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Empirical Economics

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01.12.2014

Succeeding in innovation: key insights on the role of R&D and technological acquisition drawn from company data

verfasst von: Andrea Conte, Marco Vivarelli

Erschienen in: Empirical Economics | Ausgabe 4/2014

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Abstract

This paper discusses the relationship between a company’s investment in innovation and its success in introducing new product and/or process innovations. In doing so, this analysis departs from the standard approach which puts forward a homogenous R&D-based knowledge production function by introducing different types of innovation investments (R&D and technology acquisition) for different sets of companies. Using the Community Innovation Survey (CIS) dataset comprising more than 3,000 Italian manufacturing companies, the econometric analysis adopts a set of techniques which allows to control for the sample selection, endogeneity and simultaneity problems which arise when dealing with CIS data. The main findings are summarised as follows: (1) beyond the acknowledged effect of R&D in increasing the probability of success of product innovation, a larger-than-expected role is played by technology acquisition in the innovation process; (2) the relative importance of R&D and technology acquisition varies significantly across different types of companies where crucial dimensions of analysis are company size and the technological domain of a sector.

Vorheriger Artikel Bank competition in transition countries: Are those markets really in equilibrium?

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This broader perspective is well represented by the shift from the R&D-focused Frascati Manual (‘Guidelines for the collection of R&D data’, first published in 1963) to the Oslo manual in the 1990s (OECD, 1st edn. 1997).

For an extensive review of the evolution of the literature on technological change, see Conte (2006).

See theories on the so-called ‘Schumpeterian patterns of innovation’ and ‘sectoral systems of innovation’ (Breschi et al. 2000; Malerba et al. 2008).

Santarelli and Sterlacchini (1990) pointed out the important role of embodied technological change in small Italian firms operating in traditional and mature sectors such as textiles, wood and furniture, shoes, etc.

Statistical weights are used to make the sample representative of the underlying population according to three dimensions: size, sector and (macro) regions by correcting for different response rates across groups. Statistical checks introduced in the survey (such as sampling weights, treatment of missing values, etc.) follow the guidelines of the OSLO manual (1st edn. 1997). On average, the response rate to this survey was 62 %—among the highest in Europe.

Indeed, CIS data structure does not permit the investigation of the role of R&D and TA in making a firm innovative (for a study which is based on a comparison between innovative and non-innovative Italian firms—using a different database—see Parisi et al. 2006).

In principle, current and lagged values of R&D expenditures should be included in the regressions. Unfortunately, this is not possible due to the cross-section nature of CIS data. Both R&D and TA data are flow variables in the CIS questionnaire and as such they are used in the empirical analysis. Crepon et al. (1998) underline the high cross-sectional correlation between stock and flow measures of innovative activity.

This model is discussed more extensively in Sect. 5.3.

An econometric procedure for testing a Tobit model (Tobin 1958) against a two-part Heckman-type model in the context of CIS data is provided by Conte (2009).

The correlation between actual and fitted values is 0.75 and 0.60 for R&D and TA, respectively. Both correlations are statistically significant at 99 %.

The interaction between R&D and TA is introduced as a further check into the regression. Indeed, it is important to remind that the fitted values of R&D and TA already take into account the cross-causality linkage which may affect these two inputs (see Table 8).

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Titel: Succeeding in innovation: key insights on the role of R&D and technological acquisition drawn from company data
verfasst von: Andrea Conte
Marco Vivarelli
Publikationsdatum: 01.12.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 4/2014
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-013-0779-1

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