Estimates of the Generalized Tobit model by group of countries (Eastern Europe and Baltic countries)

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Recognition of Environmental Policy as a Driver of Eco-innovation

Empirical evidence shows that the regulatory framework and environmental policy have a strong impact on eco-innovation (Green et al. 1994; Porter and van der Linde 1995; Kemp, 1997; Hemmelskamp, 1997; Cleff and Rennings, 1998; Berman and Bui, 2001). However, according to Rennings (1998), environmental product innovation tends to be more driven by the strategic market behaviour of firms (market-pulled effect). By contrast, regulation tends to drive more environmental process innovation, because the public-good character of clean technology leads to under-investment in environmental R&D (Rennings and Rammer, 2009). Therefore, environmental regulation is especially necessary to foster environmental process innovation. When the reduction of the environmental impact of a firm‘s activity offers little operational or commercialization benefits, then regulation may become the primary driver of eco-innovation (Kemp and Foxon, 2007a). For example, regulations to protect local air quality have stimulated an innovation such as catalytic converters, which have led to dramatic reductions in the emission of pollutants from vehicles (Kemp and Foxon, 2007a).
Therefore, environmental policy is a potentially strong driving force for eco-innovation, which deserves to be studied separately. Environmental policies may fall under the ―command-and-control‖ or ―market-based‖ types. Market-based instruments such as pollution charges, subsidies, tradable permits, and some types of information programs can encourage firms or individuals to undertake pollution control efforts that are in their own interests and that collectively meet policy goals (Jaffe et al., 2002). By contrast, command and control regulations tend to force firms to take on similar shares of the pollution burden, regardless of the cost. They often do this by setting uniform standards for firms, the most prevalent of which are performance- and technology-based standards (ibid).
In the wake of the Lisbon agenda, the European Council adopted in 2001 the Sustainable Development Strategy and introduced the ―Environmental Technologies Action Plan‖ (ETAP) in 2004. In the ETAP, the European Union acknowledged the strategic importance of eco-innovation. Although the actual impact of ETAP remains to be precisely assessed, it seems to have led to an increased recognition of environmental problems (and of the need for eco-innovation as an answer) in the public and political consciousness.
The results from a survey across ten OECD countries show that an increasing number of countries now perceive environmental challenges not as a barrier to economic growth but as a new opportunity (OECD, 2009). This new understanding has made environmental policy appear as an important driver of eco-innovation, thus reconciling real-world policy with the theoretical considerations of Porter and Van der Linde (1995). These authors argued in the mid-nineties that environmental progress requires companies to improve their resource productivity through dedicated innovation, so that regulation becomes not an obstacle but a driver for innovation. In Porter and Van der Linde (1995), implicitly, the more prescriptive the regulation is, the more confined will the innovation be.
More recently, Rennings (2000) also emphasized that environmental policy is becoming the main driver of eco-innovations. According to him, eco-innovations differ from normal innovations because they produce a double externality, consisting in (1) the usual knowledge externalities in the research and innovation phases and (2) externalities in the adoption and diffusion phases due to the positive impact upon the environment (Oltra, 2008). In other words, the beneficial environmental impact of environmental innovations makes their diffusion always socially desirable. However, these positive external effects lead to market failures which may hinder eco-innovation. The private return on R&D in environmental technology is less than its social return due to its public good nature, which in turn causes a lack of private incentives leading firms to under-invest in environmental R&D and innovation (Oltra, 2008). Therefore, environmental policy and/or an appropriate regulatory framework appear as a requirement for eco-innovation.

Effectiveness of Environmental Policy as a Driver of Eco-innovation

According to Porter and van der Linde (1995), environmental standards can foster innovation under three conditions. First, they must create maximum opportunities for eco-innovation, letting the industry (and not a standard-setting agency) choose its own approach to innovation. Second, regulation should foster continuous improvement, rather than locking in any particular technology. Third, the regulatory process should leave as little room as possible for uncertainty at every stage. Therefore, the type of regulation/policy and the way it is implemented is significantly important. It should lead firms to effectively address environmental problems rather than restrict firms in a specific technology and leave the environmental problem unsolved. The stringency of the policy and the terms in which it is defined are equally important, since uncertainty depends on these factors. In spite of on-going controversies on whether environmental regulation actually has an impact on innovation and on the most efficient policy instruments (see for instance Greenstone, 2002; Jaffe et al., 2002), many empirical studies (European Commission, 2001; Rennings et al. 2006, Belin et al. 2009) find a positive correlation between innovation and regulation.
Porter and van der Linde, (1995), Kemp et al. (1998) and, Jänicke and Jacob (2002) all predict that strict environmental regulations stimulate innovation in a number of ways (e.g. first mover advantages created by the development of ―green‖ technologies). These predictions are in line with the so-called ―Porter hypothesis‖ postulates that ―there are win-win opportunities through environmental regulation, where simultaneously pollution is reduced when having an increase in productivity‖. As mentioned above, this hypothesis has fuelled controversies17, but its argument remains at the core of current research on eco-innovation.
The rationale behind this argument is that firms do not detect the potential of environmental innovations because they are ―still inexperienced in dealing creatively with environmental issues‖ (Porter and van der Linde, 1995, p. 99). Environmentally and economically benign innovations are not realized because of incomplete information, and of organizational and/or coordination problems (ibid). Firms are not able to recognize the cost saving potentials (e.g. energy or materials savings) of environmental innovation (Frondel et al. 2007). This leads many of them to believe that an environmentally-virtuous behaviour is a burden rather than an asset (Kemp and Andersen, 2004). Therefore, regulations and policies can be a catalyst and help them to understand the potential benefits of environmental innovations.

