Are Socially Responsible Companies the more Successful Environmental Innovators?

Transcription

1 Are Socially Responsible Companies the more Successful Environmental Innovators? Christiane Reif and Sascha Rexhäuser March 20, 2013 Abstract The link between Corporate Social Responsibility (CSR) activities and financial performance of a firm has been intensively examined and debated, but the connection to innovation has lacked research attention. This paper investigates whether a certified CSR management system is complementary to environmental innovations so that joint introduction of both generates a higher financial performance than the introduction of environmental innovation alone. We analyze if firms with environmental innovations can generate a higher competitive advantage when they jointly implemented a certified environmental management system. For this purpose, we use panel data of environmental R&D expenditure together with a certified environmental management system and analyze their effect on the Return on Equity of a firm. The novelty of our work is the complementary approach with which we examine the joint effect of CSR and innovation on financial performance. The results indicate no complementary relation between environmental innovation and a certified environmental management system and rather implies a substitutive character. I. Introduction The effect of firms Corporate Social Responsibility activity (CSR) on their financial performance (FP) has been an intensively examined and debated issue in the economic literature, which has, however, produced heterogeneous empirical evidence so far. Meta-analyses by Orlitzky, Schmidt and Rynes (2003) and Margolis, Elfenbein and Walsh (2007) show just minor or rather neutral effects of CSR on FP. However, CSR is no marginal phenomenon, but it is noticeable that in recent years more and more companies appear to be socially and environmentally responsible (Poddi and Vergalli 2009). Lyon and Maxwell (2008) explain how firms environmental CSR engagement is driven by the demand as well as by the supply side. The latter is mainly connected to efficiency issues and cost reduction (Lyon and Maxwell 2008). Whereas demand driven CSR activities mainly relate to product differentiation and the consumer s willingness to pay a higher price for such products This work is out of the Project Impact Measurement and Performance Analysis of CSR (Corporate Social Responsibility), funded by the EU (7th Framework Program), Brussels, BE. Centre for European Economic Research (ZEW), L7,1, D Mannheim, Germany. reif@zew.de. Centre for European Economic Research (ZEW), L7,1, D Mannheim, Germany and Katholieke Universiteit Leuven (KU Leuven), Naamsestraat 69, B-3000 Leuven, Belgium. rexhaeuser@zew.de. 1

2 (Lyon and Maxwell 2008, Bénabou and Tirole 2010). Innovation is one of the keys to differentiate from competitors, but what role does CSR play in this context? Is it just another tool for differentiation, so firms can either innovate or practice CSR, like one result of the empirical study by Hull and Rothenberg (2008) implies. Is the environmental innovation itself a CSR activity and therefore leads to a competitive advantage (e.g. Porter and van der Linde 1995)? Furthermore, does a good management with competitive advantage also imply a good environmental performance? Studies by Bloom, Genakos, Martin and Sadun (2010) and Martin, Muûls, de Preux and Wagner (2012) found that modern management leads also to a significantly lower energy intensity. Based on these questions related to innovation, CSR and financial performance in the context of environmental concerns, we examine if firms with environmental innovations can generate a higher competitive advantage when they jointly implemented a certain environmental management system. Most of the former empirical research concentrated mainly on the effects of CSR on e.g. financial performance without taking innovation activity into account. Only a few studies particularly have examined the relationship of CSR, innovation and their effect on financial performance (see e.g. Hull and Rothenberg 2008, Lioui and Sharma 2012). Their approaches were based on the results by McWilliams and Siegel (2000) who proofed that ignoring innovation might lead to overestimation of the effect that CSR has on financial performance. In contrast to these studies, we investigate whether a certified CSR management system is complementary to environmental innovations so that joint introduction of both generates higher financial performance than introduction of environmental innovation alone. The assumption behind this is that environmental product innovations either introduced because of supply or demand impacts may be sold better and therefore result in better financial performance when the environmental activity is communicated to the consumer. In addition, CSR can be regarded as a credible signal to customers that an environmentally friendly process innovation was introduced to reduce environmental pressure in the production process addressing their willingness to pay for environmentally friendly production. Firms CSR activities are often self-reported and consumers are sceptical about. Certification of third parties on the CSR activity of a company can create credibility and therefore support the consumers in their purchase decision. Based on these assumptions we investigate whether firms with environmental innovations and certified environmental management systems have a higher financial performance compared to the control group, i.e. firms that introduced neither environmental innovations nor CSR. Environmental management systems such as ISO are voluntary commitments of a company on a specific management system to coordinate and support the implementation and improvement of environmental processes within the company. This is in line with the most common CSR definition in literature and practice, which includes all firm activities that integrate social and environmental concerns in business practices and that go beyond mere legal requirements. The environmental 2

