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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
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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);
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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.
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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.
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