Corporate R&D and firms’ efficiency: Evidence from Europe’s top R&D investors
Subal KumbhakarState University of New York at Binghamton / USA
Raquel Ortega-Argilés IN+ Centre for Innovation, Technology and Policy Research, Instituto Superior Técnico, Lisbon / Portugal
Lesley Potters Utrecht School of Economics / NL
Marco VivarelliCatholic University, Milan / Italy & Max-Planck Institute of Economics, Jena / Germany
Peter VoigtEuropean Commission, Joint Research Centre – Institute for Prospective Technological Studies, Seville / Spain
Main research questions
How evident is the link of R&D to productivity? Significant across sectors? If so, what are the differences in the magnitudes of these effects?
What pays off more in terms of productivity: leveraging corporate R&D activities in high- or in low-tech sectors?
Is productivity affected systematically by the amount of deployed physical capital and/or by the accumulated knowledge? Differences across sectors?
Can existing (technical) inefficiencies be empirically attributed to an inappropriate capital accumulation (capacities) or to insufficient spending on R&D (capabilities); or eventually to both?
Evidence suggest: conducting R&D may help enhancing a firm's productivitysee e.g. the seminal article by GRILICHES, 1979 – introducing the ‘knowledge production function’ approach; see also for more recent contributions: KLETTE and KORTUM, 2004; JANZ, LÖÖF and PETERS, 2004; ROGERS, 2006; LÖÖF and HESHMATI, 2006
Most studies focus either on cross-country analyses or on one specific sector, (mainly dealing with high-tech industries, e.g. pharma, ICT, …). However, considerably less attention has been devoted to determining whether the productivity gains resulting from R&D may differ across industrial sectors.
Indeed, technological opportunities and appropriability conditions are quite different across sectors (see FREEMAN, 1982; PAVITT, 1984; WINTER, 1984; DOSI, 1997; MALERBA, 2004)
Most of the previous studies rely on cross sectional methodology (no panel)
Background & literature
From a methodological point of view, 2 strands:
1. Relies on Production Functions that assume efficient use of the given inputs.If this assumption does not hold true, the parameter estimates for any marginal effects of inputs might be biased.
2. Follows the logic of two-stage approach: cross-sectional or cross-firm productivity estimates are retrieved as a residual from a production function and subject to (regression on) a set of potential determinants of productivity growth.
Background & literatureR&D-firm performance
R&D and productivity: empirical evidence
General consensus at country, sector and firm level is that there is a positive link between R&D and productivity (see e.g. Mairesse and Sassenou, 1991; Griliches 1995 and 2000; Mairesse and Mohnen, 2001)
Estimated elasticities range from 0.05 to 0.25 Wakelin (2001)
170 UK quoted firms over the period 1988-92R&D expenditure had a positive and significant role on productivity growth"net users of innovations" have a higher return on R&D
Rincon and Vecchi (2003)CompuStat micro-data over the period 1991-2001R&D-reporting firms were more productive than their non-R&D-reporting
counterparts Tsai and Wang (2004)
Panel of 156 large Taiwanese firms over the period 1994-2000R&D investment had a significant and positive impact on firm’s productivity
(elasticity ~ 0.18)Greater impact for high-techs (0.30) than for other firms (0.07)
•
• Wakelin (2001) 170 UK quoted firms over the period 1988-92 R&D expenditure had a positive and significant role on productivity growth "net users of innovations" have a higher return on R&D
• Rincon and Vecchi (2003) CompuStat micro-data over the period 1991-2001 R&D-reporting firms were more productive than their non-R&D-reporting counterparts
• Tsai and Wang (2004) Panel of 156 large Taiwanese firms over the period 1994-2000 R&D investment had a significant and positive impact on firm’s productivity (elasticity ~
0.18) Greater impact for high-techs (0.30) than for other firms (0.07)
There is a comprehensive literature dealing with empirical analyses of firm’s efficiency, based either on parametric or non-parametric frontier approaches.
Examples of R&D on firms’ inefficiency:
Sanders et al. (2007) based on firms’ life cycle where firms have the choice to achieve quality improvements or invest in R&D to achieve efficiency gains. The findings show that young firms opt for quality instead of efficiency improvements, whereas more mature firms will do both.
Bos et al. (2007 and 2008) account for inefficient use of resources and differences in the production technology across countries/ industries. Technological change, efficiency and effects implied by the input set determine endogenously technology clubs or country groups.
R&D and firm efficiency: empirical evidence
Data sources Merging of UK-DTI R&D Scoreboard and UK-DTI Value Added Scoreboard (various editions)
Unit of observation 577 top European R&D investors
Period of observation 2000-2005 (6 years)
Main variables Investment in R&D, Value Added per employee, capital expenditures, No of employees
Consistency checks Sectors with at least 5 firms; Control of outliers (Grubbs’ test); Computation of the initial capital stock; M&A.
