THE IMPACT OF R&D EXPENDITURE ON FIRM PERFORMANCE IN MANUFACTURING INDUSTRY: FURTHER EVIDENCE FROM TURKEY

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1 THE IMPACT OF R&D EXPENDITURE ON FIRM PERFORMANCE IN MANUFACTURING INDUSTRY: FURTHER EVIDENCE FROM TURKEY Dr.Erkan ÖZTÜRK Sakarya University, Department of Accounting and Taxification, Sakarya, Turkey, Dr. Feyyaz ZEREN Namık Kemal University, Department of Business Administration, Tekirdağ, Turkey, Abstract The effects of research and development (R&D) activities on firm performanceare fairly important in developing countries in which they operate. Accordingly, this study aims to determine how firms performance is affected by R&D expenditures, and this effect is tested and discussed with manufacturing firms data, which range from 2007-Q1 to 2014-Q3 in Turkey, both by using Durbin-Hausman panel cointegration test developed by Westerlund (2008) and Common Correlated Effects (CCE) coefficient estimator developed by Pesaran (2006). The findings indicate that R&D expenditures have a positive effect on sales growth in the manufacturing industry. Moreover, it has been found that this effect has continued for six months. Keywords: R&D Expenditure, Sales Growth, Panel Cointegration, Panel Estimator, Turkish Manufacturing Industry Jel Codes: O30, L25, C23 Introduction Examination of a relationship between innovation and firm performance has been a popular issue for many years. The theoretical basis of relevant studies on this issue which has been carried out up today based on Schumpeterian growth theory (1934, 1942). According to Schumpeter view, success in the firm performance depends on the entrepreneur s creativity and the unpredictability of their innovations. In other words, innovative firms gain monopoly power, thanks to R&D activities. In recent years, it is observed that interest towards R&D and innovation systems has been increasing in knowledge-based developing economies. When Turkish Statistical Institute s data is examined, it is understood that share of gross domestic expenditure on R&D (GERD) in gross domestic product (GDP) has increased and R&D activities have become much more important over the years as in many developing countries. This increase is also supported by public incentives. Figure 1. GERD/GDP % during

2 When the observed increasing trend in Figure 1 is considered with theoretical framework; it is expected that this increasing trend has positive effect both on economic development in macro perspective and on performance indicators of firms which carry out R&D activity in micro perspectives such as sales and firm growth. Reference work which examines the impact of R&D expenditure on sales growth has been developed by Scherer (1965). He looked at 365 of the largest US firms and has proven that R&D expenditures have a positive effect on firm profits via sales growth. Since then, many subsequent studies following the occupational Scherer. Most of these papers have found significant evidence about positive effect of R&D expenditure on sales growth with using different methods, such as regression analysis, cross-sectional regression analysis, parametric and nonparametric quantile regression analysis. A brief summary of the studies is presented in Table 1. Table 1. Literature Review Author Sector Country / Region Time Period Scherer (1965) Fortune s List USA Nolan et al (1980) Pharmaceutics UK Hall (1987) Manufacturing US Geroski and Machin Manufacturing UK (1992) Geroski and Toker Manufacturing UK (1996) Roper (1997) Small firms Germany, UK and Ireland Lefebvre et al (1998) Small firms Canada NA Ernst (2001) Machine tool Germany manufacturer firms Nahm (2001) Manufacturing Korea Monte and Papagni Manufacturing Italy (2003) Lööf and Heshmati CIS 1 participants Sweden (2006) Falk (2012) FFG 2 Austria García-Manjón and 1000 top R&D firms in 18 European Countries Romero-Merino (2012) EU Choi and Williams Technology intensive Korea and China (2013) industries Choi and Williams Microelectronics, China (2014) pharmaceuticals and communications 1 European Community Innovation Survey. 2 Austrian Industrial Research Promotion Fund. 3 Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Poland, Slovenia, Spain, Sweden, Netherlands and UK. 33

