Productivity growth, environmental regulation and win–win opportunities: The case of chemical industry in Italy and Germany

Accepted Manuscript

Productivity growth, environmental regulation and win-win opportunities: The case of chemical industry in Italy and Germany Alessandro Manello PII: DOI: Reference:

S0377-2217(17)30280-1 10.1016/j.ejor.2017.03.058 EOR 14342

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European Journal of Operational Research

Received date: Revised date: Accepted date:

16 February 2015 7 December 2016 22 March 2017

Please cite this article as: Alessandro Manello, Productivity growth, environmental regulation and winwin opportunities: The case of chemical industry in Italy and Germany, European Journal of Operational Research (2017), doi: 10.1016/j.ejor.2017.03.058

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Highlights • The paper proposes a new procedure for testing the Porter’s hypothesis • Directional Distance Function is used to compute environmental-economic efficiency • A sample of chemical firms from Italy and Germany is analyzed

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• Productivity growth and compliance costs are derived in a conditional settings

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• Empirical evidence supports the presence of win-win opportunities

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Productivity growth, environmental regulation and win-win opportunities: The case of chemical industry in Italy and Germany Alessandro Manello1

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University of Turin & CNR-IRCrES, cso Unione Sovietica, 218bis, Turin, Italy

Abstract

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This paper analyzes the environmental and economic efficiency of a sample of firms located in Italy and Germany, operating in the chemical sector and included in the European Pollution Release and Transfer Register (E-PRTR). The Directional Distance Function (DDF) approach in a conditional setting has been applied to obtain efficiency score and Total Factor Productivity (TFP) growth indexes considering pollution in computations. Emissions increase in absolute term between 2004 and 2007, with a worse performance of Italian firms, but efficiency indicators show a reduction of inefficiency over time, with similar performance of firms from the two countries. The formal test for the Porter’s hypothesis suggests that chemical firms suffering higher compliance costs in the first period react with investment increasing productivity in the following years. The empirical evidence, robust to different specifications and estimation methods, supports the presence of win-win opportunities.

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1. Introduction

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Keywords: Data Envelopment Analysis, Environmental and Technical Efficiency, Porter’s Hypothesis, Undesirable outputs, Productivity growth indexes

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Environmental protection is a key issue in many production processes characterized by a high level of pollution: in some cases, the additional constraints imposed by regulation are so pervasive to influence every managerial decision. In the case of mature industries, where margins are limited and competitors are numerous, environmental standards are the most important stimulus for innovations and new investments (Fukasaku, 2005). In this contest, Porter (1991) theorizes the presence of win-win opportunities in the increasing stringency of environmental regulations. The case of the Japanese manufacturing industry, able to grow more than US under more stringent environmental rules during the 80’s, is often reported as an example. In some cases, the compliance costs will be overtaken by cost savings induced by technical progress, stimulating the investment process through new standards introduced by the law. Such new investments, by embedding innovations, should

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Email address: [email protected] (Alessandro Manello) Tel.: +39-0116824942 Fax: +39-0116824966

Preprint submitted to Elsevier

March 28, 2017

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lead to a faster productivity growth and to new profitable opportunities, reducing pollution at the same time (Porter and Van der Linde, 1995). The original version of the Porter’s hypothesis (Porter’s HP) was so generic that many interpretations arise in literature. On the one hand, some authors highlight a positive relationship between regulation and innovation or R & D investments (Jaffe and Palmer, 1997), or between regulation and lower capital stock age (Hamamoto, 2006). On the other hand, other authors find a generic existence of competitive advantages for the most regulated firms (Mulatu, 2004). In the field of efficiency and productivity analysis the Porter’s HP has been rarely investigated and all previous works assume standard production models, without incorporating pollutants. Yarime (2003) estimates a measure of compliance costs through an indicator of regulatory stringency in environmental field that appears positively correlated with technical efficiency in the chlor-alkali industry in Japan. Similarly, Murty and Kumar (2003), create an index of compliance costs for manufacturing firms in India and find a significant positive correlation with technical efficiency, estimated through a parametric translog distance function. Finally, Van der Vlist et al. (2007), analyze the Dutch horticulture sector, create three dummy variables indicating different stages of environmental regulation and they find positive correlation between regulatory stringency and technical efficiency, from a parametric Cobb-Douglas production function. In some specific sectors, for instance chemicals, metals, power generation and wastes, the emission control has been so pervasive that ignoring this aspect can drive to misleading conclusion, especially in developed countries. The European Community (EC) has been one of the most important policy makers on environmental aspects during the last decades, with the aim of guarantee a high life quality in all the member states. In this spirit, the EC introduced the IPPC framework (Integrated Pollution Prevention and Control) a set of directive, regulation and decision aimed at protecting environment and reducing emissions by stimulating technical innovation in some relevant industrial sectors, chemicals among the others. The directive 1996/61/EC and the EC regulation 166/2006 impose a preventive authorization to pollute that could be obtained after proving the adoption of the Best Available Techniques (BATs) to contain emissions. Moreover, the creation of a public register, the European Pollution Release and Transfer Register (E-PRTR) similar to the US Toxic Release Inventory in the 80’s corresponds to the so called third wave of environmental policies based on reputation and information disclosure. 2 However, environmental reputation is an effective instruments if and only if information from public registers are accessible and if they are internalized in consumers choices (Caplam, 2003). Across Europe different levels of environmental sensibility coexist and this aspect can influence the utility of public register. In Northern European countries, traditionally, the attention to the environment is higher than in Southern countries. Two opposite cases are for example Germany and Italy. In Germany the informal pressure on polluters to reduce emissions and to introduce green innovations is high, also thanks to the presence of strong ecologist parties and to a 2

This third wave arrives after a first phase of command-control methods imposing pollution standards and a second market-based step introducing marketable allowances and emission fees (Canon-de-Francia et al., 2008).

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high sensibility of civil society (Rio Gonzales, 2009). In Italy, these aspects are weaker for cultural and economic reasons, and a lower attention to environmental themes can also be inferred by the Eurobarometer 3 . Many large industrial sites are located in under developed areas in the South of Italy, where the need of job opportunities overcomes the worries about the environment. 4 Furthermore, the controls on the adoption of BATs, which should represent an average level of environmental protection characterized by reasonable costs, are delegated at regional level: the effective stringency of rules can change across states due to a different institutional sensibility in releasing the authorizations to pollute. The compliance with BATs often requires additional investments on abatement equipments, changes in input-output mixes and re-organization of production: the IPPC framework may have different compliance costs across firms, as well as strong difference among counties. The industries included in the IPPC normative are nine, but the chemical industry (NACE 24) is probably one of the most important for its role of intermediate manufacturer for many other sectors. In Europe, where more than the 25% of the total world chemicals is produced, Germany and Italy are respectively the first and the third producers (Federchimica, 2010). Firms located in these two countries share many common rules, but are traditionally different in term of average size (Vitali, 2010) and other cultural aspects (Fleishman et al., 2009). The adoption of a common regulation may open important opportunities to compare economical and ecological efficiency of firms operating in the same legal framework. The empirical analysis proposed in this paper involves a sample of firms located in Italy and Germany producing chemicals products with homogeneous equipments, which are clearly defined by the IPPC framework. The existing relationship between productivity growth and compliance costs is investigated through a formal test the for Porter’s HP, highlighting the presence of win-win opportunities. The production model adopted is based on the Directional Distance Function framework, assuming weak disposability of undesirable outputs. The systemic and cultural differences between the two countries motivate the usage of conditional DDF, recently proposed by Daraio and Simar (2014) and Daraio et al., (2015) for considering different available technologies for the two set of firms. The computation of sequential Malmquist-Luenberger indexes for getting more coherent measure of productivity growth is combined with the estimate of compliance costs imposed by emission control for conducting a formal test of the Porter’s HP, the main contribution of the present paper. The results, robust to different specifications and different estimation methods, show that firms suffering higher compliance costs in the first period, react and achieve higher productivity growth hereafter, supporting the validity of the Porter’s HP in the chemical sector. The remainder of this paper is organized as follows: section 2 briefly presents the methodology, 3

