An empirical analysis of the relationship between innovation activity
Transkript
An empirical analysis of the relationship between innovation activity
An empirical analysis of the relationship between innovation activity and environmental management toward climate change Emiko Inoue PhD candidate; JSPS Research Fellow Graduate School of Economics, Kyoto University Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501 JAPAN E-mail: inoue.emiko.88r@st.kyoto-u.ac.jp Abstract No. 0201 ABSTRACT Climate change is one of the serious problems which we are facing right now. Many simulations show that a great amount of emission reduction is necessary, but it seems to be impracticable as long as we deal with in the existing framework. In this context, technological innovation is now expected to become one of the essential factors to change the existing framework and overcome this difficult situation. In order to examine what may induce innovation, previous researches (e.g. Milliman and Prince, 1989; Jaffe and Palmer, 1997) analysed the relationship between environmental policy and innovation, but there are few researches which focused on the influence of environmental voluntary approaches on innovation activity. This study scrutinises how the voluntary approaches toward climate change influence innovation activity of corporations. Using firm-level panel data of European corporations (2000-08), which was constructed based on the data of corporate responses toward “Carbon Disclosure Project”, “EU industrial R&D Investment” data, and the corporations’ CSR reports, I estimate two dynamic panel models by system GMM estimator. Innovation activity is measured by R&D investment of the corporations. The results show that one of the voluntary approaches influences on the R&D investment, and prove that corporations which disclose the information verified in whole or in part by external entities are likely to invest on R&D. This study reveals an interesting implication that it is important to implement a policy which stimulates the corporations’ incentives for disclosing the verified information by external entities in order to enhance innovation activity. Key words: Innovation, Voluntary approach, Climate change 1 1. Introduction Climate change is one of the serious problems which we are facing right now. Although the fact that a great amount of emission reduction is inevitable is suggested by many simulations such as IPCC Fourth Assessment Report (2007) and Stern Review (2006), this is not an easy task and may sometimes be impracticable without sacrificing further economic growth of both developed and less developed countries. Technological innovation is now expected to play a key role to overcome this difficult situation. This is because people begin to recognise the limits of dealing with the difficulty based on existing technology and social systems. In order to tackle the problem, it is necessary to handle it from new dimensions or frameworks which may be realised through technological innovations and innovations of social systems. Therefore, it is important to scrutinise which policies and/or factors may induce innovation. Moreover, focusing on the activities of corporations which are one of the significant actors in the economy is essential to examine effective policies and/or factors to induce innovations. When considering the influence of “eco-friendly” trend, it is also interesting to seek the relationship between corporations’ environmental management and technological innovation. This study focuses on the corporates’ voluntary approaches (henceforth VAs) toward climate change issue, and examines the influence of those environmental VAs on technological innovation which derives from R&D activity. In addition, the influence of EU emission trading scheme (henceforth EU ETS) on innovation activity is examined. Launched in 2005, EU ETS has been playing an important role in climate change policy. By adopting VAs and interaction terms regarding EU ETS, I would like to focus on the relationship between EU ETS and corporations’ innovation activities. For this research, I focus on EU corporations. Since they are facing various kinds of environmental regulations, such as carbon taxes and emission trading scheme, it is 2 worth examining how those corporations react toward environmental regulations. In addition, EU has unique data regarding environmental issues, especially climate change. As a measure of technological innovation, R&D investment, the input for innovation, is used. In order to deal with the influence of past R&D investments, dynamic panel model is assumed in this analysis. I also include the financial status and sectors which are the potential factors having impacts on R&D activity. This study shows that one of the VA positively influence the R&D investment. Corporations which disclose the information verified in whole or in part by external entities are likely to invest on R&D. Whether EU ETS positively influence on R&D investment or not is ambiguous, however, the result shows that some indirect impacts are exist. This study will contribute to the