Which business schools are more likely to produce unicorns and unicorn founders?
What did unicorn founders study at university?
To create a unicorn, what is the optimal number of co-founders?
To find answers to these and other questions about companies that achieve elusive billion-dollar status, go to … LinkedIn?
That’s right. The most rigorous, reliable data on unicorns and their founders is likely found on the LinkedIn profile of Ilya Strebulaev, a finance professor at Stanford’s Graduate School of Business who posts weekly insights from his dataset in sharp, graphically pleasing detail.
DATABASE CONTAINS 531 UNICORNS
“Venture capital research is relatively recent because there really hasn’t been any data. It hasn’t received as much attention as I think it should have received. I have amazing colleagues at other schools — at Chicago Booth, at Harvard Business School, at MIT – who are doing research on venture capital, but it’s a relatively small group of people,” says Strebulaev, a 2020 P&Q Professor of the Week.
In 2015, Strebulaev founded the Venture Capital Initiative at Stanford GSB to pull together researchers and compile more reliable data. He and his team of research assistants, PhD students, project managers, and lawyers have poured through tens of thousands of pages of VC contracts and other documents, data sets from PitchBook and VentureSource, LinkedIn and other websites, news articles and more in an effort to compile a database that includes every U.S. unicorn startup since 1995. That’s 531 unicorns so far.
“Most of those research posts on LinkedIn that you see — and many, many more are coming — are really with the support of the VCI,” Strebulaev says.
HUNDREDS OF LIKES & SHARES ON PROF’S UNICORN POSTS
After sharing insights from the dataset to his MBA students in his popular venture capital class, he figured others might be interested as well. He shared the first unicorn LinkedIn post in October: a cartogram showing the weighted-average location of American unicorns by state. (See above. Not surprising that California is the big orange blob with 309 unicorns headquartered in the state.)
A couple times per week, Strebulaev uses the dataset to answer pressing unicorn questions:
- What’s the right age to become a unicorn founder? (30-34 years)
- Do unicorn founders need an academic degree? (It helps)
- What is the founder gender of VC-backed startups between 1991 and 2018? (94.5% male! Come on, ladies! Let’s turn those ideas into companies, already.)
Strebulaev’s posts generate hundreds to thousands of likes, shares and comments. Poets&Quants talked with him recently about unicorns, unicorn founders, and the high interest in his novel data set. Keep scrolling for answers to more unicorn questions along with an Instagram announcement for P&Q readers. (This interview has been edited for length and clarity.)
First, tell us a little about yourself.
I was born and educated in Moscow, but I moved to London and did my PhD in finance at the London Business School. I came to Stanford as a professor of finance in 2004. I’ve now taught more than 2,000 students, mostly MBAs.
About 10 years ago, I became very interested in venture capital – not just about startups, but really about the entire innovative ecosystem. This is the ninth time I’ve taught the venture capital class which is now becoming very popular. Not just because of me and my co-teacher – Brian Jacobs, co-founder of Emergence Capital – but because the topic is very, very popular these days. Obviously, I’ve been impacted by my students and, in fact, most of my interesting research questions start with a question that a student asks that I don’t know how to answer. Some of them lead to really interesting discussions, debates with other colleagues, and some of them lead to fantastic research papers.
I read that collecting this data often means pouring through reams of contracts and other documents. It seems like a very labor intensive process.
It is extremely laborious. We actually have a team of lawyers who help us to interpret particularly challenging contractual details. In the Anglo Saxon legal system, contractual partners are allowed to innovate within the broad legal framework and precedence. As a result, we have tens of thousands of venture capital contracts, and really every single contract is unique. We devised a system where we have about 100 variables that we’re collecting data for from these contracts, and we have instructions that are 100-plus pages long on collecting them. Some of the variables are easier, like quantitative variables for the number of shares. Others are much more complicated, and have to be reconciled.
One of the reasons that venture capital research hasn’t gotten as much attention is that all those companies are private. Some are very small, and many of them are going to fail inevitably. So, many of these companies just disappear from the planet. To collect data from something that doesn’t exist anymore is very difficult. Many of the companies don’t really have a lot of financial data, and even if they do, there’s no one centralized place where you can look for it.
I think the VCI is in a nice position where we have gotten access to some of the data – not all the data, I would like to have more of the data – that is representative of what’s happening in the universe. Contractual data is interesting because we have a large representative sample of contractual arrangements across the venture capital industry.
A lot of the LinkedIn posts I’ve seen so far have looked at unicorn startup companies. How are you defining unicorns for the purpose of the research?
The definition of unicorns that we use is those companies that had at least one round with a reported valuation of $1 billion. When I say ‘reported’ it is important because it is not a fair market value. It is for a private company’s post-money valuation.
There are two types of unicorns: One is a company that has raised $1 billion or higher post-money valuation in private rounds. Another is a company that exited a private status – either it went public or was acquired – and, in doing so, had a valuation of over $1 billion. That is my definition. There are various other definitions used, and many people use the word ‘unicorn’ without having a precise definition.
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