Financial Well-Being and Financial Capability among Low-Income Entrepreneurs
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".