In particular Eurobarometer 2007 (wave 67, available at the http : //ec.europa.eu/public − opinion/archives/eb/eb67/eb67 − en.htm) reports a lower share of Italian people worried of global warming in comparison to Germans (50% vs 65%) or less in accordance with emission cuts (54% vs 70%). Similar differences remain also in more recent years (2015). 4 The recent case of the Italian ILVA near Taranto is emblematic. The firms is one of the largest steel mill in Europe, and even if in presence of clear violation of environmental regulation and huge amount of pollutants produced, the local public opinion is still in favor of the firm, or better, in favor of the created job.

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section 3 introduces data sources and related issues, empirical results are summarized in section 5, while section 6 concludes. 2. Literature review

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As a first step, the present work deals with the inclusion of undesirable outputs into efficiency and productivity estimates, a debated issue, solved with the concept of directional distance function (DDF) introduced by Chambers et al. (1996). The power of that tool relies in the possibility to modify the direction in which looking for the efficient counterpart of each decision making units (DMUs). The DDF envelops more stringent idea of input or output distance function and its flexibility motivates the wide application in the environmental field, both with micro and macro perspective. Production frontiers able to incorporate emissions as reducing factor of efficiency are derived for firms operating in different sectors or countries, often considering greenhouse gas emissions. The extensions of DDF to compute productivity growth indicators are based on the standard Malmquist and are named Malmquist-Luenberger (ML) productivity indexes in the case of an asymmetric treatment of undesirable outputs. The seminal work by Chung et al. (1997) represents the first empirical application of ML indexes to a sample of pulp and paper firms operating in US. Similarly, Weber and Domazlicky (2001) compute ML indexes for a sample of US manufacturing firms including air pollution as bad output; Arocena and Waddams Price (2002) estimates eco-efficiency and ML indexes for a sample of electricity generation plants in Spain, while Nakano and Managi (2008) do the same for the Japan. Yu et al. (2008) and Martini et al. (2013) estimate efficiency in the air transportation sector extending the idea of undesirable output to the case of noise or local air pollutions, both relevant issues for local communities near airports. The most recent applications of ML indexes are focused on macroeconomic or industry-level trends: Kumar and Managi (2010) estimate environmental Kuznets curves on the basis of ML growth indexes, while Zhang et al. (2011) compare environmental performance of Chinese provincial regions. Moreover, less recent applications are the following: F¨are et al. (2001) on many manufacturing sectors in US, Domazlicky and Weber (2004) on chemical 3-digit industries, Y¨or¨ uk and Zaim (2005) and Kumar (2006) on OECD countries, Kortelainen (2008) on UE member states, Zhou et al. (2010) on most polluting countries in term of greenhouse gas emissions. Generally, the TFP growth is decomposed in two terms, efficiency recovery and pure technical progress, but some inconsistency problems have been highlighted for the latter components by Aparicio et al. (2013) when considering the case of ML indexes with bad outputs. In particular, their highlight how, even in presence of a positive shift in the frontier, the empirical computation of the technological progress (TECH) components can lead to inconsistent results, especially using some specific directional vectors (i.e. g = (y, −b) like in the present paper). They show with accuracy how the approach from Oh (2010) and Oh and Hesmati (2010) can solve the issue in the environmental field, adopting the idea of sequential technology, already proposed by Tulkens and Vander Eeckaut (1995) and Shestalova (2003) for the standard Malmquist setting. For an extensive discussion on the inconsistency of ML indexes and the relative solution the main reference is Aparicio et al. (2013), while Shestalova (2003) propose a 5

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similar discussion for the standard Malmquist decomposition. Oh and Hesmati (2010) and Oh (2010) extend firstly the idea of sequential technology to the DDF framework, dealing with undesirable outputs, and they represents the main references of the present paper. Finally, the literature on DDF provides a strong theoretical background to obtain an elegant proxy of the compliance costs (i.e. total regulatory costs) paid by each firms, without any direct information on the so-called Pollution Abatement Cost or on the shadow prices of emissions (Picazo-Tadeo et al., 2005 and F¨are et al., 2007). The impact of regulation can be derived after modifying the assumption on the output set, to simulate a state of the world where emissions can be ignored due to the absence of environmental laws. The idea of relating this indicator of compliance costs with the observed TFP growth, even if using Sequential ML indexes proposed by Oh and Hesmati (2010), is based on Domazlicky and Weber (2004) that analyzing industry-level data perform a similar test.

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3. Methodology

3.1. Unconditional and conditional regulatory costs indicators using directional distances Each DMU, in this case a chemical firm, is assumed to use a vector of inputs x = N M (x1 , ..., xN ) ∈ R+ to obtain a vector of good outputs y = (y1 , ..., yM ) ∈ R+ and a vector of J bad outputs b = (b1 , ..., bJ ) ∈ R+ . The output set P (x) collects all the combinations of good and bad outputs that can be produced using a particular input vector x.

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N P (x) = {(y, b) : x can produce (y, b)}, x ∈ R+ .

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Following the axiomatic approach proposed by F¨are et al. (2007), the chemical production process can be represented by accepting standard assumption such as inactivity, compactness and free disposability of inputs. Furthermore, in the case of a polluting production process, other features of undesirable outputs modify the shape of the output set: they are jointly produced with good outputs and reducing them is costly. Two additional axioms need to be introduced to the standard settings. Null jointness. It is impossible to observe a positive amount of good outputs without observing also a positive amount of bad outputs: (y, b) ∈ P (x) and b = 0 =⇒ y = 0

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Weak disposability assumption on outputs. Each couple of vectors (y, b) is assumed to be weakly disposable, then b cannot be freely reduced: (y, b) ∈ P (x) and 0 ≤ α ≤ 1 =⇒ (αy, αb) ∈ P (x).

