Corporate R&D and firms’ efficiency: Evidence from Europe’s top R&D investors Subal Kumbhakar State 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 Vivarelli Catholic University, Milan / Italy & Max-Planck Institute of Economics, Jena / Germany Peter Voigt European 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 productivity see 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 & literature R&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 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 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 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)

Wakelin (2001)  170 UK quoted firms over the period  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  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  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 (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 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.

Methodology Log-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) v i - u i = 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 FLOW = actual expenditures OECD ANBERD and OECD STAN databases for growth rates (g) per sector (s) and country (c) over the period 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

Background and Approach Productivity paper (ROA, LP, MV) vs. SFA - Paper: Analysing R&D ~ productivity link in high-, medium- and low-tech industries X 1 / Y Production function Production frontier function (isoquante) X 2 / Y

Results (model comparison) Model SpecificsPOLSRESFAPOLSRESFAPOLSRESFAPOLSRESFA ln(K/E) (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) (0.013)(0.018) (0.011) (0.019)(0.025) (0.020) (0.018)(0.029) (0.014) (0.020)(0.031) (0.017) ln(E) (0.007)(0.013) (0.006) (0.010)(0.019) (0.009) (0.012)(0.022) (0.009) (0.014)(0.022) (0.010) Constant (0.183)(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 intensity (0.830)(2.285)(0.192)--- Capital intensity (2.887)(0.295)---(0.190) Constant (0.345) Noise term No. of employees (0.004)(0.012)(0.061)(0.081) Constant (0.548) (0.721) Wald: time-D's (jtl.) P-value Wald: sector-D's (PF) P-value Wald: sector-D's (TE) P-value firms observations Note 1 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 wholeHigh-techMedium-techLow-tech

Results (SFA) Whole SampleHigh-techMed-highLow-tech Model Specificationcoefficient P-Value ** coefficient P-Value ** coefficient P-Value ** coefficient P-Value ** ln(Knowledge stock/Employee) ln(Capital stock/Employee) ln(E) [workforce] time Constant sector dummies * U(het): [Inefficiency term] R&D intensity Capital intensity time year dummies * sector dummies * Constant V(het): No. of employees Constant Wald (overall) / prob > chi Log likelihood firms observations 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) obsMean Std. deviationMinMax overall sample high-tech sectors medium-techs low-techs Marginal effects on efficiency obsMean Std. deviationMinMax overall sample high-tech sectors medium-techs low-techs

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. possibilities 2) 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 policies TE estimates marginal effect of R&D-intensity on firms' Technical Efficiency R&D intensityfirmsobservationsmeanminmaxmeanminmax High-tech Technology hardware & equipment Pharmaceuticals & biotechnology Leisure goods Aerospace & defence Automobiles & parts Software & computer services Electronic & electrical equipment Medium-high-tech Chemicals Industrial engineering Health care equipment & services Household goods General industrials Food producers Media Low-tech Fixed line telecommunications Industrial metals Electricity Oil equipment, services & distribution General retailers Support services Construction & materials Banks Gas, water & multiutilities Oil & gas producers Mobile telecommunications Industrial transportation Beverages Mining Total 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