An alternative approach towards the knowledge production function on a regional level - applications for the USA and Russia

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe present study picks up on the aspect of knowledge generation -a key part of every national innovation system -in the context of the USA and the Russian Federation. Following Fritsch and Slavtchev (2006) a knowledge production function can be used to account for the efficiency of an innovation systems.In detail this study provides a quantile regression estimation of the knowledge production function to account for a possible non-linear relationship between knowledge inputs and knowledge output. Using regional data for researchers, expenditures on R& D and patent grants for the USA and the Russian Federation -motivated by the results of a kernel density estimation and transition matrices -a quantile regression is performed for a basic knowledge production function design; for Russia as well for an extended design.The results show that in both countries there exist groups of regions with smaller sized research systems that report significantly different dynamics and thus knowledge production functions than regions with larger sized research systems.

The Key Actors of Knowledge Production Function in Turkey: The Role of Universities as a Knowledge Producer

“…They reveal that the R&D staff and the R&D expenditures of universities have significant effect on patent activities. Perret (2016) examines the knowledge production process, which he thinks as the key for national innovation system, for the USA and Russian Federation. Knowledge production is thought as an element that reveals the efficiency of innovation system.…”

An empirical knowledge production function of agricultural research and extension: The case of the University of California Cooperative Extension

“…We use a log-log formulation for the knowledge production function, which is standard in the literature (Czarnitzki et al, 2009;hUallachain and Leslie, 2007;Perret, 2016) and is based on the assumption of a Cobb-Douglas-type production function with no restriction on returns to scale. Econometrically, this functional form is informative about input elasticities.…”

“…It is driven through producing knowledge by firms and individuals, which allows them to stay competitive in the market (Buesa et al, 2010). Since the seminal paper by Griliches (1979), the concept of the knowledge production function has been further developed in theory (Czarnitzki et al, 2009) and applied at national (Perret, 2016), regional (Fritsch, 2002;hUallachain and Leslie, 2007;Charlot et al, 2014), sectoral (Gurmu et al, 2010), levels, and even using a metaanalysis of 15 individual studies (Neves and Sequeira, 2018).…”