The Environmental Design Research Association (EDRA), in partnership with Project for Public Spaces (PPS), has recognized evolveEA’s work in Upper Lawrenceville, also known as Pittsburgh’s 10th Ward, with a Great Places Award. The Upper Lawrenceville Targeted Development Strategy was developed through a series of charrettes led by evolveEA in which they helped the community craft a neighborhood identity and a series of principles guiding future development to achieve the community’s long-term livability goals. The principles built upon the existing physical and cultural legacy of Upper Lawrenceville but also were aspirational, seeding a vision for a future yet to come focused on economic, cultural and environmental issues.
Today’s economy elevates the value of higher education institutions to the highest degree of public awareness ever demonstrated. Higher education institutions impact their community in a host of very obvious ways, such as:
- Supporting the development of 21st century talent armed with skills to drive modern business;
- Employing a range of professionals in a sector often recognized as the largest in many small communities;
- and Initiating research and development initiatives supporting the advancement of technology and improved economic performance.
These examples speak to the common ways nearly every institution engages. Yet, what does it mean for a campus to be truly connected to its community? Continue reading “Trends in Town-Gown Collaboration”
A recent National Science Foundation (NSF) report revealed that R&D expenditures among U.S. higher education institutions remained flat in FY2012 compared to FY2011, and, when adjusted for inflation, declined by 1.1 percent. “This represents the first constant-dollar decline since 1974 and ends a period of modest growth during FYs 2009–11, when R&D expenditures increased an average of 5% each year.”
While this statistic is newsworthy, it may not be as much of a crisis as it first seems. Continue reading “Declining Higher Ed R&D Expenditures: A Crisis or an Opportunity?”
In recent months our Fourth Economy team has been hard at work on several town-gown development projects. It’s time to share a few lessons learned. First, if you live in a smaller town and are fortunate enough to have an institution of higher education close by, don’t squander the opportunity to build upon this high value asset – embrace it, leverage it, and cultivate it.
While the many positives associated with town-gown partnerships may be obvious to most of us, surprisingly those positives often need to be clearly identified, communicated and tactically acted upon. Continue reading “Small Towns, Great Gowns, Big Opportunities”
The Fourth Economy team has been fortunate to have had many project experiences in our first two-years of life. When asked what has been the most notable observation or question that I can take away from our work thus far, it is this – Do markets define a “place” or can a “place” define the market? Throughout our national travels and work locally within the Pittsburgh region, it is clear that many second and third class cities across the country are increasingly realizing new and financially viable mixed-use development and higher density housing projects. Continue reading “Do Markets Define the Place or Can a Place Define the Market?”
IBM estimates that we create 2.5 quintillion bytes of data every day. They do not, however, estimate how much of this data is duplicated – all of the documents emailed between friends and coworkers that get stored on personal devices, corporate servers and cloud machines. By my own unscientific and completely arbitrary estimate, at least 55 percent of our daily data production is duplication.
Big Data includes a lot of transactional data – what you purchase from stores as well as your Google searches or the fact that Person A sent an email to Person B, as well as the content of that email. There is also data from sensors used in industrial production as well as climate, weather and traffic monitoring. It includes Twitter and other social media posts, digital photos, Wikipedia entries and data produced by researchers, scientists, corporations and government agencies.
Big Data is often unstructured but it is usually timely. It is not simply an aggregation of a bunch of data. The challenge is to structure this data and make sense of it. Economists and regional developers have been behind in tapping into Big Data but it can be useful in a number of ways. Much of it enables firms to better segment customers or develop next generation products. It can also provide value in itself by selling access to that data for specific types of users and uses.
One of the problems we have with a lot of economic data is that it is too structured or aggregated to make it useful for data mining and other Big Data analytical techniques. For instance, our use and definition of industry sectors (NAICS) hampers analysis of emerging industries. This structure is used to provide anonymity and confidentiality but it also distorts the kind of variation that is useful to better understanding how our economy works. For one, we have no idea what happens within a nondisclosed NAICS code. But even within a sector we don’t know how many firms are growing or declining or the magnitude of those changes. For most economic developers working within a local or regional economy, it can make a big difference whether an apparent “industry trend” is broadly shared by companies in the sector or if there are diverging patterns.
Currently there are few sources of Big Data for economic development analysis, but job postings, social media feeds and patent data are a few that Fourth Economy has been working on to yield new insights on economic trends. Patent data has been particularly ripe for this analysis, in part because it is so unstructured that it is difficult to analyze with traditional tools and techniques. These can be frustrating times for analysts and for anyone seeking answers. There is a wealth of data out there, but too often we aren’t able to hammer it into useful information.
We’ve set up a quick poll to gather some data of our own. It’s only one question, so share your thoughts.
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.
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.
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.