literatures in many points. It is unique in terms of using data of Carbon Disclosure Project (henceforth CDP) which has not been used so much for academic purposes. CDP enables us to examine first-hand information of corporations’ responses regarding climate change issue, which may extract intriguing factors. As far as I recognise, this is the first literature which focuses on how VAs regarding climate change influence corporates’ R&D investment by using CDP survey data. Moreover, this research may give interesting implications for environmental policy to be effective on encouraging corporations’ innovation activity. This paper is structured as follows: Section 2 provides the background literature, and Section 3 mentions an overview of VAs and R&D. In Section 4, econometric models and methodology used for the analysis are presented, and in Section 5, data description is shown. The results are examined in Section 6, and finally a conclusion and an implication of the study are discussed in Section 7. 3 2. Literature review The role of technological innovation has received great attentions in the field of environmental economics (Jaffe et al., 2003). When facing on the long term environmental problems, such as climate change, they remind us how technological innovation is vital for society. Since the benefits of innovations tend to diffuse into society, market mechanism alone has been little power to stimulate R&D incentive. On the other hand, environmental regulation or public funding of R&D has often provided opportunities of R&D activity (Popp et al., 2010). Therefore, technological innovation has often been scrutinising in the context of environmental policy. At the beginning, in 1970s, an interest was focused on which policy measurement may induce innovations (e.g. Magat, 1979; Milliman, and Prince, 1989). In 1990s, Porter Hypothesis brought a sensational argument to the relationship between environmental policy and technological innovation; because the argument was unlike a traditional view which economists had that time. Many economists, at that time, believed that strengthened environmental regulation may impose additional costs to corporations in order to comply with. However, in this hypothesis, Porter mentioned that when corporations face with strengthened environmental regulations, they are inclined to look for potential technological innovation (Porter, 1991). Through the process of seeking for unnoticed seeds of innovation, and positive involvement in R&D activity, corporations can encourage innovations which may boost the competitiveness in the international market (Porter and Linde, 1995). Many studies on the Porter Hypothesis were done thereafter. Lanjouw and Mody (1996) examined the relationship between patents and pollution abatement cost, a proxy for the stringency of environmental regulation, based on a data set of Japan, U.S., and Germany. Their results showed that pollution abatement cost successfully affected the number of patents, but they did not control for other factors which may 4 influence technical innovation. Jaffe and Palmer (1997) analysed how the stringency of environmental regulations may affect R&D expenditure and patents. Pollution abatement cost was also used as a proxy for the severity level of regulations in this study. They concluded that pollution abatement costs positively affected only R&D expenditures. In order to scrutinise the Porter Hypothesis, Jaffe and Palmer (1997) also categorised it into three types: (i) narrow version, (ii) weak version, and (iii) strong version. The first version mentions that flexible environmental policies may stimulate corporations’ incentives to innovate more than prescriptive ones. Also it says that environmental regulations should focus on outcomes rather than processes. The second version states that “certain kinds” of environmental innovations may be stimulated by environmental regulation. And the last type asserts that when environmental regulations are properly designed, cost-saving innovations may be induced. This is most attractive version of hypothesis, which would be related with competitiveness. Hamamoto (1997) also examined whether the Porter hypothesis can be observed in Japan by utilising the similar model as seen in Jaffe and Palmer (1997). The result of this study which used Japanese industry-level data showed that pollution prevention investment and low-energy consuming investment increased R&D expenditures. Both investments were used as a proxy for the stringency of environmental regulations. Brunnermeier and Cohen (2003) empirically analysed the relationship between regulation and the environmental related technological innovation. As a proxy for the stringency of regulation, they used pollution abatement costs and the number of inspections committed by regulatory institutions. Although the pollution abatement costs increased the environmental related patents, inspections did not affect technological innovation. Arimura et al. (2007) analysed what may induce environmental R&D, and 5 tested the three variations of the Porter Hypothesis proposed by Jaffe