Journal of Productivity Analysis

, Volume 36, Issue 1, pp 55–69 | Cite as

Innovation and export activities in the German mechanical engineering sector: an application of testing restrictions in production analysis

  • Torben SchubertEmail author
  • Léopold Simar


Since Solow (Q J Econ 70:65–94, 1956) the economic literature has widely accepted innovation and technological progress as the central drivers of long-term economic growth. From the microeconomic perspective, this has led to the idea that the growth effects on the macroeconomic level should be reflected in greater competitiveness of the firms. Although innovation effort does not always translate into greater competitiveness, it is recognized that innovation is, in an appropriate sense, unique and differs from other inputs like labor or capital. Nonetheless, often this uniqueness is left unspecified. We analyze two arguments rendering innovation special, the first related to partly non-discretionary innovation input levels and the second to the induced increase in the firm’s competitiveness on the global market. Methodologically the analysis is based on restriction tests in non-parametric frontier models, where we use and extend tests proposed by Simar and Wilson (Commun Stat Simul Comput 30(1):159–184, 2001; J Prod Anal, forthcoming, 2010). The empirical data is taken from the German Community Innovation Survey 2007 (CIS 2007), where we focus on mechanical engineering firms. Our results are consistent with the explanation of the firms’ inability to freely choose the level of innovation inputs. However, we do not find significant evidence that increased innovation activities correspond to an increase in the ability to serve the global market.


Data envelopment analysis Bootstrap Subsampling Nonparametric efficiency estimation, technical efficiency Production Innovation Exports CIS Mechanical engineering Germany Discretionary 

JEL Classification

C14 C40 C60 D20 L60 O30 



L. Simar acknowledges support from the “Interuniversity Attraction Pole”, Phase VI (No. P6/03) of the Belgian Science Policy, from the Helga & Wolfgang Gaul Stiftung, Fakultät für Wirtschaftswissenschaften, Universität Karlsruhe and from the Chair of Excellency “Pierre de Fermat”, Région Midi-Pyrénées, France.


