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Producing Innovations: Determinants of Innovativity and Efficiency

  • Jaap W.B. Bos
  • Ryan C.R. van Lamoen
  • Mark W.J.L. Sanders

In this paper we estimate, using stochastic frontier estimation techniques, the relationship between R&D inputs a innovative output in a sample of Dutch firms. We find that over 63% of between firm variation in observed "innovativeness" can be attributed to inefficiency in the innovation process. The remainder is due to differences in the innovation production process itself. We derive our results including the usual controls and find in addition that large firms tend to look more innovative. But when considered more carefully large firms turn out to be less efficient. With standard estimation techniques this inefficiency is masked by a more productive innovation technology. We thus find evidence of economies of scale in line with the Schumpeter mark II hypothesis (large firms are more innovative), but also show that large firms tend to operate at lower levels of efficiency.

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File URL: http://degit.sam.sdu.dk/papers/degit_16/c016_060.pdf
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Paper provided by DEGIT, Dynamics, Economic Growth, and International Trade in its series DEGIT Conference Papers with number c016_060.

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Length: 17 pages
Date of creation: Sep 2011
Date of revision:
Handle: RePEc:deg:conpap:c016_060
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  1. Aghion, P. & Howitt, P., 1990. "A Model Of Growth Through Creative Destruction," DELTA Working Papers 90-12, DELTA (Ecole normale supérieure).
  2. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth Through Creative Destruction," Scholarly Articles 12490578, Harvard University Department of Economics.
  3. Acemoglu, Daron, 2002. "Directed Technical Change," Review of Economic Studies, Wiley Blackwell, vol. 69(4), pages 781-809, October.
  4. Dinopoulos, Elias & Thompson, Peter, 1998. " Schumpeterian Growth without Scale Effects," Journal of Economic Growth, Springer, vol. 3(4), pages 313-35, December.
  5. Philippe Aghion & Nicholas Bloom & Richard Blundell & Rachel Griffith & Peter Howitt, 2002. "Competition and innovation: an inverted U relationship," IFS Working Papers W02/04, Institute for Fiscal Studies.
  6. Daron Acemoglu, 2002. "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, American Economic Association, vol. 40(1), pages 7-72, March.
  7. George E. Battese & Greg S. Corra, 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 169-179, December.
  8. Acemoglu, D., 1997. "Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality," Working papers 97-14, Massachusetts Institute of Technology (MIT), Department of Economics.
  9. Baltagi, Badi H & Griffin, James M, 1988. "A General Index of Technical Change," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 20-41, February.
  10. repec:hal:journl:hal-00287137 is not listed on IDEAS
  11. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  12. Battese, George E. & Corra, Greg S., 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 21(03), December.
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