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Can R&D Reduce Technology Gaps in European Manufacturing?

Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Monday, April 02, 2007 Slide 1 of 11. Can R&D Reduce Technology Gaps in European Manufacturing?. Jaap Bos Claire Economidou and Mark Sanders m.sanders@econ.uu.nl. Presentation by Mark Sanders for GMU PhD-students

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Can R&D Reduce Technology Gaps in European Manufacturing?

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  1. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Monday, April 02, 2007 Slide 1 of 11 Can R&D Reduce Technology Gaps in European Manufacturing? Jaap Bos Claire Economidou and Mark Sanders m.sanders@econ.uu.nl

  2. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 2 of X Introduction Stochastic Frontier Analysis Technology Gaps and R&D Preliminary Results A Model to explain them

  3. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 3 of X Stochastic Frontier Y/K Efficient Frontier Y/L

  4. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 4 of X Stochastic Frontier Efficiency Gap: The distance to the industry frontier. Technology Gap: The distance to the meta-frontier. Y/K Industry j’s Efficient Frontier TG TE Industry i’s Efficient Frontier Y/L

  5. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 5 of X Technology Gaps In a dataset of 20 OECD countries (j) with 21 industries (i) and 25 years (t) we have:

  6. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 6 of X Technology Gaps In effect we benchmark the performance of industry i in country j at time t to the performance of other industries and countries. The implicit assumption being that all industries in all countries could in principle operate on the same frontier.

  7. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 7 of X Preliminary Results Note that this is estimated in levels such that TE=exp[te] and TGR=exp[tgr] θ SD z P>z 95% CI R&D intensity 0.010 0.022 0.450 0.652 -0.034 0.054 TE -0.068 0.013 -5.210 0.000 -0.093 -0.042 (R&D intensity)2-0.027 0.016 -1.720 0.085 -0.058 0.004 TE2 0.073 0.010 7.350 0.000 0.053 0.092 TE*R&D intensity 0.025 0.010 2.520 0.012 0.006 0.044 Constant 0.996 0.004 250.580 0.000 0.988 1.004 Fixed effects estimation in levels, with country-industry specific fixed effects (126 groups). Number of bootstraps=1000. Wald _2(5) =527.1. R2 = 0.2782 (within); 0.0298 (between); 0.1886 (overall).

  8. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 8 of X Hypotheses Hypothesis I: Corporate R&D in mature industries aims to reduce the efficiency and technology gaps. Hypothesis II: Young industries are “fluid” and therefore have a less clear R&D-efficiency nexus. Hypothesis III: Moreover on average their TE and TGR is larger due to larger heterogeneity.

  9. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 9 of X The Model • Now to explain this we need a model that: • 1. Has corporate R&D in mature industries aim for efficiency improvements. • 2. Has new industries aim for something else; quality improvements. • Predicts that the R&D-TE and R&D-TGR nexus is strong for mature and weak for new industries. • Endogenize the transition from new to mature.

  10. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 10 of X The Model

  11. Presentation by Mark Sanders for GMU PhD-students Fairfax, VA Tuesday, April 2, 2007 Slide 11 of X Testing Test: R&D intensity at industry level should predict size of (and change in) TE and TGR, more so in mature industries. Estimation in progress…

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