and Palmer (1997). For this analysis, data of manufacturing facilities in seven OECD countries was used. The results showed that public policy could induce environmental R&D investment. The adoption of an environmental accounting system also induced environmental R&D investment, which was promoted by flexible policy instruments. The direct relationship between flexible policy instruments and environmental R&D investment was not proved. “Strong” version of the Porter Hypothesis was indirectly supported in their study. Lanoie et al. (2011) also tested the Porter Hypothesis by using same data as Arimura et al. (2007) did. In this study, four main elements: environmental policy, R&D, environmental performance, and commercial performance were examined. “Weak” version was strongly supported, “narrow” version was conditionally observed, and “strong” version was not supported in their study. In terms of the VAs, various studies analysed the determinants of Environmental Management System (henceforth EMS) certificate adoption and the effectiveness of implementing EMS on environmental performance. Nakamura et al. (2001) found that several factors, such as firm size, the average age of employees and export ratio affected the ISO 14001 adoption by using data of Japanese manufacturing corporations. Nishitani (2009) mentioned that the determinants differed depending on the year of adoption, and the relationship between economic performance and initial ISO 14001 adoption was positively proved. Regarding the effectiveness of certified EMS on environmental performance, Potoski and Prakash (2005) found that ISO 14001 show a significant influence on reducing the environmental impacts of US corporations. However, in Germany case, Ziegler and Rennings (2004) could only find a significantly weak positive effect on environmental innovation and abatement behaviour. Using Japanese facility-level data, Arimura et al. (2008) examined the effectiveness of VAs (implementing ISO 14001 6 and publishing environmental reports) on the environmental performance. The results showed that both VAs contributed to reduce all the impacts, and environmental regulations did not exert a negative effect on ISO 14001. They also found that ISO adoption was encouraged by assistance programs of authorities which is one of the VAs as well. Few studies investigated the effects of VAs besides EMS on environmental innovation. Demirel and Kesidou (2011) examined how external policy instruments and firms’ VAs influence three different types of eco-innovations: end-of-pipe technologies, cleaner production technologies, and environmental R&D. They found that first and second types of innovation are influenced by VAs which improves efficiency of machinery and equipment. The results also showed that environmental regulations play an effective role to stimulate the first and last types of innovation. 3. Overview of VAs and R&D investment 3.1. What is the VA? In the theory of environmental economics, the VAs are considered as a supplemental measurement to traditional economic policies which use market mechanisms (Pearce and Turner, 1990). However, if VAs are judged in more empirical context, they have started to recognise as being more flexible, effective, and sometimes cost less than those traditional economic policies (Alberini and Segerson, 2002; Arimura et al., 2008). Moreover, VAs are considered to be one of the most important policy instruments to reach environmental targets (Vogel, 2005). Therefore, it is worth scrutinising the VAs. The term “VA” has a broad meaning which includes several kinds of approaches, such as “self-regulation”, “voluntary agreements”, and “voluntary initiatives”, to name just a few. Many studies (EEA, 1997; Carrao and Leveque, 1999; Segerson and Li, 1999; Alberni and Segerson, 2002; Lyon and Maxwell, 2002; Brau 7 and Carraro, 2004) have agreed that VAs can be classified into three categories. The first category is a unilateral action by a single polluter or a group of polluters, which develops without any regulatory involvement. These unilateral commitments consist of environmental improvement programmes set up by corporations. The second type is a bilateral agreement or negotiated agreement between public authorities and a polluter or group of polluters. Under this bilateral agreement, the terms of agreement are determined through negotiation process between authorities and polluters. The last case is voluntary government programmes or public voluntary schemes such as Eco-management and Auditing Scheme (EMAS) initiated by EU. In this case, authorities unilaterally determine both rewards for and obligations towards participation, and criteria for participation. In this study, I focus on the first category. This type is a basic form of VAs, which occurs voluntary responding to the surrounding circumstances. Facing on the world-wide trend towards increased environmental responsibility, there is no doubt that corporations have been influenced by this trend and have been trying to become eco-friendly. Not only the pressure from outside, this type of VAs is also strongly connected with corporations’ characteristics and internal decision making, thus it is interesting to examine. 