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Record W4323565257 · doi:10.3390/jrfm16030181

Financial Well-Being and Financial Capability among Low-Income Entrepreneurs

2023· article· en· W4323565257 on OpenAlexvenueno aff
Baorong Guo, Jin Huang

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
FundersEwing Marion Kauffman Foundation
KeywordsFinanceBusinessFinancial analysisFinancial ratioMainstreamFinancial servicesFinancial riskFinancial crisisIndirect financeFinancial planEconomics

Abstract

fetched live from OpenAlex

Financial well-being is a key component of quality of life and overall well-being and is likely to affect other aspects of quality of life, such as health and health care. The COVID-19 pandemic presents an immense crisis of financial well-being among low-income entrepreneurs and has left many small-scale entrepreneurs financially fragile. We argue that promoting the financial capability of low-income entrepreneurs is effective in protecting their financial well-being from a crisis. To examine the association between financial capability and the financial well-being of low-income entrepreneurs, we use the 2016 National Financial Well-Being Survey, which provides the latest and comprehensive measurement of financial capability, including financial knowledge, financial skills, and access to financial products and services. Our analyses show that, compared to their higher-income counterparts, low-income entrepreneurs have statistically lower levels of financial well-being, financial knowledge, financial skills, and access to mainstream financial products; they also have a statistically higher risk of using high-fee alternative financial products. In addition, low-income entrepreneurs have larger barriers to accessing mainstream financial products than low-income non-entrepreneurs. The results indicate that financial capability plays a significant role in promoting the financial well-being of low-income entrepreneurs.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations27
Published2023
Admission routes1
Has abstractyes

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