Advancing Concepts and Models for Measuring Innovation Proceedings of a Workshop (2017) / Chapter Skim
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3 Innovation Beyond R&D and Conventional Input Measures
3 Innovation Beyond R&D and Conventional Input Measures
Pages 25-46
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-- conducted by the Census Bureau through an interagency agreement with the National Center for Science and Engineering Statistics (NCSES) -- asks firms in manufacturing and service industries for information about newly introduced or improved products and processes, and about purchased R&D inflows and outflows, revenue from the sale of patents and patent licensing, and a number of activities related to intellectual property (IP)
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From page 26... ...
innovations, new-to-the-firm innovations, and significantly improved products during the 3-year reference period. The CIS and DoIL surveys generated the following estimates of the share of manufacturing firms introducing innovations for the 2007-2009 period in selected European countries and the United States: New-to-Firm Innovation – Germany: 49% 1While Arora et al.
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In order to identify innovating firms, the DoIL survey asked: "In 2009, have you earned revenue from any new or significantly improved goods or services in [INDUSTRY] introduced since 2007, where ‘new' means new to your firm?
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Analysts can set the criteria -- for example, the percentage of sales -- for what is considered important. Summary statistics reveal an NTM innovation rate in the United States of about 16 percent for the 2007-2009 period across all manufacturing industries, with innovation occurring disproportionately among large firms (38% versus 23% and 13%, respectively, for medium and small firms)
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Cohen used Figure 3-1 to present findings about the percentage of business unit sales from focal innovation. The vertical axis is the percent age of innovating firms, and the horizontal axis indicates the percentage of sales accounted for by that group of innovating firms.
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Y axis is the percentage of innovating firms in each category of the x axis. X axis categorizes other kinds of innovations, further classified by percentage of sales accounted for by the focal innovation (< 50% sales, > 50% sales)
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At about this same time, Arora noted, many statistical agencies began developing their models of innovation measurement, which influenced methodological decisions. Arora noted that fast-forwarding to more recent years, data from the National Science Foundation (see Figure 3-3)
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Given the diminishing role of internal research in large private-sector companies, understanding the mechanisms of external knowledge production and spillovers from universities, startups, and government sources, as well as non-U.S. sources of invention and knowledge, is becoming increasingly important.5 Regarding the organizations doing the inventing, in the United States, 5Additionally, some companies are moving away from introducing new products and into financial engineering, globalization, and buying businesses (where innovations may be harder to detect and measure)
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The National Science Board Science and Engineering Indicators publications provide information about the extent to which invention originates from science by showing the percentage of patents citing science and engineering literature.6 In open discussion, commenting on external sources of inventions (and inventive labor) , Alfonso Gambardella (Bocconi University)
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RESEARCH USING LONGITUDINAL BUSINESS DATA AND OTHER STATISTICAL AGENCY DATA THAT HELP MEASURE INNOVATION Javier Miranda (U.S. Census Bureau)
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However, if the decline has been in transformational entrepre neurs creating innovative businesses, then it would be a greater concern. Research by Miranda and his colleagues (e.g., Decker et al., 2015)
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Likewise, as shown in Figure 3-5, the decline in the share of employment in high tech jobs accounted for by young firms began in 2000 and, by 2011, it was about half of what it had been a decade earlier. The decline coincides with a slowdown in productivity growth as measured using official statistics (or, Miranda pointed out, using Fernald's numbers cited by Sichel above)
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As in past collaborations between agencies, such as between NCSES and Bureau of Economic Analysis in developing new data for adding R&D and intangibles into the National Income and Product Accounts, questions of data confidentiality are regularly dealt with. DESIGN INNOVATION AS AN ALTERNATIVE OR COMPLEMENT TO R&D Bruce Tether (University of Manchester)
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The modern view of design is that it brings together creativity through insights and integration of ideas. Tether suggested a good example is IDEO, a global design firm, which created a program for Bank of America called "Keep the Change" to entice people to save more (every time customers buy something with their debit card, the bank rounds up the purchase to the nearest dollar and transfers the difference from their checking into a savings account)
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. SOURCE: Workshop presentation by Bruce Tether, May 19, 2016; data from Innobarometer 2015 (Montresor and Vezzani, 2015)
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Work by Galindo-Rueda and Millot (2015) using the Danish CIS data reveal an increasing impact from using design more strategically.
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Design is important in low tech sectors and, increasingly, in services. • Among the challenges in measurement are (1)
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This research finds that use of robotics led to, on average, a 0.36 percentage point increase in a country's annual labor productivity growth, accounting for slightly more than one-tenth of overall GDP growth during that time. Although small, Seamans pointed out that its impact is similar in magnitude to that resulting from steam engines in the United Kingdom in the late 19th century -- and about one-tenth of the productivity growth that occurred during this time period.
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indicate that total annual shipments of industrial robotics began to increase rapidly around 2010, and they have nearly doubled since then.10 Similarly, beginning around 2011-2012, there was a large uptick in the number and rate of patents that cite the robot class. These trends suggest, according to Seamans, that the 0.36 percentage point contribution to productivity growth estimated by Graetz and Michaels (2015)
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and probability of automation by occupation. SOURCE: Workshop presentation by Rob Seamans, May 19, 2016; wage data from Bureau of Labor Statistics; probability of automation estimates from Frey and Osborne (2013)
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" and "Has your establishment considered using robotics instead of human labor? " In discussion, Sheryl Winston Smith (Temple University)
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