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Therefore, only proportional contractions of both good and bad outputs are feasible (F¨are et al., 1989), because the decrease on bad outputs can only be performed by reducing desirable outputs if inputs are fixed. Free disposability remains valid only on the subset of good outputs, for which every reduction is technically feasible without costs. 6

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The Directional Output Distance Function model (DODF), defined on the output set respecting 2 and 3, gives the maximum feasible expansion of outputs in a pre-assigned direction with unchanged inputs. → −W D O (x, y, b; gy , gb ) = max{β : (y, b) + (βgy , βgb ) ∈ P (x)} (4)

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J M . The asymmetrical treat, gb ∈ R+ where g = (gy , gb ) is the directional vector and gy ∈ R+ ment of good and bad outputs comes from the appropriate choice of the directional vector (Chambers et al., 1998): for example g = (y, −b) is a common assumption in literature, for the scale-free values of βs that are immediately comparable among DMUs. 5 DODF allows to look at the efficient counterpart of each firms along non-radial projections, and β takes a value equal to 0 for efficient DMUs and increase with inefficiency. The choice of the particular directional vector g = (y, −b) derives from the IPPC directive itself: on the one hand the regulation require a continuous reduction in emissions for obtaining preventive authorization to pollute, on the other hand managers want to increase good outputs. However, as highlighted in the introduction, the application of the IPPC directive and the preventive authorization to pollute are delegated to regional government and then the local (i.e. national in this case) sensibility to the environment may deeply influence the technology requirements. The comparison of firms operating in different context leads to overestimates of efficiency scores in some cases and underestimates in other cases. Some recent results of efficiency theory have been embedded in the model to consider the differences in the operating framework for Italian and German firms. In particular, Daraio and Simar (2014) and Daraio et al., (2015) extend the ideas of conditional efficiency measures based on input and output distance function to the case of directional distance function. Conditional DDF model allows including in the first stage of efficiency estimates some factors influencing the shape of the technology (i.e. the relationship between inputs and outputs), for which the separability assumptions are not verified. The computation of conditional DDF models implies complicated smoothing procedure if continuous variables are involved as environmental factors as reported by Daraio and Simar (2005) or Daraio and Simar (2007), but in case of categorical / qualitative variables, the situation is simpler, in particular when a binary variable (i.e. the firm is Italian, Yes or Not) is the unique aspect of interest. In such case, the smoothing procedure cannot be applied (i.e. only two groups can be identified) and according to Daraio et al. (2015), conditional efficiency scores can be obtained by simply dividing the sample on the basis of such variable, and running separately a standard efficiency model for each sub-sample. That procedure gives as results efficiency scores that are conditioned on the bases of the categorical variable used thanks to sub-sampling (i.e. firms operating into a country face a specific frontier that is not the same for the other country). The deep differences between the two countries suggest a separation of firms based on their nationality; therefore DDF 5

Other choices on the directional vector can be valid, even if obtained betas are not scale free. See for example the vector g = (1, −1) used by Picazo-Tadeo et al., (2005) and Macpherson et al., (2010) with similar properties of g = (y, −b), but no scale invariance, or g = (1, 0) proposed by Picazo-Tadeo and Prior (2009) to consider only good output expansions with unchanged emission levels.

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values should be computed separately for Italian and German firms, according to the simplest conditional version of the standard DDF model. The resulting separation of DMUs according to a single binary variable, leads to a framework very similar to that introduced by O’Donnel et al. (2008) 6 . Moreover, given the focus on regulatory costs imposed to each firm, the DDF literature has derived a useful indicator based on the comparison of the regulated and unregulated scenario and then, a comparison of efficiency scores obtained under weak and free disposability hypothesis on bad outputs. The case of free disposability of emissions, does not represent a technologically feasible production plans in a real world, in particular such assumption of strong disposal disrupts the physical relationship between good output and bad output, because it simply ignore emissions. In particular, there are no way to estimate the potential good output production if any environmental rule have been introduced over time. All the costs in term of past investment in emission saving technologies, as well as the time devoted by manager or employees to comply with regulation cannot be consistently quantified. However, the strong disposability scenario is just intended to model a sort of virtual world which only makes sense if is interpreted in terms of costs, i.e., reducing undesirable outputs has no cost in terms of desirable output, as proposed by F¨are et al., (1989). Following Domazlicky and Weber (2004), the difference in the estimated DDF for each firm under the two state of the world, the regulatory impact indicator (RII) can be used as a proxy of the compliance costs:

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→ − → − k k k k RII = D F0 (xk , y k , bk ; y k ) − D W 0 (x , y , b ; y )

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Where superscripts F and W indicate respectively free and weak disposability on bad outputs. The RII indicator is particularly useful in the present application: previous papers, dealing with the problem of emissions, normally end at this point, with an estimation of the global impact of regulation in terms of potential good output not produced, at firm level. In this case, it represents the first element to formally test for the validity of the Porter’s HP, as clarified in previous sections. An important aspect of novelty relies in the use of the conditional DDF approach: the resulting RII are conditional indicators, never used in the literature.

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3.2. TFP growth through sequential Malmquist-Luenberger indexes As anticipated in the introduction, the second ingredient for the test are TFP growth rates, computed under the sequential technology assumption extended to environmental DDF by Oh and Heshmati (2010) and Oh (2010). The obtained TFP indexes are more reliable and robust, because potential inconsistency, due to implausible downward shift related to particular directions, are reduced according to Aparicio et al. (2013). The basic

6 O’Donnel et al. (2008) propose efficiency comparisons across groups through the estimate of a metafrontier, computed from an unresctricted technology set where all DMUs-years observations have been pooled, and group frontiers which are computed according to restricted technology sets (i.e. observation have been separated on the basis of each groups).

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assumption is that in each t all previous technological choices are still available, then the frontier at each time envelops all data points observed up to that time, eliminating implausible negative shifts, observed in some empirical works. Starting with the contemporaneous output set assumed in standard ML setting: P t (xt ) = {(y t , bt ) | xt can produce (y t , bt )}, with t = 1, ...T

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The sequential idea of technology is based on the superset of each single contemporaneous production possibility set, defined over previous axioms, in the following way: [ [ [ P t (xt ) = P 1 (x1 ) P 2 (x2 ) .. P t (xt ) (7)

SM Lt+1 t

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Standard ML indexes by Chung et al., (1997) can be re-defined on the sequential output set (7) to obtain their sequential version named Sequential Malmquist-Luenberger indexes (SML) proposed by Oh and Hesmati (2010). The formulation is standard, where only the big S refer to the aforementioned augment (i.e. sequential) output set. 7 → − → − i 21 t t t t (1 + D t+1 (1 + D tS (xt , y t , bt ; g t )) S (x , y , b ; g )) · = → − → − t+1 , y t+1 , bt+1 ; g t+1 )) (1 + D tS (xt+1 , y t+1 , bt+1 ; g t+1 )) (1 + D t+1 S (x h

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The SML index is built as the geometrical mean of two components - one based on technology at time t and the other based on technology at time t+1 - both calculated as the ratio of the DODFs calculated on quantities at time t and t+1. A SM L = 1 indicates an absence of productivity growth between t and t + 1, while increasing productivity emerges in case of SM L > 1, the converse indicates a deterioration in the firms’ position. The main advantage of that approach is a more robust and reliable definition of the frontier that makes SML indexes consistent when measuring environmental productivity changes because the technology envelops all previous observations (Aparicio et al., 2013). As in more standard case, the TFP growth can be divided into two parts: on the one hand the efficiency gain over the considered time period (EFF ), on the other hand technical progress in the production of chemical products (TECH ).

