6

Developing Subnational Datasets and Indicators

The panel uses the term “subnational” to refer to state and finer geographical levels, including metropolitan areas. The National Center for Science and Engineering Statistics (NCSES) publishes state statistics on science and technology education and workforce inputs, financial research and development (R&D) inputs, R&D outputs, and the relationship of science and technology indicators to general economic indicators. Indeed, NCSES has a new interactive online tool for the Science and Engineering Indicators 2012 state data. However, statistics at geographic levels finer than states are often not published. The omission of subnational data is important because technology transfer between universities and firms (and venture capital investments) can occur in immediate proximity, or crossing state and even national boundaries. In the knowledge economy, political boundaries, rivers, and mountain ranges are not likely to determine the location and transfer of innovation activities, while colleges, universities, local enterprise zones, and venture capitalists are quite likely to determine where “hot spots” of innovation are located and transfers among them.

POLICY RELEVANCE

Subnational statistics on science, technology, and innovation are especially policy relevant at this time when the nation’s economy is struggling to adjust to structural displacement of workers. How does innovation activity in a given firm at a given place contribute to that firm’s productivity, employment and growth, and perhaps also to these characteristics in the surrounding area? How do those innovation supply chains work within a state? Are firms principally acquiring new knowledge from customers or from universities? These are the questions for which governors as well as state and municipal representatives want data in order to be able to answer. From an investment perspective, policy makers, university administrators, and business developers want to see science, technology, and innovation (STI) indicators at scales that match their geographic interests.

During the panel’s workshop, Robert Atkinson (Information Technology and Innovation Foundation) said that subnational information would be particularly helpful for technology and innovation policy. Other workshop participants described subnational decompositions in several countries. Based on her extensive research on STI hot spots, Maryann Feldman (University of North Carolina) emphasized that “economic growth does occur within these finer geographic units.” She went on to stress that decision makers in the states and metropolitan areas are in great need of data on innovation activities at subnational levels. She said that NCSES needs to work with users to determine what statistics would be useful at the subnational level and what some of the users have already created that could be useful inputs into NCSES’s subnational indicators.

There are a few caveats to expanding NCSES’s national indicators to include a variety of subnational measures. For example, Christopher Hill (George Mason University) raised the issue that there might be disparities between the disaggregated data and data at the national level if the



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6 Developing Subnational Datasets and Indicators The panel uses the term “subnational” to refer to state and finer geographical levels, including metropolitan areas. The National Center for Science and Engineering Statistics (NCSES) publishes state statistics on science and technology education and workforce inputs, financial research and development (R&D) inputs, R&D outputs, and the relationship of science and technology indicators to general economic indicators. Indeed, NCSES has a new interactive online tool for the Science and Engineering Indicators 2012 state data. However, statistics at geographic levels finer than states are often not published. The omission of subnational data is important because technology transfer between universities and firms (and venture capital investments) can occur in immediate proximity, or crossing state and even national boundaries. In the knowledge economy, political boundaries, rivers, and mountain ranges are not likely to determine the location and transfer of innovation activities, while colleges, universities, local enterprise zones, and venture capitalists are quite likely to determine where “hot spots” of innovation are located and transfers among them. POLICY RELEVANCE Subnational statistics on science, technology, and innovation are especially policy relevant at this time when the nation’s economy is struggling to adjust to structural displacement of workers. How does innovation activity in a given firm at a given place contribute to that firm’s productivity, employment and growth, and perhaps also to these characteristics in the surrounding area? How do those innovation supply chains work within a state? Are firms principally acquiring new knowledge from customers or from universities? These are the questions for which governors as well as state and municipal representatives want data in order to be able to answer. From an investment perspective, policy makers, university administrators, and business developers want to see science, technology, and innovation (STI) indicators at scales that match their geographic interests. During the panel’s workshop, Robert Atkinson (Information Technology and Innovation Foundation) said that subnational information would be particularly helpful for technology and innovation policy. Other workshop participants described subnational decompositions in several countries. Based on her extensive research on STI hot spots, Maryann Feldman (University of North Carolina) emphasized that “economic growth does occur within these finer geographic units.” She went on to stress that decision makers in the states and metropolitan areas are in great need of data on innovation activities at subnational levels. She said that NCSES needs to work with users to determine what statistics would be useful at the subnational level and what some of the users have already created that could be useful inputs into NCSES’s subnational indicators. There are a few caveats to expanding NCSES’s national indicators to include a variety of subnational measures. For example, Christopher Hill (George Mason University) raised the issue that there might be disparities between the disaggregated data and data at the national level if the 31