Hindrances to Diffusion of Eco-innovation

There are specific problems inherent to eco-innovative activities which make the diffusion of eco-innovations difficult. One of the main problems is the double externality issue mentioned in Section 3.1: since this double externality leads firms to under-invest in eco-innovation, it also strangles the diffusion of eco-innovations at its very root.
Another problem associated with eco-innovation is that in many cases the adoption and the diffusion of environmental technologies can be viewed as a typical case of technological competition between an established technology and an alternative environmental technology, or a set of alternative environmental technologies (Oltra, 2008). In order to be successful in the diffusion process, the alternative technology should become a viable substitute to the existing technology. The more efficient the new environmental technology is on the mainstream characteristics, the more likely its diffusion becomes (Christensen, 1997). But, the superiority of the eco-innovation compared to the established technology is always a debatable issue and needs further investigation. Moreover, most industries shy away from technological environmental innovations in the first instance because of their complexity and of the financial risks they entail.
The heterogeneity of potential adopters only adds to the second problem: a technology that is generally superior will not be equally superior for all potential users, and may remain inferior to the existing technology for some users for an extended period of time after its introduction (Jaffe et al. 2002). Therefore, it is important to understand how potential users, be they consumers or firms, value the environmental characteristics of the new technology. Very often, clean technologies or products are adopted at the expense of other technologies or other products. As a result, potential users want to gain some advantages from their investments (e.g., an increased competitiveness in the case of firms that have adopted a clean technology) and generally want to know the expected returns before the implementation process.
In general, when users are consumers (and not firms) they do not have adequate information about the new technology or product. This lack of adequate information constitutes another great obstacle to the diffusion of the new technology. Consumers have to cope with the typical problem of experience goods since they cannot know ex ante the environmental characteristics of goods and/or technologies (Oltra, 2008). This issue gives more importance to communication channels, since better explanations and increasing awareness about environmental issues may enable a more widespread adoption of eco-innovations. Increasing awareness about the shortcomings of the existing technologies and identifying the required technological improvements may also increase the speed of adoption. This is the reason why policy instruments such as information provision and eco-labels are necessary to inform consumers efficiently and to stimulate the diffusion of environmental products (Oltra, 2008). For example, ISO 14000 standards can be a good tool for increasing environmental awareness. All the above-mentioned obstacles raise the question of how to give a kick not only to the creation of eco-innovations, but also to their diffusion process. So far, however, the empirical literature seems to have focused on the determinants and/or effects of eco-innovation, or on testing the Porter Hypothesis. There is still scope for empirical work on the diffusion processes of environmental innovations.

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Explanatory Variables and Descriptive Statistics

We are first interested in observing the impact of a firm‘s size on voluntary EMS adoption decision. We measure the firm size with its actual number of workers. We also included ―Size²‖ variable, which is measured by taking the square of a firm‘s size. These two variables together could point out the direction of the relationship between a firm‘s size and their propensity to adopt the standards. In practice, large firms have higher chances of securing additional financial resources for innovative activities and they are more likely to be faced with growing international pressures to clean up their operations and minimize environmental hazards. Most empirical studies found that the probability of implementing environmental standards increases with firm size (e.g. Delmas and Montiel, 2009; Grolleau et al. 2007a, b). However, difficulties in reorganization and stabilization of certain routines, red-tape, lack of managerial capabilities, complex, costly and the time-consuming nature of the adoption process should also be accounted for. Firms that are too large may find the adoption of voluntary EMS unnecessary and its adoption process highly demanding, which in return may have a significant impact on a firm‘s adoption decision.
Being part of a ―Group‖ is another control variable and it would help us to understand whether being part of a group gives an advantage when adopting voluntary EMS. According to Pekovic (2010) and Zyglidopoulos (2002), a group firm may have relatively easier access to a larger amount of financial resources to invest in new practices. However, having a larger amount of financial resources is only one side of the coin. We should also consider the organisational challenges pertinent to large organisations that comprise several sister companies. Accordingly, the relationship among the parent and sister companies and their respective organisational, structural, managerial and other characteristics may play significant role in the adoption decision. Accordingly, if the sister companies are bounded to the parent company‘s strategic decision-making and general business strategy, being part of a larger group of firms may also constitute certain organisational issues. Hence, firms may or may not have to adopt the standards depending on parent company‘s business strategy.

