1. Impact of Corporate R&D on Efficiency in OECD Industries
The 9th Annual  EUROMED Academy of Business (ΕΜΑΒ) Conference: Innovation, Entrepreneurship and Digital Ecosystems
Impact of Corporate R&D on Efficiency in OECD Industries
Maria Dos Santos – ESCS/IPL and DINÂMIA´CET-ISCTE-IUL
Henrique Diz – Universidade Lusófona do Porto

2. Agenda and presentation topics
· Introduction and main goals
Literature review
· The paper's research problem and why it is interesting
· Methodology, methods and data
· Results
· Conclusion

3. Introduction
Efficiency and productivity have always been a crucial issue in economics issues.
The research and development (R&D) literature generally assumes that corporate R&D activities have a positive impact on firm productivity, at country level (Griliches 1979; Kumbhakar, et al., 2012) or multi-country level, both on high-tech, medium-tech and low-tech industries (Verspagen, 1995; Liik et al., 2014);
Currently, the alleged advantage of low-tech over high-tech sectors in achieving more efficiency gains from (additional) R&D investment is being debated.
Some authors argue that the relationship between R&D and productivity growth would be expected to be weaker in high-tech than in low-tech sectors. This hypothesis contrasts with previous empirical evidence that additional R&D activities make a bigger marginal impact in high-tech sectors and that additional capital investment makes a bigger marginal impact in low-tech sectors (Kumbhakar et al., 2012; Liik, et al., 2014);

4. Introduction and main goals
That question (R&D impact on industries) has been understood by policymakers around the world and reflected in different countries setting targets for innovation inputs and outputs, probably the most well-known one being the Lisbon target, replaced by Strategy Europe 2020 setting the R&D expenditures to the 3% level of GDP.
The main goal of this study is to analyze the impact of corporate R& D in the performance of low, medium and high- tech industries, in the main OECD countries.
The paper aims to answer the questions: Does the impact of R&D is significant for all types of industries? If so, what are the differences and the magnitude of these effects in each of these types of industries?

5. Literature review
Studies on firm performance can be divided into two main strands:
Relies on production functions that assume efficient use of the given inputs. If this assumption does not hold true, the parameter estimates and associated marginal effects of inputs might be biased according Kumbhakar et al., (2012).
Follows the logic of a two-stage approach; cross-sectoral or cross- firm productivity estimates are retrieved as a residual from a production function and subject them to a regression on a on a set of potential determinants of productivity growth (Bos et al. 2010a, b; Kumbhakar et al., 2012).
Relatively small attention has been paid to studying whether the productivity growth stemming from R&D activities differ across industries.

6. Literature review
Griliches and Mairesse (1982) and Cuneo and Mairesse (1983) focusing on sectoral comparisons based on production function methodology. The authors conducted two comparable studies, used micro-level data and drew a distinction between firms in science-related sectors and those in other sectors:
They found that the impact of R&D on productivity was significantly higher for science-based firms (elasticity 0.20) than for others (0.10).
Verspagen (1995) carried out a multi-country study, three sectors: high-tech, medium-tech and low-tech, according to the OECD classification (Hatzichronoglou 1997). The major finding of his study was that the impact of R&D was significant and positive only in high-tech sectors;
More recently Ortega-Argile´s et al., ( 2010) examined the top EU and USA R&D investors concluded that this impact increases from low-tech through medium–high to high-tech sectors. For capital input, the results are the opposite; they appear to be quite high for low-tech sectors, tend to be lower for medium-techs, and are insignificant for high-tech sectors;

7. The model and data
The production function selected was Cobb-Douglas (CD) (Greene, 2008, p. 98)
The study uses a stochastic frontier (SF) production function approach (Aigner, Lovell, & Schmidt, 1977) according to Kumbhakar et al., (2012) and Liik et al., (2014);
To examine the extent to which the input (R&D capital) influences the productivity of the industry;
The assumption of a common frontier across sectors is a sensitive issue.
Nevertheless, many studies do assume such a common frontier. This study avoids assuming a common technology by estimating group-specific technology levels and running the corresponding analyses in parallel according according Kumbhakar et al., (2012) and Liik et al., (2014);

8. Data
The study is based on industry-level at two digits (by Industries International Standard Industrial Classification of All Economic Activities (ISIC4));
Data from two OECD combined datasets:
OECD STAN Database for Structural Analysis (ISIC Rev. 4, combined with ISIC Rev. 3) - for measures of output, labour input, and capital and OECD ANBERD STAN3 and STAN 4 for R&D expenditures in Industries (ISIC Rev. 4)
over a 12-year period from 2000 to 2011 the latest data available, forming an unbalanced panel;
low-tech industries (D10 to D12); medium-low-tech ( D19 and D22) and high-tech industries (D21 D26) according the ISIC 4 classification after conversion to ISIC3 (with different nomenclature for the missing countries and values).