3 Data and Methodology This study aims to determine long-term relationship between R&D expenditure and sales growth data for 26 Turkish manufacturing firms which range from 2007-Q1 to 2014-Q3. For this purpose, the relationship between R&D expenditure data 4 which are reported in accordance with International Financial Reporting Standards (IFRS) and are classified as research activities in accordance with International Accounting Standard (IAS) 38 with sales revenue data are tested by panel data analysis unlike the methods of Table 1. In this study, the long-term relationship between the panels has been tested by Durbin-Hausman panel cointegration test. In order to obtain results of Durbin-Hausman cointegration test, firstly, candidate cointegration must be estimated and thus residuals be obtained. Then, in order to consider cross-correlation, these residuals are decomposed by using principal components method. After observing common factor, if obtained error terms are stationary, it is determined that there is cointegration among equity variables. In this context, one of the two tests recommended by Westerlund (2008) is Durbin-Hausman panel test. This test performs analysis by assuming that the autoregressive parameters don t change between industries. According to this test, a rejection of null hypothesis means that there is a cointegration relationship in all panels. On the other hand, the second test is Durbin-Hausman group test which also allows parameter differentiation between industries. Alternative hypothesis of this test does not indicate that there is a cointegration relationship in all panels. It indicates that there is at least one cointegration relationship in panel (Bayar and Tokpınar, 2014). In order for the implementation of this test, there is not also condition that series are stationary at the same level; however, independent variable must be stationary I(1) level, and may be stationary I(0) or I(1). After this step, CCE estimator, which forecast long-run regression coefficient of the explanatory variable under cross section dependency, recently advanced by Pesaran (2006), must be estimated in order to interpret the analysis results. Empirical Findings Slope Homogeneity and Cross Section Dependency Test have an important place in determining panel cointegration test used here. Accordingly, both panels have heterogeneous structure according to Slope Homogeneity test in Table 2. With respect to cross sectional dependency, instead of using Breusch and Pagan (1980) CDLM test whose results are not dependable when individual means are different than zero, Peseran's (2008) CDLM adj Test is used that gives test statistics by taking into accounting variation and means. Table 2.Pesaran and Yamagata (2008) Slope Homogeneity Test Results Test Statistics P-Values adj Table 3. Cross Section Dependency Test Results R&D Expenditure Sales Growth CDLM (1980) (0.00)* (0.00)* Bias-adjusted CDLM (2008) 1.58 (0.06)** 1.52 (0.06)** Note: Values in parentheses indicate p-values. *, ** indicate significant at 1% and 10% level. 4 According to IAS 38, R&D expenditures which its benefits are consumed in the same period are classified as expense, while R&D expenditures which its benefits will be consumed in following periods are classified as intangible assets. 34

4 There is evidence that there is the cross section dependency in both panels according to findings in Table 3. Therefore, second generation panel unit root and cointegration test should be used in next steps. Table 4.Im, Lee andtieaslau (2012)Panel Unit Root Test Results R&D Expenditure Sales Growth Level (0.00)* (0.10) 1 st difference (0.00)* Note: Values in parentheses indicate p-values. * indicates significant at 1% level. While R&D expenditure panel is stationary in level according to Im, Lee and Tieaslau (2012) panel unit root test, sales growth panel has a unit root in a level. When we take first differences, this panel becomes stationary. The use of the Durbin-Hausman Panel Cointegration Test has been found convenient in such a case that both panels have a heterogeneous structure and are stationary in difference level. While analysis is applied, it investigated the effects of R&D expenditure on sales growth in 1, 2, 3 and 4 lagged periods (3, 6, 9 and 12 months). Table 5.Durbin-HausmanPanel Cointegration Test Results R&D Expenditure -Sales Growth Group Panel Synchronously 9.16 (0.00)* 4.18 (0.00)* Sales(+3) 9.17 (0.00)* 1.77 (0.04)** Sales(+6) 4.61 (0.00)* (0.01)** Sales (+9) 1,28 (0.83) (0.22) Sales (+12) 0.90 (0.91) 1.14 (0.87) Note: Values in parentheses indicate p-values. *, ** indicate significant at 1% and 5% level. The effect of R&D expenditures on sales has continued for 6 months for all panel according to findings in Table 5. As it is seen that there is cointegration between R&D expenditure and sales growth for 1 and 2 lagged model, yet there is no relation for 3 and 4 lagged model. Moreover, group statistics which propose at least one cointegration in panel is seen significant up to first 6 months. Direction and coefficient of this cointegration are estimated by CCE. It is determined that R&D expenditures have the positive effect on firm sales growth according to Table 6. This effect has increased during first 3 months, it tends to decrease following 3 months. This positive effect disappears after 6 months. Table 6.CCE Estimation Results Dependent Variable Coefficient T Statistics Synchronously Sales (+3) Sales (+6) Concluding remarks Empirical evidence from Turkey suggests that R&D expenditures have a positive effect on sales growth in the manufacturing industry. This result conforms to the findings of previous studies that are relevant in the literature. However, because of using research activities data which are classified as the expense, the effectiveness of this effect decreases up to 6 months as expected. In brief, this study provides even stronger evidence of the effect of R&D expenditures on sales growth by using panel data methods from manufacturing industry. 35

