Inequality and Entrepreneurship: Institutional Barriers Faced by Underrepresented Entrepreneurs
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».