3 management certification serves therefore as CSR indicator. In this sense, we expect that CSR increases the marginal financial returns to introducing environmental innovation compared to a control group. This is because firms with environmental innovations and an environmental management system are expected to have a better financial performance as they can differentiate from their competitors by verifying their environmentally friendly behaviour and convincing consumers. For this purpose, we use panel econometric methods and apply it to methods aiming to test for complementarity between two business activities (see Athey and Stern 1998, for an overview on these methodologies).the alternative relation between environmental innovation and a certified environmental management system being substitutive is tested with this approach at the same time. A substitutive connection would imply that a concentration on one of the two activities should be preferred with respect to financial performance at least in the short-run. Existing empirical research based on the complementerity methodology are for example Cassiman and Veugelers (2006) or Hottenrott, Rexhäuser and Veugelers (2012). Cassiman and Veugelers (2006) examined the complementarity between internal and external innovation activities and which factors influencing the strength of this complementarity. Hottenrott et al. (2012) analyzed the complementarity between green innovations and organizational changes. II. Previous Research on CSR and Financial Performance The term Corporate Social Responsibility (CSR) is not commonly defined. In literature and practice several definitions exist, but two common aspects of CSR can be found in these definitions: CSR activities relate to social and environmental issues and go beyond legal requirements. Besides this broad sense of definition, which covers profit-maximizing, profit-neutral and profit-sacrificing CSR action, also a narrower interpretation of CSR can be found. It started with the famous and often cited article by Friedman (1970) in which he claims that CSR means "[... ] spending someone else s money [... ]" (Friedman 1970, p.2) and he emphasizes that "[... ]there is one and only one social responsibility of business to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in open and free competition without deception or fraud[... ]" (Friedman 1970, p.6). Reinhardt, Stavins and Vietor (2008) also use this understanding of CSR and refer to Elhauge (2005) by saying that CSR means sacrificing profits. This profit-sacrificing type of CSR is also called altruistic CSR (see e.g. Baron 2001, Baron 2010, takes the altruistic explanation in a broader sense with the term reciprocal altruism, which is conditional on partners action and benefits). These different viewpoints on what activities count as CSR result in diverse approaches how CSR and financial performance are connected. 3

4 II.A CSR and the Theory of the Firm The relationship of CSR and financial performance (FP) has been examined in several empirical studies, which were recently reviewed by Orlitzky et al. (2003) and Margolis et al. (2007). The authors criticize that in most of the studies only unilateral relationships between CSR and the financial performance of a company are taken into account (Margolis et al. 2007), but the mechanism and direction of causality are unclear. Two main theories on the connection of CSR and financial performance can be found in literature: the slack resources and the stakeholder theory. The slack resources theory (see e.g. Orlitzky et al. 2003, Waddock and Graves 1997) or also called available funding theory (Preston and O Bannon 1997) implies that prior FP is associated with subsequent CSR. This means that doing CSR depends on the current economic situation of the company. One example for such a CSR activity is giving donations. At least the decision on the extent of the donation depends on the financial resources of the company. Based on this theory the financial performance of a firm influences the firm s CSR engagement. This would imply that firms with a better financial performance than their counterparts have more free available resources, which can be used for CSR activities. Examples for empirical analyses on the slack resources theory are McGuire, Sundgren and Schneeweis (1988) and Waddock and Graves (1997). For a detailed discussion on CSR resource approaches see Branco and Rodrigues (2006). The contrary theory that CSR influences financial performance is based on the stakeholder or social impact hypothesis by Preston and O Bannon (1997). This approach is explained by Waddock and Graves (1997) as good-management. In general, the stakeholder concept means the inclusion of interests not only of shareholders, but also of outside stakeholders. Freeman (2010, p.25) describes stakeholders as "[... ] any group or individual who can affect or is affected by the achievement of the firm s objectives." Donaldson and Preston (1995) put it this way "Stakeholder analysts argue that all persons or groups with legitimate interests participating in an enterprise do so to obtain benefits and that there is no prima facie priority of one set of interest and benefits over another." Involving groups like governments, investors, political groups, suppliers, trade associations, employees, communities, customers (Donaldson and Preston 1995, figure 2). According to Freeman (2010), the difference of CSR and the stakeholder concept lies in the additional "[... ] nontraditional stakeholder groups who are usually thought of as having adversarial relationships with the firm[... ]" of the CSR approach. Although Donaldson and Preston (1995) confess that the connection of CSR and financial performance is not easily converted into stakeholder theory. Preston and O Bannon (1997) explain their social impact hypothesis as a version of stakeholder theory. Considering the needs of stakeholders, also including the demand for CSR activities by the company, the company is rewarded with a better financial performance (Waddock and Graves 1997). Preston and O Bannon (1997) denote this as a lead-lag relationship, which means 4