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→ − 1 + D tS (xt , y t , bt ; g t ) = → − t+1 , y t+1 , bt+1 ; g t+1 ) 1 + D t+1 S (x

− t+1 t t t t → − h (1 + → t+1 t+1 t+1 t+1 i 1 D S (x , y , b ; g )) (1 + D t+1 , y , b ; g )) 2 S (x = · → −t t t t t → − t t+1 t+1 t+1 t+1 (1 + D S (x , y , b ; g )) (1 + D S (x , y , b ; g ))

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M Lt,t+1 = SM Lt,t+1

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4. Data, empirical strategy and main assumptions

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A efficiency component EF F > 1 shows a catching-up process based on technological diffusion and imitation: the observed distance from the frontier is decreasing over time and it indicates an increasing homogeneity of performances. Of course, the reference piecewise linear frontier can change over time and this effect is picked by the other term. Technical progress represents the share of TFP growth linked to new opportunities emerging from innovations, but in short time periods this component shows a negligible impact for the limited technological shocks and efficiency recovery is, in general, larger.

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Environmental data come from the European Pollution Release and Transfer Register (E-PRTR), a public register published on-line by the European Environment Agency (EEA). The European Pollution and Emission register has been introduced formally with the Directive 1996/61/EC, but it becomes effective only after 2000. The register collects data from 2001 to 2004 afterward, with the regulation 166/2006 EC its application is enlarged and also transfer activities of pollutants are traced. E-PRTR is relatively young in comparison with the Toxic Release Inventory in US born in 1986 and not many studies investigate it. European firms operating in nine sectors must declare their emissions if a double thresholds, on production capacity and emissions level, are overcome. In this paper only data for firms included in the activity code number 4, Chemical industry (Nace code 24), are used to guarantee homogeneity in firms activities. Moreover, Regulation CE 166/2006 provides a list of equipment and minimal production capacity to be included in the register. No information are provided on the share of that activities on the global turnover, but the large dimension required for each equipment suggests a significant contribute in total firm’s production value. The list of activities involved in the reporting cover all base-chemical products: all plants for the production on industrial scale of basic organic or inorganic chemicals, fertilizers, basic pharmaceutical products and explosive have to measure and control pollution. General information, release means (air, water or soil), methods of measurement, particular installations and emission quantities must be declared in the E-PRTR. The level of information is fine: data must be delivered for each production plant and for each of 91 chemicals that are listed in the directive. Data release starts in 2001, but 2004 and 2007 observations are more reliable, complete and comparable: these data after an aggregation procedure of emissions from air, water and soil have been used here. Emissions from each production plants are summed up by release means using a weighting scheme based on the specific toxicity level and are ri-aggregated by firm. For each substance, the inverse of the allowed thresholds is assumed as indicator of the dangerousness, relatively to each substance and release mean (Ca˜ non-de-Francia et al., 2008). The implicit idea is that the higher is the threshold the lower is the associated toxicity: such indicators are computed for each release means and then summed up in a single Environmental Impact Indicator (EII), used as bad output in DDF computation. In notation: EII =

3 X 91 X j=1 g=1

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dgj qgj

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Where dg = T1g , g indexes pollutants, j indexes release means, k indexes firms, T represents thresholds specific for each pollutant and q is the total quantity released. Economic data come from Amadeus on-line database, by Bureau Van Djick, collecting balance-sheets of European firms. Physical data on production and inputs are not available, and then economic proxies are derived from financial accounts. Good output is measured by total revenues (Y). Capital stock (K) is measured as the net value of tangible fixed asset while Labor (L) is proxied by the total labor costs to partially consider quantity and quality of human resources. Intermediate goods (M) are obtained by the sum of raw material costs (net of inventory changes) and costs of services. All variables are included in Euros at constant 2004 prices, deflated using sector-specific deflators from OECD 8 Differences in prices between the two countries, which can be ignored in case of physical quantities, must be considered for the use of monetary proxies. The Purchasing Power Parity transformation has been applied here in combination with the constant prices transformation, in order to get comparable measures of inputs and outputs across years and countries9 . Table 1 shows descriptive statistics for inputs and outputs Table 1: Descriptive statistics of Input and Output variables (2007)

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Variable mean min max sd Inputs (1000s of euros, constant prices 2004 & PPP) Assets 119,236 51.7 1,401,390 247,045 Intermediate Goods 314,558 1,830.2 2,787,152 516,628 Labour costs 64,315 138.4 758,782 124,795 Good Output (1000s of euros, constant prices 2004 & PPP) Turnover 526,922 9,100.4 3,721,139 805,998 Bad Output (environmental impact index) EII 72.24 1.02 469.87 121.82

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Some problems arise for financial data due to Mergers and Acquisitions or transformations. Firstly, some firms are excluded for their abnormal growth rate in inputs or outputs10 . Secondly, only firms with unchanged name and complete balance sheet data in both periods are included in the sample. Finally, only firms declaring emissions in both periods are analyzed, even if this condition may be potentially restrictive because it excludes virtuous

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The Gross Output deflator is applied to Y, the intermediate input deflator is applied to actualize M and L, and the deflator for Gross Fixed Capital is used for K. All deflators are relative to manufacturing sector and data come from OECD Stan Database for Structural Analysis. 9 PPP indexes, as well price indexes relative to the chemical sector, relative to 2004 and 2007 come from OECD Stan 10 This is the case of the biggest Italian produced of polymers and resins, Polimeri Europa SPA, part of the Eni Group, excluded by the analysis because only 2007 economical data are reliable. In the period 2004 and 2007 Polimeri Europa receive all profitable activities by Syndial and by other firms of Eni. In 2004 the financial situation reveals a transition phase where all human resources were already transferred to Polimeri Europa but not jet industrial activities. Then from 2004 and 2007 the firms show an increase of revenues from 5 million to around 7 billion, with a similar trend in fixed capital.

