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Innovation behaviour of firms in a small open economy: the case of the Czech manufacturing industry

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Abstract

This paper describes the role of R&D and analyse its impact on productivity in the Czech economy in a CDM model. Four CIS waves (2001, 2003, 2006, and 2008) were used in the CDM model. The estimated low innovation input elasticity around 9 % describes the Czechs as poor innovators in the EU. This economy was a developing country until 2006 and we have observed a substantive FDI inflow since 1998. Multinationals have a higher sales share now and are an essential part of the economy. Multinationals engage less in innovation, but innovating MNEs spend more on R&D per employee and appropriate more from their innovated goods. The FDI inflow was a form of innovation wave. Innovation output is an important determinant for boosting productivity among SME’s. Public support had positive effect on innovation intensity; however, no additional effect on innovation output.

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Notes

  1. For justification of basic research—see Arrow (1962).

  2. There are neoclassical theories of Romer (1986) and Lucas (1988). There are also competing Schumpeterian theories of Grossman (1993), Aghion and Howitt (2005), and others. There are Evolutionary theories of Nelson (1982), and Dopfer (2005). End eventually complexity science theories (path dependence) of David (1975) and Liebowitz and Margolis (1995).

  3. For more on the issue and distinction between invention, innovation and imitation (see Brozen 1951; Schmookler 1966; Godin 2008).

  4. The National Innovation Strategy (Government Resolution No. 207 of 24 March 2004) and National Innovation Policy of the Czech Republic for 2005–2010 (Government Resolution No. 851 of 7 July 2005) defined state support opportunities and proffered areas of research.

  5. European patent office (EPO) patents, there are about 10 million inhabitants in the Czech Republic. Patent are count in Fractional count, inventor’s country, priority date.

  6. This concept (Hashi and Stojcic 2013) was also used by authors Zemplinerová and Hromádková (2012). This paper will follow to some extent the H-CDM framework, but uses R&D expenditures and output per employee to allow for comparison with other papers.

  7. See identification issues, OLS bias, self-reporting issues, under-representation, linear assumptions, productivity function bias and more in Loof and Hesmati (2006), Janz et al. (2004), or Hashi and Stojcic (2013).

  8. During last 3 years this firm had non-zero sales from innovated products and services and non-zero R&D expenditures in one of these areas: Intramural and external R&D, new equipment, new knowledge, training, advertisement and costs related to introduction of goods to the markets, and other expenditures related to introduction of new products and processes. These expenditures are associated with product, process, organizational, or marketing innovation and they resulted in new to the firm innovation.

  9. Not all models are specified to the number of employees. Some are not normalized, i.e. plain innovation sales variable is used.

  10. CIS data—Innovation survey (TI), Firms’ data—The annual statement of economic entities in selected production sectors (P5-01), R&D data—Annual Report on research and development (VTR 5-01). Representativeness of data samples is provided by the CZSO surveys are carried out according to the State Statistical Service (No. 89/1995 Sb.). A detailed description of data and questionnaires can be found here: http://apl.czso.cz/pll/vykazy/pdf1. The CIS data analysis was possible at CERGE—EI.

  11. Hampering factors refer to barriers, which prevents a firm to innovate or innovate more (lack of information, skilled personnel, technology, finances, high costs, and prior innovation). Demand pull factors refer to information sources, which a firm use to market analyses and possible innovation projects. There are important information sources inside the firm, and outside the firm (suppliers, clients, competitors etc.). Technology push variables refer to a pressure of new technologies to the production function of a firm, which is motivated to introduce new product range, approach new markets, increase the quality of goods, production capacity, lower the costs, environmental impact, material and energy requirements. See the CIS questionnaire for full definitions (CZSO 2001).

  12. See Appendix Tables 11 and 19, there is a year dummy variable for the next wave—next year.

  13. We performed control estimation without individual effects with jackknife robust standard error. Statistically significant were only firms with 100–999 employees (3rd equation). The computation requirements for 3SLS jackknife procedure with fixed effects were too high, but we suspect similar results showing so called “inverse-U” relationship between innovation output and firm size.

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Acknowledgments

This paper is a result of the academic work at the University of J. E. Purkyně in Ústí nad Labem, Czech Republic.

