Technical efficiency in a technological innovation system perspective: The case of bioenergy technologies R&D resources mobilisation in a sample from EU-28

Author/s Alessandro Fiorini
Publishing Year 2017 Issue 2016/2 Language English
Pages 21 P. 107-127 File size 262 KB
DOI 10.3280/EFE2016-002006
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Building upon mainstream literature on concept and measures of innovation efficiency, this paper introduces a new method for the evaluation of R&D resources mobilisation in a Tech-nological Innovation System framework. The method is based on a quantitative set up that aims to improving both the representation and assessment of system dynamics. An application to the case of the EU28 bioenergy technological system shows that almost 20% of the R&D potential was under-utilised in 2011. Main determinants of this systemic failure were specific economic and policy factors, which conditioned allocation decisions. In particular, the preference bias induced by the general investment capacity of the system, competitive pressure on entities undertaking innovation activities and the intensity of policy production. Conversely, a relatively higher share of educated population and the evidence of an effective progress towards the achievement of specific policy targets positively impacted on the efficient utilisation of R&D resources.

Keywords: Technological innovation system, innovation efficiency, bioenergy, two-stage DEA, truncated regression

Jel codes: O32, Q42, C14

  1. Banker R.D., Charnes A. and Cooper W.W. (1984). Some Models for Estimating Technical and Scale Efficiencies in Data Envelopment Analysis. Management Science, 30 (9): 1078-1092.
  2. Banker R.D. and Natarajan R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56 (1): 48-58.
  3. Bebczuk R.N. (2002). R&D expenditures and the role of government around the world. Estudios de Economia, 29-1: 109-121.
  4. Bergek A., Jacobsson S., Carlsson B., Lindmark S. and Rickne A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37: 407-429.
  5. Bogetoft P. and Otto L. (2011). Benchmarking with DEA, SFA, and R. New York: Springer.
  6. Brolund J. and Lundmark R. (2013). Induced innovation and renewable energy policies for bioenergy: an econometric analysis. Forest Biomass Conference, 07-09 October 2013, Dobiegniew, Poland.
  7. Cai Y. (2011). Factors affecting the efficiency of the BRICSs’ National Innovation Systems: a comparative study based on DEA and panel data analysis. Economics E-Journal, Discussion Paper 2011-52.
  8. Carlsson B., Jacobsson S., Holmén M. and Rickne A. (2002). Innovation systems: analytical and methodological issues. Research Policy, 31: 233-245.
  9. Carlsson B. and Stankiewicz R. (1991). On the nature, function and composition of technological systems. Journal of Evolutionary Economics, 1: 93-118.
  10. Cazals C., Florens J.P. and Simar L. (2002). Nonparametric frontier estimation: a robust approach. Journal of Econometrics, 106: 1-25.
  11. Charnes A., Cooper W.W., Golany B., Seiford L. and Stutz J. (1985). Foundations of Data Envelopment Analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30, 91-107. DOI: 10.1016/0304-4076(85)90133-2
  12. Charnes A., Cooper W.W. and Rhodes E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2: 429-444. DOI: 10.1016/0377-2217(78)90138-8
  13. Cherchye L., Moesen W., Rogge N., Van Puyenbroeck T., Saisan M., Saltelli A., Liska R. and Tarantola S. (2008). Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index. Journal of the Operational Research Society, 59 (2): 239-251.
  14. Cooper W.W., Seiford L.M. and Zhu J. (2011). Handbook on Data Envelopment Analysis. 2nd ed. New York: Springer.
  15. Cullman A., Schmidt-Ehmcke J. and Zlaczysti, P. (2012). R&D efficiency and barriers to entry: a two stage semi-parametric DEA approach. Oxford Economic Papers, 64: 176-196.