  1. Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica 60:323–351CrossRefGoogle Scholar
  2. Alvarez R (2007) Explaining export success: firm characteristics and spillover effects. World Dev 35:377–393CrossRefGoogle Scholar
  3. Becchetti L, Santoro MIM (2001) The determinants of small and medium-sized firm internationalization and its relationship with productive efficiency. Welt-wirtschaftliches Archiv Rev World Econ 137:297–319CrossRefGoogle Scholar
  4. Beise-Zee R, Rammer C (2006) Local user-producer interaction innovation and export performance of firms. Small Bus Econ 27:207–222CrossRefGoogle Scholar
  5. Bickel PJ, Sakov A (2008) On the choice of m in the m out of n bootstrap and confidence bound for extrema. Stat Sin 18:967–985Google Scholar
  6. Bleaney M, Wakelin K (2002) Efficiency, innovation and exports. Oxf Bull Econ Stat 64:3+CrossRefGoogle Scholar
  7. Braunerhjelm P (1996) The relation between firm-specific intangibles and exports. Econ Lett 53:213–219CrossRefGoogle Scholar
  8. Charnes A, Cooper WW, Rhodes E (1978) Measuring the inefficiency of decision making units. Eur J Oper Res 2(6):429–444CrossRefGoogle Scholar
  9. Debreu G (1951) The coefficient of resource utilization. Econometrica 19:273–292CrossRefGoogle Scholar
  10. Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc A(120):253–281Google Scholar
  11. Grossman G, Helpman E (1993) Innovation and growth in the global economy. The MIT Press, CambridgeGoogle Scholar
  12. Grupp H (1997) Messung und Erklärung des Technischen Wandels. Springer, BerlinCrossRefGoogle Scholar
  13. Grupp H, Maital S (1998) Interpreting the sources of market value in a capital goods market: R&D management in industrial sensors. R&D Manag 68:65–77CrossRefGoogle Scholar
  14. Grupp H, Schubert T (2010) Review and new evidence on composite innovation indicators for evaluating national performance. Res Policy 39:67–78CrossRefGoogle Scholar
  15. Hay DA, Liu GS (1997) The efficiency of firms: what difference does competition make?. Econ J 107:597–617CrossRefGoogle Scholar
  16. Harris H, Li QC (2009) Exporting, R&D, and absorptive capacity in UK establishments. Oxf Econ Pap New Ser 61:74–103CrossRefGoogle Scholar
  17. Kneip A, Simar L, Wilson PW (2008) Asymptotics and consistent bootstraps for DEA estimators in non-parametric frontier models. Econom Theory 24:1663–1697CrossRefGoogle Scholar
  18. Lachenmaier S, Wossmann L (2006) Does innovation cause exports? Evidence from exogenous innovation impulses and obstacles using German micro data. Oxf Econ Pap New Ser 58:327–350Google Scholar
  19. OECD (2005) OSLO manual: guidelines for collecting and interpreting innovation data, OECD and Eurostat, 3rd edn. OECD, ParisGoogle Scholar
  20. Pla-Barber J, Alegre J (2007) Analysing the link between export intensity, innovation and firm size in a science-based industry. Int Bus Rev 16:275–293CrossRefGoogle Scholar
  21. Penrose ET (1959) The theory of the growth of the firm. Basil Blackwell, BerlinGoogle Scholar
  22. Politis DN, Romano JP, Wolf M (2001) On the asymptotic theory of subsampling. Stat Sin 11:1105–1124Google Scholar
  23. Rodriguez JL, Rodriguez RMG (2005) Technology and export behaviour, a resource-based view approach. Int Bus Rev 14:539–557CrossRefGoogle Scholar
  24. Romer RM (1986) Increasing returns and long-run growth. J Polit Econ 94:1002–1037CrossRefGoogle Scholar
  25. Romer PM (1990) Endogenous technological change. J Polit Econ 98:72–102CrossRefGoogle Scholar
  26. Schubert T (2010) Marketing and organisational innovations in entrepreneurial innovation processes and their relation to market structure and firm characteristics. Rev Ind Organ 36:189–212CrossRefGoogle Scholar
  27. Schubert T, Grupp H (2009) Tests and confidence intervals for a class of scientometric, technological and economic specialisation ratios. Appl Econ. doi: 10.1080/00036840802600160
  28. Serfling RS (1980) Approximation theorems of mathematical statistics. Wiley, New-YorkCrossRefGoogle Scholar
  29. Simar L, Wilson P (1998) Sensitivity of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44(1):49–61CrossRefGoogle Scholar
  30. Simar L, Wilson P (2001) Testing restrictions in nonparametric efficiency models. Commun Stat Simul Comput 30(1):159–184CrossRefGoogle Scholar
  31. Simar L, Wilson PW (2008) Statistical inference in nonparametric frontier models: recent developments and perspectives. In: Fried H, Lovell CAK, Schmidt S eds) The measurement of productive efficiency, 2nd edn. Oxford University Press, OxfordGoogle Scholar
  32. Simar L, Wilson PW (2010) Inference by the m out of n bootstrap in nonparametric frontier models. J Prod Anal (forthcoming). doi: 10.1007/s11123-010-0200-4
  33. Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70:65–94CrossRefGoogle Scholar
  34. Sterlacchini A (1999) Do innovative activities matter to small firms in non-R&D-intensive industries? An application to export performance. Res Policy 28:819–832CrossRefGoogle Scholar
  35. Tomiura E (2007) Effects of R&D and networking on the export decision of Japanese firms. Res Policy 36:758–767CrossRefGoogle Scholar
  36. Wakelin K (1998) Innovation and export behaviour at the firm level. Res Policy 26:829–841CrossRefGoogle Scholar
  37. Wilson PW (2008) FEAR 1.0: a software package for frontier efficiency analysis with R. Socioecon Plann Sci 42:247–254CrossRefGoogle Scholar
  38. Yang CH, Chen JR, Chuang WB (2004) Technology and export decision. Small Bus Econ 22:349–364CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Competence Center Policy and RegionsFraunhofer Institute for Systems and Innovation ResearchKarlsruheGermany
  2. 2.Chair of Innovation EconomicsTechnical University of BerlinBerlinGermany
  3. 3.Institute of StatisticsUniversité Catholique de Louvain-la-NeuveLouvain-la-NeuveBelgium
  4. 4.Toulouse School of EconomicsUniversité de ToulouseToulouseFrance

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