Effective Database - Unbalanced longitudinal database with 532 firms belonging to 28 manufacturing and service sectors
- But, sample bias in terms of large firms!
Dataset
Descriptive statistics
Variable All firms High-tech Medium-tech Low-tech
Mean SD Mean SD Mean SD Mean SD
VA/E 0.068 0.062 0.063 0.037 0.053 0.024 0.095 0.100
R&D/E 0.032 0.049 0.062 0.069 0.021 0.026 0.012 0.013
C/E 0.473 1.756 0.158 0.400 0.135 0.176 1.280 3.091
E 36120 62434 40626 73890 22736 38350 48258 69635
Low-tech industries appear to be highly productive (labour productivity)
But this certainly is not because of R&D accumulation rather than due to …
higher capital intensity and scale economies
Methodology SFA (Stochastic Frontier Analysis) with time and sector dummies (PF) thus controlling for
the effects of R&D and capital stocks, time (time trend) and sector specifics on firms technical inefficiency >>> K/E is used as the pivotal impact variable. Furthermore, marginal effects were calculated to allow detailed investigation of the impact of external factors on inefficiency.
To compare: POLS (Preliminary pooled OLS) with time and sector dummies Random effects (RE) model rather than fixed effects for various reasons:
• unbalanced short panel (average of 3.4 observations available per firm) severely affects within-firm variability ;
• the within-firm component of the variability of the dependent variable is overwhelmed by the between-firms component (0.15 vs 0.58);
• the Hausman selection test (Chi-squared=4.65, p-value=0.79) clearly rejected fixed effects model;
• in the fixed effects model time-invariant regressors, such as the two-digit sectoral dummies, are automatically wiped-out.
ii uvEECEKEVA )ln()/ln()/ln()/ln( 3210
MethodologyLog-linear Production Function:
all variables in natural logarithms and deflated according to the different national GDP deflators during the 6 year period
VA/E = labour productivity
K/E = R&D stock per employee
C/E = (physical) capital stock per employee
E = employment (company size control)
vi - ui = compound error term
Note: Time (alternatively time trend) and two-digit sector dummies used in order to control for market / macroeconomic shocks (learning curve effects ) and sectoral peculiarities
with: i = 1…532; t = 2000…2005; u and ν as the error term components
Methodology: Stock variables• Calculating R&D and capital stocks: Perpetual Inventory Method
00
,( )t
ts c j
FLOWSTOCK
g
with: s = 1,…, 28 c = 1,…,14 j = 1,2,3 t0 = 2000
tjtt FLOWSTOCKSTOCK )1(1 with: t = 2000,…,2005
FLOW = actual expenditures
OECD ANBERD and OECD STAN databases for growth rates (g) per sector (s) and country (c) over the period 1990-1999
Different depreciation rates (δ) depending on sector group (j):
R&D stock [see e.g. Hall, 2007]
20% in case of high-tech firms, 15% for medium-techs, 12% for low-techs
Capital stock [see e.g. Nadiri and Prucha, 1996]
8% in case of high-tech firms, 6% for medium-techs, 4% for low-techs
Results (model comparison)
Model Specifics POLS RE SFA POLS RE SFA POLS RE SFA POLS RE SFA
ln(K/E) 0.123 0.125 0.087 0.180 0.160 0.162 0.138 0.146 0.102 0.048 0.068 0.051
(0.014) (0.015) (0.009) (0.018) (0.029) (0.018) (0.012) (0.026) (0.012) (0.014) (0.021) (0.013)
ln(C/E) 0.122 0.117 0.075 -0.011 0.014 0.035 0.133 0.137 0.132 0.23 0.21 0.105
(0.013) (0.018) (0.011) (0.019) (0.025) (0.020) (0.018) (0.029) (0.014) (0.020) (0.031) (0.017)
ln(E) -0.063 -0.092 -0.043 -0.036 -0.074 -0.007 -0.061 -0.072 -0.035 -0.084 -0.113 -0.087
(0.007) (0.013) (0.006) (0.010) (0.019) (0.009) (0.012) (0.022) (0.009) (0.014) (0.022) (0.010)
Constant -0.189 0.096 -2.015 -1.863 -1.571 -1.792 -1.412 -1.231 -1.326 -0.598 -1.443 -0.306
(0.183) (0.220) (0.086) (0.149) (0.221) (0.151) (0.149) (0.309) (0.141) (0.188) (0.252) (0.211)
Inefficiency term
R&D intensity1 -3.992 -7.66 -0.694 ---
(0.830) (2.285) (0.192) ---
Capital intensity1 -12.715 0.675 --- -0.424
(2.887) (0.295) --- (0.190)
Constant --- --- --- 1.040
--- --- --- (0.345)
Noise term
No. of employees -0.255 -0.295 -0.454 -0.714
