Abstract
We examine the empirical link between collaborative R&D strategies and the research and innovation performance of small- and medium-sized enterprises in peripheral locations. Using a survey of German firms combined with time series information on patent applications obtained from the European Patent Office, we apply a comparison-group approach and estimate different “treatment effect” models to assess the notion of causality underlying this relationship. Besides accounting for observed and unobserved firm-specific heterogeneity, we thereby also control for the likely endogeneity of R&D collaboration as a strategic choice in the course of research and innovation activities. Our results for the period 2001–2007 indicate that engaging in R&D collaboration vis-á-vis a non-collaborative research strategy is related to higher outcome levels for a firm’s key research and innovation indicators such as R&D and patent intensity. We also find that this latter link varies by firm size and the organizational type of cooperating partner, and especially those firms that simultaneously engage in research collaborations with other private businesses and research institutes/universities show above average innovation performance. In contrast, the notion of spatial proximity to research partners is shown to be of less importance. Our results may be of help for the future design of regional policies supporting the research and innovation activity of small firms in peripheral and remote locations outside large metropolitan areas.
Similar content being viewed by others
Notes
The terms “R&D collaboration” and “R&D cooperation” are used synonymously throughout this analysis.
For Germany, area boundaries are defined by the Conference of Ministers for Spatial Planning (Schäfer et al. 2007). Only from 2009 onwards were parts of the Federal State of Thuringia connected to the European metropolitan region “Mitteldeutschland” as an extension of the former metropolitan region “Sachsendreieck.”
For details on innovation support in Germany, see, for instance, http://www.germaninnovation.org, and for an overview of current EU funding schemes within the Horizon 2020 framework program for research and innovation, see, for example, http://ec.europa.eu/programmes/horizon2020/en/area/smes.
Firm size may also be interpreted as a measure of market power and the ability of firms to determine the underlying contractual basis of R&D collaborations. In this case, the measured difference in output from joint R&D activities could be biased in favor of large firms so that the “absorptive capacity” argument has to be interpreted carefully.
Schubert (2015) has recently shown that the risk of infringement of intellectual property may depend on the type of innovation and collaborative partnership.
The basic argument in favor of superior returns on local collaborations relative to non-local is related to the costs associated with the coordination and transfer of knowledge over larger distances. Thus, geographical proximity may significantly facilitate innovation intensity in local networks, especially if knowledge is tacit and relies heavily on face-to-fact contact and personal relations. On the other hand, opponents of this theory argue that the emergence of modern information and communication technologies has made knowledge networks less spatially delimited than previously (Boschma 2005).
See Kleinknecht et al. (2002) for a discussion of the advantages and disadvantages of different innovation indicators.
This survey was conducted in the evaluation process of two direct enterprise support schemes, namely the “Joint Task for the Promotion of Industry and Trade” and the “Promotion of Joint Research Projects,” on behalf of the Thuringian Ministry of Economics and the Thuringian Ministry of Science, Research and Arts. For the survey, a total of 6861 firms in the manufacturing and service sectors were contacted. The response rate was around 21 %. For an earlier scientific study using these data, see Alecke et al. (2012), who also report tests for sample distribution and representativeness.
However, as this variable is a binary indicator, we cannot identify the actual intensity of individual and cooperative R&D activity for the individual firm. This may have an additional impact on the output variables analyzed. Our results should thus be seen as a necessary first step, which calls for further research.
The questionnaire also asks about collaboration within a narrow 30 km radius around each responding firm; however, in this case, the subset of regional collaborations would be very small, making statistical inference unfeasible.
Collaboration can take place in either the same or different R&D projects.
PATSTAT is jointly developed by the EPO and the OECD (for further information, see www.epo.org).
The manual check for patent applications was based on https://depatisnet.dpma.de/.