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  1. Faculty of Social and Economic Studies, University of J. E. Purkyně in Ústí nad Labem, Moskevská 54, 400 96, Ústí nad Labem, Czech Republic

    Marek Vokoun

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Appendices

Appendix 1: CDM unreported results for the first period 1999–2003

1.1 Appendix 1.1: Probit marginal effects, robust standard error, Model 1 unreported variables

See Table 6.

Table 6 Decision to innovate, Czech CIS waves 2001 and 2003, Model 1 unreported variables
Full size table

1.2 Appendix 1.2a: Linear regression, cluster robust standard error, Model 3 unreported variables

See Table 7.

Table 7 Innovation input intensity, Czech CIS waves 2001 and 2003, Model 3 unreported variables
Full size table

1.3 Appendix 1.2b: Linear regression, cluster robust standard error, public support variables in Model 3

See Table 8.

Table 8 Innovation input intensity, Czech CIS wave 2001 and 2003, Model 3 with additional variables
Full size table

1.4 Appendix 1.3a: Three-stage least-squares jackknife regression, Models 5 and 7 unreported variables

See Tables 9, 10 and 11.

Table 9 3SLS model overview, Czech CIS wave 2001 and 2003, Model 5 and 7 unreported variables
Full size table
Table 10 Innovation output, Czech CIS wave 2001 and 2003, Model 5 and 7 unreported variables
Full size table
Table 11 Labour productivity output, Czech CIS wave 2001 and 2003, Model 5 and 7 unreported variables
Full size table

1.5 Appendix 1.3b: 3SLS jackknife regression, additional public support variables in Model 5 and 7

See Tables 12, 13 and 14.

Table 12 3SLS model overview, Czech CIS wave 2001 and 2003, Model 5 and 7 with additional variables
Full size table
Table 13 Innovation output, Czech CIS wave 2001 and 2003, Model 5 with additional variables
Full size table
Table 14 Labour productivity output, Czech CIS wave 2001 and 2003, Model 7 with additional variables
Full size table

Appendix 2: CDM results for the second period 2004–2008

2.1 Appendix 2.1: Probit marginal effects, robust standard error, Model 2 unreported variables

See Table 15.

Table 15 Decision to innovate, Czech CIS wave 2006 and 2008, Model 2 unreported variables
Full size table

2.2 Appendix 2.2: Linear regression, cluster robust standard error, unreported variables (Model 3)

See Table 16.

Table 16 Innovation input intensity, Czech CIS wave 2006 and 2008, Model 4 unreported variables
Full size table

2.3 Appendix 2.3: Three-stage least-squares jackknife regression, Models 6 and 8 unreported variables

See Tables 17, 18 and 19.

Table 17 3SLS model overview, Czech CIS wave 2006 and 2008, Model 6 and 8 unreported variables
Full size table
Table 18 Innovation output, Czech CIS wave 2006 and 2008, Model 6 and 8 unreported variables
Full size table
Table 19 Labour productivity output, Czech CIS wave 2006 and 2008, Model 6 and 8 unreported variables
Full size table

Appendix 3: CDM sensitivity tests (panel estimation) for the whole period 1999–2008

3.1 Appendix 3.1: Probit random effect model, average marginal effects, unreported panel results for decision to innovate variable

See Table 20.

Table 20 Decision to innovate, Czech CIS waves 2001, 2003, 2006, and 2008
Full size table

3.2 Appendix 3.2: Random-effects, GLS regression, unreported panel results for R&D expenditures intensity (Log of R&D expenditures per employee)

See Table 21.

Table 21 Innovation input intensity, Czech CIS waves 2001, 2003, 2004 and 2008
Full size table

3.3 Appendix 3.3: Three-stage least-squares regression, fixed effects (i.year, i.id dummy variables), unreported panel results for innovation output (log of sales from innovated goods per employee) and output (log of sales per employee)

See Tables 22, 23 and 24.

Table 22 3SLS model overview, Czech CIS waves 2001, 2003, 2006 and 2008
Full size table
Table 23 Innovation output, Czech CIS waves 2001, 2003, 2006 and 2008
Full size table
Table 24 Labour productivity output, Czech CIS waves 2001, 2003, 2006 and 2008
Full size table

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Vokoun, M. Innovation behaviour of firms in a small open economy: the case of the Czech manufacturing industry. Empirica 43, 111–139 (2016). https://doi.org/10.1007/s10663-015-9296-0

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