Abstract
This paper explores R&D offshoring’s role in radical product innovations. These innovations are important for companies’ growth strategies, and we check the extent to which companies rely on external sources, which may bring knowledge that differs significantly from that already present internally. The evidence for Spanish firms between 2004 and 2013 shows that R&D offshoring influences significantly the intensity of radical but not of incremental innovations. This influence is apparently smaller when external knowledge comes from universities or research institutions rather than from the business sector. The recent financial crisis also exerted a detrimental effect on this influence, as compared with the previous period of economic growth.
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Notes
By radical innovations we mean those that embed a more novel component than in the case of incremental innovations. As explained in the data section, we use information on new or significantly improved products for the market as a proxy for radical innovation (as compared with new or significantly improved products only for the firm). As signaled by a referee, it is obvious that not everything that is new to the market is a radical or breakthrough innovation. However, this is the only proxy that we can obtain for radical innovations with the information contained in a CIS-type survey, and it has been used by prior studies for measuring breakthrough or radical innovations (Coad et al. 2016; Laursen and Salter 2006; Tether and Tajar 2008; Van Beers and Zand 2014). Thus, we decided to keep the term radical innovations, despite being aware that it could overstate the variables. We thank an anonymous referee for highlighting this point.
Beliefs and ways of solving problems that allow decision making in certain directions (see Phene et al. 2006).
We thank the editor for highlighting this point.
The excluded variables are presented in Sect. 5.2. These exclusion restrictions guarantee the identification of the system and avoid problems of collinearity in the last step.
We decided to estimate bootstrap errors because of the use of the generated variables (Mill’s ratios) in this second stage. As explained by Heckman (1979), the non-inclusion of those ratios can be seen as an omitted variable problem due to the fact that the expected value of the dependent variable depends on the selection term—the probability of being an innovative firm—leading to an inconsistency of the parameters of interest in the second stage (Wooldridge 2010, p. 805).
The exogenous variable could be correlated with managerial abilities, which are unobserved.
In this case, \( X_{it} \) and \( Z_{it} \) can have possibly common elements.
We interact the inverse Mill’s ratios with time dummy variables in order to allow γ to be different across \( t \).
Firms with more than 20% of the market share in a given sector represent around 0.19% of total observations and 0.07% of the enterprises in the sample. The threshold of 20% of the market share was chosen following previous evidence that is also based on the PITEC survey, such as López-García and Montero (2010). Additionally, in the case of those observations for which internal R&D expenditures are more than two times the volume of sales, we have replaced such values with a maximum value of 2—representing around 0.6% of total observations. Although the selection of a value of 2 is arbitrary, other smaller values did not imply any change in the results. These additional estimates are available upon request.
Most R&D offshoring of European firms is conducted between firms within the European Union (Tübke and Van Bavel 2007).
Following previous studies that use CIS-type survey data, we develop the ratio between the percentage of sales over one minus the percentage of sales, and take the logs of the ratio. As the log of the bounds (zero and one) are not defined, we apply a winsorizing process for the extreme values, assigning 0.9999 to 1 and 0.0001 to 0 (see Klomp and Van Leeuwen 2001; Mohnen et al. 2006; Raymond et al. 2010; Robin and Schubert 2013). We decided to use this transformation because it is closer to a normal distribution and lies in the set of real numbers that vary from -∞ to +∞. As the variable is very skewed, this is a necessary transformation in order to get close to a normal distribution.
The offshoring variable, as in the PITEC database, refers to the acquisition of knowledge through licensing and does not include joint ventures.
As stressed in the hypotheses section, in order to consider whether there is a different role of offshoring in large and small enterprises, we split the sample into large enterprises (LEs), those firms with more than 200 workers, and small and medium-sized enterprises (SMEs), with 200 workers and fewer, following the classification in the PITEC survey. The results of the Chow tests at the bottom of columns 2, 4, and 6 in Table 5 stress the significant differences between SMEs and LEs. Thus, we test our first two hypotheses taking into account this difference. In the case of our third hypothesis—different impact of offshoring before and during the crisis—we decided to use two dummy variables: one for the pre-crisis period, and another one for the crisis, and interact them with the offshoring variable (columns 7 and 8 of Table 5). This procedure allows a fair comparison between the parameters while avoiding an important reduction in the number of observations in each subsample.
The sectoral dummy variables are at the two-digit level (NACE 1.1). For a detailed list, see the following website (p. 11): https://icono.fecyt.es/PITEC/Documents/2016/dise%C3%B1oregistro_sindelimitadores2014%20(2017).pdf
We acknowledge the possibility of reverse causality, as detailed in Sect. 6.1.
We should also be aware that the share of firms that purchase technology from foreign research centers or universities is very small as compared with the share that purchase from the business sector (see also Gutiérrez Gracia et al. 2007).
We also run the regressions for a balanced panel for hypotheses 1–2, thereby trying to take into account a possible attrition problem; and the results barely change (the results are available from the authors on request). This seems to show that there is no problem of attrition as we would expect since the rate of dropout from the panel is very small. We thank the editor for pointing this out.
The major issues reported include: a firm belonging to a sector with high employment turnover; an acquired firm; a change in the unit of reference; a change in or abandonment of activity; a firm remaining from an acquisition process (not part of the acquisition); a firm in liquidation; a merged firm; a firm that has employees ceded by other firms; a consequence of the crisis; and a firm that cedes employees to other firms. The time frame for the pre-crisis period is 2004‒2008, while the crisis period is 2009‒2013. The reasoning comes from the fact that the crisis started to show its impact in 2009 (Hud and Hussinger 2015).
We thank the editor for highlighting this point (results upon request from the authors).
Not only the number of enterprises but also the amount of money that is allocated to this strategy has been reduced among those enterprises that conducted R&D offshoring throughout the entire period.
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Acknowledgements
We thank Antonio Di Paolo, Anastasia Semykina, the participants of the DRUID Academy Conference 2017, especially Valentina Tartari, and finally the editor and two anonymous referees for their very useful comments. Rosina Moreno acknowledges financial support from the project “Redes de colaboración tecnológica e innovación. Determinantes y efectos sobre la competitividad de las empresas españolas”, which was funded by the Fundación BBVA Ayudas a Proyectos de Investigación 2014, Ayudas Fundación BBVA as well as the Ministerio de Economía y Competitividad for the project entitled “Innovation and locational factors: Diversification, knowledge and the environmental revolution”, ECO2017-86976-R and the Program ICREA Acadèmia. Damián Tojeiro-Rivero gratefully acknowledges financial support from the University of Barcelona (APIF). Erika Badillo wishes to acknowledge the financial support from the Universidad Autónoma Latinoamericana (UNAULA) Research Fund, 26-000019. The usual disclaimer applies.
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Tojeiro-Rivero, D., Moreno, R. & Badillo, E.R. Radical Innovations: The Role of Knowledge Acquisition from Abroad. Rev Ind Organ 55, 173–207 (2019). https://doi.org/10.1007/s11151-018-9659-3
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DOI: https://doi.org/10.1007/s11151-018-9659-3