can r&d reduce technology gaps in european manufacturing? jaap bos claire economidou and mark...
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Stochastic Frontier Presentation by Mark Sanders for GMU PhD-students Fairfax, VATuesday, April 2, 2007Slide 3 of X Y/K Y/L Efficient FrontierTRANSCRIPT
Can R&D Reduce Technology Gaps in European Manufacturing?
Jaap Bos Claire Economidou
and
Mark [email protected]
Presentation by Mark Sanders for GMU PhD-studentsFairfax, VA Monday, April 02, 2007 Slide 1 of 11
IntroductionStochastic Frontier AnalysisTechnology Gaps and R&DPreliminary ResultsA Model to explain them
Presentation by Mark Sanders for GMU PhD-studentsFairfax, VA Tuesday, April 2, 2007 Slide 2 of X
Stochastic Frontier
Presentation by Mark Sanders for GMU PhD-studentsFairfax, VA Tuesday, April 2, 2007 Slide 3 of X
Y/K
Y/L
Efficient Frontier
Efficiency Gap: The distance to the industry frontier.Technology Gap: The distance to the meta-frontier.
Presentation by Mark Sanders for GMU PhD-studentsFairfax, VA Tuesday, April 2, 2007 Slide 4 of X
Y/K
Y/L
TETG
Industry j’s Efficient Frontier
Stochastic Frontier
Industry i’s Efficient Frontier
Presentation by Mark Sanders for GMU PhD-students
Fairfax, VA Tuesday, April 2, 2007 Slide 5 of X
Technology Gaps
ijtijtijttijtttijt νulβkβαy ,2,1
ijttijtttijt lβkβαy ,2,1*
ijtijtijt yyte *
** yytgr ijtijt
In a dataset of 20 OECD countries (j) with 21 industries (i) and 25 years (t) we have:
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 ofindustry i in country j at time t to the performanceof other industries and countries.
The implicit assumption being that all industries in all countries could in principle operate on the same frontier.
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% CIR&D intensity 0.010 0.022 0.450 0.652 -0.034 0.054TE -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 0.004TE2 0.073 0.010 7.350 0.000 0.053 0.092TE*R&D intensity 0.025 0.010 2.520 0.012 0.006 0.044Constant 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).
ijtijtijtijtijtijtijijt TEDRθTEθDRθTEθDRθθTGR *&&& 52
42
321
Hypothesis I: Corporate R&D in mature industriesaims 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.
Presentation by Mark Sanders for GMU PhD-studentsFairfax, VA Tuesday, April 2, 2007 Slide 8 of X
Hypotheses
Presentation by Mark Sanders for GMU PhD-students
Fairfax, VA Tuesday, April 2, 2007 Slide 9 of X
The ModelNow 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.3. Predicts that the R&D-TE and R&D-TGR nexus
is strong for mature and weak for new industries.
4. Endogenize the transition from new to mature.
Presentation by Mark Sanders for GMU PhD-students
Fairfax, VA Tuesday, April 2, 2007 Slide 10 of X
The Model
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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…
Presentation by Mark Sanders for GMU PhD-studentsFairfax, VA Tuesday, April 2, 2007 Slide 11 of X
Testing