Review of Regional Research

, Volume 38, Issue 1, pp 77–109 | Cite as

The innovation efficiency of German regions – a shared-input DEA approach

  • Tom Broekel
  • Nicky RoggeEmail author
  • Thomas Brenner
Original Paper


The paper contributes to the debate on how to measure regions’ innovation performance. On the basis of the concept of regional innovation efficiency, we propose a new measure that eases the issue of choosing between industry-specific or global measures. We argue for the use of a robust shared-input DEA-model to compute regions’ innovation efficiency in a global manner, while it can be disaggregated into industry-specific measures.

We illustrate the use of the method by investigating the innovation efficiency as well as its change in time of German labor market regions. It is shown that the method treats regions that have industry structures skewed towards industries with high and low innovation intensities more fairly than traditional approaches.


Regional innovation efficiency Shared-input DEA Nonparametric efficiency analysis Regional innovation 

JEL codes

R12 O18 O31 


  1. Arundel A, Kabla I (1998) What percentage of innovations are patented? Empirical estimates for European firms. Res Policy 27(3):127–141CrossRefGoogle Scholar
  2. Audretsch D (1998) Agglomeration and the location of innovative activity. Oxf Rev Econ Policy 14(2):18–29CrossRefGoogle Scholar
  3. Bade F‑J (1987) Regionale Beschäftigungsentwicklung und produktionsorientierte Dienstleistungen. Sonderheft, vol. 143. Deutsches Institut für Wirtschaftsforschung, BerlinGoogle Scholar
  4. Beasley JE (1995) Determining teaching and research efficiencies. J Oper Res Soc 46:441–452CrossRefGoogle Scholar
  5. Bonaccorsi A, Daraio C (2006) Econometric approaches to the analysis of productivity of R&D systems. Handbook of quantitative science and technology research handbook of quantitative science and technology research – the use of publication and patent statistics in studies of S&T systems. Springer, Dordrecht, pp 51–74Google Scholar
  6. Brenner T, Broekel T (2011) Methodological issues in measuring innovation performance of spatial units. Ind Innov 18(1):7–37CrossRefGoogle Scholar
  7. Broekel T (2012) Collaboration intensity and regional innovation efficiency in Germany – a conditional efficiency approach. Ind Innov 19(3):155–179CrossRefGoogle Scholar
  8. Broekel T (2015) Do cooperative R&D subsidies stimulate regional innovation efficiency? Evidence from Germany. Reg Stud 49(7):1087–1110CrossRefGoogle Scholar
  9. Cazals C, Florens JP, Simar L (2002) Nonparametric frontier estimation: a robust approach. J Econom 106:1–25CrossRefGoogle Scholar
  10. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444CrossRefGoogle Scholar
  11. Chen K, Guan J (2012) Measuring the efficiency of China’s regional innovation systems: application of network data envelopment analysis (DEA). Reg Stud 46(3):355–377CrossRefGoogle Scholar
  12. Chen KH, Guan JC (2010) Modeling macro-R&D production frontier performance: an application to Chinese province-level R&D. Scientometrics 82(1):165–173CrossRefGoogle Scholar
  13. Cohen WM, Klepper S (1996) A reprise of size and R&D. Econ J 106:925–951CrossRefGoogle Scholar
  14. Cook WD, Green RH (2004) Multicomponent efficiency measurement and core business identification in multiplant forms: a DEA model. Eur J Oper Res 157:540–551CrossRefGoogle Scholar
  15. Cooke P, Uranga MG, Etxebarria G (1997) Regional innovation systems: Institutional and organisational dimensions. Res Policy 26(4–5):475–491CrossRefGoogle Scholar
  16. Daraio C, Simar L (2007) Advanced robust and nonparametric methods in efficiency analysis: methodology and applications. Series: Studies in Productivity and Efficiency. Springer, New YorkGoogle Scholar
  17. Deyle H, Grupp H (2005) Commuters and the regional assignment of innovative activities: a methodological patent study of German districts. Res Policy 34(2):221–234CrossRefGoogle Scholar
  18. De Witte K, Kortelainen M (2013) What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables. Appl Econ 45(17):2401–2412CrossRefGoogle Scholar
