Synergy in the knowledge base of U.S. innovation systems at national, state, and regional levels: The contributions of high‐tech manufacturing and knowledge‐intensive services

Abstract

Using information theory, we measure innovation systemness as synergy among size‐classes, ZIP Codes, and technological classes (NACE‐codes) for 8.5 million American companies. The synergy at the national level is decomposed at the level of states, Core‐Based Statistical Areas (CBSA), and Combined Statistical Areas (CSA). We zoom in to the state of California and in more detail to Silicon Valley. Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states; the CBSA are too small, so that systemness spills across their borders. Decomposition of the sample in terms of high‐tech manufacturing (HTM), medium‐high‐tech manufacturing (MHTM), knowledge‐intensive services (KIS), and high‐tech services (HTKIS) does not change this pattern, but refines it. The East Coast—New Jersey, Boston, and New York—and California are the major players, with Texas a third one in the case of HTKIS. Chicago and industrial centers in the Midwest also contribute synergy. Within California, Los Angeles contributes synergy in the sectors of manufacturing, the San Francisco area in KIS. KIS in Silicon Valley and the Bay Area—a CSA composed of seven CBSA—spill over to other regions and even globally.

Evolutionary and Institutional Triple Helix Models

“…In a series of studies-usually co-authored with colleagues from these nations-a number of national systems of innovation were thus analyzed and the results were decomposed in terms of both regions and technological sectors: Germany (Leydesdorff & Fritsch, 2006;Ruhrmann et al, under review), the Netherlands (Leydesdorff, Dolfsma, & van der Panne, 2006), Sweden , Norway , Italy (Leydesdorff & Cucco, 2019), Hungary (Lengyel & Leydesdorff, 2011), Spain , the Russian Federation (Leydesdorff, Perevodchikov, & Uvarov, 2015), the USA (Leydesdorff, Wagner, Porto-Gomez, Comins, & Phillips, 2019),and China (Leydesdorff & Zhou, 2014).…”

Constructing the national innovation system in Rwanda: efforts and challenges

“…A number of frameworks aiming at the facilitation of the use of knowledge have been established and tested in different contexts. Several frameworks consider learning and interactions as main processes and where institutions and knowledge are major components (Fagerberg, Bengt-Åke, and Srholec 2018;Leydesdorff et al 2019). Key frameworks and concepts include networks, value chains, clusters, development blocks, complexes, innovation milieu, complex products and systems, competence blocs and innovation systems.…”

“…However, the adaptation and contextualization of the IS in a certain context require a proper understanding of the concept and exploitation of supporting instruments in terms of policy and institutional frameworks. The geopolitical patterns are key in the shaping of NIS in this case and it might be wise to position a NIS in its regional context for better understanding its construction process, as it is likely to be influenced by regional plans and transboundary operations (Leydesdorff et al 2019;Fagerberg, Bengt-Åke, and Srholec 2018;Coenen 2006) 2.2. National innovation system: an emerging model in developing countries…”

“…This might be also a tool for local and regional innovation systems development, which can enhance industrial development in specific regions and sectors depending on existing potential and their efficient exploitation. The understanding of innovation propensity in potential socio-economic sectors plays an important role in resources allocation and specialization in tools and mechanisms for interaction and learning process (Freeman 1995;Leydesdorff et al 2019). The observed high potential in agriculture and ICT sectors, if well exploited, can influence other sectors as both are comprehensive sectors covering a wide range of actors and different localities across the country.…”

The Structures Interaction Model Of Universities And Business

“…A group of American scientists, led by the Dutch scientist Loet Leydesdorf, applied an informationtheoretical approach based on Shannon's entropy calculations to study the knowledge base of the American economy and calculated the synergy of innovation (Leydesdorff, Wagner, Porto-Gomez, Comins, & Phillipse, 2017), which is expressed in the negative value of entropy (∆ ).…”