Busting the Startup Myth: Implications for Tech Transfer
In the article, entrepreneurship ecosystems pioneer Daniel Isenberg asks an important question: Do Startups Really Create Lots of Good Jobs? He suggests that the startups-create-jobs idea has experienced “the truth effect,” gaining legitimacy and becoming accepted as fact when the reality is far different:
- The “startups create jobs” statement is true only for the very small percent that survive the first year, not for the statistical whole.
- Many of the startups that make it to year 5 employ mainly low-paying/low-skill workers, or the high-skill workers are collecting little or no salary while they bootstrap. (Average revenue after 6 years is a mere $180K.)
- Since the 2007–08 recession in the U.S., middle-market enterprises (with $10M to $1B in revenues) created 92% of net new jobs.
- States and countries with the highest amount of startup activity—Montana, Brazil, Angola, China, and Chile—are sparsely populated areas with lower infrastructure and a lack of larger businesses as part of the regional economy.
Dr. Isenberg’s article is worth reading in its entirety, especially because the most important point is at the very end:
“public and business leaders as well as policymakers in the U.S. and elsewhere must see startups accurately and in perspective in order to foster growth and long-term economic prosperity.” (emphasis mine)
Substitute “in order to have a successful tech transfer program,” and longtime readers will see that this has been my mantra for a few years now.
So what does this mean for tech transfer? In a nutshell: Don’t put all of your eggs in the startup basket. Here are two specific suggestions for those setting policy for university and government tech transfer programs.
1. Define What a Startup Is
Programs that offer special options and licenses for startups but don’t clearly define them risk having large, established companies “game the system.” I’m not making this up: We were helping a client who was determined to license to a startup, and a company actually stated they were considering setting up a temporary subsidiary to access the special rates.
So when you write the rules about licensing to or providing funding programs for startups, define what qualifies as a startup. Is it:
- Years in business?
- Number of employees?
- Or some other characteristic(s)?
Focus on the intention of the special category to determine how it should be defined. Better yet, make the “special offer” for small businesses rather than startups. It might not be as trendy as “startups,” but “small-and-medium enterprise (SME)” is easier to define. Plus it’s likely to result in more successes that lead to better economic development.
Which brings me to my second recommendation…
2. Focus on Finding the Right Company
Whether you’re licensing a technology or securing a partnership for collaborative R&D, worry less about it being a startup and more on whether this is the right company. We often are pointing out to our clients that:
- An existing small businesses is more stable and usually has greater long-term viability than a startup.
- Helping an existing small business to succeed takes fewer resources for you, since unlike startups they probably already have the basic infrastructure in place as well as some of the needed resources.
- You can take a hybrid approach, licensing one portion to an existing organization while licensing another portion to the startup that is most appropriate for that early-stage development environment. This approach reduces the risk and secures some licensing revenue sooner (since startups usually have longer lead times to product, if they ever actually get there), yet it still can allow the startup to have exclusivity for some part of the innovation.
So I applaud Dr. Isenberg for his insightful article, and I urge university and government policymakers to heed these insights to avoid translating the startup myth “unthinkingly and uncritically into policy and practice.”