  19. Eckey HF, Kosfeld R, Türck M (2006) Abgrenzung deutscher Arbeitsmarktregionen. Volkswirtschaftliche Diskussionsbeiträge. Universität Kassel, KasselGoogle Scholar
  20. Edquist C, Zabala-Iturriagagoitia JM (2015) The Innovation Union Scoreboard is flawed: The case of Sweden – not the innovation leader of the EU – updated version. Papers in Innovation Studies, Paper no. 2015/27. CIRCLE, LundGoogle Scholar
  21. Emrouznejad A, Thanassoulis E (2005) A mathematical model for dynamic efficiency using data envelopment analysis. Appl Math Comput 160(2):363–378Google Scholar
  22. Färe R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. Am Econ Rev 81(1):66–83Google Scholar
  23. Farrell MJ (1957) The measurement of productive efficiency. J Royal Stat Soc Ser A 120(3):253–290CrossRefGoogle Scholar
  24. Foray D (2015) Smart specialisation. Opportunities and challenges for innovation policy. Routledge, Regional Studies Association, AbingdonGoogle Scholar
  25. Frenken K, van Oort FG, Verburg T (2007) Related variety, unrelated variety and regional economic growth. Reg Stud 41(5):685–697CrossRefGoogle Scholar
  26. Fritsch M (2000) Interregional differences in R&D activities – an empirical investigation. Eur Plan Stud 8(4):409–427CrossRefGoogle Scholar
  27. Fritsch M (2003) How and why does the efficiency of regional innovation systems differ. In: Bröcker J, Dohse D, Soltwedel R (eds) Innovation clusters and interregional competition. Springer, BerlinGoogle Scholar
  28. Fritsch M, Slavtchev V (2011) Determinants of the efficiency of regional innovation systems. Reg Stud 45(7):905–918CrossRefGoogle Scholar
  29. Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10(1):92. doi: 10.2307/3003321 CrossRefGoogle Scholar
  30. Jaffe A (1989) Real effects of academic research. Am Econ Rev 79(5):957–970Google Scholar
  31. Jeong SO, Park BU, Simar L (2010) Nonparametric conditional efficiency measures: asymptotic properties. Ann Oper Res 173(1):105–122CrossRefGoogle Scholar
  32. Lybbert T, Zolas N (2013) Getting patents and economic data to speak to each other: an ‘algorithmic links with probabilities’ approach for joint analyses of patenting and economic activity. Res Policy 43(3):530–542CrossRefGoogle Scholar
  33. Malerba F, Orsenigo L (1993) Technological regimes and firm behavior. Ind Corp Change 2(1):45–74CrossRefGoogle Scholar
  34. Malerba F, Orsenigo L, Breschi S (2000) Technological regimes and Schumpeterian patterns of innovation. Econ J 110(463):388–410CrossRefGoogle Scholar
  35. Malmquist S (1953) Index numbers and indifference surfaces. Trabajos de estadística 4(2):209–242Google Scholar
  36. Pedraja-Chaparro F, Salinas-Jiménez J, Smith P (1999) On the quality of the data envelopment analysis model. J Oper Res Soc 50(6):636–644CrossRefGoogle Scholar
  37. Schmoch U, Laville F, Patel P, Frietsch R (2003) Linking technology areas to industrial sectors. Final report to the European Commission. DG Research, Karlsruhe, Paris, BrightonGoogle Scholar
  38. Simar L, Wilson PW (2002) Non-parametric tests of returns to scale. Eur J Oper Res 139(1):115–132CrossRefGoogle Scholar
  39. Stern S, Porter ME, Furman JL (2002) The determinants of national innovative capacity. Res Policy 31(6):899–933CrossRefGoogle Scholar
  40. Wang MW, Stanley JC (1970) Differential weighting: a review of methods and empirical studies. Rev Educ Res 40:663–705CrossRefGoogle Scholar
  41. Wilson PW (1995) Detecting influential observations in data envelopment analysis. J Prod Analysis 6(1):27–45CrossRefGoogle Scholar
  42. Zabala-Iturriagagoitia JM, Voigt P, Gutiérrez-Gracia A, Jiménez-Sáez F (2007) Regional Innovation Systems: How to Assess Performance. Reg Stud 41(5):661–672CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Institute of Economic and Cultural GeographyLeibniz University of HanoverHanoverGermany
  2. 2.Faculty of Business and EconomicsKULeuvenBrusselBelgium
  3. 3.Economic Geography and Location ResearchPhilipps University MarburgMarburgGermany

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