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firms able to reduce emissions under the thresholds. The latter point is a possible, but unusual possibility and, in many cases, it is related to dismissing plants. All these restrictions reduce the sample size to a final sample of 43 11 , 23 from Italy and 20 from Germany, but the sample is able to represent more than 23 billion of total production, around 10% of the whole chemical industry in Italy and Germany12 , and around the 20% of base chemicals13 (Federchimica, 2010). Emissions at firms’ level give some first intuitions on recent trends and on protection activities. Environmental Impact Indicators in absolute level and in relation to turnover are reported in table 2, showing an increase of average pollutions in the period 2004-2007, both in absolute and relative term. Italian firms, which initially show lower emission level than their German counterparts, obtain a worst performance in 2007, with a strong increase in absolute and relative EII. The Italian performance is still worst in term of emissions per unit of turnover: if German and Italian firms produce similar level of emission per thousand of turnover in 2004, in 2007 Italian chemical firms pollute 5 times more than their German counterparts per unit of turnover produced. Without substantial investments in new equipment, Italian firms expand production volume, in a phase of growth, with a steeper increase of pollution, subsequent to a initial under-utilization of production capacity: Italian emissions increase linearly with the turnover path. The situation is exactly the opposite for German firms which are able to increase production and reduce pollutants at the same time, maybe for their constant investment in new equipments, a trend also partially confirmed by the assets dynamics. Table 2: Enviromental Impact Indicator, absolute values and over turnover (000s, price at 2004)

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EII 2004 2007 GERMANY 72.0 53.8 ITALY 45.0 86.5 Total 57.5 71.3

EII / Turnover 2004 2007 0.271 0.168 0.307 0.985 0.290 0.605

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4.1. Empirical strategy for testing the Porter’s HP Win-win opportunities, coherent with the Porter’s HP, emerge if firms exposed to more stringent environmental protection invest in innovation able to boost their productivity growth and, at the same time, to reduce their emission levels. The adoption of the vector g = (y, −b) in the DODF framework, is particularly coherent for this purpose. A reduction in the average βs between t and t + 1, in a period of increasing environmental standards, can be interpreted as first superficial evidence supporting the Porter’s HP, because the 11

Firms included in E-PRTR for which is possible to collect complete economical data are respectively 79 and 70 regarding 2004 and 2007, but only 44 are observed in both period. 12 The cumulative turnover of the sample is respectively 5 and 18 billion of Euros for Italy and Germany and the country production was in 2008 respectively 57 and 139 billion 13 Base chemical represent 55% of the chemical sector

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average inefficiency of the group is decreasing. The conclusion in favor of the Porter’s HP, proposed by van der Vlist et al. (2007) in a stochastic frontier framework, is based on the same argument: decreasing of average inefficiency in a period of increasing stringency of environmental legislation. Similarly, in the context of non-parametric DDF, F¨are et al. (2007) observe contraction over time of the average DDF value and they conclude in favor of win-win opportunities. The first formal test for the Porter’s HP has been proposed by Domazlicky and Weber (2004), through the estimation of standard ML indexes. They use the value of the estimated regulatory costs (i.e. similar to the proxy RII derived before) at time t as a measure of the initial regulatory stringency for each observed firm, then they analyze the correlation between regulation costs and the computed ML indexes, but they find no evidence (no positive significant correlation) supporting the Porter’s HP. The approach proposed here is similar, but more complete and up to date. ML indexes are replaced by their sequential version for avoiding potential inconsistency, while all DDF are computed in a conditional setting, for getting more robust and reliable results. Moreover, different regression models, as well different estimation methods, have been used instead of a simple correlation test. The computed SML indexes are the dependent variable, while the measures of regulatory costs (RII) and a set of firm-level control variables are the regressors. The estimated model is the following: SM Lt,t+1 = α + θRIit + ρZi + εi . i

(13)

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Where the matrix Zi contains a set of control variables such as the firm’s size, the vertical disintegration index and a dummy variable indicating the nationality of firms. The latter dummy has been included to catch the remaining effect of firm nationality and the informal pressure on firms to adopt BATs, after eliminating the specificity of each country through the conditional setting14 . The test for the validity of Porter’s HP is based on the inference for the θ coefficient: if it is significant and positive the relation between productivity growth and the regulatory costs in the first period is positive. In this case the Porter’s HP can be accepted; otherwise the test does not support the hypothesis.

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4.2. Assumption on the return to scale All the required DDF computations necessary to obtain efficiency estimates (relative to 2004 and 2007) as well as the SML components have been computed by solving different linear programs for each firm, often reported in previous works 15 and for each linear program an assumption on the prevailing return to scale must be made. From the point of view of theory and literature, the assumption on the return to scale in measuring economic/environmental efficiency is not a clear-cut, as it is recently highlighted by Picazo-Tadeo et al., (2012). The differences between the two assumptions, constant versus variable, are conceptual (i.e. relative to the production process) as well computational, because in case of constant return to 14

A different degree of adoption of the BATs will influence TFP growth, according (Cole et al.2005) We refer to F¨ are et al. (2007) for linear programs regarding DDF and weak/free disposability assumption and to Oh and Hesmati (2010) for linear programs regarding inter-temporal DDF 15

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scale (CRS), the sum of the element of the intensity vector is free and not constrained to one. In fact, from an ecological perspective the economic activity is commonly characterized by CRS (Picazo-Tadeo et al., 2012), mainly for the focus on the total pressure generated on the environment, for which the size of the production activity seems not matter too much (Kortelainen and Kousmanen, 2004). Constant return to scale are assumed by almost all previous papers estimating Malmquist-Luenberger indexes (i.e. TFP growth indexes incorporating bad outputs), see for example Chung et al., (1997); Domazlicky and Weber (2004); Zhang et al. (2011); Arabi et al. (2015), as well as by all the papers assuming sequential technologies (Oh and Hesmati, 2010; Oh, 2010; Aparicio et al., 2013). Some recent contributions propose more complex models for considering variable return to scale (VRS) and modeling weak disposability, see for instance Sueyoshi and Goto (2010) or Podinowsky and Kousmanen (2011), but these extensions go beyond the purposes of the present analysis. In fact, here measures of economic/environmental efficiency are the background for obtaining compliance costs indicators and TFP growth indexes, and the additional computation burden for analyzing them in a regression phase recommends maintaining relatively simple frameworks by assuming CRS (F¨are et al., 2016). From an empirical point of view, several testing procedures have been proposed in order to verify if the CRS assumption is valid and leads to meaningful efficiency scores. I apply different tests in order to add robustness and reliability to my results. I compute the DDF values under the assumption of CRS and VRS: as argued by Simar and Wilson (2002), the CRS estimator of efficiency scores lead to meaningful results only if the assumption on CRS is valid, while the VRS estimator lead to consistent estimates of the true efficiency in any case. Therefore, if CRS and VRS scores are not different in statistical terms, the CRS assumption can be accepted. I perform the test proposed by Simar and Wilson (2002) without rejecting the H0 of CRS validity, according to both a simple T-test and nonparametric sign rank test. Moreover, I also perform a test recently proposed by Kneip et al., (2016) based on the random shuffling of observations into two groups. Such two independent sets of efficiency scores, obtained under CRS as well as VRS assumption, have been used for creating a normally distributed test statistics that have been used to verify the consistency of the assumption on return to scale. Also in this case, the null hypothesis of Constant Return to Scale validity cannot be rejected. In light of such tests, in the present empirical analysis the CRS assumption seems to fit the data structure better than the VRS assumption and, at the same time, the necessity of maintaining simpler efficiency models lead to the choice of assuming Constant Return to Scale along all the DDF computations. 5. Results