Of course, the authors are aware that the use of patent information also has certain shortcomings as pure patent counts are not capable of controlling for the quality or breadth of a patent. Also, as pointed out by an anonymous reviewer, the use of R&D expenditures as an outcome variable for the process of collaborative R&D activities is limited as an increase in R&D spending (e.g., through the hiring of skilled employees) may also indicate a strategic input in the conduct of R&D collaboration. Interpretations thus have to be made with care.
The average degree of missingness is 6.51 %. Detailed information can be obtained upon request.
The idea of the MI approach proposed by Rubin (1987) is to estimate missing observations based on non-missing sample information. MI assumes that the set of covariates to be imputed is distributed from a multivariate normal distribution. As the distributional parameters are unknown in practical applications, Gibbs samples are required to generate multiply imputed samples from the posterior predictive distribution of unobservable information given the observable sample information for the covariates.
A convenient overview of the matching approach for treatment effect estimation is given, for instance, by Angrist and Pischke (2009).
Or at least in its weaker form as the conditional independence assumption on \(y_{0}\) states, given by: \(E(y_{i,0}\vert \mathbf{X}_{i}, d=1) = E(y_{i,0}\vert \mathbf{X}_{i}, d=0) = E(y_{i,0}\vert \mathbf{X}_{i})\) (see Todd 2008).
One major advantage of the Kernel procedure, compared to NN matching, is the lower variance of the estimator as more information is used (Caliendo and Kopeinig 2008).
As we are using multiply imputed data, we estimate the above parameter for each of the five imputed datasets separately and compute the unweighted average of these estimates to test for the statistical significance of the difference in outcome between treated and comparison firms.
We set the number of bootstrap replications equal to 500.
We use the term “pseudo” panel here since our combined dataset only fulfills the minimum requirements for a panel data analysis given that we only observe the dependent variable over time and can construct a time-varying interaction term based on the treatment indicator and a time dummy, but we do not have time-varying information for the set of covariates. Further details will be given below.
Moreover, we include sectoral dummies for two-digit industries to account for cross-industry heterogeneity in the R&D collaboration strategy.
In Table 2, the matching estimation was carried out for the outcome variable R&D intensity. The results for the two alternative patent outcome variables yield almost the same results and can be obtained from the authors upon request.
Here we have used the Epanechnikow Kernel and a bandwidth parameter of \(h=0.06\).
Likewise, for patent intensity the relative difference in outcome is 59 %.
For patent intensity, we find that collaborating firms have a relative outcome difference of 0.19. The reference level for (matched) comparison firms is a patent intensity of 0.32 patents per employee.
For the Heckman model, we use additional instruments related to the wage compensation for employees (pay rates above or below industry collective agreement), as well as the number of R&D personnel, which can be seen as relevant indictors for the decision to engage in R&D collaboration or not. Summary statistics for these additional instruments can be found in the “Appendix.”
Empirical support for this argument is also given by additional estimation results (not reported), using survey information on the introduction of product/process innovations according to the OSLO manual OECD (2005). Here, small firms with 11–50 employees are found to derive the greatest benefits from joint R&D activity compared to firms with individual R&D activity. This finding is in line with Gallego et al. (2012), who argue that small firms in particular benefit from organizational innovations in combination with an intensive use of external knowledge.
The reader should note that although the number of treated firms is sufficiently large in the overall sample to conduct statistical inference by means of matching, for the size-related subsamples the number of observations can become quite small. As Antonietti and Antonioli (2011) point out for a comparable matching approach with a fairly small number of treated firms, in these cases the empirical exercise should merely be considered an explorative experiment.
We focus on the overall sample results here. Results by size group can be obtained from the authors upon request.
Although there have been similar follow-up programs for this type of public support scheme (see, for instance, information on the web presence of the Thuringian Ministry of Economics, Science and Digital Society, http://www.thueringen.de/th6/tmwat/technologie/technologie_innovationen/-foerderung/richtlinien/verbund), it is reasonable to assume that the redefined fixed program periods result in the firm’s choice of adopting a particular R&D strategy being driven by the exogenously determined possibility of receiving public support for joint R&D activities.
