Inequality and Entrepreneurship: Institutional Barriers Faced by Underrepresented Entrepreneurs
Bibliographic record
Abstract
Entrepreneurs from under-represented groups, inherently face inequalities in starting and succeeding in their entrepreneurial endeavors. In recent years, significant progress has been made in understanding the entrepreneurial challenges faced by diverse under-represented groups, including racial minorities, women, immigrants, and justice-impacted individuals. While such previous work has been influential in identifying barriers such as restricted access to human, social, or financial resources (Kim, Aldrich, and Keister 2006) and biased evaluators (Fairlie and Robb 2008) faced by under-represented entrepreneurs, we have limited knowledge on how institutional barriers – ranging from formal regulations to informal societal and cultural norms – shape and aggravate entrepreneurial inequalities. Thus, our symposium aims to address the underexplored role and impact of diverse and novel institutional contexts in shaping entrepreneurial inequality for under-represented groups. This symposium addresses this question by focusing on different under-represented populations including individuals with criminal records, women, and racial minorities, leveraging a diverse set of experimental and archival methods. Each paper in our symposium explores distinct and novel institutional contexts encountered by under-represented groups such as formal regulations on financial access for individuals with criminal records, informal legacies from historical slavery, social and cultural norms around women entrepreneurs in Mexico, and gender bias in the start-up employee market. Our presenters further showcase novel consequences of such institutional contexts, by documenting that institutional barriers to entrepreneurship not only leads to stunted entry and success by under-represented entrepreneurs, but also perpetuate inequalities in unforeseen areas by exacerbating gender-bias in innovation and increasing crime among the most vulnerable populations. These presentations collectively broaden our understanding of the impact of institutional barriers on under-represented entrepreneurs, examining novel mechanisms across a variety of institutional contexts as well as unique consequences. Through our symposium, we hope to underscore the importance of creating inclusive formal and informal institutional ecosystems, which are crucial for leveling the playing field in entrepreneurship. The Effect of Barriers to Credit on Justice-Involved Entrepreneurs Author: Keith Finlay; U.S. Census Bureau Author: Kylie Jiwon Hwang; Northwestern Kellogg School of Management Author: Michael Mueller-Smith; U. of Michigan Author: Brittany Street; U. of Missouri Policy and Patriarchy: Changes in Startup Costs and the Entrepreneurial Gender Gap in Mexico Author: Grady Wallace Raines; Cornell SC Johnson College of Business Author: Peter Polhill; Cornell U. Author: Ryan Scott Coles; U. of Connecticut Long-term Effects of Institutional Slavery on Black Representation in Entrepreneurship Author: Kunyuan Qiao; Georgetown U. Author: John Dencker; Northeastern U. Does the gender of an idea matter? Evidence from the market for startup talent Author: Solene Delecourt; UC Berkeley Author: Sahiba Chopra; Haas School of Business, UC Berkeley
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".