5.1. Environmental efficiency scores The set of linear programs, assuming weak and free disposability of bad output (i.e. EII indicator), as well all the components of SML indexes, are written and solved using 14

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R16 . Computed efficiency scores, for the two years and for the two different frameworks of unconditional and conditional frontier, are reported in table 3. Unconditional DDF scores have been obtained by a standard DDF model applied to the whole sample, while conditional DDF scores have been obtained by applying the model separately for two groups of Italian and German firms. Before results interpretation, it should be underlined that efficiency is a relative concept and then each firm is compared with the best of the sample in each specific time period. Estimated βs represent the maximal feasible expansion of good output and contextual reduction of bad outputs, maintaining inputs unchanged. The average distance from the frontier is higher in 2004 and it decrease in 2007, highlighting an increase of environmental efficiency, or better a decrease in the average distance from the reference frontier. The larger values of βs in 2004 indicates a poorer initial performances of firms highlighting a smaller probability of lying on the frontier in 2004 in respect to 2007. In other words, a group of very efficient firms, adopting the best available techniques, dominates some less advanced plants which adopt the minimal technical requirement for obtaining the authorization to pollute. However, the distance between the two group is decreasing after an initial shock due to the introduction of E-PRTR in 2001 and the subsequent crowding-out effect. Chemical firms react in the following period (2004-2007) and many opportunities arise as highlighted by the decrease of average DODF value from 0.122 to 0.09, from 0.075 to 0.05 in the conditional setting. The less restrictive interpretation of Porter’s HP, proposed by Murty and Kumar (2003), can be here accepted: inefficiency decreases over time and this highlights the presence of win-win opportunities. Nevertheless, good possibilities to enhance green performances still remain in 2007: turnover can be expanded and emissions reduced on average by 9% if the best technologies have been adopted by all the observed firms. This potential recovery in term of turnover and reduction in emission drops to a more reliable 5% in the conditional setting, recognizing that nationality matters in the technical relationship between inputs and outputs. The efficiency scores computed from the separated sample cannot be directly compared among Italy and Germany, because derive from two different reference frontiers. However, the two subgroups of Italian and German firms show very similar performance in 2004 and 2007: non-parametric Kruskal-Wallis tests and parametric T-test reject the hypothesis of differences between the two groups in both periods, for both unconditional and conditional scores. This substantial homogeneity in the two groups of firms is also confirmed when βs are computed assuming free disposability. In the case of conditional DDF under free disposability, the contribution to the frontier of Italian firms are ignored and the reference frontier for German firms shifts down with a consequence decrease of the average βs, suggesting that best performers are more often Italian. The evidence in favor of Italian firms in the unconditional setting is partially driven by the nature of data: when a firm invests in greener equipment and specialized labor force, the value of both tangible Assets and Labor costs increase. In fact, labor costs are higher in Germany, due to higher labor productivity that go beyond the traditional higher salary standards, already moderated by the PPP transformation. Moreover, the investments in 16

All the codes are available upon request

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¯ averages Table 3: Computed efficiency scores β,

Free disposability assumed βunconditional βconditional 2004 2007 2004 2007 0.239 0.244 0.129 0.060 0.167 0.119 0.133 0.118 0.200 0.177 0.131 0.091

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Weak disposability assumed βunconditional βconditional 2004 2007 2004 2007 GERMANY 0.158 0.139 0.070 0.043 ITALY 0.090 0.060 0.081 0.057 Total 0.122 0.097 0.076 0.050

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Pollution Abatement Controls are higher and the value of tangible assets increases proportionally. German firms appear more inefficient due to higher, but costly in monetary term, attention to the environmental footprints. On the contrary, the advantages of Italian firms disappear when the different nature of the technology (i.e. the different operating system in term of sensibility to the environment) in Italy and Germany is considered: the performance of German firms overcame those of the Italian counterparts, or better their average distance from the reference frontier drops. Table 4: Regulatory Impact Indicators

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Unconditional RI 2004 2007 GERMANY 0.081 0.105 ITALY 0.077 0.059 Total 0.079 0.080

Conditional RI 2004 2007 0.059 0.017 0.052 0.061 0.055 0.041

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The firm-level differences between DODF under weak and free disposability assumption are used to obtain RII, a proxy of the compliance costs, reported in table 4, on average for 2004 and 2007. The second and the third columns report unconditional RIIs which appear higher than their conditional counterpart (columns 4 and 5), more precise and reliable because discount the specificities of the two cultural systems. On average the IPPC directive, and more generally environmental rules, cause compliance costs near the 5% of the turnover in 2004 and near 4% in 2007 in the chemical sector. However the situation appears different in the two periods: if regulatory costs are equally distributed in 2004, they drop to 2% in 2007 for German firms while increase to 6% for Italian firms, even focusing on conditional DODF (columns 4 and 5, table 4). The reasons of this differential can be drawn from the decreasing level of absolute and relative emissions for German firms observed in the previous section, an aspect that deeply influences the free disposable frontier. 5.2. Productivity dynamics SML indexes are estimated on the basis of theoretical considerations depicted previous sections and are computed by solving mixed period frontiers17 . The time window considered 17

All programs are computed in R. All codes are available upon request

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covers the period from the end of 2004 to the end of 2007, then all computed indexes refer to the productivity growth over three years. Table 5 shows results for the 2 subgroup and only results from the conditional SML have been reported, by computing geometric averages as suggested by the index nature of SML indicators. Values larger than 1 indicate an observed TFP growth, while values smaller than 1 show a regress in the level of observed TFP. Results of computations under weak disposability mainly confirm the trend emerging by purely ecological performances, showed in table 2. Italian firms show a positive TFP growth around +3%, that seems incoherent with the observed jump in emissions levels, but coherent with the larger average inefficiency recorded in table 3. On the contrary, German firms show a lower growth of TFP around -1% in three years, even if they are able to reduce emission levels in absolute term and in relation to turnover. In Italy, chemical firms show a positive TFP growth mainly thanks to a higher contribution of the frontier swifts compared with the German case.