References
Alecke B, Otto A, Untiedt G (2010) FuE und Innovationen in Ostdeutschland: Strukturelle Unterschiede bestimmen den Rückstand. Informationen zur Raumordnung 10/11.2010:759–771
Alecke B, Mitze T, Reinkowski J, Untiedt G (2012) Does firm size make a difference? Analysing the effectiveness of R&D subsidies in East Germany. Ger Econ Rev 13(2):174–195
Angrist J, Pischke J (2009) Mostly harmless econometrics: an empiricist’s companion. Princeton University Press, Princeton
Anselin L, Varga A, Acz Z (1997) Local geographical spillovers between university research and high technology innovations. J Urban Econ 42:422–488
Antonietti R, Antonioli D (2011) The impact of production offshoring on the skill composition of manufacturing firms: evidence from Italy. Int Rev Appl Econ 25(1):87–105
Ashenfelter O (1978) Estimating the effect of training programs on earnings. Rev Econ Stat 60:47–57
Asheim B, Isaksen A (2002) Regional innovation systems: the integration of local ‘sticky” and global “ubiquitous” knowledge. J Technol Transf 27(1):77–86
Ball G, Kesan J (2009) Transaction costs and trolls: strategic behavior by individual inventors, small firms and entrepreneurs in patent litigation. Illinois Law and Economics Papers Series Research Papers Series No. LE09-005
Bathelt H, Malmberg A, Maskell P (2004) Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Prog Hum Geogr 28:31–56
BBSR - Bundesinstitut für Bau-, Stadt- und Raumforschung (2009) Positionierung Europäischer Metropolregionen in Deutschland. BBSR-Berichte KOMPAKT 3/2009, Bonn
Belderbos R, Carree M, Lokshin B (2006) Complementary in R&D cooperation strategies. Rev Ind Organ 28(4):401–426
Bertrand M, Duflo E, Sendhil M (2004) How much should we trust difference-in-difference estimates? Q J Econ 119(1):249–275
Bjerke L, Johansson S (2015) Patterns of innovation and collaboration in small and large firms. Ann Reg Sci 55(1):221–247
Blomqvist K, Levy J (2006) Collaboration capability—a focal concept in knowledge creation and collaborative innovation in networks. Int J Manag Concepts Philos 2(2):31–48
Breschi S, Malerba F (1997) Sectoral systems of innovation. In: Edquist C (ed) Syst Innov. Pinter, London, pp 130–156
Boschma R (2005) Proximity and innovation: a critical assessment. Reg Stud 39(1):61–74
Boschma R, Frenken K (2010) The spatial evolution of innovation networks. A proximity perspective. In: Boschma R, Martin R (eds) The handbook of evolutionary economic geography. Edward Elgar, Cheltenham, pp 120–135
Broekel T, Buerger M, Brenner T (2015) An investigation of the relation between cooperation and the innovative success of German regions. Spat Econ Anal 10(1):52–78
Caliendo M, Hujer R (2006) The microeconometric estimation of treatment effects—an overview. Allgemeines Statistisches Archiv 90(1):199–215
Caliendo M, Kopeinig S (2008) Some practical guidance for the implementation of propensity score matching. J Econ Surv 22(1):31–72
Cantner U, Kösters S (2012) Picking winners? Empirical evidence on the targeting of R&D subsidies to start-ups. Small Bus Econ 39(4):921–936
Chun H, Mun S (2012) Determinants of R&D cooperation in small and medium-sized enterprises. Small Bus Econ 39(2):419–436
Cohen W, Klepper S (1996) A reprise of size and R&D. Econ J 106:925–951
Czarnitzki D, Ebersberger B, Fier A (2007) The relationship between R&D collaboration, R&D subsidies and R&D performance: evidence from Finland and Germany. J Appl Econom 22(7):1347–1366
d’Aspremont C, Jacquemin A (1988) Cooperative and noncooperative R&D in duopoly with spillovers. Am Econ Rev 78(5):1133–1137
