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Re-Evaluating the Knowledge Production Function for the Regions of the Russian Federation

Jens K. Perret ()
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Jens K. Perret: International School of Management

Journal of the Knowledge Economy, 2019, vol. 10, issue 2, No 10, 670-694

Abstract: Abstract The present study picks up on the aspect of knowledge generation—a key part of every national innovation system—in the context of the Russian Federation. Following Fritsch and Slavtchev (2006), a knowledge production function can be used to account for the efficiency of an innovation system. In detail, this study implements a panel quantile regression estimation approach and thus presents a novel approach in studying national innovation system and, more specifically, their efficiency. In particular, a non-linear knowledge production function is estimated to quantify for a possible non-linear impact of knowledge inputs on domestically—sing patents from the Russian Patent Office—and internationally—using patents from the European Patent Office—oriented knowledge output. Using regional data, it is shown that a non-linear impact of the inputs especially on Russian domestic patents can be found. The results offer new insights into the structure of the Russian innovation system as a threshold is identified where the innovation system switches from increasing returns of researcher input to decreasing returns. This implies that only smaller research systems work efficiently, and starting from a size of approximately 900 researchers, their efficiency steadily decreases.

Keywords: Russian federation; Knowledge; Knowledge production function; Knowledge generation; Quantile regression; Regional economics (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13132-017-0475-z

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