5 that CSR creates reputation and in a next step influences the company s financial performance. This generation of reputation influences different groups employees, customers, investors, communities, etc. (Waddock and Graves 1997, Poddi and Vergalli 2009, Barnett 2007), which is also in line with the demand driven CSR engagement explained by Lyon and Maxwell (2008). The positive influences of CSR on profits was subject of several studies. One empirical example related to environmental concerns is the study by Hart and Ahuja (1996) who found positive effects of emission reductions on ROS, ROA and ROE. Waddock and Graves (1997) cannot only verfiy the slack resources theory by their empirical research, but also the direction of the CSR-FP-relationship based on the stakeholder theory. Therefore, they discuss the virtuous circle of CSR and financial performance in which CSR influences financial performance and the better economic position of the company also influences again the CSR activity. In such an approach it is unclear if CSR or FP is the initial trigger. II.B CSR, Innovation and Financial Performance An overwhelming number of studies address the connection of CSR and financial performance and mostly focus on how CSR influences FP, which can be based on the stakeholder theory. The meta-analyses by Orlitzky et al. (2003) and Margolis et al. (2007) give an impression on the huge number of this research. Their overall result indicates a slight positive influence of CSR on FP. Nevertheless, the empirical analyses that specifically include innovation as a factor are rare. Hart and Ahuja (1996) include innovation as a control variable for their analyses how CSR effects different types of operational and financial performances of a firm. McWilliams and Siegel (2000) argue that innovation has a positive effect on FP and that CSR and innovation are strongly correlated. Therefore, measuring the effect of CSR on FP needs to consider innovation as one explanatory variable of FP. In their analyses they verify their statements and found a neutral effect of CSR on FP when innovation is included as explanatory variable. Hull and Rothenberg (2008) extend the study by McWilliams and Siegel (2000) with an additional model in which CSR and innovation are not only included as single variables, but also as interaction term. They base their approach on the assumption that firms can differentiate via innovation or CSR. They conclude that CSR positively influences FP, innovation is an important explanatory variable in this context, and innovation and CSR are substitutes concerning differentiation. Another study based on McWilliams and Siegel (2000) is conducted by Lioui and Sharma (2012) who examine how environmental CSR affects ROA and Tobin s Q directly and indirectly via R&D. They construct a possible additional return on R&D investments of CSR firms with an interaction term of CSR and innovation. They explain the negative direct effect of CSR on FP with costs of CSR and attribute the positive indirect effect to more efficient R&D (Lioui and Sharma 2012). The previous research shows on the one hand that innovation is an important factor 5

6 of financial performance and furthermore that in the past innovation was included as control or explanatory variable in single form or as interaction term with CSR. The inclusion of innovation and CSR as complementary factors was not yet empirically analyzed to the best of our knowledge. Our complementary approach tests if the joint introduction of innovation and CSR generates higher financial performance. An interaction term measures the additional effect that innovation and CSR would have if they were both implemented. Contrary, the complementary approach enables us to differentiate between three exclusive types of engagement: companies just doing innovation alone, companies only engaged in CSR, companies innovating and jointly engaged in CSR, and companies neither innovating nor being active in CSR. This allows us not only to analyse if environmental innovation and environmental management systems are complementary, but also the contrary substitutive relation if joint implementation would lead to lower financial performance. For our analysis we focus on environmental concerns of CSR and innovation. We base the complementary approach of our analysis on two main considerations: Previous empirical studies found a connection of CSR and innovation. One example by Wagner (2010) shows that CSR can drive innovations with social benefits (defined as environmental or social benefits). The further step how this affects FP was not made in the study. Another research by Bocquet, Le Bas, Mothe and Poussing (2011) examined the effect of CSR on different types of innovation and found that companies doing strategic CSR tend to innovate in respect to products and that responsive CSR is not likely to promote organizational innovations. These examples show the connection of CSR and innovation. Furthermore, there is asymmetric information on the firm s environmental performance. The firm knows about their environmental innovations, but their consumers, workers and investors do not have this information, which is not easily to get, to aggregate or to compare (Bénabou and Tirole 2010). Information by third parties such as certified environmental management systems can serve as signal and support consumers, workers and investors in their decisions. On the other side, firms are willing to provide such proven information to gain advantage of their environmental product and process innovations. The communication of the firm s environmental performance can help the firm in their competition for socially responsible consumers (Kitzmueller and Shimshack 2012). Based on the found relation of CSR and innovation and the signaling effect of certified CSR, we examine whether firms with environmental innovations and environmental management systems have higher financial performance compared to the control group. Contrary to the neoclassical assumption that CSR is always sacrificing profits (Friedman 1970) we rather orient on a win-win assumption of CSR and FP. This is in line with the conclusion by Orlitzky (2005) that CSR and FP are no trade-offs and legally it is not easy for a manager to sacrifice profits in the social interest (Reinhardt et al. 2008). Nevertheless, our approach aims to shed light on the connection of CSR and innovation and their effect on FP complementary or substitutive. We will test the hypothesis if firms jointly implementing environmen- 6