(0.004) (0.012) (0.061) (0.081)
Constant --- --- 1.176 3.93
--- --- (0.548) (0.721)
Wald: time-D's (jtl.) 8.8 95.28 53.51 3.3 29.53 7.89 3.66 32.22 20.69 7.17 58.15 55.2
P-value 0.000 0.000 0.000 0.006 0.000 0.162 0.003 0.000 0.001 0.000 0.000 0.000
Wald: sector-D's (PF) 46.62 368.21 1455.85 38.07 54.76 95.15 14.89 19.49 136.22 45.51 186.66 858.23
P-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.000 0.000
Wald: sector-D's (TE) 75.9 79.64 59.22 75.45
P-value 0.000 0.000 0.000 0.000
firms 1787 600 671 516
observations 532 170 196 166
Note1 Intensity refers to calculated R&D (capital) stocks per employee, standardized by the sample mean.Robust standard errors in parenthesis; all coefficients are significant at 95% confidence level (unless those that are underlined)
Sample as a whole High-tech Medium-tech Low-tech
Results (SFA)
Whole Sample High-tech Med-high Low-tech
Model Specification coefficient P-Value** coefficient P-Value** coefficient P-Value** coefficient P-Value**
ln(Knowledge stock/Employee)ln(Capital stock/Employee)ln(E) [workforce]timeConstant sector dummies*
0.08700.0744-0.04310.0330-2.05201462.41
0.0000.0000.0000.0000.0000.000
0.1536------
0.0288-1.9007
145.15
0.0000.1620.6130.0000.0000.000
0.1038 0.1307-0.03730.0176-1.2650134.40
0.0000.0000.0000.0030.0000.000
---0.1584-0.09660.0486
---1292.92
0.4990.0000.0000.0000.1110.000
U(het): [Inefficiency term] R&D intensityCapital intensitytimeyear dummies*
sector dummies*
Constant
-3.992-12.700
------
75.86---
0.000 0.0000.2650.7070.0000.838
-5.5144---------
87.50---
0.001 0.0830.4790.6230.0000.984
-0.6861---------
61.64---
0.000 0.1770.2890.0970.0000.216
-0.4683---------
135.311.9146
0.0000.1920.4000.3420.0000.000
V(het): No. of employeesConstant
-0.2545---
0.0000.975
-0.3020---
0.0000.636
-0.4448 1.1138
0.0000.042
-0.84854.7825
0.0000.000
Wald (overall) / prob > chi2 2639.39 0.000 441.61 0.000 545.48 0.000 27755.95 0.000
Log likelihood -449.441 -140.4168-35.599
-146.69
firms 1787 600 671 516
observations 532 170 196 166
Note * Significance of all variables belonging to the corresponding group was tested jointly (joint Wald-test) Note** Variables not found to be significant at α 0.05 have been removed from the estimation (though the corresponding P-values were kept and are reported in the table in italics
in order to demonstrate the level of insignificance / to justify the removal).
SFA: dependent variable VA/E, HN-distribution of inefficiencies
Results: Firm level inefficiencies
Efficiency (TE) obs MeanStd.
deviation Min Max
overall sample 1787 0.822 0.1597 0.145 1.000
high-tech sectors 600 0.819 0.1473 0.161 1.000
medium-techs 671 0.870 0.1182 0.284 1.000
low-techs 516 0.732 0.2086 0.041 0.970
Marginal effects on efficiency obs Mean
Std. deviation Min Max
overall sample 1787 -0.033 0.0290 -0.131 0.000
high-tech sectors 600 -0.040 0.0304 -0.132 0.000
medium-techs 671 -0.052 0.0465 -0.264 0.000
low-techs 516 -0.092 0.0848 -0. 473 -0.011
02
46
810
Den
sity
0 .2 .4 .6 .8 1ef
all companies high-tech industriesmedium-tech industries low-tech industries
R&D intensity groupsEfficiency
-.5
-.4
-.3
-.2
-.1
0
R&D intensity groupsMarginal effects of R&D-intensity on firms' ineffeciency
all companies high-tech industriesmedium-tech industries low-tech industries
Summary of empirical results
In general, corporate R&D affects company performance positively! toehold for R&D policy
However, at firm level there are two effects to be distinguished both leveraging productivity…
1) advancing technology, shifting the frontier (technological progress) increase in prod. possibilities2) creating (tacit) knowledge, which helps avoiding / reducing waste increase in firms’ efficiency
high / medium-tech industries : (1) + (2)low-tech industries : (1)
Accordingly, returns to an additional investment in R&D tend to be higher in high-tech industries compared to low-techs due to the double edge effect of R&D. For capital accumulation it is vice versa! (because embodied TCH is more important for low-techs than for high-tech industries remind TCH…!)