Weak SML Italy 1.031 Germany 0.987 Total 1.010

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Table 5: Sequential Malmquist-Luenberger indexes in the conditional setting

disposability assumed EFF TECH 0.976 1.056 0.964 1.024 0.970 1.041

Free disposability assumed SML EFF TECH 1.039 0.975 1.066 0.973 0.966 1.008 1.008 0.971 1.039

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Computation of SML under the assumption of free disposability allows deriving conclusion on the effectiveness of productivity enhancements without considering pollution. A priory expectation suggests that if a firm invests more in greener assets or greener inputs, observed capital and intermediate goods tend to increase with a negative effect on purely technical efficiency. From table 5 results partially confirm these expectations, with German firms showing a TFP contraction more severe (0.973 < 0.987) over the period under the assumption of free disposability. On the contrary, Italian evidence underlines a lower attention to limit pollution, as suggested by the faster productivity growth (1.039 > 1.031) in case of an unregulated scenario (i.e. emissions are ignored). Moreover, the contribution of EFF and TECH components helps in understanding these unexpected results. In both case, under weak and free disposability, the TECH component is lower for German firms, highlighting a limited contribution of technical progress in sustaining TFP, because firms are already operating near the frontiers. In this situation, the potential TFP gains cannot be reached by imitating other firms, because others are, on average, worst. 5.3. Testing for the Porter’s HP Different models have been estimated on the basis of equation 13, using two different estimation methods for testing the robustness of results which substantially coincide. The estimates of productivity growth (i.e SML indexes) from a regulated and unregulated frameworks, share the assumption on conditional technology: given the strong influence of local authorities on emission control, firms from Italy and Germany are considered separately when SML are computed. 17

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The firm-level variables included within the regression models are limited and many of them have been already presented, like the RII indicators and SML indexes, while the controls need to be better specified. Three elements characterize each firm: a measure of size, a measure of vertical integration and nationality of the firm, the latter is indicated through a dummy isolating Italians. The measure of size, given by the log of the average annual turnover, catches dimensional effect and potential evidence of scale dis-economies, given the large dimension of firms in the sample. In some empirical works dealing with efficiency, the effect of size is isolated in the first stage of computations using conditional DEA, but here all firms are at least medium by definition18 . Finally, the measure of vertical dis-integration, obtained as the ratio of intermediate goods over turnover, is included to control for different strategies and position along the chemicals value chain19 . Previous studies investigating the determinants of TFP growth, apply different econometric techniques in order to get consistent coefficients’ estimates. Nakano and Managi (2008) use system GMM to solve the serial correlation of Malmquist-Luenberger indexes, while Watanabe and Tanaka (2007) apply a censored Tobit model. Other studies dealing with efficiency scores, introduce a truncated regression model based on the econometric theory by Simar and Wilson (2007). In the present paper the estimation of one TFP growth index for each firm simplifies the problem and the robust OLS can be applied properly. However, given the nature of SML indexes, and its substantial truncation at zero values, also the truncated regression model, estimated via Maximum Likelihood can be a valid alternative. Results under the hypothesis of weak disposability on undesirable outputs in the computation of SML are reported in table 6, while table 7 collects estimates under free disposability. In all estimates, models seem to fit well and R2 s seem acceptable, even if the small sample size represents a serious limitation. Signs of coefficients are robust and coherent across all specifications and estimation methods. The results support the Porter’s HP and show a positive and significant correlation between compliance costs in the first period and TFP growth indexes, computed including (or not) emissions into computation. As previously highlighted, RII2004 represents the potential good output lost during the first period due to environmental regulation and it can be considered a good proxy of the compliance costs for each firms. Therefore, the larger is RII2004 the stronger is the impact of environmental laws for each particular firm. According to the Porter’s HP, these costs will stimulate investment as reaction of the firm, and the productivity growth, observed in the following period, will be higher for firms paying initially higher costs. Of course additional variables can interact within this process of adjustment, motivating the estimate of regression models instead of a simple correlation matrix. 18

According to the E-PRTR, only firms over a certain threshold in term of production capacity have to declare emission and adopt BATs to reduce its environmental impact. 19 The Adelman index of integration given by the ratio of value added over turnover is the measure of vertical integration commonly accepted, but in this case value added cannot be computed without losing at least 3 observations. It is also impossible to obtain it in an indirect way from EBIT because depreciation is loosed, then a rough but more correct indicator of vertical disintegration has been chosen.

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Table 6: Test for Porter HP, assuming conditional technology and weak disposability

Sequential ML indexes 0.510*** 0.509*** 0.475*** 0.510*** 0.509*** 0.475*** (0.152) (0.152) (0.157) (0.148) (0.146) (0.150) Size -0.0206 -0.0151 -0.0152 -0.0206 -0.0151 -0.0152 (0.0138) (0.0136) (0.0141) (0.0134) (0.0131) (0.0134) IT — 0.0280 0.0270 — 0.0280 0.0270 (0.0243) (0.0243) (0.0234) (0.0231) VI — — 0.0332 — — 0.0332 (0.115) (0.109) Constant 1.238*** 1.155*** 1.141*** 1.238*** 1.155*** 1.141*** (0.178) (0.179) (0.152) (0.173) (0.173) (0.145) Sigma — — — 0.0970*** 0.0963*** 0.0963*** (0.0287) (0.0286) (0.0283) Observations 43 43 43 43 43 43 R-squared 0.156 0.168 0.170 Chi-square 11.97 12.28 15.56 Log Lik 39.29 39.60 39.64 Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1

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VARIABLES RII2004

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Table 7: Test for Porter HP, assuming conditional technology and free disposability

VARIABLES RII2004

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0.843*** (0.200) Size -0.0186 (0.0161) IT — VI

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0.841*** (0.194) -0.00584 (0.0163) 0.0646* (0.0323) —

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Constant 1.195*** 1.006*** (0.207) (0.216) Sigma — —

Sequential 0.753*** (0.190) -0.00619 (0.0168) 0.0618* (0.0330) 0.0877 (0.131) 0.967*** (0.198) —

ML indexes 0.843*** (0.195) -0.0186 (0.0157) —

0.841*** 0.753*** (0.186) (0.181) -0.00584 -0.00619 (0.0157) (0.0160) 0.0646** 0.0618** (0.0311) (0.0314) — — 0.0877 (0.124) 1.195*** 1.006*** 0.967*** (0.202) (0.209) (0.188) 0.118*** 0.115*** 0.114*** (0.0275) (0.0278) (0.0270) 43 43 43

Observations 43 43 43 R-squared 0.198 0.240 0.246 Chi-square 18.79 20.41 22.01 Log Lik 30.92 32.08 32.24 Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1 19

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A positive reaction of productivity to regulatory stringency is the main point argued by Porter and it is confirmed by the positive coefficients for RII2004 which are statistically significant along all the model specifications and all the procedures of estimate (table 6). Also when emissions are ignored, the Porter’s HP appears statistically verified: win-win opportunities emerge clearly even in this case. If the impact of environmental protection is high (in term of cost imposed) in t, managers may react by investing in new equipments able to sustain productivity, in term of turnover produced per unit of inputs, but also able to protect the environment. Win-win opportunities seems to be verified for both Italian and German firms, and they are similar for both groups, as suggested by the insignificant coefficient estimated for the dummy Italy in table 6. However, if emissions are ignored in the computation of SML (table 7), a better performance of Italian firms emerges in combination with a confirmed Porter’s HP: Italian firms seem less aware on the consequences of their production process in term of environmental damages. Moreover, this evidence resists even if differences between Italian and German firms have been already discounted through a separate computation of the reference frontier. In other words, after discounting for cultural differences between Italy and Germany in term of a different sensibility on environmental themes by maintaining the two production technologies separated, the results suggest that new investments of Italian firms, stimulated by environmental regulation, are less devoted to reduce emissions than the new investment pursued by German firms. In particular, the proposed TFP growth, computed via SML, can increase for two reasons: an increase in the economic returns from inputs or a reduction in emission produced. Both the economical or environmental components are valid instruments to increase global productivity and their weight on the final results cannot be easily predicted. However, in the chemical sector the environmental components seems to be the preferred leverage of German firms, while the economic side seems the way chosen by Italian firms. The influence of country-level informal pressure on firm to enhance environmental sustainability emerges clearly, even if differences among countries have been discounted in the first-stage of productivity computations. Finally, the other variables introduced in the regression models (size and Vertical Integration) seem not to influence the trend in SML indexes and then, no additional considerations on the outsourcing strategy can be drawn from the present analysis. Therefore, the suspect that outsourcing more polluting processes can be used as a way to bypass the stringency of European environmental protection seems to be excluded for the chemical industry.

