👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Tuesday, I publish “Insights” to digest the most relevant startup research & reports, and every Thursday, I publish “Essays” that cover hands-on insights about data-driven innovation & AI in VC. Follow along to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
Efficiency, effectiveness, and inclusiveness are our three guiding principles why investors need to become more data-driven. While we focused many episodes on the first two dimensions, we didn’t spend enough time on the latter. A bit more than a year ago, Prof Isabell Welpe and Nadja Born shared their research on the impact of gender inequality on startups in a guest post.
It’s important to note that gender is a prominent yet (only) one of many dimensions of diversity. Let’s not forget about factors such as religious beliefs, race, martial status, ethnicity, parental status, age, education, physical and mental ability, income, occupation, language, geographic location, and many more components. While we’d love to explore the impact on startups of as many dimensions of diversity as possible, the majority of the available “diversity in startups” research is focused on gender.
Extending on the incredibly insightful guest post from last year, we are going to examine the state of gender diversity more broadly today. This is a topic with many perspectives and extensive literature. Our aim is to provide a comprehensive overview of the current landscape, analyze the existing research on how gender diversity—or the lack thereof—impacts startups, and offer practical, research-based advice for entrepreneurs looking to enhance their teams' performance, their companies' success, and outcomes for their stakeholders.
We should consider how our societal and entrepreneurial structures can be optimized to maximize positive impacts, whether measured in technological progress, financial performance, or addressing major challenges like climate change, diseases, and economic inequality. And finally, beyond moral contemplation, if you believe the literature and the data, we can’t afford to leave half the population by the wayside for petty considerations.
Before we dive into the literature and the data, here are some quick definitions of terms that we are going to use throughout this episode. Just to make sure we’re on the same page:
Diverse team: A team of founders that includes at least one woman and at least one man. This is not to say that these teams fulfill all considerations around diversity.
All-female team: A team of founders (or solo founders) that includes only female founders.
All-male team: A team of founders (or solo founders) that includes only male founders.
Non-binary teams: While we recognise the importance of inclusivity, it's worth noting that this episode does not include data on non-binary individuals. This omission is due to the lack of comprehensive data and research on the topic in Europe. While still far from great, we found that the data is a little better in the US. This is partly rooted in legal challenges that can make it difficult to survey data on sexual orientation, and gender identity, in many European countries.
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