Linking collaborative R&D strategies with the research and innovation performance of SMEs in peripheral regions: Do spatial and organizational choices make a difference?


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.

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  1. 1.

    The terms “R&D collaboration” and “R&D cooperation” are used synonymously throughout this analysis.

  2. 2.

    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.”

  3. 3.

    For details on innovation support in Germany, see, for instance,, and for an overview of current EU funding schemes within the Horizon 2020 framework program for research and innovation, see, for example,

  4. 4.

    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.

  5. 5.

    Schubert (2015) has recently shown that the risk of infringement of intellectual property may depend on the type of innovation and collaborative partnership.

  6. 6.

    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).

  7. 7.

    See Kleinknecht et al. (2002) for a discussion of the advantages and disadvantages of different innovation indicators.

  8. 8.

    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.

  9. 9.

    For the time period of the analysis, Thuringia was not included in any of the European metropolitan regions (BBSR 2009; Schäfer et al. 2007).

  10. 10.

    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.

  11. 11.

    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.

  12. 12.

    Collaboration can take place in either the same or different R&D projects.

  13. 13.

    PATSTAT is jointly developed by the EPO and the OECD (for further information, see

  14. 14.

    The manual check for patent applications was based on

  15. 15.

    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.

  16. 16.

    The average degree of missingness is 6.51 %. Detailed information can be obtained upon request.

  17. 17.

    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.

  18. 18.

    A convenient overview of the matching approach for treatment effect estimation is given, for instance, by Angrist and Pischke (2009).

  19. 19.

    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).

  20. 20.

    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).

  21. 21.

    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.

  22. 22.

    We set the number of bootstrap replications equal to 500.

  23. 23.

    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.

  24. 24.

    Moreover, we include sectoral dummies for two-digit industries to account for cross-industry heterogeneity in the R&D collaboration strategy.

  25. 25.

    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.

  26. 26.

    Here we have used the Epanechnikow Kernel and a bandwidth parameter of \(h=0.06\).

  27. 27.

    Likewise, for patent intensity the relative difference in outcome is 59 %.

  28. 28.

    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.

  29. 29.

    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.”

  30. 30.

    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.

  31. 31.

    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.

  32. 32.

    We focus on the overall sample results here. Results by size group can be obtained from the authors upon request.

  33. 33.

    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,, 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.


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Correspondence to Timo Mitze.

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The authors declare that they have no conflict of interest.


This research work did not receive any financial funding.

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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.



See Table 11.

Table 11 Variable definition and descriptive statistics for the sample of R&D-active firms

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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).

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JEL Classification

  • C14
  • C21
  • O32
  • O38