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

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.

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Notes

  1. 1.

    A comprehensive analysis of the knowledge generation and transmission process can be found in Perret (2013).

  2. 2.

    At least, if transition statistics of the EBRD’s transition reports are considered, e.g., EBRD (2016), and one acknowledges that the transition process by far has not been successfully concluded, as fitting shown by Kasparov (2015).

  3. 3.

    Audretsch et al. (2014) provide a concise overview on the links between knowledge and innovation.

  4. 4.

    Note that Mathew (2012) provides an analysis, using quantile regressions, that is comparable in the same aspects to the one done in this study.

  5. 5.

    For example, Bitzer (2003) suggests a different approach to modeling a KPF and Kang and Dall’Erba (2015) argue on the importance of including spatial effects in the analysis.

  6. 6.

    Perret (2013) has shown that if both researchers and expenditures on R&D are included, biases due to high multi-collinearity arise. A large share of expenditures on R&D are spent on paying researchers, and thus, a high correlation between both variables arises.

  7. 7.

    See Griliches (1979).

  8. 8.

    Fritsch (2002) and Fritsch and Franke (2004) advocate the use of either researchers or R&D expenditures as knowledge inputs, though not both at the same time. As a large share of the R&D expenditures is used to pay for the researchers including both would lead to biases in the regression results.

  9. 9.

    In some sectors, like pharmaceuticals, the time frame might be much longer, while in other sectors, like food products, it can be much shorter.

  10. 10.

    See WIPO (2012b)

  11. 11.

    Officially, an application should be processed after 18 months.

  12. 12.

    While Fritsch and Slavtchev (2006) suggest that a 3-year lag offers the best alternative; Perret (2013) finds for the Russian Federation a lag of 2 years to provide the best results.

  13. 13.

    Note that in cases where a logarithm, aside from logarithm naturalis, has been implemented, the exponential function needs to be subsidized by a corresponding power function.

  14. 14.

    Even though it is recognized that limiting the study to the year 2012 does exclude recent development trends from a purely formal perspective, it is very convenient to stop in 2012. Until 2012, the regional layout across the Russian Federation has been relatively stable and all changes that did occur were only intra-regional or of an nominative nature. Stopping in 2012 excludes the regional re-allocations between Moscow city and the Moscow Oblast as well as having to argue the exclusion of the two de facto objects of Crimea and Sevastopol—if data were available at all—as they would significantly bias the results.

  15. 15.

    Furthermore, this high correlation might be considered a first indicator that both parts of the Russian NIS are connected.

  16. 16.

    The Nenetsia Autonomous Okrug is considered part of the Arkhangelsk Oblast and the Yamalia and Khantia-Mansia Autonomous Okrugs are considered parts of the Tyumen Oblast.

  17. 17.

    All variables implemented in this section enter the regression in logarithmized terms, except for shares. It can be argued that scaling the researcher variable by using per capita values would be more suitable for the overall validity of the estimation, to ensure comparability with other studies of KPF; however, absolute numbers are considered.

  18. 18.

    Integrating the GRP also allows one to control for business cycle effects.

  19. 19.

    See, for example, Kim (2010).

  20. 20.

    Imports impact innovativeness as it relieves pressure from domestic firms to innovate and provide products for their home market. Lichtenberg and van Pottelsberghe de la Potterie (1998) stresses that it is not so much the intensity of imports but the distribution of the countries of the origin of imports that matters; however, these effects are not accounted for in this study.

  21. 21.

    It is noted that a feedback relation between the generation of knowledge via patents and FDI flows seems highly likely even though respective tests do not yield corresponding results in this context.

  22. 22.

    The link between the market structure and the innovative output, the innovativeness of a region, is argued in detail already by Mansfield (1981), Cohen et al. (1987), Rothwell (1989), and Levin et al. (1991).

  23. 23.

    See respective reports by Yuri Levada Analytical Center (2012) or Russian Public Opinion Research Center / VCIOM (2012).

  24. 24.

    See Netter and Megginson (2001).

  25. 25.

    As with the researchers, it can argued that it might be more prudent to use per capita values instead.

  26. 26.

    Leite and Weidmann (1999) argues that corruption depends on natural resources, while Tompson (2006), a little less drastically, links corruption to large state-owned firms, which in Russia in particular persist in the oil and gas industry. Additionally, Verisk rates the Russian oil and gas sector as a sector of extremely high risk of corruption, going hereby in a similar direction as the EY report (EY 2014). It can, rightfully, be argued that in recent times, corruption also persists in other high-yield sectors like the defense industry or high-tech industries like the nano-tech industry; however, on the one hand, this study focusses on a period up to 2012, and thus, recent trends are only of partial interest. Also on the other hand, data has been available comprehensively for the full time period only for the oil and gas sector.

  27. 27.

    Note, in this context, also the proclaimed negative relation between resource endowments and economic growth which, in the literature, is referred to as the resource curse. See for a discussion of this phenomenon for example Auty (1993).

  28. 28.

    The indicator is calculated as the relation of the sum of exports and imports against the GRP. Even though the exports are as well part of the openness indicator, multicolinearity is no problem in this context.

  29. 29.

    The inclusion of the openness indicator might, however, not generate significant additional information as it basically replicates the effects of exports, imports and GRP in a composite form.

  30. 30.

    All monetary variables including the GRP, the exports, and imports as well as the FDI enter the model in real terms with the base year 1995.

  31. 31.

    Estimations have been carried out for a 2- and a 3-year time lag. In both cases, the results look almost identical. Therefore, only the results for 2-year time lag are presented herein.

  32. 32.

    Note that while the international perspective with patents from the EPO is mainly skipped due to the fact that the coefficients are nearly constant, a preliminary analysis of the residual deviation shows it to be highly non-linear and comparable to either a polynomial of the fifth order or a trigonometrical function. As both cases are not exactly solvable, this study restricts itself solely to the domestic perspective.

  33. 33.

    A figure for the case with a break at 25 patents has not been presented separately as the results would more or less copy Figs. 1114. Table 1 furthermore underlines the similarity of both cases.

  34. 34.

    Asterisks are used to signify significance. * is an error margin of 10%, ** of 5% and *** of 1%.

  35. 35.

    It is taken into account that piecewise functions are regularly defined via the independent variable and not as has been the case here via the dependent variable.

  36. 36.

    While this procedure makes the KPF continuous at the value R = 897.188, it still remains non-differentiable. This design flaw needs to be remarked upon as the basic KPF has been noted in its log-linear form, and the logarithm as well as the first order derivative are linked to the growth rate of the function. This problem, however, will not be treated in the present study.

  37. 37.

    Note that in both cases, a fixed effects estimator has been implemented and thus the resulting KPFs are comparable.

  38. 38.

    Note that these observations relate mostly to specific smaller regions which report consistently small values and are not limited to the years of the 1990s and thus to effects of the transition recession.

  39. 39.

    This development can be seen from ongoing political decisions but also from the studies by Chebankova (2008) and Solnick (2016).

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Acknowledgements

The author would like to thank Mr. David Hanrahan for editorial support.

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Perret, J.K. Re-Evaluating the Knowledge Production Function for the Regions of the Russian Federation. J Knowl Econ 10, 670–694 (2019). https://doi.org/10.1007/s13132-017-0475-z

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Keywords

  • Russian federation
  • Knowledge
  • Knowledge production function
  • Knowledge generation
  • Quantile regression
  • Regional economics