3.2. Impacts of VAs on R&D investment Based on the argument that innovation is connected with competitiveness by Porter and Linde (1995), R&D investment, an input for innovation, is the source of competitiveness. Since R&D investment is vital for corporations to success in the market, they strategically consider the optimal conditions for investment (e.g. the amount of investment, timing to invest), which strongly affected not only by the factors like size, economic performance and human resources, but also the internal decision 8 making process. In addition, in this global economy, corporations are influenced by pressures from outsides, such as stakeholders, media and global issues. Climate change issue is one of serious global problems we have to face on. VAs regarding climate change is one of the measurements which corporations have taken to deal with this issue. These VAs are basically implemented by corporations without any regulatory involvement. Therefore, VAs would become an indicator which shows how the corporations think about the issue, and what they do to deal with. In this context, VAs may become one of the ways to improve the appropriateness of its actions within the regulations, brand images, and legitimacy. Not only stimulated by incentives to improve their values, corporations also aim to find seeds to attain potential opportunities for further growth. Some of the corporations are keen to implement VAs because through the implementing process, they might seize the fact and circumstance precisely, which would help them to integrate the strategy toward climate change to their business. The corporations may also gain an important insight and information regarding R&D investment through VAs. Therefore, my hypothesis is that VAs regarding climate change are more likely to encourage R&D investment. 4. Model In order to examine the relationship between environmental VAs and innovation, I estimate two types of models. In Model 1, R&D investment divided by net sales is used as the dependent variable. In Model 2, the dependent variable is R&D investment divided by number of employees. I assume that corporation’s environmental R&D investment is expressed as: , 9 (1) where i denotes corporations and t years. Lagged dependent variable , adopted as one of the explanatory variables, as well as other control factors ( is and ). Eq. (1) is a dynamic model which shows that R&D investment is determined by its own past realisations. According to Baumol (2002), corporations which invest great on R&D activity are likely to encourage R&D in the future as well. Many reasons for this tendency can be explained by the corporate actions, for example, if a company developed a new product in the process of R&D activity, a company may invest on R&D further in order to improve the product or create superior substitution next. Dependent variable in this study may also be influenced by past realisations of itself, thus it is essential to include its lagged variable in a set of explanatory variables. is dummy variables of corporations’ voluntary approaches. of control variables. , and individual corporation-specific effect, is a set are parameters to be estimated. is a time specific effect, and finally is an is the error term. To control for time-dependent determinants of R&D investments such as changes in environmental policies which affect corporations’ overall R&D incentives, time specific effects ( ) are included. Corporation-specific effects ( ) captures the response of each corporation to external factors such as regulatory shocks. Several econometric problems may arise from estimating the dynamic model equation above. Firstly, VA is assumed to be endogenous variables which may correlate with the error term due to two-way causality between VA and R&D investment. If we fail to take this relationship into account, a simultaneity bias may occur. The fact that endogenous variables are included in this model makes it difficult to comply with the “strict exogeneity” condition. “Strict exogeneity” means that correlation between the error term and explanatory variables across all time periods may occur (Wooldridge, 2010). If variables which are not strictly exogenous are included, OLS estimator may 10 be inconsistent. Secondly, the lagged dependent variable is endogenous to the individual effects , which may give rise to a dynamic panel bias. When “small T, large N" panels (few time periods and many individuals) are used for estimation, this bias may become significant (Roodman, 2006). Since the lagged dependent variable would be correlated with the error term, OLS estimator is inconsistent. Based on these econometric problems, I must therefore focus on estimation methods that can be used for explanatory variables or instruments which are not strictly exogenous. In this study, generalized method of moments (henceforth GMM) is used for the dynamic models of panel data. These models adopt lags of the dependent variables as covariates, and include unobserved individual