Women-Owned businesses and access to bank credit: Evidence from three surveys since 1987
Bibliographic record
Abstract
Women-owned businesses are often thought to face difficulties in applying for and securing bank loans (Schwartz, 1979 Schwartz, E. B. 1979. Entrepreneurship: a new female frontier. Journal of Contemporary Business, 4: 47–76. [Google Scholar]; Riding and Swift, 1990 Riding, A. and Swift, C. 1990. Women business owners and terms of credit: some empirical findings of the Canadian experience. Journal of Business Venturing, 5(5): 327–340. [Crossref], [Web of Science ®] , [Google Scholar]; Buttner and Rosen, 1992 Buttner, E. H. and Rosen, B. 1992. Rejection in the loan application process: male and female entrepreneurs' perceptions and subsequent intentions. Journal of Small Business Management, 30(1): 58–65. [Google Scholar]; Fabowale et al., 1995 Fabowale, L., Orser, B. and Riding, A. 1995. Gender, structural factors, and credit terms between Canadian small businesses and financial institutions. Entrepreneurship Theory and Practice, 19(4): 41–65. [Crossref] , [Google Scholar]; Haines et al., 1999 Haines, G. H. Jr, Orser, B. J. and Riding, A. L. 1999. Myths and realities: an empirical study of banks and the gender of small business clients. Canadian Journal of Administrative Sciences, 16(4): 291–307. [Crossref], [Web of Science ®] , [Google Scholar]; Coleman, 2000 Coleman, S. 2000. Access to capital and terms of credit: a comparison of men- and women-owned small businesses. Journal of Small Business Management, 38(3): 37–52. [Web of Science ®] , [Google Scholar]). While there may always be individual instances of difficulties with credit availability that might receive the attention of the media, the more important issue—especially given the increasing contribution of women-owned business to growth in the US economy, is whether women-owned businesses face any systemic, non-economic discrimination in applying for credit. We test three questions related to the success of women-owned businesses in accessing commercial bank financing. First, are women-owned businesses less likely to apply for bank loans than businesses owned by men? Second are women-owned businesses more likely to be turned down in their most recent loan application? And finally, if approved on their most recent loan application, are they more likely to receive a smaller loan? We found gender to be related to the application for bank loans as well as the size of the loans but not to the frequency of turndowns. These findings may be due to an omitted variable that could capture women's concerns about maintaining control over their business.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 0.001 |
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".