  16. Cullmann A., Schmidt-Ehmcke J. and Zlaczysti P. (2009). Innovation, R&D efficiency and the impact of the regulatory environment – A two stage semi-parametric DEA approach. DIW Berlin, Discussion Paper 883.
  17. Dahmén E. (1988). “Development blocks” in industrial economics. Scandinavian Economic History Review, 36 (1): 3-14. DOI: 10.1080/03585522.1988.10408102
  18. Daraio C. and Simar L. (2007). Advanced Robust and Nonparametric Methods in Efficiency Analysis. New York: Springer.
  19. de Rassenfosse G. and van Pottelsberghe de la Potterie B. (2009). A policy insight into the R&D-patent relationship. Research Policy, 38: 779-792.
  20. Debreu G. (1951). The Coefficient of Resource Utilization. Econometrica, 19 (3): 273-292. DOI: 10.2307/1906814
  21. Emrouznejad A., Parker B. and Tavares G. (2008). Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Journal of Socio-Economics Planning Science, 42 (3): 151-157.
  22. European Commission (1997). Communication “An Energy Policy for Europe”. COM(2007)1, 10.01.2007, Brussels.
  23. European Commission (2007). Communication “Energy for the Future: Renewable Sources of Energy. White Paper for a Community Strategy and Action Plan”. COM(1997)599, 26.11.1997, Brussels.
  24. European Commission (2015). Communication “A Framework Strategy for a Resilient Energy Union with a Forward-Looking Climate Change Policy”. COM(2015)80, 25.02.2015, Brussels.
  25. Farrel M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A, 120, 3: 253-290.
  26. Fiorini A., Georgakaki A., Lepsa B.-N., Pasimeni F. and Saltó L. (2016). Estimation of corporate R&D investment in Low-Carbon Energy Technologies: a methodological approach. R&D Management Conference 2016, 3-6 July 2016, Cambridge, United Kingdom.
  27. Freeman C. (1995). The ‘National System of Innovation’ in Historical Perspective. Cambridge Journal of Economics, 19: 5-24.
  28. Griliches Z. (eds.) (1984). R and D, Patents and Productivity. Chicago: University of Chicago Press.
  29. Guan J. and Chen K. (2012). Modeling the relative efficency of national innovation system. Research Policy, 41: 102-115.
  30. Guan J. and Zuo K. (2014). A cross-country comparison of innovation efficiency. Scientometrics, 100: 541-575.
  31. Hekkert M. and Negro S.O. (2009). Functions of innovation systems as a framework to understand sustainable technological change: Empirical evidence for earlier claims. Technological Forecasting and Social Change, 76: 584-594.
  32. Hekkert M., Negro S.O., Heimeriks G. and Harmsen R. (2011). Technological Innovation System Analysis. A manual for analysts. Utrecht: Copernicus Institute for Sustainable Development and Innovation, University of Utrecht, Faculty of Geosciences.
  33. Hekkert M., Suurs R.A.A., Negro S.O., Kuhlmann S. and Smits R.E.H.M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74: 413-432.
  34. International Energy Agency (2015). World Energy Outlook 2015. OECD/IEA, Paris.
  35. Jin B. and Rousseau R. (2004). Evaluation of research performance and scientometric indicators in China. In: Moed H.F., Glänzel W. and Schmoch U. (eds.). Handbook of Quantitative Science and Technology Research. The Use of Publication and Patent Statistics in Studies of S&T Systems: 497-514. New York: Kluwer Academic Publishers.
  36. Johnson A. (2001). Functions in Innovation System Approaches. DRUID Nelson and Winter Conference, 12-15 June 2001, Aalborg, Denmark.
  37. Johnson A.L. and Kuosmanen T. (2012). One-stage and two-stage DEA estimation of effects of contextual variables. European Journal of Operational Research, 220: 559-570.
  38. Koopmans T.C. (1951). Activity Analysis of Production and Allocation. New York: Wiley.
  39. Kotsemir M. (2013). Measuring National Innovation Systems efficiency – A review of DEA approach. Higher School of Economic Research, WP BRP 16/STI/2013.
  40. Lee H.Y. and Park Y.T. (2005). An international comparison of R&D efficiency: DEA approach. Asian Journal of Technology Innovation, 3 (2): 207-222. DOI: 10.1080/19761597.2005.9668614
