National Fourth Economy Community Index Lists Top 10 Mega-Sized Counties for 2015

141204-FECIndexFourth Economy Consulting announces the latest release of its national community index, listing top counties from across the nation. The Fourth Economy Index highlights those communities ideally positioned to attract modern investment and managed economic growth within the fourth economy.

PITTSBURGH, PA – The latest release of the Fourth Economy Community Index (FEC Index, #FECIndex) was announced today listing the nation’s top ten mega-sized Fourth Economy Communities. These communities are recognized as the regions ideally positioned to attract modern investment and managed economic growth among all regions with a population greater than 500,000 people.
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How Many Startups can University Research Support?

Utah made headlines by generating more startups in 2009 than MIT on ¼ of their budget. Interest and activity in university spinoffs continues to grow. A number of new initiatives have launched recently to promote the commercialization of university technology and more specifically the development of startup companies.

  • Texas is a building a $7 million, 20,000SF accelerator facility, the Center for Research Commercialization. The CRC will provide green and biotech startups with access to Texas State faculty and labs.
  • The Auburn Business Incubator, located on the Auburn University campus is a new incubator facility to link startups to a network of services from university and community sources.
  • Carnegie Mellon University, a perennial startup powerhouse, recently launched a new initiative, Greenlighting Startups, which leverages their ‘Five Percent, Go in Peace‘ policy to generate university startups. One new twist is the Open Field Entrepreneurs Fund (OFEF) that provides early-stage business financing to alumni who have graduated from CMU within the past five years.

University startups are one of the most visible ways in which academic innovation produces regional economic benefits. Startups, however, require more effort than licensing agreements, and it is not an appropriate strategy for commercializing every technology.

The Association of University Technology Managers (AUTM), which began in 1974 as the Society of University Patent Administrators, provides data on these startups and university technology transfer. As more universities emphasize startups or other aspects of technology commercialization, it will be important to have good benchmarks in terms of the effort required and the expected return.

[Table 1]

Institutions emphasize different aspects of the commercialization process and may prefer licenses and patents to startups. The AUTM data doesn’t tell us the strategic emphasis of the institutions, so the average for how many startups you can expect out of a given amount of research expenditure is skewed by including institutions that never attempt to create a spinoff firm. Analyzing the AUTM data from 2003 to 2009, there are 133 institutions that produce less than one startup per year (Table 1). A number of these schools have very small budgets and are not oriented towards creating startups; in fact, only 24 of the 133 (18 percent) have annual R&D budgets above $100 million.

When we look at the institutions that generate at least one or more startups per year, we see why the $100 million threshold matters (Figure 1). It does not take $98 million or $100 million of research to generate a startup, but you can’t tell which research and which technology will lead to a startup, so you need to have a lot of research activity going on in order to find those opportunities to produce a new startup. At less than $100 million in R&D, you will need to be either very lucky or very good to consistently create startups.

[Figure 1]

As the volume of research increases, institutions become more efficient. At $200 million to $400 million in R&D, institutions can expect only a modest increase in startup rates – getting one startup for every $92 million in research. The very best schools, those that produce more than 4 startups per year, are able to generate one startup for every $77 million in research. For the smaller institutions, implementing the best practices and doing everything you can to be efficient at producing startups might add one more startup every other year.

An improvement in the data collected by AUTM would be to have more specific data on research expenditures and commercial outcomes by sector so that institutions have a better idea of how they stack up. AUTM reports the number of university startups and research expenditures but it does not provide specifics on the technology sectors for those indicators. For example, it is more expensive to develop a technology and launch a startup in biotech versus a web application, but all of those numbers are mixed together in the AUTM data.

There is also a need for more and better data about the quality and performance of university startups. The AUTM data does not distinguish various qualitative factors on startup development, or their ultimate level of success. Is a legally incorporated shell company with no employees, no investment and no revenue equal to, less than or greater than three committed entrepreneurs who have invested $50,000 of their own money to develop a prototype but they haven’t legally filed for incorporation? These are questions that require more long-term study and data collection. A few universities have collected this data for economic impact studies, but the variety of methods employed make it difficult to compare performance.

I am certainly a believer in the power of university based economic development, but I also know that it is not easy to succeed with that strategy and it is not the right fit for every university. With the data currently available, we can’t accurately answer the question of how many startups a university can expect to produce from its research base. If you have thoughts on how to improve the information about university commercialization and specifically startups, let us know by email or leave a comment below.