Nevertheless, corporate R&D has a positive impact on company performance in all industries!What is about sector specifics in terms of the effect of R&D on efficiency?Evidence of underinvestment in R&D?
Target sectors for R&D policiesTE estimates
marginal effect of R&D-intensity on firms' Technical Efficiency
R&D intensity firms observations mean min max mean min max
High-tech 0.21 170 600 0.819 0.161 1.000 -0.040 0.000 -0.132
Technology hardware & equipment 0.41 22 77 0.604 0.161 0.885 -0.103 -0.050 -0.132
Pharmaceuticals & biotechnology 0.28 30 120 0.863 0.708 0.943 -0.026 -0.012 -0.035
Leisure goods 0.25 7 25 0.693 0.362 0.906 -0.070 -0.062 -0.074
Aerospace & defence 0.20 21 82 1.000 1.000 1.000 0.000 0.000 0.000
Automobiles & parts 0.16 37 140 0.812 0.565 0.957 -0.038 -0.027 -0.040
Software & computer services 0.16 21 56 0.899 0.863 0.960 -0.019 -0.010 -0.021
Electronic & electrical equipment 0.15 32 100 0.779 0.401 0.909 -0.047 -0.021 -0.051
Medium-high-tech 0.08 196 671 0.870 0.284 1.000 -0.052 0.000 -0.264
Chemicals 0.12 42 154 0.895 0.716 0.996 -0.039 -0.001 -0.063
Industrial engineering 0.08 58 209 0.918 0.771 0.966 -0.030 -0.011 -0.038
Health care equipment & services 0.08 14 43 0.754 0.477 0.930 -0.098 -0.030 -0.141
Household goods 0.06 18 51 0.729 0.414 0.945 -0.112 -0.041 -0.132
General industrials 0.05 20 69 1.000 1.000 1.000 0.000 0.000 0.000
Food producers 0.05 31 105 0.858 0.659 0.936 -0.055 -0.022 -0.063
Media 0.05 13 40 0.640 0.284 0.961 -0.173 -0.044 -0.264
Low-tech 0.02 166 516 0.732 0.041 0.970 -0.092 -0.011 -0.473
Fixed line telecommunications 0.03 14 43 0.783 0.321 0.947 -0.064 -0.041 -0.080
Industrial metals 0.02 14 39 0.837 0.654 0.943 -0.046 -0.025 -0.059
Electricity 0.02 13 43 0.720 0.371 0.911 -0.106 -0.033 -0.146
Oil equipment, services & distribution 0.02 7 22 0.134 0.041 0.206 -0.386 -0.181 -0.473
General retailers 0.02 9 29 0.800 0.588 0.932 -0.055 -0.039 -0.064
Support services 0.02 22 67 0.703 0.297 0.898 -0.090 -0.034 -0.112
Construction & materials 0.02 15 65 0.931 0.821 0.965 -0.017 -0.014 -0.019
Banks 0.02 6 6 0.647 0.411 0.930 -0.414 -0.364 -0.446
Gas, water & multiutilities 0.01 23 75 0.694 0.359 0.954 -0.088 -0.039 -0.103
Oil & gas producers 0.01 13 48 0.787 0.530 0.970 -0.058 -0.028 -0.081
Mobile telecommunications 0.01 6 17 0.550 0.167 0.955 -0.161 -0.011 -0.199
Industrial transportation 0.01 11 23 0.848 0.568 0.943 -0.044 -0.018 -0.052
Beverages 0.01 8 20 0.752 0.481 0.927 -0.073 -0.057 -0.082
Mining 0 5 19 0.471 0.190 0.913 -0.199 -0.186 -0.212
Total 0.09 532 1787 0.822 0.041 1.000 -0.033 0.000 -0.473
Remark: The highest marginal effects of R&D-intensity on inefficiency were found for sectors that have a rather low mean TE, suggesting underinvestment in R&D and a toehold for R&D policy. In turn, in some sectors almost no marginal effect of R&D-intensity on TE could be found (suggesting an already almost optimal R&D-intensity…).
Policy relevant messages
– R&D policy may primarily focus on high-/medium tech industries (due to higher return).– However, supporting corporate R&D in medium-/low tech industries may pay off as well with
regard to leveraging company performance!– In general… Marginal effects of R&D spending were found to differ significantly among sectors / industries.
Accordingly, a targeted policy approach is suggested taking sector specifics into account rather than applying an ‘equal to all’ type of public intervention.
In fact, the allocation of the supportive efforts in Europe focusing on company performance and competitiveness is as much as important as its general increase.
– In detail… encourage corporate R&D to improve the (frontier) technology of high / medium-techs facilitate capital accumulation for low-techs ( foster embodied technological change) focus on rising R&D-intensity in all industries in order to increase overall efficiency level