  41. Liu J.S., Lu L.Y.Y., Lu W.-M. and Lin B.J.Y. (2013), Data Envelopment Analysis 1978-2010: a citation based literature survey. Omega, 41: 3-15.
  42. Lundvall B.-A. (eds.) (1992). National Systems of Innovation: Towards a theory of Innovation and Interactive Learning. London: Pinter.
  43. Malerba F. (2002). Sectoral systems of innovation and production. Research Policy, 31: 247-264.
  44. McDonald J. (2009): Using least squares and tobit in second stage DEA efficiency analysis. European Journal of Operational Research, 197: 792-798.
  45. Nasierowski W. and Arcelus F.J. (2003). On the efficiency of national innovation system. Socio-Economic Planning Sciences, 37: 215-234.
  46. Nicholls J., Mawhood R., Gross R. and Castillo-Castillo A. (2014). Evaluating renewable energy policy: a review of criteria and indicators for assessment. IRENA-UKERC Policy Paper, 01.
  47. OECD (1997). National Innovation Systems. Organisation for Economic Co-operation and Development, Paris.
  48. OECD/IEA, FAO (2017). Technology Roadmap: How 2 Guide for Bioenergy. Organisation for Economic Co-operation and Development, Paris.
  49. Pestana Barros C. and Assaf A. (2009). Bootstrapped efficiency measures of oil blocks in Angola. Energy Policy, 37: 4098-4103.
  50. Pestana Barros C. and Dieke P.U.C. (2008). Measuring the economic efficiency of airports: A Simar-Wilson methodology analysis. Transportation Research – Part E, 44: 1039-1051.
  51. Prodan I. (2005). Influence of research and development expenditures on number of patent applications: selected case studies in OECD countries and Central Europe, 1981-2001. Applied Econometrics and International Development, 5-4: 5-22.
  52. Rousseau S. and Rousseau R. (1997). Data Envelopment Analysis as a tool for constructing scientometric indicators. Scientometrics, 40 (1): 45-56.
  53. Sharma S. and Thomas V.J. (2008). Inter-country R&D efficiency analysis: an application of data envelopment analysis. Scientometrics, 76 (3): 483-501.
  54. Shephard R.W. (1953). Cost and Production Functions. Princeton, NJ: Princeton University Press.
  55. Simar L. and Wilson P.W. (2007). Estimation and inference in two-stage semi-parametric models of production processes. Journal of Econometrics, 136: 31-64.
  56. Simar L. and Wilson P.W. (2011). Two-stage DEA: caveat emptor. Journal of Productivity Analysis, 36: 205-218.
  57. The World Bank/International Finance Corporation (2012). Doing business in a more transparent world. Comparing regulation for domestic firms in 183 economies; The International Bank for Reconstruction and Development/The World Bank, Washington.
  58. Walrave B. and Raven R. (2016). Modelling the dynamics of technological innovation systems. Research Policy, 45: 1833-1844.
  59. Wang N. and Hagedoorn J. (2012). The lag structure of the relationship between patenting and internal R&D revised. Research Policy, 43: 1275-1285.
  60. Wieczorek A.J. and Hekkert M.P. (2012). Systemic instruments for systemic innovation problems: A framework for policy makers. Science and Public Policy, 39: 74-87.
  61. Wiesenthal T., Leduc G., Haegeman K. and Schwarz H.-G. (2012). Bottom-up estimation of industrial and public R&D investment by technology in support of policy-making: the case of selected low-carbon energy. Research Policy, 41: 116-131.
  62. Zhou P., Ang B.W. and Pho K.L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189: 1-18.
  63. Zhu J. (2009). Quantitative Models for Performance Evaluation and Benchmarking. Data Envelopment Analysis with Spreadsheets. New York: Springer.

Alessandro Fiorini, Technical efficiency in a technological innovation system perspective: The case of bioenergy technologies R&D resources mobilisation in a sample from EU-28 in "ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT" 2/2016, pp 107-127, DOI: 10.3280/EFE2016-002006