7 tal innovation and certified environmental management systems do better than the companies that neither introduced environmental innovations nor CSR. II.C EMAS and Financial Performance The certified environmental management system variable serves as CSR indicator in our approach. Therefore empirical studies about EMAS can be of guidance for our analysis. Mainly the empirical research focus on either the determinants influencing the firm s engagement in environmental management systems (see e.g. Nakamura 2001, Potoski and Prakash 2005, Nishitani 2009, Nishitani 2010) or the effect on environmental performance to evaluate the usage of EMAS as policy instrument (see e.g. Dasgupta, Hettige and Wheeler 2000, King and Lenox 2000). Of more relevance for our study is the effect of EMAS on the innovation activity of a firm or the financial performance. The study by Melnyk, Sroufe and Calantone (2003) examine the effects relative effects of uncertified and certified EMS on environmental performance as well firm performance (e.g. reduction of costs). They based their research on a survey among North American mangers. The results show that certified EMS have a positive effect on environmental as well as operations performance. An analysis by Wagner (2007) on German manufacturing firms found a significantly positive effect of EMS on process innovation but not for product. innovations. Rennings, Ziegler, Ankele and Hoffman (2006) examined both the the effect of EMAS on technical innovations as well as on financial performance. They found a positive impact of EMAs on environmental process innovations and the learning process of EMAS have a positive effect on product innovations and influence the economic performance positively. Another relevant strand of literature for our study is research on the effect of environmental performance on profitability, where environmental performance is not necessarily measured as certified EMAS. One example is the empirical analysis by Klassen and McLaughlin (1996) examines the effect of environmental awards as proxy for strong environmental management on financial performance in form of event studies on the firm level. They found that significantly positive effects of these awards on financial performance. Another study by Konar and Cohen (2001) shows that negative environmental performance effects financial performance negatively. The environmental performance was measured as emitted toxic chemicals and as lawsuits against the firm. Russo and Fouts (1997) used environmental ratings as environmental performance indicators and found a positive impact on financial performance. These empirical studies show effects of environmental performance on financial performance and some also examine the relationship of innovation and environmental performance. Based on these empirical research our complementary approach of innovation and environmental performance and the effect on financial performance seems plausible and to the best of our knowledge has not been examined in this context. 7

8 III. Data and Choice of Variables We base our research on worldwide company panel data from the Thompson and Reuters ASSET4 (A4) database, which allows us to the get better insights into the process organization of a company than only using CSR score data (like in the CSR context often used Kinder, Lydenberg, Domini (KLD) database). The panel data structure allows us to control of unobservable factors and endogenous factors in a fixed effect model, because these factors are assumed to be stable over time (see Nickel 1996). The unbalanced panel A4 contains a collection of environmental, social, governance and financial data of more than 3,000 global companies listed in major indices such as S&P 500, MSCI Europe, FTSE 350 and the MSCI World Index, Stoxx 300, Nasdaq, ASX 200. Publicly available information of a company (e.g. reports, but also other publicly available sources) is gathered yearly beginning in 2002 by specially trained analysts. The A4 data allows us to provide formal tests whether CSR complements firms environmental innovations in terms of financial performance or has a substitutive character. For this purpose we exploit the panel data structure to estimate the effects on FP. The relation of a specific CSR activity on a specific FP implies for empirical analysis that the results strongly depend on how CSR and FP are measured (see Margolis et al. 2007). Concerning CSR, if it is related to the business or not and regarding FP, if it is an accounting-based or market-based measurement (see Margolis et al. 2007). For the market-based the stock market performance is taken into consideration. Whereas accounting-based measures include company internal financial data like Return on Equity (see Margolis et al. 2007). Orlitzky et al. (2003) also differentiate between market-based as external market responses and accounting-based as internal efficiency financial performance measurement. We concentrate on firm-based data and therefore use certified environmental managements systems, which are business related as CSR indicator and Return on Equity (ROE) as accounting-based financial performance indicator. The innovation variable environmental R&D is also business related. It takes the value one if a firm has indicated to have done environmental R&D and zero otherwise. Likewise, the CSR variable takes the value of one from that year when a firm has introduced a certified environmental management system (more on this later). IV. Estimation Strategy Why do we expect complementarity to be present? Corporate social responsibility alone may not be a source of increasing financial performance. However, such an activity could be seen as a signal to potential customers for advanced environmentally-friendly production technologies otherwise unobservable by them. In this sense, voluntary certified environmental management systems may increase the utility of customers with some environmental preferences for the firms products 8

