INNOVATION AND ECONOMIC PERFORMANCE: AN ANALYSIS AT THE FIRM LEVEL IN LUXEMBOURG

1. INNOVATION AND ECONOMIC PERFORMANCE: AN ANALYSIS AT THE FIRM LEVEL IN LUXEMBOURG Vincent Dautel CEPS/INSTEAD Seminar “Firm Level innovation and the CIS - Is there a common story across EU countries?” October 24, 2005, Lisboa.

2. Research questions How innovation in the manufacturing and service sector impacts economic performance ? How innovation impacts the level of labour productivity How innovation impacts the growth of labour productivity

3. Plan of lecture • Introduction • Data used • Formulation of the model • Specification of the model • Main econometric results • Conclusion

4. Introduction • Empirical literature on innovation at the firm level focuses mainly on the manufacturing sector. • Moreover, a limited literature has considered the service sector in the relationship between innovation (technological change) and economic performance (especially at the firm level). • Based on the manufacturing sector, a first attempt to model the link between innovation input, innovation output and economic performance has been carried out by Crepon and al. (1998). A slightly modified version of this model has been introduced by Lööf and Hesmati (2001). A complementary approach can be found in Klomp and Van Leeuwen (2001). • Very recently, the inclusion of the service sector has been considered in empirical application linking the different steps of the innovation process (Conceiçao and Veloso (2004), Faria (2004), Lopes and Godinho (2005), Musolesi (2005)).

5. Why the inclusion of the service sector matter? • Crucial role of the service sector in the European Economy - 2/3 of employment and GDP (Eurostat 1999) - 2/3 of economic and employment growth in the business sector - 54% of the total value added in 2001 (34% from the manufacturing sector) • Service sectors are innovating (CIS2 and CIS3, Eurostat) - Innovative activities occur at different extents and different forms across the service sectors - service sectors are not only technology adopters

6. But measures of the innovation activities matter in order to estimate service sectors contribution • Innovation, following a technological approach, is mainly defined through new product and process. • Consequently innovation measures are biased toward technological change and do not capture the whole spectrum of changes characterizing the service sector • Even with the limitation imposed by the available data, it seems very useful to introduce the service sector in the analysis, which will be based on a technological approach.

7. Other important strategic and organisational changes, EU, 1998-2000 (%)

8. The data • The Micro data come from the CIS3 survey carried out in Luxembourg (survey based on the 3 years of observation 1998-2000). • For this survey a sample of 440 observations is available • The data include 4 types of crucial measures for our analysis, which allows to consider the different steps of the innovation process • Innovating or not • Innovation effort (expenditures in innovation activities) • Innovation output (percentage of turnover from innovative products) • Innovation performance (labor productivity measured as sales per employee )

9. Many other variables are available at the firm level. Some of them are have been collected for firms innovating or not, some variables are only available for innovators. • Data used refer to the whole of the firms surveyed in CIS3 : i.e. firms operating, with at least ten employees, in the manufacturing sector or in a selection of the service sectors. • In order to allow statistical inference, weighting factors have been applied. • 75% of the target population belongs to the service sectors

10. Formulation of the model Two main econometric problems arise for the estimation - Selectivity: not all firms are engaged in innovation activity and some information is not available for non-innovating firms - Simultaneity: several steps of the innovation process are considered simultaneously, as a consequence some explanatory variables may be determined jointly. Following Lööf and Hesmati (2001) and Janz, Lööf and Peters (2003) we apply the following approach: - We estimate the effect of the innovation output on productivity only for the innovators • We allow feedback effect of productivity on innovation output • We estimate the model (4 equations) in two steps

11. In a first step a generalized tobit model is applied including the selection equation and the innovation input equation. The estimates resulting from this step are used to estimate the inverse Mills’ ratio included in order to correct for potential selection bias • In a second step the productivity (first productivity level, second productivity growth) and innovative output are estimated in a simultaneous equation system (3 sls estimation)

12. Specification of the model • In each equation we include a identical set of variables: firm’s size, sectorial dummies (9 sectors) firm type (belonging or not to a group and origin of the group) • We assume that several variables can affect innovation output directly or indirectly and include them in the innovation input and innovation output equation: demand pull, lead time advantage on competitors, secrecy practice. • Additional variables have been introduced in order to characterizing each equation

13. Main econometric results Innovating in product • Positive and significant effect of firm’s size • The product lifetime seems to have a role to play • Introducing other changes increases the propensity to innovate • Market competition may increase the incentive to innovate (i.e. lead time advantage on competitors has a positive and significant effect) - The market orientation does not seem to increase the propensity to innovate

14. Intensity of innovation expenditure • Innovation input decreases with firm size (small firms invest more resources in innovation) • Being a process innovator increases the innovation expenditure intensity • Belonging to a Luxemburger group has a positive and significant impact (in comparison with firms not belonging to a group) • Demand pull is negatively linked with innovation expenditure intensity • Having a continuous R&D activity has a positive and significant impact - The lack of appropriate source of finance has a negative impact

15. Innovation output • Innovation input has a positive but not significant impact on innovation output (p=0.22) • The feedback effect of productivity is positive and significant • The size has a negative impact • Belonging to an extra-European group (mainly firms from the US) increases innovation output - Positive effect of external knowledge • Demand pull has a positive and significant impact - The lack of information on technology and/or qualified personnel decreases innovation output

16. Level of labour productivity • Sales per head are mainly determined by the innovation output and the investment effort (i.e. innovation is essential) • The share of employees with higher education has a negative impact • Being an European group has a positive impact (in comparison to firms not belonging to a group) - Being a new firm decreases the level of labor productivity

17. Labour productivity growth • Innovation output is positive but not significant at the 10% level (p=0.17) • The share of employees with higher education has a positive impact - Being a Luxemburger group has a negative impact (in comparison to firms not belonging to a group)

18. Conclusion • The main Results found for the manufacturing sector are also found with the service sector inclusion • Considering the level of labour productivity, innovation is essential • Considering the growth of labour productivity, innovation influence is less obvious • In addition to these first results, a reliability analysis has to be carried out

19. Thank you for your attention For additional information Vincent Dautel vincent.dautel@ceps.lu