Producing Innovations: Determinants of Innovativity and Efficiency

1. Producing Innovations: Determinants of Innovativity and Efficiency 09 September 2011 DEGIT XVI, St-Petersburg Jaap W. Bos Maastricht University Ryan van Lamoen Utrecht School of Economics and Mark Sanders Utrecht School of Economics m.w.j.l.sanders@uu.nl

2. Motivation The Importance of Innovation: From Smith (1776) to Aghion and Howitt 2009), Acemoglu 2010, and Galor (2011) “Innovation drives long-run economic growth” Innovation is endogenous The Production of Innovations: Theory from Hicks and Kennedy to Romer and Jones Empirics from Griliches (1980) to Mairesse and Mohnen (2002) The European Paradox (e.g. Figel 2006) Innovation at the firm level (e.g. Thompson 2001) Inefficiency in Producing Innovations: Not all R&D creates innovations to the same degree Eliminating inefficiency Biased parameter estimates

3. Research Questions How can we estimate the KPF? 1. Production Function Analogy (Mairesse & Mohnen, 2002) 2. Functional Form (CD, CES, TrLog) Is there inefficiency in innovation? 1. How to estimate (in)efficiency (SFA vs DEA) 2. How important is it? Country Level: Wang (2007); Wang&Huang (2007); Fu & Yang (2009); Firm Level: Gantumur and Stephan (2010) (67%) Can we explain inefficiency in innovation? 1. Environment (competition) 2. Firm Characteristics (size) 3. Innovation Process (cooperation, funding )

4. Methodology Y = A F(K, L) Y is innovative sales K is knowledge stock (e.g. Jones 1995) L is R&D labor flow A is total factor innovativity (e.g. Mairesse and Mohnen, 2002) A = E x T A is innovativity (measure of ignorance/residual) E is innovation efficiency (e.g. Weil 2004) T is innovation technology E ≤ 1(00%) E = G(X) E is innovation efficiency X is a vector of explanatory variables

5. Methodology Y B A C L

6. Methodology and Data Yit is innovative sales Kitis knowledge stock (perp. inv. method e.g Hall and Jones 1999) Lit is R&D labor flow (in FTE) Dtis a time dummy (e.g. Baltagi and Griffin 1988) βi,j are firm i and industry j fixed effects vitis i.i.d. error term -uit is i.i.d. inefficiency term (SFA e.g. Aigner et. al. 1977)

7. Methodology and Data uit is innovation inefficiency zitis the vector of dummies (Fund and CoopComp) Cit is price-cost margin (e.g. Aghion et. al. 2005) FSit is firm size (number of employees) Simultaneous estimation of (1) and (2) using ML

8. Methodology and Data Yit is innovative sales zitis the vector of dummies Cit is price-cost margin FSit is firm size

9. Methodology and Data Community Innovation Surveys (CIS) and Production Statistics (PS) CIS in 5 Bi-annual waves, PS annually 1994-2004 Firm Level by CBS Census (>50) and Stratified Random Sample (<50) Firms in both samples only Firms with positive sales only Selection Bias in CIS Table 1: Descriptive statistics Symbol Variable Unit Mean SD Min Max Yit Sales from innovations €1000 7481.567 14319.87 1.521 218986.6 Kit Knowledge stock €1000 3466.236 4885.73 18.61 28876.23 Lit Research labor Fte 2.442 3.965 0.029 40 DCCit Cooperation with competitors dummy 0.118 0.323 0 1 DCOitCooperation with other institutions dummy 0.378 0.485 0 1 DFUitFunding from the government dummy 0.638 0.481 0 1 Cit Price cost margin fraction 0.248 0.1119 -0.816 0.704 FSit Number of employees # 187.04 350.678 0 10857 The descriptive statistics are based on the sample in Column v in Table 2 (1,366 observations).

