The impact of innovation activities on firm performance using a multi-stage model: Evidence from the Community Innovation Survey 4

TL;DR: In this paper, a multi-stage approach to innovation is applied to the firm level data from the fourth Community Innovation Survey (CIS4), covering some 90,000 firms.

About: This article is published in Research Policy.The article was published on 2013-03-01 and is currently open access. It has received 333 citations till now. The article focuses on the topics: Mature market.

Summary

1. Introduction

• The traditional economic theory predicts that in the long run all firms will converge to their long run steady state equilibrium position and optimum size.
• In last few decades a large number of studies have attempted to map the channels and mechanisms through which new knowledge is transformed into better performance.
• The evidence from this literature is inconclusive thus calling for further research.
• The failure to achieve this goal can be traced to many factors including the inability to stimulate R&D spending and enhance the innovation activities of firms in EU countries, particularly the new members from Central and Eastern European Countries which are seriously lagging behind.
• The results of their baseline specification are presented in section 6.

2. Theoretical framework

• Innovation refers to all scientific, technological, organisational, financial and commercial activities which lead to, or are intended to lead to, the implementation of technologically new or improved products or services (OECD/Eurostat, 1997 p. 39).
• Building on these foundations, Klette et al., (2000) developed the multi-stage model of firm behaviour in which they argue that the growth of a firm is determined by the quality and price of its own and its competitors’ products and that the quality of its products can be improved through innovation.
• As with other variables the findings based on different datasets exhibit considerable variations.
• The empirical evidence has been found only in few cases (Klomp and Van Leeuwen 2001, Loof and Heshmati 2002).
• In addition to these determinants, various firm characteristics such as the firm size have been included as the determinants of the innovation output but mostly they have been insignificant (Loof and Heshmati 2002).

4. Data

• The empirical analysis in this paper is based on the data from fourth Community Innovation Survey (CIS4), conducted in 2004.
• The comparison of average productivity of firms in different sub-groups across the two sub-samples shows substantial differences, with firms based in old EU group of countries having much higher levels of productivity.
• The anonymised data was made available to the Microdyn project on CDROM by Eurostat.
• The authors are grateful to Sergiu-Valentin Parvan for facilitating access to data and confirming the regression results produced at the Centre.
• For expositional convenience, hereafter, the authors will refer to the two groups as Central and East European countries and West European countries.

Access to subsidies

• Received subsidies from local sources Source: CIS Database, Eurostat Turning to the most important issue, the difference in performance between innovating and non-innovating firms, it is evident that in both sub–samples, firms undertaking some innovation activities in the three previous years, performed much better than their rivals which did not engage in any innovation activities.
• One explanation for this finding may be that greater competition on the EU and foreign markets forces firms to be more innovative and efficient – but it also reflects the fact that firms withstanding foreign competition are likely to be more efficient in the first place.
• With respect to factors hampering innovation, cost obstacles seem to be the most prominent.
• Moreover, innovating firms in different sub-samples vary considerably with respect to their performance.
• It appears that apart from innovation, market orientation and creation of networks with other enterprises and research institutions have positive effect on firm’s performance while the factors hampering innovation such as cost and knowledge obstacles as well as the use of subsidies are negatively related to the firm performance.

5. Model specification

• The  literature  on  innovation  and  firm  performance  identifies  two  major  problems  with  the  econometric specification of this relationship, namely the selectivity bias and simultaneity bias.
• The  selectivity bias arises from the fact that not all firms engage in innovation and some innovations are  not successful.
• In addition, we have already argued that there are many factors which can influence  both  firms’  decision  to  innovate,  its  level  of  expenditure  on  innovation  as  well  as  its  final  performance.
• The four‐stage model discussed earlier (Crepon, Duguet  and Mairesse 1998) is capable of addressing these problems.
• The  third  stage  is  a  knowledge  production  function  linking  innovation  input  and output.

5.1. General specification of the system model

• The and are random error terms with zero mean, constant variances and not correlated with the explanatory variables.
• It is assumed that the two error terms are correlated with each other on the basis of unobservable characteristics of firms.
• This fact is acknowledged and controlled for with proper instrumentation.

5.2. Definition of variables and specification of the model

• The four dependent variables of the model and the explanatory variables in each stage are defined as follows.
• For a fuller definition of variables.
• It is specified as a function of: firm size; innovation output from third stage; organisational and marketing innovations; factors hampering innovation (all mentioned earlier); two dummy variables for sources of innovation indicating if the improvements in products and processes in the previous three years were developed within enterprise or in the wn efforts cooperation with other firms and institutions.
• All four models contain three dummy variables for industry specific effects for manufacturing, services and trade sectors.
• As the authors already mentioned, these two equations are estimated as a system in a framework of simultaneous equations where the feedback is allowed from productivity to the innovation output.

6. Interpretation of findings

• In this section the authors report the results of the estimation procedure.
• The analysis was conducted for the full sample and the two sub-samples (Western Europe and CEECs) separately.
• For expositional convenience the results for each stage of the innovation process will be presented in separate sub-sections.