5 References Bayar G. And Tokpınar S. (2014) Türkiye İmalat Sanayi Alt Sektörleri Üretiminin Belirleyicileri-Panel Veri Analizi, Business and Economics Research Journal, 5, Choi S. B. and Williams, C. (2013) Innovation and Firm Performance in Korea and China: a Cross-context Test of Mainstream Theories, Technology Analysis & Strategic Management, 25, Choi S. B. and Williams, C. (2014) the Impact of Innovation Intensity, Scope, and Spillovers on Sales Growth in Chinese firms, Asia Pasific Journal of Management, 31, Ernst, H. (2001) Patent Application and Subsequent Changes of Performance: Evidence from Time-series Cross-section Analysis on the Firm Level, Research Policy, 30, Falk, M. (2012) Quantile Estimates of the Impact of R&D Intensity on Firm Performance, Small Business Economics, 39, García-Manjón, J. V. and Romero-Merino M. E. (2012) Research, Development, and Firm Growth. Empirical Evidence from European Top R&D Spending Firms, Research Policy, 41, Geroski P. A. and Machin S. (1992) Do Innovating Firms Outperform Non-innovators?, Business Strategy Review Summer, Geroski P. A. and Toker S. (1996) the Turnover of Market Leaders on UK Manufacturing Industry, , International Journal of Industrial Organization, 14, Hall, B. (1987) the Relationship between Firm Size and Firm Growth in the US Manufacturing Sector, Journal of Industrial Economics, 35, International Accounting Standard 38: Intangible Assets. Lefebvre, E., Lefebvre, L.A. and Bourgault, M. (1998) R&D-related Capabilities as Determinants of Export Performance, Small Business Economics, 10, Lööf H. and Heshmati A. (2006) On the Relationship between Innovation and Performance: A Sensitivity Analysis, Economics of Innovation and New Technology, 15, Monte A. and Papagni E. (2003) R&D and the Growth of Firms: Empirical Analysis of a Panel of Italian firms, Research Policy, 32, Nahm, J. (2001) Nonparametric Quantile Regression Analysis of R&D-Sales Relationship for Korean Firms, Empirical Economics, 26, Nolan, M. P. Oppenheim, C. and Withers K. (1980) Patenting, Profitability and Marketing Characteristics of the Pharmaceutical Industry, World Patent Information, 2, Official Websites of Turkish Statistical Institute. Pesaran, M. H. (2006) Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74, Pesaran, H. M., Ullah, A. and Yamagata T.(2008) a Bias-Adjusted LM Test of Error Cross- Section Independence,Econometrics Journal, 11, Roper, S. (1997) Product Innovation and Small Business Growth: A Comparison of the Strategies of German, UK and Irish Companies, Small Business Economics, 9, Scherer, F. M. (1965) Corporate Inventive Output, Profits, and Growth, Journal of Political Economy, 73, 3, Schumpeter, J. A. (1934) Entrepreneurship as Innovation, In R. Swedberg (Ed.), Entrepreneurship The Social Science View, 51 75, Oxford: Oxford University Press. Schumpeter, J. A. (1942) Capitalism, Socialism and Democracy, Harper, New York. Westerlund, J. (2008) a Panel Cointegration Tests of the Fisher Effect, Journal of Applied Econometrics, 23,

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