9 and especially is expected to promote the sales of environmental product innovations of these firms. Studies on the determinants of environmental management systems revealed that consumer preferences have strong impact on firm s engagement in EMAS certification (see e.g. Nakamura 2001, Potoski and Prakash 2005, Nishitani 2009, Nishitani 2010). The descriptive analysis of environmental innovation and environmental management systems indicates a correlation between both strategies. In the sample the joint realization of environmental innovation and certified EMAS occurs with a higher frequency than the implementation of environmental innovation alone (Table 1). Table 1: Adoption Decision Frequencies and Relative Frequencies CSR CSR EVINNO 0 1 Total EVINNO 0 1 Total 0 8,429 4,003 12, % % % (7,708) (4,724) (62.00 %) (38.00 %) ,322 1, % % % (981) (601) (62.00 %) (38.00 %) Total 8,689 5,325 14,014 Total % % % Expected frequencies appear in parentheses. Pearson chi 2 (1) = 1.6e+03 Pr = Kendall s tau-b = , P > z = Table 1 also shows the frequency under the assumption of independency of both firm strategies in each second line. Interestingly, if both strategy variables were independent, we would expect that only 601 firms had introduced both strategies jointly. However, the firms that actually implemented both is more than twice the number we would expect in case of independency. Together with the very high coefficient of association (Kendall s tau-b), table 1 offers strong evidence for high correlation between environmental innovation and certified EMAS of a firm. This correlation, of course, is not sufficient for complementarity to be present. Whether this correlation really stems from complementarity in firm strategies or not is subject to all that follows. IV.A Estimation Complementarity in Business Strategies Complementarity (in the sense of Edgeworth) in firm strategies means that doing more of one activity increases the marginal returns of doing more of the other. In principle, there are two ways to estimate such a relationship: the adoption approach and the productivity approach, which is central to the present paper. Roughly put, the adoption approach relies on the correlation of two firm strategies as to 9

10 account for complementarity 1. Unfortunately, the decision to implement CSR and to introduce environmental innovations are, by their nature, discrete choices. Miravete and Pernías (2010) show that in exactly this case the adoption approach leads to incoherence between a certain combination of activities and the error terms. The so called productivity approach, however, also works well with dichotomous variables and accounts for the performance effects of the potential complementary variables with respect to an objective function in our case ROE. To see this, suppose that the objective function would be smooth and twice differentiable as well as the two activity variables, a positive mixed partial derivative 2 of the objective function with respect to the two variables indicates complementarity of the objective function in its two arguments. In this case, the objective function is supermodular in firm strategies. The concept of supermodularity is directly related to complementarity (Milgrom and Roberts 1990). To make use of this concept when having binary strategy variables, Milgrom and Roberts (1990) define the two binary variables over a sub lattice 3. Roughly put, this means to impose an order on each pairwise combination of the variables. Let {0, 0} be the smallest element under the order and denote that neither CSR nor environmental R&D is done. In a similar fashion, {1, 0} and {0, 1} are higher elements under the order representing environmental R&D only and CSR only, respectively. Finally, the highest element under to order ({1, 1}) accounts for joint use of the two firm strategies. It is exactly this order which allows to translate discrete strategy choices similar to an increase in these strategies if they are continuously measurable. Thus, the condition saying that increasing the value of one activity increases the marginal return of the other reads in the binary case as adopting a strategy increases the returns to the other strategy that is already adopted. Moreover, this condition implies that adopting both jointly leads to a higher performance than adopting both in isolation, simply because the one increases the marginal returns of the other. Formally, the condition for supermodularity and complementarity reads as follows: f(1, 0) + f(0, 1) f(1, 1) + f(0, 0), (1) where f(.) represents the objective function, i.e. ROE, see Milgrom and Roberts (1990). In what follows, the central interest lies in how to appropriately test this condition meaning how to specify the objective function. Let ROE it=1 denote firm i s absolute difference in ROE between period t 1 and t. Taking time differences allows to eliminate any time consistent determinants of ROE that vary between 1 The adoption approach can be traced back to the work of Arora and Gambardella (1990). They show that a positive covariance among a pair of activity variables (for instance innovation adoption decisions) indicate complementarity if the activity variables are conditioned on any other firm-specific characteristics. 2 This is represented by an interaction term in the estimation equation 3 See also Milgrom and Roberts (1995) for a very good and easily understandable overview on lattice theory and complementarity. 10

11 cross-sectional units and which we cannot control for. ROE it=1 = α 0 + α 1 P AT it=1 + α 2 P RISA it=1 + α 3 P RIEAR it=1 IV.B Measuring the Model s Variables +β 10 EV INNO_ONLY it=1 + β 01 EMAS_ONLY it=1 +β 11 BOT H it=1 + α C CONT ROL + ɛ it (2) ROE is an accounting-based measure of financial performance (see Margolis et al. 2007, Orlitzky et al. 2003) used in other studies on CSR before, i.e. by Hart and Ahuja (1996), Waddock and Graves (1997) or Poddi and Vergalli (2009). In general, it is defined as income divided by total equity in percentage. In the A4 database it is specifically calculated as income minus preferred dividends divided by total common equity. It measures profitability by revealing how much profit a company generates with the money shareholders have invested. We normalized it by multiplying with 100 and use the change of ROE (ROE) between t 1 and t as dependent variable. This approach allows us to focus on our main aim to explain if firms that implemented environmental innovation and CSR jointly can gain a better FP than their counterparts rather than explaining the effect of both variables on the level of ROE. Furthermore, unobservable time consistent factors like management abilities are considered at least partly with this approach. Nevertheless, we will test two other models with ROE not measured as difference. One including and one not including time lagged ROE as explanatory variable. The lagged ROE to control for time consistent determinants of ROE is also included in a fourth model with the dependent variable measured as difference. The explanatory variables we are focusing on in our research are innovation and CSR. Concerning the measurement of innovation, input and output of innovation can be differentiated. Smith (2005, p.151) discusses the traditional categorization of innovation into ideas, learning, creation of knowledge on the one hand and competence and capabilities on the other hand. In the Oslo Manual innovation input is explained as investments, which includes R&D (see also Kleinknecht, Van Montfort and Brouwer 2002) and other innovation related investments. Furthermore, other kinds of preparation for different types of innovation like relationships to universities to deploy external knowledge can be treated as innovation input (OECD and Eurostat 2005). In empirical literature R&D expenditures are often taken as input innovation variable, because of its long history of collection and the comparability between countries in contrast to other input innovation determinants (Kleinknecht et al. 2002, Smith 2005). For a deeper discussion on R&D as innovation input, please see e.g. Kleinknecht et al. (2002) or Smith (2005). In the A4 database the environmental R&D investment (EVINNO) serves as environmental innovation input variable. It is measured as a dichotomous variable with the value one if the company invested in environmentally friendly products or services and zero otherwise. 11