6.1. Decision to innovate

• Table 2 presents the results of the estimation of the first stage for three samples.
• The results are very similar for the three samples.
• In general it can be concluded that the probability of engagement in innovation for a typical firm increase with firm size.
• This suggests that knowledge accumulated from previous innovative activities (even when they were not successful) as well as knowledge transfer from other parts of group motivates firms to engage in new innovations.
• The market factors and other factors hampering innovation (such as the existence of previous innovations) have negative impact on the probability of the firm’s decision to engage in innovation activities.

6.2. Innovation investment

• The results from the estimation of the innovation investment equation are presented in Table 3.
• Investment in innovation is higher in firms which are part of a group and which previously had abandoned some innovation efforts.
• Local subsidies have negative sign in the full sample and the old EU sample while the coefficient for firms in new EU countries is statistically insignificant.
• The likely explanation for this is that when deciding on provision of subsidies local administrations may have other objectives and may base their decision on political factors.
• In all three regressions other sources of information (conferences, scientific journals and professional and industry associations) are statistically insignificant.

6.3. Innovation output

• The third stage consists only of firms that have reported positive amount of innovation output.
• The results of the estimation are presented in Table 4.
• In the other two cases the coefficient of the Mill’s ratio is statistically insignificant.
• While for the full sample the authors find a positive and statistically significant coefficient, for the sub-sample of firms in CEECs countries the effect of productivity on innovation output is negative and for the sub-sample of West European firms it is insignificant.
• In addition the product oriented effects of innovation such as improvements in quality or the wider range of goods and services are positively correlated with the innovation output.

6.4. Productivity (performance) and innovation output

• The result of the estimation of the fourth stage of the model is presented in Table 5.
• The table indicates that the firm’s productivity increases significantly with innovation output.
• 27    Somewhat puzzling is the negative coefficient on organisational innovations found for the full sample and the sub-sample of firms in Western Europe.
• Therefore, change in organization results in lowering performance of firms in the short run.
• In addition, the coefficient for New EU countries is statistically significant in all but the first stage equation.

7. Sensitivity analysis

• To check the sensitivity of above results the authors exclude feedback effect from the third stage, productivity to innovation output, equation and estimate the system for innovation output and productivity with two-stage least squares method.
• The only exceptions are coefficients for sources of innovation being cooperation with other enterprises or institutions in the full sample, which becomes significant, and the cost factors hampering innovation whose effect in the Western Europe sub-sample diminishes.
• In mature market economies the feedback is not significant while in case of transition countries the authors observe negative effect from productivity to innovation output, probably reflecting the strong specialisation of firms from these countries in labour intensive products.
• In Innovation and Firm Performance: Econometric Exploration of Survey Data, edited by A. Kleinknecht and P. Mohnen, 310-320.

The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and

• The Business Cycle. New York: Oxford University Press, 1934.
• 36    Stoevsky, G. »Innovation and Business Performance of Bulgarian Companies (structural econometric analysis at firm level).« Economic Research and Projections Directorate Working Paper. Bulgarian National Bank, 2005.

Independent variables

• Number of employees in 2004 National market Dummy variable; 1 if firm in past 3 years sold goods on national market EU Market Dummy variable; 1 if firm in past 3 years sold goods on EU, EFTA or EU candidate countries marketsa.
• All other countries Dummy variable; 1 if firm in past 3 years sold goods on markets of other countries Part of a group Dummy variable; 1 if firm is part of an enterprise group Abandoned or ongoing innovations Dummy variable; 1 if firm in past 3 years had any abandoned or ongoing innovations.
• In cooperation with other enterprises or institutions Dummy variable; 1 if enterprise developed product innovations in 3 years prior to survey in cooperation with other enterprises or institutions (base category: developed by other enterprises or institutions).
• Sources of process innovations Within enterprise Dummy variable; 1 if enterprise developed process innovations in 3 years prior to survey alone (base category: developed by other enterprises or institutions).

Figures

Table 3: Results from the innovation investment equation Total CEECs WE

Table 4: Results from the innovation output equation Total CEECs WE

Table 5: Results from the productivity equation Total CEECs WE

Table 2: Results from the selection equation Total CEECs WE

Table 1: Average productivity of different groups of firms in the database Sample All countries CEECs Western Europe Criterion Yes No Yes No Yes No

Table 7: Results from the productivity equation 2sls Total CEECs WE

Table 6: Results from the innovation output equation 2sls Total CEECs WE

Frequently asked questions

Q1. What is the main topic of his research?

His research has focused on microeconomic problems of transition to a market economy in Central and Eastern Europe, especially in areas such as: privatisation, corporate governance, enterprise restructuring, competitiveness, bankruptcy and reorganisation, employee financial participation and competition policy. 

Q2. What is the definition of the system model?

5.1. General specification of the system model.............................................................165.2. Definition of variables and specification of the model ..........................................186.1. Decision to innovate..................................................................................................206.2. Innovation investment...............................................................................................226.3. Innovation output ......................................................................................................236.4. Productivity (performance) and innovation output ................................................26References ............................................................................................................................33Appendix ...............................................................................................................................373   Iraj Hashi is a CASE Fellow and Professor of Economics at Staffordshire University, United Kingdom.