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 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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