1. R&D and Productivity: Testing Sectoral Peculiarities Using Micro Data R&D and Productivity: Testing Sectoral Peculiarities Using Micro Data Raquel Ortega-Argilés IN+ Centre for Innovation, Technology and Policy Research IST-UTL, Lisbon Lesley Potters Utrecht School of Economics, Utrecht Marco Vivarelli Università Cattolica, Dipartimento di Scienze Economiche e Sociali, Piacenza Centre for the Study of Globalisation and Regionalisation (CSGR), Warwick Institute for the Study of Labour (IZA), Bonn “Encontros Ciência 2010” 5th July 2010, Lisbon

2. QUESTIONING TWO ASSERTIONS FROM THE RECENT DEBATE: 1)"Lavish R&D Budgets Don't Guarantee Performance" (Booz-Allen-Hamilton reports: see Jaruzelski, Dehoff, and Bordia, 2005 and 2006 ). R&D PRODUCTIVITY ECONOMIC PERFORMANCE We test only the first link, the second being affected by many exogenous factors other than R&D such as advertising, scale economies, market power, demand and so on. 2)Catching-up low-tech sectors are investing less in R&D but enjoy a "late- comer advantage", while high-tech are affected by decreasing returns (see Marsili, 2001; Von Tunzelmann and Acha, 2005; Mairesse and Mohnen 2005). Motivation and Novelties

3. Starting from the seminal article by Griliches, 1979 (proposing the so called “knowledge production function”) and up to the more recent contributions (see Klette and Kortum, 2004; Janz, Lööf and Peters, 2004; Rogers, 2006; Lööf and Heshmati, 2006), previous empirical works have found a significant contribution by R&D in enhancing a firm's productivity. Most of these studies focus either on cross-country analyses or on one specific sector, mainly dealing with high-tech sectors such as the pharmaceutical or ICT – related sectors. In contrast, considerably less attention has been devoted to determining whether the productivity returns from R&D are different across industrial sectors. Indeed, technological opportunities and appropriability conditions are so different across sectors (see Freeman, 1982; Pavitt, 1984; Winter, 1984; Dosi, 1997; Malerba,2004)

4. Previous empirical evidence General consensus See e.g. Mairesse and Sassenou, 1991; Griliches 1995 and 2000; Mairesse and Mohnen, 2001 Positive link between R&D and productivity, estimated elasticities range from 0.05 to 0.25 Verspagen (1995) OECD sectoral-level data Singled out three macro sectors: high-tech, medium-tech and low-tech Impact of R&D only significant in the high-tech ones. Harhoff (1998) 443 German manufacturing firms over the period 1977-1989 R&D elasticity was significant and positive and higher for high-technology firms. 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 equal to 0.18) Greater impact for high-tech firms (0.3) than for other firms (0.07)

5. Data sources: Merging of UK-DTI R&D Scoreboard and UK-DTI Value Added Scoreboard editions Unit of observation: 577 top European R&D investors (very large firms) Time period: 2000-2005 (6 years) Main variables: R&D, Value Added per employee, capital expenditures, employees 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 Biased dataset towards the large firms, “pick the winner” effect in low-tech sectors. Dataset

7. Low-tech sectors appear to be more productive, in terms of labour productivity but not because of greater R&D accumulation rather because of scale economies and higher physical capital intensity Descriptive statistics

8. Cobb-Douglas Production Function: where: all variables in natural logarithms all variables deflated according to the different national GDP deflators during the 6 year period VA/E = labour productivity R/E = R&D stock per employee C/E = (physical) capital stock per employee E = employment (if greater than zero, it indicates increasing returns) Time and two-digit sector dummies implemented in order to take into account both common macroeconomic effects and sectoral peculiarities Methodology

9. As is common in this type of literature (see Hulten, 1991; Jorgenson, 1990; Hall and Mairesse, 1995; Bönte, 2003; Parisi, Schiantarelli and Sembenelli, 2006), stock indicators (rather than flows) were inserted as impact variables; indeed, a firm's productivity is affected by the cumulated stocks of physical and R&D and not only by current or lagged flows: To compute the growth rates (g) for K and C, we used the OECD ANBERD and the OECD STAN databases respectively (compounded average rates of change in real R&D and fixed capital expenditures in sector (s) and countries (c) over the period 1990-1999) The Perpetual Inventory Method

10. The depreciation rate problem: We applied different δ to the three sectoral groups (j: high-tech, medium-high- tech and medium-low/low-tech); in fact, advanced sectors are characterised - on average - by shorter product life cycles and by faster technological progress that accelerates obsolescence. Accordingly, we applied δ equal to 20%, 15% and 12% to R&D stock (K) and 8%, 6% and 4% to capital stock (C). The resulting weighted averages (15.6% for K and 6.0% for C) are very close or identical to the 15% and 6% commonly used in the literature. The Perpetual Inventory Method

11. Preliminary pooled ordinary least squares (POLS) with time and sectoral dummies Random (RE) 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 refused fixed effect; in the fixed effects model time-invariant regressors, such as the very significant two-digit sectoral dummies, are automatically wiped-out. Robustness checks: lagged regressors (t-1) inclusion of spillovers (see Bernstein and Nadiri, 1989; Los and Verspagen, 2000; Medda and Piga, 2007), we proxied intra-sectoral spillovers through total sectoral R&D expenditures (specific national/sectoral figures from the OECD-ANBERD database); consistently with previous variables, we used spillover stocks per employee (S/E). Method Methodology

12. Results

13. Results: Robustness Checks

14. All shown coefficients are significant at at least the 95% level of confidence Results

15. The positive and significant impact of R&D on productivity is always confirmed. While this result does not fully dispel the concern about the lack of a link between R&D and the ultimate economic performance of a firm it clearly suggests that R&D is a fundamental determinant of possible competitive advantage. Firms in high-tech sectors not only invest more in R&D, but also achieve more in terms of efficiency gains connected with research activities. In contrast with recent acceptance of low-tech sectors as favourite targets for R&D investment, our results show that firms in high-tech sectors are still far ahead in terms of the productivity impact of their research activities, at least among the top European R&D investors. Productivity growth in low-tech firms is still heavily dependent on investment in physical capital (embodied technological change). While these results cannot readily be generalised to the overall economy, they do not support the idea that "low R&D" is "more efficient R&D", but rather the opposite view. Further research – based on larger and more comprehensive samples – is needed to see whether this result can be further qualified. Conclusions