12 As CSR indicator we use a certified environmental management system (EMAS). This indicator fulfils the main components of the exisiting CSR definition explained before: voluntary action of companies that goes beyond legal requirements in environmental concerns. Furthermore, the CSR variable is business related because it is a management system integrated into the business of the company and therefore ensures that the measurement of FP as accounting-based fits to the measurement of CSR as described in Margolis et al. (2007). Additionally, a certified environmental management system has a better signaling effect than non-certified CSR activities of a company to their consumer, workers and investors because of its trustability (see also Melnyk et al. 2003). We generated this binary variable by connecting the data from two binary variables in the A4 database, namely ISO14000 certification and certified EMAS. If the company has had at least one of these certified environmental management systems it was counted as a certified environmental management system. According to Hart and Ahuja (1996) CSR engagement only effects ROE in a two-year time lag. They explain it, on the one hand, with the capital structure ROE is reflecting and therefore investment decisions like CSR have a lagged effect (Hart and Ahuja 1996). On the other hand, they explain the lagged effect with the longer time needed for investment in CSR to work as reputation for the company (Hart and Ahuja 1996). The engagement in CSR and the investment in environmental R&D decisions of a manager might rely on factors not observable in our data, i.e. a manager s own pro-environmental attitude (Bénabou and Tirole 2010). We assume that such unobservable factors to be time consistent at least for the time period we look at, which means that unobservable management abilities and attitudes do not change fast over time. The above explained estimation strategy of using the difference of ROE as dependent variable or the inclusion of a lagged ROE as explanatory variable do control for such time constant unobservables. Another explanatory variable for financial performance are the US patents (PAT ) hold by the specific company. Patents are indicators for temporary limited monopoly. The company holding a patent has a technological advantage, which can be the reason for price differences resulting in a better performance. The benefit of the usage of US patents is their consistent measurement method and the relevancy of these patents. We use the number of real hold patents by a company in the specific year from the A4 database for our calculations because we assume that this hold patents effect ROE in the same time period and not time lagged. As indicator for market power, which might influence the FP by possible markups, we choose the price per sales ratio (PRISA). It reflects the value placed on sales by past performance, other companies or the market. The profit margin affects the price to sales ratio and therefore is a good indicator for market power. We use it for the current time period. Another indicator for market power is the price earnings ratio. The price per earnings inform about the stock performance of a company and higher values might indicate technical advantages. We also use the current time period in 12

13 our estimations. In the A4 database other measurement methods for market power like market share or the Herfindahl index are not available. Especially the large firms indexed on the stock market in A4 are worldwide acting firms and therefore the usage of such market power indicators is questionable, as Aghion, Bloom, Blundell, Griffith and Howitt (2005) point out. These indicators are limited concerning worldwide acting companies, because the geographic market definition is unclear. Alternatives like the Lerner index (see e.g. Aghion et al. 2005)or price-to-cost ratio (see e.g. Gorodnichenko, Svejnar and Terrell 2010) are also not included in the A4 database. Therefore the price per sales ratio and the price earnings ratio are the only explanatory variables for market power. Nevertheless, the panel data structure allows to control for unobservable but time consistent factors of market power (see e.g. Nickel 1996). As Nickel (1996) explains that unobservable factors changes correlate with the changes of the observable variables. In our case the inclusion of the lagged ROE and the usage of difference of ROE as dependent variable reflect these unobservable factors. Other control variables (C ) are the size of the company measured in number of employees, the geographical location of the company included as continental affiliation and the industry sector. The size of a company is measured by employees. However, mainly large companies are included in the A4 database. We categorized the companies location according to their continent of company domicile, while we differentiate US, Canadian, Asian, and European companies. African, Central and Southern American, and Oceanian companies are not included in our analysis because the representation rate of each is only below 0.5% in the dataset. In the A4 database the industrial sector is covered by the Standard Industry Classification system. We clustered the companies according to their SIC code into eleven sector categories. The agricultural, construction and public administration sector was not taken into account because of the few included data. The geographical location and the sectors are time consistent variables. We conducted preliminary tests on fixed effects versus random effects model. As a first step, the F-test on the null hypothesis of no fixed effects is rejected. The Hausman-Test with the null hypothesis of no correlation was rejected. Therefore, we use a fixed-effects model for the following estimations. V.A Base Model Results V. Results Additionally to the Model described in equation (2) with ROE as dependent variable measured in differences, we estimated three other models. One with ROE as dependent variable and the same independent variables as described in equation (2). This is referred to as MODEL_1. We estimate also another model (MODEL_2) with ROE as dependent variable, but additionally ROE in t 1 as explanatory 13