Doran J, Jordan D, O’Leary E (2012) The effects of the frequency of spatially proximate and distant interaction on innovation by Irish SMEs. Entrep Reg Dev 24(7–8):705–727
Destatis (2008) Klassifikation der Wirtschaftszweige. Mit Erläuterungen. Wiesbaden, Statistisches Bundesamt
DPMA - Deutsches Patent und Markenamt (2006) Patentatlas Deutschland. Regionaldaten der Erfindungstätigkeit, München
Edquist C (1997) Systems of innovation: technologies, institutions, and organizations. Pinter, London
Freel M (2000) External linkages and product innovation in small manufacturing firms. Entrep Reg Dev 12(3):245–266
Fritsch M, Slavtchev V (2011) Determinants of the efficiency of regional innovation systems. Reg Stud 45(7):905–918
Gallego J, Rubalcaba L, Hipp C (2013) Organizational innovation in small European firms: a multidimensional approach. Int Small Bus J 31(5):563–579
Gatrell J (2000) Integrated dependence: knowledge-based industries in peripheral regions. Econ Dev Rev 17(3):63–69
GEFRA, MR Gesellschaft für Regionalberatung, TraSt (2004) Ergebnisse der Unternehmensbefragung, Investitionsförderung und Regionale Wirtschaftsförderung’ im Rahmen der Gemeinschaftsaufgabe, Verbesserung der Regionalen Wirtschaftsstruktur’ 1997–2003 in Thüringen, Münster
GEFRA, GEWIMAR, TraSt (2005) Evaluierung der Förderung von Verbundprojekten im Bereich Forschung und Entwicklung im Freistaat Thüringen 1997–2003, Münster
Grillitsch M, Nilsson P (2015) Innovation in peripheral regions: Do collaborations compensate for a lack of local knowledge spillovers? Ann Reg Sci 54(1):299–321
Guo S, Fraser M (2014) Propensity score analysis: statistical methods and applications, 2nd edn. SAGE Publications, Thousand Oaks
Harrigan K (1988) Joint ventures and competitive strategy. Strateg Manag J 9(2):141–158
He Z, Wong P (2012) Reaching out and reaching within: a study of the relationship between innovation collaboration and innovation performance. Ind Innov 19(7):539–561
Heckman J (1979) Sample selection bias as a specification error. Econometrica 47(1):153–161
Heckman J, Ichimura H, Todd P (1997) Matching as an econometric evaluation estimator: evidence from evaluating a job training programme. Rev Econ Stud 65:261–294
Hemphill T, Vonortas N (2003) Strategic research partnerships: a managerial perspective. Technol Anal Strateg Manag 15(2):255–271
Hussinger K (2008) R&D and subsidies at the firm level: an application of parametric and semiparametric two-step selection models. J Appl Econom 23(6):729–747
Katz M (1986) An analysis of cooperative research and development. RAND J Econ 17(4):527–543
Keeble D (1997) Small firms, innovation and regional development in Britain in the 1990s. Reg Stud 31:281–293
Kleinknecht A, van Montfort K, Brouwer E (2002) The non-trivial choice between innovation indicators. Econ Innov New Technol 11(2):109–121
Koberg C, Detienne D, Heppard K (2003) An empirical test of environmental, organizational, and process factors affecting incremental and radical innovation. J Technol Manag Res 14:21–45
Lechner M (2011) The estimation of causal effects by difference-in-difference methods. Found Trends Econom 4(3):165–224
Lööf H, Broström A (2008) Does knowledge diffusion between university and industry increase innovativeness? J Technol Transf 33(1):73–90
Lopez-Fernandez C, Serrano-Bedia A, Garcia-Piqueres G (2012) Innovation capacity in European peripheral regions: determinants and empirical evidence. In: Katzy B, Holzmann T, Sailer K, Thoben D (eds) Proceedings of the 2012 8th international conference on engineering, technology and innovation, pp 1–10
Lynch S, Locke W (2003) Determinants of R&D activities in SMEs located in peripheral regions. Paper presented at the 55th International Atlantic Economic Conference, Vienna, March 12–16, 2003