effects. The difference GMM, a consistent GMM estimator of dynamic panel models, is developed by Arellano and Bond (1991). In order to eliminate the individual effects, the difference GMM uses Eq. (2) by first-differencing Eq. (1), and adopts previous observations of the endogenous variables and lagged dependent variable as instruments. It is based on the assumption that the error term is not serially correlated and the explanatory variables are not correlated with future realisations of the error term (Roodman, 2006). ∆ ∆ , ∆ ∆ ∆ (2) However, this difference GMM has limitations that the lagged levels may become weak instruments for the first-differences when the explanatory variables are persistent over time, and this may lead to a large finite sample bias (Blundell and Bond, 1998). In my study, some of the explanatory variables are persistent over time, and there is some possibility that it may lead to the bias and imprecision. Therefore, to deal with this problem and to generate consistent and efficient 11 estimates, I selected the System GMM estimator, which is proposed by Blundell and Bond (1998) as an extended version of the differenced GMM estimator. Blundell and Bond (1998) mention that it is possible to overcome the bias and poor precision by using two equations. One is the difference Eq. (2) adopting suitably lagged levels as instruments, and second equation is the Eq. (1), the equation in levels, which uses suitably lagged differences of the explanatory variables as instruments. One-step estimate is implemented to obtain the results. A misspecification test for second-order serial correlation in the first-differenced error term is done. If we cannot reject the null hypothesis that there is no second-order serial correlation in the differenced residual, the error term (in levels) is not serially correlated at order 1. In this case, the estimated model is supported. I also perform a Sargan test of overidentifying restrictions (OIR), which tests for the validity of the instruments used in the model. The null hypothesis is that overidentifying restrictions are valid. If this null hypothesis is not rejected, the instruments can be considered valid. 5. Data description 5.1. CDP survey data and R&D investment data To scrutinise the relationship between VAs regarding climate change issue and R&D investment, the firm level dataset (fiscal year 2000-08) of EU corporations (301 corporations) is structured based on data of “CDP”, “EU industrial R&D Investment”, and corporations’ CSR reports. The Carbon Disclosure Project, CDP, is an independent non-profit organisation working on how to actualise greenhouse gas emissions reduction and sustainable water use. Working with the world’s largest investors, businesses and governments, CDP engages in the actions to realise a more sustainable economy, and 12 provides measurement and information for thousands of companies and cities to improve their management toward environmental risk. As said in an old management adage “You can't manage what you don't measure”, it is difficult to manage for improvement if we do not measure to see what is going on. CDP also thinks that evidence and insight is vital for real change, and gives an incentive to corporations and cities to measure and disclose their greenhouse gas emissions, potential risks and opportunities related to climate change, and strategies for managing those risks and opportunities by leveraging market forces including shareholders, governments and rival corporations (CDP, 2012). Figure 1: Number of institutional investors and the amount of funds under management (Trillion USD) Source: CDP website (2012) On behalf of institutional investors, CDP requests information on greenhouse gas emissions, energy use and the risks and opportunities from climate change from thousands of the world’s largest companies. This programme started in 2003, and the questionnaire has being sent to companies each year. The quantity and quality of data which disclosed by companies has advanced significantly, and the number of responding companies has been growing since the first CDP report in 2003 (see figure.2). In 2009, backed by 475 institutional investors with $55 trillion in assets (see Figure 1), the questionnaires were sent to more than 3,700 of world’s largest 13 companies, and about 60% of companies responded. If you focus on Global 500 companies (500 largest companies in the FTSE Global Equity Index Series), the overall response rate for CDP2009 was 82% (409 companies) (CDP, 2012) Figure 1 and 2 show that CDP has been gaining its influence on business arena and financial market. Those globally collected climate change data is now utilised for the evidence of investment and policy decision making. CDP data is increasingly becoming more integrated into financial analysis, and indices such as the Carbon Disclosure Leadership Index (CDLI) and the Carbon Performance Leadership Index (CPLI) are focused on. CDLI is an index which indicates high disclosure quality of companies’ responses, and CPLI index is qualified to companies which are taking positive measures on