14 variable to control for unobservable time consistent determinants of ROE varying between cross-sectional units. Finally, we provide evidence for a model (MODEL_4) where the dependent variable measured in absolute differences between t and t 1 is regressed, among other regressors, on lagged ROE. We will concentrate our interpretation on the last model (MODEL_4), because it has the two features of including the lagged ROE as explanatory variable and the difference of ROE as dependent variable to explain the growth of ROE. The estimation results are provided in Table 2. Table 2: Estimation Results Model 1 (fe) Model 2 (fe) Model 3 (fe) Model 4 (fe) Dependent Variable: ROE ROE ROE ROE lag_roe 0.122*** *** (0.011) (0.011) ln_pat (0.209) (0.216) (0.284) (0.216) ln_size *** (0.555) (0.622) (0.818) (0.622) PRISAL 2.601*** 2.688*** 2.211*** 2.688*** (0.142) (0.151) (0.199) (0.151) PRIEAR 0.055*** 0.062*** 0.067*** 0.062*** (0.008) (0.008) (0.011) (0.008) EVINNO_ONLY 2.695** (1.469) (1.506) (1.982) (1.506) EMAS_ONLY * (0.647) (0.709) (0.933) (0.709) BOTH *** *** *** (0.879) (0.931) (1.223) (0.931) Constant 7.254*** ** (5.161) (5.824) (7.662) (5.824) R 2 (within) Observations 14,014 11,199 11,199 11,199 * p<0.10, ** p<0.05, *** p<0.01, robust standard errors appear in parentheses The dependent variable (Return on Equity) is a financial performance indicator that is sensitive to a firms competitive position, especially market share and maybe even more important market power. All things equal, firms with high market power are expected to have a high markup ratio so that the performance indicator ROE is very sensitive to unobserved differences in the firms competitive environment. This is one reason for the relatively poor model fit as seen in Table 2. Unfortunately, the Asset4 database does not offer any good indicators to account for this. The only obvious candidates Price per Sales (PRISAL) and Price per Earnings (PRIEAR) are both highly statistically significant but cannot solve this problem entirely. As to limit these problems, we use in the Models 2 and 4 lagged dependent variables to account for any factors that explain the level of ROE we could otherwise not sufficiently control for. Model 4 goes even further by using the absolute difference of ROE between t and t 1 as a dependent variable and control for lagged ROE. This has the feature that we can ask whether the two potentially complementary firm strategies certified environmental management and environmentally friendly inno- 14

15 vations can explain the growth in ROE condoned on its previous values. Roughly put, this model structure is somewhat similar to convergence growth models. In our case, a one unit higher ROE in t 1 is associated with a unit smaller growth (in absolute numbers) in ROE. This means that the better a firm is, the less potential for further growth can be realized. Please note that the models 2 and 4 are in principle the same, where model 2 reports this rate of convergence and Model 4 reports the value 1-β lag_roe. Of more interest are the signs of the strategy variables EVINNO_ONLY, EMAS_ONLY, and BOTH. If, and only if, certified environmental management and environmentallyfriendly innovation would be complementary, the following conditions must hold: f(1, 0) + f(0, 1) f(1, 1) + f(0, 0), or: β EV INNO_ONLY + β EMAS_ONLY β BOT H 0, (3) where β NEIT HER is linearly dependent and serves as the control group (and is therefore exactly zero). Obviously, this condition is not satisfied in any model specification. In Model specification 4, using a one-sided F-test, we can reject the Null formulated in equation 3 with a p-value of (the condition itself has the value of 0.05 and is thus far away from being smaller than zero). Put it otherwise, we cannot provide robust evidence that both certified environmental management and environmentally-friendly innovations are complements with respect to firms financial performance. In this way, the (very preliminary) econometric analysis does not confirm the evidence from the simple correlation analysis presented in Table 1 and rather implies a substitutive character of these activities. Finally, it is also worth to name some caveats of the present analysis. First of all, environmentally-friendly innovation is a binary indicator measured using information whether a firm has allocated expenditures to environmental research and development. Research and development, of course, is an input indicator to environmental innovation providing positive returns in future periods. This could be a reason for no robust evidence for complementarity. In the robustness checks we include models to avoid this problem. A second concern stems from the measure of CSR with respect to the environment. Certified environmental management is a rather crude measure since it neglects other voluntary activities of firms that may be beneficial for the environment. Unfortunately, the Asset4 database does not include many certified CSR indicators. A robustness check on another certified indicators is included in the next section. Thirdly and finally, we have serious concerns regarding potential endogeneity of the indicator variables of environmental CSR and environmentally-friendly innovations since both are endogenous choice variables of the firm. Fortunately, the panel structure of the Asset4 database allows to tackle this problem, which is subject to what follows. 15