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proper weights are not applied in the aggregation process. Hill also voiced concern that data might not maintain the same type of meaning at disaggregate levels as they do at the national level. For example, he said, “year after year the state-level data for Virginia show substantially higher levels of federal funding to private industry for R&D than is shown in the expenditures by private companies.” He concluded that “there’s a whole lot more money going into the state than companies report spending.” Hill also said: “While place matters, place also is very leaky both in and out.” Therefore, it is not necessarily clear what the geographical span of impact is for a university or a firm in a given locale. There is also the problem that multiplant firms that span more than one state might have difficulty allocating activities accordingly. This potential problem would clearly affect data quality and reliability at finer geographical levels. USERS’ PERSPECTIVES Users want more disaggregated STI information on multiple levels. They want STI comparisons across U.S. regions and between U.S. and foreign regions. Also, for some smaller countries, such as Finland or Sweden, comparisons to U.S. regions may be more appropriate. The panel’s workshop yielded a plethora of subnational STI indicators that users said would be helpful. Participants mentioned a very wide range of information:  state, county, and metropolitan tables of data from the Business R&D and Innovation Survey (BRDIS) (covering R&D performance, workforce, and intellectual property);  degrees granted in science, technology, engineering, and mathematics (STEM) (production and migration);  academic R&D expenditures;  federal R&D expenditures;  total R&D (from a resurrected nonprofit R&D survey);  STEM jobs (Occupational Employment Statistics from the Bureau of Labor Statistics [BLS]);  STEM workforce migration (data on Local Employment Dynamics from the Census Bureau);  patent applications, grants, and citations (from the U.S. Patent and Trademark Office);  STI equity investments (from various sources);  STEM occupational projections (from BLS and the Employment and Training Administration [ETA]);  STEM occupation classification (from ETA);  STEM graduate and workforce migration (National Center for Education Statistics, from the Census Bureau and BLS); firm innovation processes (from the Economic Research Service [ERS] at the U.S. Department of Agriculture [USDA]);  propensity to innovate ratings;  mappings of entrepreneurial density;  industry support for R&D in universities;  firm births, mergers and acquisitions, deaths (“business dynamics” as characterized by Haltiwanger in the July 2011 workshop, including geography, industry, business size, business age); 32

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 venture capital investments;  state and federal grants and loans (STAR METRICS);1  initial public offerings;  new products (from Thomasnet.com);  drug and other approvals (from the Federal Drug Administration);  data on dealmakers and entrepreneurs, including number of connections among dealmakers and entrepreneurs); and  data on emerging industries, based on universities, government laboratories, firms, value chains, key occupations, and individuals. This long list clearly includes much that would be very difficult or expensive to collect, and would require consultation with state institutions, which in turn would require recognition that states drive industrial and trade policies. New sources for subnational data need to be considered. For instance, the Association of Public and Land Grant Universities (APLU)—funded by the National Science Foundation and the U.S. Department of Commerce—has developed a template for new measures of university contributions to regional economies under its Commission on Innovation, Competitiveness, and Economic Prosperity (CICEP). The goal of CICEP is “to create a resource for universities to better measure and describe the broad range of their contributions to regional (and national) innovation and economic growth” (Association for Public and Land Grant University, 2011, p. 1). At the panel’s workshop, David Winwood and Robert Samors presented an overview of CICEP. They noted that there are dozens of measures that APLU would like universities and other organizations to collect in a range of categories: material transfer agreements; consortia agreements; sponsored research by industry; clinical trials; service to external clients; student employment on funded projects; student economic engagement; student entrepreneurship; alumni in the workforce; incubation and acceleration program success; relationships between clients/program participants and host university; ability to attract external investment. The U.S. Department of Agriculture’s Economic Research Service is planning to conduct an establishment survey on innovation activities in rural areas. This survey will be based on the European Union’s Community Innovation Survey. Researchers funded by the National Science Foundation, through the Science of Science and Innovation Policy program have also developed datasets that can be used to create subnational STI indicators. Some of those data are already available for public use. These and other issues are knotty problems that NCSES will have to navigate as it endeavors to create meaningful subnational STI indicators. In spite of the problems, however, the panel believes that the value of STI indicators at the state level and for regions where there are relatively high levels of science, technology, and innovation activities would contribute information needed by policy makers. 1 The STAR METRICS project is a partnership between science agencies and research institutions to document the outcomes of science investments to the public. 33

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CONCLUSIONS AND RECOMMENDATION The panel in its continuing work will determine the relative merits of NCSES’s augmenting its indicators program to produce finer geographical granularity of STI indicators. It should be noted that NCSES could provide some of these indicators with data from current surveys. Data from other sources, both profit and nonprofit, could be made available through NCSES. The data from outside providers would necessarily be vetted through NCSES’s data evaluation process. As a clearinghouse of STI data, NCSES could make great strides toward making high-utility indicators available to users, especially researchers and government organizations. RECOMMENDATION 5: The National Center for Science and Engineering Statistics should host working groups in the near future to further develop subnational science, technology, and innovation indicators. Participants in the working groups should be both users and providers of the data. A main focus of the discussion should be on data reliability, particularly at fine geographical scales. Potential indicators should include subnational research and development statistics, and subnational science, technology, engineering, and mathematics workforce statistics. 34