climate change mitigation. The responses and indices are disclosed to public. CDP data is very unique and valuable because it is possible for us to examine the first-hand response from corporations, which is not always possible in other surveys. However, there are few academic papers using this CDP data, and the data is not fully scrutinised. This study is original in terms of using new dataset constructed mainly based on this CDP data. Figure 2: Number of responding companies to CDP Year Source: CDP website (2012) 14 Regarding R&D investment, data of “EU industrial R&D Investment” and corporations’ CSR reports are used. The EU Industrial R&D Investment Scoreboard was first issued in 2004 and has been providing firm level data of R&D investment since then. This data has been practically used to monitor the situation on R&D in the EU. CSR reports of each company are also used in this research to supplement CDP data and R&D investment data. 5.2. Variables A set of variables, besides the lagged dependent variable, is summarised in Table 1. As for characteristics of corporations, the following variables: “lnPROFIT” and “lnMRKTCPTL” are focused on. Corporations’ economic performance is explained by the variable “lnPROFIT” which indicates operating profit. Since corporations with better economic performance are more likely to pursue environmental goals (Nakamura et al., 2001), the coefficient for “lnPROFIT” is expected to be positive. Market capitalisation (lnMRKTCPTL) shows not only the economic performance but also explains overall evaluations of corporations in the market. Stakeholders’ decision is one of the most important external factors which influence corporate actions. It is plausible that larger corporations may experience more pressure to be green from stakeholders (Nishitani, 2009). Corporations with high value of market capitalisation also tend to be affected by those pressures. This external pressures may encourage corporations to be green and may increase R&D investment. Considering this possibility, I predict that the expected sign for this variable becomes positive. Regarding corporations’ VAs toward climate change, the following seven variables (“RES”, “PLAN_GHG”, “TRG”, “EMISSION”, “ENERGYCOST”, “INFO_VERIF”, and “EUETSSTR”) are noticed. All variable take the value 1 if the facility has implemented or had following VAs: whether a Board Committee or other 15 executive body have overall responsibility for climate change issue (RES); whether the corporation implements GHG emission reduction plans (PLAN_GHG); whether the corporation has emissions reduction targets with time frames to achieve (TRG); whether the corporation discloses GHG emissions (EMISSION); whether the corporation discloses the total cost of its energy consumption e.g. from fossil fuels and electric power (ENERGYCOST); whether any of the information disclosed has been externally verified/assured in whole or in part (INFO_VERIF); whether the corporation has a strategy for EU ETS (EUETSSTR). “PLAN_GHG”, “TRG” may act on corporations to integrate a measure toward climate change issue to their business with clear timeline. By doing “RES”, they may also consider the issue more specifically. Disclosure is one of the important factors which encourage corporations to grasp the situation of the issue precisely. Therefore, level of disclosing “EMISSION”, “ENEGYCOST” may become an indicator to show how seriously the corporations deal with the climate change issue. Considering the fact that whether the reported information has externally verified or not is one of the vital factors for corporations to receive high CDLI and CPLI score, “INFO_VERIF” is essential not only for their environmental management but also for success in financial market. These VAs may play positive roles to tackle the climate change issue, and drive corporations to be greener. As a result, corporations may encourage their environmental R&D investment in order to become more cost effective and energy efficient by innovating new technology, products and production process, for instance. In fact, according to some corporations’ responses to CDP survey, the corporations which consider the climate change issue seriously are inclined to encourage innovation activity. Some corporations might even think that this risk will give a potential business opportunity to them, and increase their R&D investment in order to reinforce their status as leading corporations and gain competitiveness as a result of innovations. Therefore, I assume 16 that these variables are influential to R&D investment. And finally, in order to consider the impact of EU ETS, a variable “EUETSSTR” is added in the model. Corporations might invest on R&D as one of the internal strategy toward EU ETS. This is because it is necessary for them to control CO2 emissions in order to comply with EU ETS, and changing and innovating processes and/or products may be one of means to achieve emission abatement. Therefore, this variable may encourage R&D investment. Since corporation specific factors, such as corporations’ internal decision making system and managers’ attitude toward climate change, are likely to be correlated with these VA variables, they are assumed to be potentially endogenous variables. Sector dummy variables are created by referring to NACE code system and Industry Classification Benchmark (ICB). The former is the European standard for industry classifications and the latter one is an industry classification developed by Dow Jones and FTSE. In addition, year dummy are created. These variables are treated to be strictly exogenous in the model. Lastly, interaction terms of “EUETS” and sector dummy are included in the models. “EUETS” is a dummy variable which takes the value “1” if the corporations have involved in EUETS. Interaction terms of “EUETS” and “EUETSSTR”. These interaction terms are included in order to examine the influence of EUETS on innovation activity. 6. Results and Discussion In this study, two types of models were estimated; the model using the ratio of R&D investment to net sales as a dependent variable (Model 1) and the other using the amount of R&D investment per employee as a dependent variable (Model 2). The estimation results are shown in Table 2. The results of the Arellano–Bond test show that in both Model 1 and Model 2, 17 the null hypothesis was not rejected. In other words, there is no evidence of serial correlation and does not imply model misspecification. Both models also pass the Sargan test for overidentifying restrictions which confirms that the instruments can be considered valid. In both models, In Model 1, the estimated coefficient of the lagged dependent variables is positive and statistically significant at the 1% level. Regarding VAs, it is interesting to notice that “INFO_VERIF” is positively significant at the 10% level. This indicates that corporations whose disclosed information reported in CDP responses has been externally verified in whole or in part are likely to encourage R&D investment and to increase the ratio of R&D investment to net sales. This implies that VA such as disclosing verified information is important for corporations, which enhance the accuracy of information, and give a chance to examine the relationship between corporate actions and climate change issue deeply. This process may lead corporations to notice the necessity of R&D investment. Other VAs, however, did not show significant influences on R&D investment. Some of the interaction terms of EUETS dummy and sector dummy are negative and statistically significant (EUETS*DS1: 5% level; EUETS*DS2: 5% level; EUETS*DS6: 10% level). These results show that corporations in the sector 1 (electric utilities, gas and water supply), sector 2 (oil, metals, mining, paper and forest products) or sector 6 (automobiles and auto parts) which are involved EU ETS are not likely to invest on R&D. Sector 1, 2 and 6 are energy-intensive industries and the important industries which are controlled by various environmental policies. The results are unexpected, however, they may indicate some possibilities that these industries have been proactively dealt with environmental policies and have already invested great amount budgets on R&D activities. Since EU ETS has started, those corporations might have changed their policies by cutting down the R&D investment, and have 18 started to spend on other areas besides R&D activities, such as increasing the accuracy of monitoring the impact of their environmental actions. Other variables, including sector dummies and time dummies, are not significant. In Model 2, similar to the result of Model 1, “INFO_VERIF” is positive and statistically significant at the 10% level. The results imply that if any of the information reported has been externally verified, corporations are inclined to boost their R&D investment per employees. Year dummy of 2006 has a positive coefficient, which is significant at the 5% level. Since EU ETS has started in 2005, this result might indicate some positive impacts of EU ETS. 7. Conclusion Using the data set of EU corporations which constructed based on “CDP”, “EU industrial R&D Investment data” and CSR report, the effects of VAs on R&D investment are scrutinised. System GMM was employed in order to estimate dynamic panel models. The findings of this study show that a VA positively influenced on R&D investment. A tendency that corporations which disclose the information verified in whole or in part by external entities are likely to invest on R&D was observed in both Model 1 and 2. This robust result may remind an important message I previously mentioned: “You can't manage what you don't measure”. In order to disclose the verified information, corporations may try to measure the environmental impacts with high accuracy, and this process will provide some chances to closely examine the relationship between corporate actions and the climate change issue. Corporations may be able to aware the importance of innovation activity through this commitment and tend to encourage R&D investment. While interaction terms of “EUETS” and sector dummy (sector 1, 2 and 6) have negative coefficients, year dummy 2006 has positive coefficients and apt to 19 encourage R&D investment. The results of interaction terms show that EU ETS may give negative impact on R&D investment, however, according to the result of year dummy 2006, it is difficult to assert the negative influence of EU ETS on innovation activity. This is a unique study which shed a light to the relationship between VAs and R&D investment, but estimation methods may be able to improve further. Also increasing the number of years and/or corporations’ data makes this study more persuasive. In addition, since this study could not prove the direct impacts of EU ETS toward innovation, I would like to scrutinise the direct influences of EU ETS in next study by utilising different estimation methods. 