Producing Innovations:Determinants of Innovativity and Efficiency

Jaap W. BosMaastricht University

Ryan van LamoenUtrecht School of Economics

and

Mark SandersUtrecht School of Economics

m.w.j.l.sanders@uu.nl

09 September 2011DEGIT XVI, St-Petersburg

MotivationThe 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 inefficiencyBiased parameter estimates

Research QuestionsHow 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 )

MethodologyY = A F(K, L)

Y is innovative salesK is knowledge stock (e.g. Jones 1995)

L is R&D labor flowA is total factor innovativity (e.g. Mairesse and Mohnen,

2002)

A = E x TA is innovativity (measure of ignorance/residual)

E is innovation efficiency (e.g. Weil 2004)

T is innovation technologyE ≤ 1(00%)

E = G(X)E is innovation efficiencyX is a vector of explanatory variables

Methodology

A

B

C

Y

L

Methodology and Data

Yit is innovative salesKit is knowledge stock (perp. inv. method e.g Hall and Jones

1999)

Lit is R&D labor flow (in FTE)

Dt is a time dummy (e.g. Baltagi and Griffin 1988)

βi,j are firm i and industry j fixed effectsvit is i.i.d. error term -uit is i.i.d. inefficiency term (SFA e.g. Aigner et. al. 1977)

€

lnYit =

βK lnK it + βL lnLit +12βKK lnK 2

it +12βLL lnL2 it + βKL lnK it lnLit

+τtDt + βzzit + β j + β i + v it −uit

Methodology and Data

uit is innovation inefficiencyzit is 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)

€

uit =γzzit + γCC it + γFSFSit + wit

Simultaneous estimation of (1) and (2) using ML

Methodology and Data

Yit is innovative saleszit is the vector of dummiesCit is price-cost marginFSit is firm size

€

lnYit =(1)+βzzit + βKz lnK itz it + βLz lnLitzit

+ 12βKKz lnK it

2z it +

12βLLz lnLit

2zit + βKLz lnK it lnLitzit

+βCC it + βFSFS it

Methodology and Data

Table 1: Descriptive statisticsSymbol Variable Unit Mean SD Min MaxYit Sales from innovations €1000 7481.567 14319.87 1.521 218986.6Kit Knowledge stock €1000 3466.236 4885.73 18.61 28876.23Lit Research labor Fte 2.442 3.965 0.029 40DCCit Cooperation with competitors dummy 0.118 0.323 0 1DCOit Cooperation with other institutions dummy 0.378 0.485 0 1DFUit Funding from the government dummy 0.638 0.481 0 1Cit Price cost margin fraction 0.248 0.1119 -0.816 0.704FSit 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).

Community Innovation Surveys (CIS) and Production Statistics (PS)

CIS in 5 Bi-annual waves, PS annually 1994-2004Firm Level by CBSCensus (>50) and Stratified Random Sample (<50)Firms in both samples onlyFirms with positive sales onlySelection Bias in CIS

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 Kit

2 0.059 (0.044)

1/2ln Lit2 -0.031

(0.042) ln Kit ln 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

If correctly specified (in)efficiency 99% of variation => (in)efficiency matters)

Output elasticities K and L 0.41 and 0.18 resp. Sum <1 => Reject CRS in Innovation

ResultsTable 3: Decomposing the change in innovativenessVariable Average change ShareT -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 yearsInefficiency accounted for 62% of this deterioration

ResultsPanel 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 Kit

2 0.059 -0.004

(0.044) (0.049) 1/2ln Lit

2 -0.031 -0.174*** (0.042) (0.048)

ln Kit ln 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 InsignificantOutput Elasticities on K and L

0.16 and 0.33 resp.Still reject CRSRelative size switchedDeterminants directly affect innovation?

Signs as expected (?):Cooperation reduces

inefficiencyFunding has no impactCompetition has no impactFirms Size reduces

inefficiencyVariation due to inefficiency drops

ResultsPanel A: Determinants of Innovation

Specification (iii) Translog (iv) Translogln Kit 0.176 -0.065

(0.363) (0.267)ln Lit -0.957*** 0.042

(0.296) (0.221)1/2ln Kit

2 -0.004 0.111*(0.049) (0.058)

1/2ln Lit2 -0.174*** -0.010

(0.048) (0.035)ln Kit ln 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

Output Elasticities on K and L0.24 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 innovationFirm Size (now) increases inefficiency

Conclusions

(In)Efficiency Matters (a lot)Across firms between 50-99%Across countries ?This may bias estimated parametersThis may point to low hanging fruit

Competition is correlatedWith innovativity (+) With innovative efficiency (-)

Size is correlated With innovativity (+) With innovative efficiency (-)

Cooperation and Government Funding are hardly significant and not very robust

Policy Implications

For FirmsLarge firms should organize R&D on small scaleLarge inefficiencies are an opportunity

For Policy MakersFunding does not target winners very wellCooperation among competitors has little impact

For ScientistsConsider (in)efficiency in estimating KPFConsider more flexible functional forms