Propensity Score Matching and Fully Interacted Linear Matching

The aim of this research is twofold. First, we are interested in discovering the intrinsic characteristics of firms that may have an impact on the adoption of environmental standards. Secondly, discovering the causal effect of adoption of environmental standards on the firms performance (the average effect of treatment on the treated – ATT) through Total Value Added (VA). Even though, there are several other performance measures such as labour, material, energy productivity, specific issues and problems call for an appropriate measure of performance. In our approach, we use a performance indicator to measure efficiency in the use of resources approximated with total price of sales minus the total production cost including factor costs (i.e., material cost along with subventions, taxes and subsidies) as the Porter hypothesis postulates. Therefore, we consider it appropriate to use Total Value Added (VA) as the relevant measure of performance proxy. In order to check the robustness of our empirical estimation, we also specified other performance measures i.e. VA / Total turnover. Nevertheless, the estimation results obtained with alternative dependent variables remains robust to our main results. Therefore, we will only discuss the results pertinent to our benchmark model.
In order to estimate the impact of adoption on adopters‘ VA, we need to compare the average VA of these firms to the average VA that these same firms would have achieved had they not adopted the standards. However, since a firm either adopts the standards or does not, the average VA that firm would have achieved had they not adopt the standards remains an unobserved counterfactual since only one outcome is observed. More precisely, what would have resulted had the firm not been adopted the standards (treated) cannot be observed, which gives a rise to the ‗Evaluation Problem‘ (Vandenberghe and Robin, 2004). The evaluation problem consists in providing unbiased estimates of this average counter-factual through the use of appropriate methods and usually untestable assumptions (Goodman and Sianesi, 2005). Hence, we used the Propensity Score Matching (PSM) method proposed by Rosenbaum and Rubin (1983) and further developed by Heckman et al. (1997, 1998). The matching method is a non-parametric alternative to Instrumental Variable (IV) and Heckman type models for estimating a causal effect net of endogeneity bias. In our research, relying on Ordinary Least Square method (OLS) may produce biased results when considering the fact that there would be some firms adopting the standards, which are not comparable to the firms in the non-adopting sample. In this sense, performing OLS might hide the fact that we are actually comparing incomparable firms by using the linear estimation (Goodman and Sianesi, 2005). The propensity score matching method (PSM) then offers a unique advantage by excluding the firms from the sample that are not comparable to any other firm in the non-adopting sample, leaving only firms from both group that have same features and hence, comparable. Even though, the PSM offers certain advantages over the OLS, we can bring the OLS estimations closer to the PSM estimates by imposing common support before running the OLS and by allowing the impact of treatment (in our case adoption) to vary for each observable variable. This technique is generally referred as Fully Interacted Linear Model (FILM) and it allows the impact of adoption to vary for each observable variable. This aspect of the FILM allows us to test the presence heterogeneous return (Goodman and Sianesi, 2005). If the method does not provide evidence for heterogeneous return, the estimation results will simply coincide with simple OLS. Hence, both the PSM and FILM methods have been carried out in this research in order to provide comparability between the estimation results.

Table of contents :

Chapter 2
3.1. Summary of key variables before and after merging the datasets
3.1.1. Frequency of adopters and non-adopters according observation periods
3.1.2. Summary statistics on the variables used in the econometric analysis
3.1.2. Aggregated Nace codes according to OECD‘s technology classification
4.1. Estimation Results
4.2. Average Treatment Effect on the Treated (ATT) obtained from PSM and FILM
4.2. Summary statistics on the choice of variables before and after matching
Chapter 3
3.3. Distribution of Eco-innovators and innovators according to industry classifications
3.4. Summary statistics of the variables used in the econometric analysis
3.4. Descriptive statistics of the regressors for eco-innovators and conventional innovators
4.1. Estimates of the Generalized Tobit Model for eco-innovation
4.2. Estimates of the Generalized Tobit model by group of countries (Western Europe and Mediterranean)
4.2. Estimates of the Generalized Tobit model by group of countries (Eastern Europe and Baltic countries)
Chapter 4
3.3. Taxonomy of Firms (Terms used in this study)
3.4. Summary statistics on the variables used in the empirical estimation
3.4. Summary statistics on the variables used in the estimations by type of eco-innovator
3.5. Descriptive statistics according to industry affiliations
3.5. Distribution of eco-innovators across industries for each type of eco-innovator
3.5. Summary statistics for different types of eco-innovators across all manufacturing industries and service sectors
4.1. Marginal effects for the whole sample
4.2. Distribution of innovators across countries
4.2.1. Marginal effects for the Western European group
4.2.1. Marginal effects for the Baltic group
4.2.1. Marginal effects for the Mediterranean group
4.2.1. Marginal effects for the Eastern European group


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