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A Discrete Choice Analysis at the Firm Level,” ZEW Discussion Paper, No.04-30 23 Table 1 Summary statistics Number of observations Variables Mean Std. Dev. Min Max RDSALES R&D investment/net sales 1098 0.041 0.181 0 5.149 lnRDEMPLO R&D investment/number of employees (log) 1097 1.676 1.058 0 5.519 lnPROFIT Operating profit (log) 772 6.845 1.798 -0.036 15.156 lnMRKTCPTL Market Capitalisation (log) 1040 8.922 1.594 3.555 12.169 RES A Board Committee or other executive body have overall responsibility for climate change issue 626 0.891 0.311 0 1 PLAN_GHG Implements GHG emission reduction plans 605 0.853 0.355 0 1 TRG Has emissions reduction targets with time frames to achieve 607 0.743 0.437 0 1 EMISSION Discloses GHG emissions Discloses the total cost of its energy consumption e.g. from fossil fuels and electric power Any of the disclosed information has been externally verified/assured in whole or in part 735 0.875 0.331 0 1 684 0.482 0.5 0 1 533 0.775 0.418 0 1 ENERGYCOST INFO_VERIF EUETSSTR Has a strategy for the EU ETS 431 0.826 0.38 0 1 DS1 Electric Utlities, Gas and water supply 1690 0.092 0.289 0 1 DS2 Oil, Metals, Mining, Paper and Forest products 1690 0.103 0.304 0 1 DS3 Chemicals and Pharmaceuticals 1690 0.125 0.331 0 1 DS4 Food, beverage, tobacco 1690 0.047 0.212 0 1 DS5 Industrial machinery, high-tech 1690 0.206 0.404 0 1 DS6 Automobiles and auto parts 1690 0.046 0.21 0 1 DS7 Other manufacturing goods 1690 0.11 0.313 0 1 DS8 Bank and diversified financials, other services 1690 0.27 0.444 0 1 DY2000 Dummy variable for year 2000 1690 0.041 0.198 0 1 DY2001 Dummy variable for year 2001 1690 0.043 0.202 0 1 DY2002 Dummy variable for year 2002 1690 0.066 0.248 0 1 DY2003 Dummy variable for year 2003 1690 0.117 0.321 0 1 DY2004 Dummy variable for year 2004 1690 0.148 0.355 0 1 DY2005 Dummy variable for year 2005 1690 0.166 0.372 0 1 DY2006 Dummy variable for year 2006 1690 0.161 0.368 0 1 DY2007 Dummy variable for year 2007 1690 0.147 0.355 0 1 DY2008 Dummy variable for year 2008 1690 0.112 0.315 0 1 EUETS*EUETSSTR Interaction term of EUETS dummy and EUETSSTR 431 0.513 0.5 0 1 EUETS*DS1 Interaction term of EUETS dummy and DS1 1690 0.045 0.207 0 1 EUETS*DS2 Interaction term of EUETS dummy and DS2 1690 0.04 0.195 0 1 EUETS*DS3 Interaction term of EUETS dummy and DS3 1690 0.037 0.188 0 1 EUETS*DS4 Interaction term of EUETS dummy and DS4 1690 0.021 0.142 0 1 EUETS*DS5 Interaction term of EUETS dummy and DS5 1690 0.023 0.15 0 1 EUETS*DS6 Interaction term of EUETS dummy and DS6 1690 0.015 0.123 0 1 EUETS*DS7 Interaction term of EUETS dummy and DS7 1690 0.017 0.128 0 1 EUETS*DS8 Interaction term of EUETS dummy and DS8 1690 0.009 0.094 0 1 24 Table 2 Estimation results Variables Model 1 Model 2 R&D investment/sales R&D investment/employees Coefficient (Robust Std. Err.) Coefficient (Robust Std. Err.) 0.914 (0.059)*** - L. lnRDEMPLO - 0.894 (0.058)*** lnPROFIT 0.002 (0.003) 0.008 (0.081) L. RDSALES lnMRKTCPTL 0.001 (0.003) 0.016 (0.079) RES -0.001 (0.003) -0.183 (0.145) PLAN_GHG -0.010 (0.009) -0.146 (0.229) TRG 0.007 (0.007) 0.052 (0.145) EMISSION -0.004 (0.010) 0.024 (0.175) ENERGYCOST 0.001 (0.003) -0.045 (0.084) INFO_VERIF 0.010 (0.006)* 0.313 (0.163)* EUETSSTR -0.015 (0.011) -0.107 (0.203) DS1 0.013 (0.009) 0.035 (0.373) DS2 0.006 (0.006) -0.101 (0.162) DS3 0.018 (0.012) 0.262 (0.174) DS4 0.006 (0.007) 0.006 (0.151) DS5 0.003 (0.009) 0.145 (0.191) DS6 0.011 (0.007) 0.171 (0.181) DS7 -0.001 (0.007) 0.004 (0.152) DY2001 - - DY2002 - - DY2003 - - DY2004 -0.007 (0.008) 0.036 (0.137) DY2005 -0.000 (0.007) 0.152 (0.117) DY2006 -0.001 (0.002) 0.136 (0.066)** DY2007 0.001 (0.002) 0.086 (0.052) DY2008 - - EUETS*EUETSSTR 0.008 (0.007) 0.119 (0.147) EUETS*DS1 -0.021 (0.010)** -0.243 (0.383) EUETS*DS2 -0.016 (0.008)** -0.010 (0.196) EUETS*DS3 -0.016 (0.011) -0.223 (0.163) EUETS*DS4 -0.011 (0.008) -0.130 (0.193) EUETS*DS5 -0.009 (0.011) -0.234 (0.184) EUETS*DS6 -0.016 (0.008)* -0.194 (0.191) EUETS*DS7 -0.007 (0.007) -0.200 (0.204) Number of observations 230 230 Arellano-Bond test for AR(2) in first differences Pr > z 0.694 0.273 Sargan test of OIR 0.535 0.385 Note: System GMM, Robust one-step. Standard errors are shown in parentheses. *, ** and *** indicate the significance at the 10%, 5% and 1% levels, respectively. Constant is included in the model, though its coefficient is not reported here. 25