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6. Conclusion and discussion This work contributes to the long run debate on the effect of environmental regulation on innovation and firms’ productivity. This effect may be positive according to the Porter’s HP that arguing positive productivity effects from an increasing stringency of environmental regulation. The paper is probably one of the first attempts to investigate this issue at international level, using firm-level data from different countries. The directional distance function, a well known methodology to get reliable efficiency estimates in presence of bad outputs, and its recent conditional version have been applied to a sample of Italian and German firms operating in the chemical sector during the period 2004-2007. Emissions 20

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data are drawn from the European E-PRTR register and are aggregated to create a global index of environmental impact used as bad output in efficiency computations. Concerning the problem of international comparability of emissions level, that is sometimes poor; this paper takes emission data from the same official source for both analyzed countries. The considered sample, even if it is small in size, covers a significant share of total volumes and values of the base chemicals industry in both countries. Two separate frontiers have been obtained for 2004 and 2007 through the application of conditional DDF framework; the potential differences between the two economic systems have been neutralized. The results show a substantial alignment in environmental-economic performance of Italian and German firms, partially overturning the evidence based on emissions per turnover rates. In general, the average distance between best technology and marginal firms is declining over the period, first weak evidence in favor of the Porter’s HP, probably due to the EPRTR regulation itself, but the level of homogeneity seems higher in Germany. Firm-level indicators of the global compliance costs related to environmental regulation have been computed in a conditional DDF setting to get more reliable and conservative results. Also the distribution of regulatory burden seems similar between the two countries, and shows an average compliance cost around the 4/5 % of the total turnover. Environmental productivity growth rates sensible to emissions levels have been computed in light of the Sequential Malmquist-Luenberger indexes in order to minimize potential inconsistency of standard ML indexes already highlighted by previous works on that field. The last and most important contribution of the paper is a precise and formal test for the validity of the Porter’s hypothesis, using Sequential ML indexes and compliance cost indicators as primary ingredients. The empirical evidence for chemical firms supports the existence of win-win opportunities: the level of compliance costs in the first period appears significantly and positively related with productivity growth indexes. The evidence is robust to different model specifications and different estimation methods. Therefore, empirical findings strongly support the validity of the Porter’s HP, showing that firms more penalized by the stringency of environmental rules are stimulated in adopting new technologies allowing for less emissions and stronger productivity growth in the future. The policy implication is important. In mature industries like chemicals, the presence of the so called BATs for receiving the preventive authorization to pollute has been able to stimulate the adoption of new technologies reducing emissions and increasing productivity. Accordingly, policy makers may be less aware of imposing more stringent environmental standards because, after the starting crowing-out and compliance costs, firms react by renewing equipments for switching towards new green technologies. This process seems able to guarantee, on the one hand higher productivity growth compensating compliance burdens, and, on the other hand, minor emissions which justify the policy itself. The net effect on economical activities at industry level cannot be easily estimated and additional empirical work is necessary in this sense. Differences between Italian and German firms emerge in favor of the former only if emissions are ignored. In Germany, environmental protection and economic efficiency appear more balanced in firms’ choices, while the weaker informal pressure in favor of environmental protection allows Italian firms being more oriented on economic aspects, with lower 21

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consideration of emissions. Such findings partially confirms that community pressure plays an important role in affecting green performance of firms, as recently reported by Fres and Reynaud (2012). In this sense, policy instruments able to increase the sensibility of the public opinion on environmental topics can be a valid strategy to increase the informal pressure on firms, stimulating them to become more focused on eco-efficiency themes during the investment phase. Even if this study represent one of the first attempt to formally test for the presence of win-win opportunities using firm-level data in a specific sector across different countries, the results should be interpreted with care. Some recent contribution (see Dakpo et al., 2016 for a recent review) highlight some criticism on the weak disposability assumption, suggesting to use more complex methods recently developed. Moreover, given the small sample size as well as the existence of potential (even if limited) endogeneity issues concerning compliance costs and productivity growth, the presented results should be interpreted or extended with care. For instance, methodological developments, as well as richer data, are still needed to better deal with the potential estimation biases arising from unobserved heterogeneity at firm level. Acknowledgements

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The author gratefully acknowledge Dr. Secondo Rolfo and Prof. Gianmaria Martini as well all the participant to HAWEPA 2012 (IV Halle Workshop on Efficiency and Productivity Analysis) in Halle (Germany) and IAERE 2013 (1st Annual Conference of the Italian Association of Environmental and Resource Economists) in Ferrara (Italy) for valuable suggestions on the early version of the paper. I also thank three anonymous referees and the editor for their valuable comments; of course any remaining errors are solely my responsibility. This study has been financed by Piedmont Region through the project ICT Converging on Law: Next Generation Services for Citizens, Enterprises, Public Administration and Policymakers related to the announcement Converging Technologies 2007.

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[1] AA.VV., 2010. Chimica in cifre. Federchimica, Milan. [2] Aparicio J., Pastor J.T. and Zofio J.L., 2013. On the inconsistency of the MalmquistLuenberger index, European Journal of Operational Research 229, 738-742. [3] Arabi B., Munisamy S., Emrouznejad A., 2015. A new slacks-based measure of MalmquistLuenberger index in the presence of undesirable outputs, Omega 51, 29-37. [4] Arocena, P. and Waddams Price, C., 2002. Generating efficiency: economic and environmental regulation of public and private electricity generators in Spain. International Journal of Industrial Organisation 20 (1), 41-69. [5] Caon-de-Francia J., Garcs-Ayerbe C. e Ramrez-Alesn M., 2008. Analysis of the effectiveness of the first European Pollutant Emission Register (EPER). Ecological Economics 67, 83-92. [6] Caplan A. J., 2003. Reputation and the control of pollution. Ecological Economics 47, 197-212. [7] Chambers R. G., Chung Y. and F¨are R., 1996.Benefit and distance function. Journal of Economic Theory 70, 407-419. [8] Chambers R. G., Chung Y. and F¨are R., 1998. Profit, directional distance function and Nerlovian efficiency. Journal of Optimisation Theory and Applications 98 (2), 351-364.

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