16 V.B Robustness Checks This section provides robustness checks to overcome the previous mentioned limits of the basic model. Firstly, estimate a model to cope with the endogeneity problem. Secondly, we include the innovation indicator as lagged variable to control for time-lagged impact of innovation input. Thirdly, another certified CSR variable is used to check if the results are the same for a different CSR activity. Fourthly, the sample is restricted to just European countries. Fifthly, the sample is limited to only the manufacturing and mining sector, because we assume that the innovation input variable environmental R&D is especially relevant for these sectors. Since both the introduction of environmental innovation and the EMAS certification are endogenous choice variables to the firms, there is no reason to belief both are strictly exogenous. On the one hand, the explained slack resources theory or the virtuous circle theory implies endogeneity of CSR and environmental innovation because the decision for both investments might be driven by the current FP of a firm. If we assume that both variables reflect strategic management decisions, they are based on previous FP with the aim to gain advantage from the investment in later higher FP, which is in line with the win-win approach of CSR discussed i.e. by Lyon and Maxwell (2008) or Bénabou and Tirole (2010). On the other hand, a good manager may know that CSR can serve as a signal of environmentallyfriendly production techniques to the customers and so may promote sales of new (also environmentally-friendly) products. The indicator variable of joint use of CSR and environmental R&D maybe therefore correlated with unobserved management quality that itself is expected to be highly correlated with the error term, i.e. unexplained residuals in ROE. BOT H is therefore expected to be endogenous. The other two indicators are therefore potentially endogenous too. Unfortunately, the Thompson and Reuters Asset4 database does not offer a broad set of potential instrument variables. We therefore make use of the panel structure and include one year lagged observations of the potentially endogenous regressors. Since the endogenous regressors are binary variables, little time variation may limit the use of lagged variables as instruments. Therefore, we construct instruments of the three potentially endogenous variables by taking their means across sectors and size classes for all but the respective firm. The latter feature ensures also variation across cross-sectional units. Using these instruments, the regression based test (see Wooldrige 2010, p.118 ff) provides evidence for the endogeneity of the variable BOT H. The single introduction of EMAS (EMAS_ONLY ) or environmental innovation(evinno_only ) seem not to be endogenous in our sample based on our tests. The different kinds of instrumentation with lagged variables for EMAS_ONLY, EVINNO_ONLY, and BOT H or their means across sectors and size classes do not change the results massively. We choose models instrumenting the variable BOT H because of the endogeneity test results including the lagged as well as the means across sector and size classes (Table 3). 16

17 Table 3: IV Estimation Results Model 1 (IV, fe) Model 2 (IV, fe) Model 3 (IV, fe) Model 4 (IV, fe) Dependent Variable: ROE ROE ROE ROE lag_roe 0.126*** *** (0.011) (0.011) ln_pat (0.234) (0.217) (0.285) (0.217) ln_size ** (0.683) (0.644) (0.846) (0.644) PRISAL 2.624*** 2.617*** 2.143*** 2.617*** (0.164) (0.154) (0.202) (0.154) PRIEAR 0.049*** 0.058*** 0.063*** 0.058*** (0.009) (0.008) (0.011) (0.008) EVINNO_ONLY (1.686) (1.578) (2.071) (1.578) EMAS_ONLY ** *** *** *** (1.533) (1.446) (1.897) (1.446) BOTH *** *** *** *** (3.001) (2.811) (3.684) (2.811) Constant * (6.252) (5.894) (7.743) (5.894) R 2 (within) Observations 11,157 11,199 11,199 11,199 * p<0.10, ** p<0.05, *** p<0.01, robust standard errors appear in parentheses Again, we restrict attention to Model 4 only. Also for the IV estimates, we can reject the Null formulated in equation 3. A one-sided Wald test against this Null reports a z-value of providing robust evidence for certified environmental management and environmentally-friendly innovation not to be complementary in firms financial performance. The second robustness check is referred to the innovation variable environmental R&D. This innovation input variable might at first reduce the ROE and only generates innovations in the future. Therefore we estimated a model with a one year time lag of the used innovation factor in classes (Table 4). Here also Model 4 and the related tests show no complementarity of certified EMAS and one-year time-lagged environmental R&D on financial performance. 17