McAdam R, McConvery T, Armstrong G (2004) Barriers to innovation within small firms in a peripheral location. Int J Entrep Behav Res 62(4):489–501
McAdam R, Reid R, Shevlin M (2014) Determinants for innovation implementation at SME and inter SME levels within peripheral regions. Int J Entrep Behav Res 20(1):66–90
Noteboom B (1994) Innovation and diffusion in small firms: theory and evidence. Small Bus Econ 6(5):327–347
OECD (2005) Oslo manual. Guidelines for collecting and interpreting innovation data. OECD, Paris
Quandt R (1958) The estimation of parameters of linear regression system obeying two separate regimes. J Am Stat Assoc 55:873–880
Quandt R (1972) A new approach to estimating switching regressions. J Am Stat Assoc 67:306–310
Revilla A, Fernandez Z (2012) The relation between firm size and R&D productivity in different technological regimes. Technovation 32:609–623
Rosenbaum P, Rubin D (1983) The central role of the propensity score in observational studies of causal effects. Biometrika 70(1):41–55
Rubin D (1987) Multiple imputation for nonresponse in surveys. Wiley, New York
Sakakibara M (1997) Evaluation of government-sponsored R&D consortia in Japan: Who benefits and how? Res Policy 26:447–473
Sianesi B (2004) An evaluation of the active labour market programmes in Sweden. Rev Econ Stat 86(1):133–155
Schäfer R, Stellmacher F, Lutter H (2007) Initiativkreis Europäische Metropolregionen in Deutschland. Praxis Heft 52, Bonn: BBR Werkstatt
Schubert T (2015) Infringement of intellectual property in innovation partnerships. R&D Manag. doi:10.1111/radm.12128
Schwartz M, Peglow F, Fritsch M, Günther J (2012) What drives innovation output from subsidized R&D cooperation? Project-level evidence from Germany. Technovation 32:358–369
Todd P (2008) Matching estimators. In Durlauf S, Blume L (eds) The new palgrave dictionary of economics, 2nd edn. Palgrave Macmillan, New York. http://www.dictionaryofeconomics.com/article?id=pde2008_M000365 (Last Accessed 24 Sep 2015)
Trippl M (2011) Regional innovation systems and knowledge-sourcing activities in traditional industries—evidence from the Vienna food sector. Environ Plann A 43(7):1599–1616
Vega-Jurado J, Gutiérrez-Gracia A, Fernández-de-Lucio I (2009) Does external knowledge sourcing matter for innovation? Evidence from the Spanish manufacturing industry. Ind Corp Change 18(4):637–670
Vonortas N (1994) Inter-firm cooperation with imperfectly appropriable research. Int J Ind Organ 12(3):413–435
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Funding
This research work did not receive any financial funding.
Human and animal rights
Human participants surveyed during this research project were informed about its scope and consented to the scientific use of the survey results. The research did not involve animals.
Additional information
The authors thank Rannveig Edda Hjaltadóttir, Charlie Karlsson and Christoph M. Schmidt for helpful comments on this manuscript as well as Audur Inga Rúnarsdóttir for excellent research assistance in building up a patent database. We further acknowledge the valuable advice received from two anonymous referees and the Editor-in-Chief Martin Andersson.
Appendix
Appendix
See Table 11.
Rights and permissions
About this article
Cite this article
Mitze, T., Alecke, B., Reinkowski, J. et al. Linking collaborative R&D strategies with the research and innovation performance of SMEs in peripheral regions: Do spatial and organizational choices make a difference?. Ann Reg Sci 55, 555–596 (2015). https://doi.org/10.1007/s00168-015-0719-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00168-015-0719-4