10. Results Table 2: Results Specification Cobb Douglas Trans Log ln Kit 0.431*** -0.033 (0.031) (0.325) ln Lit 0.161*** -0.213 (0.033) (0.264) 1/2ln Kit2 0.059 (0.044) 1/2ln Lit2 -0.031 (0.042) ln Kitln Lit 0.054 (0.035) u/(u + v) 0.220 0.989 Observations 1,367 1,367 Industry Dummy yes yes Time Dummy no no The dependent variable is sales from innovations. Standard errors (between parentheses) are robust against heteroskedasticity. Asterisks indicate significance at the following levels: * – 0.10, ** – 0.05, and *** – 0.01. Jointly Significant => Reject CD Output elasticities K and L 0.41 and 0.18 resp. Sum <1 => Reject CRS in Innovation If correctly specified (in)efficiency 99% of variation => (in)efficiency matters)

11. Results Table 3: Decomposing the change in innovativeness Variable Average change Share T -0.018 13.099% (-1)K/K -0.021 15.465% (-1)L/L -0.012 8.746% TE -0.085 62.691% INN -0.136 100% The decomposition of the productivity change is based on Column ii in Table 2. The share of each decomposition component in explaining productivity changes is based on the average change in the decomposition components. Innovativity fell by 13.6% over 10 years Inefficiency accounted for 62% of this deterioration

12. Results Panel A: Determinants of Innovation Specification (ii) Translog (iii) Translog ln Kit -0.033 0.176 (0.325) (0.363) ln Lit -0.213 -0.957*** (0.264) (0.296) 1/2ln Kit2 0.059 -0.004 (0.044) (0.049) 1/2ln Lit2 -0.031 -0.174*** (0.042) (0.048) ln Kitln Lit 0.054 0.178*** (0.035) (0.039) Panel B: Determinants of inefficiency DCCit -0.528*** (0.176) DCOit -0.184** (0.091) DFUit -0.072 (0.082) Cit 0.319 (0.420) FSit -0.003*** (0.0001) u/(u + v) 0.989 0.499 Observations 1,367 1,366 Industry Dummy yes yes Time Dummy no no The dependent variable is sales from innovations. Standard errors (between parentheses) are robust against heteroskedasticity. Asterisks indicate significance at the following levels: * – 0.10, ** – 0.05, and *** – 0.01. L Jointly Significant K Jointly Insignificant • Output Elasticities on K and L • 0.16 and 0.33 resp. • Still reject CRS • Relative size switched • Determinants directly affect innovation? Signs as expected (?): Cooperation reduces inefficiency Funding has no impact Competition has no impact Firms Size reduces inefficiency Variation due to inefficiency drops

13. Panel A: Determinants of Innovation Specification (iii) Translog (iv) Translog ln Kit 0.176 -0.065 (0.363) (0.267) ln Lit -0.957*** 0.042 (0.296) (0.221) 1/2ln Kit2 -0.004 0.111* (0.049) (0.058) 1/2ln Lit2 -0.174*** -0.010 (0.048) (0.035) ln Kitln Lit 0.178*** 0.005 (0.039) (0.049) DCCit -0.020 (0.127) DCOit 0.154* (0.086) DFUit 0.037 (0.081) Cit -1.361*** (0.342) FSit 0.005*** (0.001) Panel B: Determinants of inefficiency DCCit -0.528*** 0.155 (0.176) (0.736) DCOit -0.184** -0.238 (0.091) (0.529) DFUit -0.072 -0.727 (0.082) (0.485) Cit 0.319 -3.405* (0.420) (1.969) FSit -0.003*** 0.006*** (0.0001) (0.001) u/(u + v) 0.499 0.918 Results • Output Elasticities on K and L • 0.24 and 0.08 resp. • Reject CRS (scale effects/directed TC) • Relative size as before (stock>flow) • Determinants directly affect innovation! • Competition increases innovation (significant at 1%) • Competition increases inefficiency (significant at 10%) • Firm Size increases innovation • Firm Size (now) increases inefficiency

14. Conclusions (In)Efficiency Matters (a lot) Across firms between 50-99% Across countries ? This may bias estimated parameters This may point to low hanging fruit Competition is correlated With innovativity (+) With innovative efficiency (-) Size is correlated With innovativity (+) With innovative efficiency (-) Cooperation and Government Funding are hardly significant and not very robust

15. Policy Implications For Firms Large firms should organize R&D on small scale Large inefficiencies are an opportunity For Policy Makers Funding does not target winners very well Cooperation among competitors has little impact For Scientists Consider (in)efficiency in estimating KPF Consider more flexible functional forms