9. Data The variables computed from the database was:
E – Number of employees per industry typology;
GFCF – Gross fixed capital formation and current prices;
VALD – Value added at current prices;
R&D – R&D expenditures in Industry at current prices.
- Input and output variables are transformed at constant prices (OCDE database)
- R&D expenditures and investments into physical capital was capitalized, in order to provide R&D and physical capital stock variables by perpetual inventory method

10. Data
a starting value of R&D capital is calculated at the time period t0
g - the growth rate of R&D expenses
- the depreciation rate
SF model for panel data, based on the Cobb-Douglas production function:

11. Heteroscedasticity test
Results
Table 1. Models’ parameters and efficiency estimates for different sectors.
Source: authors’ calculations. Note: *p < .05; **p < .01; ***p < .001.
Model
Whole sample
Low-Tech
Medium-tech
High-tech
ln R&D/E -
0,28***
ln K/E
0,690**
0,742*
0,221***
0,259**
constant
0,356**
0,441*
2,501***
1,857**
time
0,021
0,001
0.0670
0,023
Heteroscedasticity test
(p-value)
2,77614
0,0000
2,184
- 01,3021
12,1 
0,0002
Mean efficiency (TE)
0,367
0,1050
0,368
0,409

12. Results

13. Results

14. Conclusion
High-tech industries are generally associated with the higher efficiency and productivity values than the other industries with medium and low technologies;
These industries are also associated with higher growth rates of knowledge;
These results were according the previous authors;
The R&D is crucial for the development of a competitive high tech European industry across the world;
For medium and mainly low tech industries the capital accumulation seems crucial your development;
So the public and privates funds must charter the industries according to their needs and technological performance;
That implies that decisions makers may give more attention to European programs about the European funds distribution. In particular, more funds on R&D must be attributed to the high-tech industries across all the industries and European countries. On the opposite way the accumulation of capital must give priority to the low and medium tech industries.

15. Suggestions are welcoming
Thanks so much
Suggestions are welcoming

16. Notes:
(1) e.g. by the so-called Swedish paradox (moderate innovation output despite very high innovation inputs) (Edquist & McKelvey, 1998).
Business enterprise expenditure on R&D (BERD) -covers R&D activities carried out in the business - sector by performing firms and institutes, regardless of the origin of funding (European Commission, 2014).
(2) The EU innovation policy is focusing on R&D: increasing BERD is the rationale behind the “Lisbon agenda 2000” to make Europe the most dynamic knowledge economy in the world by 2010 and the more specific “Barcelona target,” which— two years later—committed the EU to reaching the objective of an R&D/GDP level of 3%, two thirds of which accounted for BERD (European Commission 2002; European Council 2002). The recent “Innovation Union” document consistently advocates for a boost in R&D on order to increase the competitiveness of the European private sector (European Commission 2010a).
(3) In order to use the SFA method, the production function must be selected, for the best practice frontier. Two forms of functions are dominating in empirical applications (Greene, 2008, p. 98): Cobb-Douglas (CD) and translogarithmic. These functions are related, CD is a constrained version of translogarithmic function. With respect to efficiency estimates, there is no significant difference between these two, also the ranking of efficiency scores is strongly correlated (Zhang, 2012).

17. Notes:
The SFA model selection is generally similar to panel data models – among the options are fixed and random effect models. The difference between these two is related to the assumptions about the asymmetric error term. The fixed-effect model does not need any assumptions about probability distribution of asymmetric error, which may be correlated with regressors or random noise, becoming a part of the producer-specific intercept parameter (Kumbhakar & Lovell, 2000).
SFA models are even more vulnerable than linear regression models, because there are two affected error components (Kumbhakar & Lovell, 2000). For such a case, robust White (1980) estimates could be used for standard errors. White’s estimate is appropriate also in cases when the structure of heteroscedasticity is not known (Greene, 2007).

18. Technological intensity
Appendix A1- Standard Industrial Classification of all Economic Activities 2011
Appendix A1- Standard Industrial Classification of all Economic Activities 2011
Code
Description
Technological intensity 05
Mining of coal and lignite
- (2) 06
Extraction of crude petroleum and natural gas 07
Mining of metal ores
09, 08
Other mining and quarrying, Mining support service activities 10
Manufacture of food products
Low
12, 11
Manufacture of beverages, manufacture of tobacco products 13
Manufacture of textiles 14
Manufacture of wearing apparel 15
Manufacture and processing of leather and related products 16
Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials 17
Manufacture of paper and paper products 18
Printing and reproduction of recorded media
Low (3)
182
Reproduction of recorded media
Medium-low 19
Manufacture of coke and refined petroleum products 20
Manufacture of chemicals and chemical products
Medium-high 21
Manufacture of pharmaceutical products and homeopathic pharmaceutical
High
preparations 22
Manufacture of rubber and plastics products 23
Manufacture of other non-metallic mineral products 24
Manufacture of basic metals 25
Manufacture of fabricated metal products, except machinery and equipment
281
Manufacture of general-purpose machinery 28
Manufacture of machinery and equipment n.e.c.
275
Manufacture of domestic appliances 26
Manufacture of computer, electronic and optical products 27
Manufacture of electrical equipment

19. Manufacture of domestic appliances
Appendix A1- Standard Industrial Classification of all Economic Activities 2011
Appendix A1- Standard Industrial Classification of all Economic Activities 2011 (continuação)
275
Manufacture of domestic appliances
Medium-high 26
Manufacture of computer, electronic and optical products
High 27
Manufacture of electrical equipment
29, 30
Manufacture of motor vehicles, trailers and semi-trailers, Manufacture of other transport equipment
301
Building of ships and boats
Medium-low
303
Manufacture of air and spacecraft and related machinery 31
Manufacture of furniture
Low 32
Other manufacturing
Low (5)
325
Manufacture of medical, dental and orthopedic instruments and supplies