Are the Energy Efficiency Technologies efficient?

Economic Modelling 27 (2010) 274–283

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Economic Modelling j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c m o d

Are the Energy Efficiency Technologies efficient? Kostas Kounetas, Kostas Tsekouras ⁎ Department of Economics, University of Patras, University Campus – Rio Patras 26500, Greece

a r t i c l e

i n f o

Article history: Accepted 14 September 2009 JEL classification: Q42 D24 L6 Keywords: Energy Efficient Technologies Productive efficiency Non-neutral frontiers

a b s t r a c t This paper investigates a rather neglected issue regarding the impact of Energy Efficiency Technologies (EETs) on firms' productive performance. Possible influences may arise in the context of internal cost of adjustment, learning by doing effects and the capital vintage. A unique dataset was used which has resulted from a survey carried out among a sample of Greek EET adopters in the manufacturing sector. An econometric framework based on nested non-neutral frontiers, was developed to estimate the influence and the decomposition of EETs on firms' productive performance. The empirical findings reveal that the EETs affect positively the firms' technical efficiency and negatively the deterministic part of the frontier. Significant variations among industries and size groups appear to be present. Some policy implications are derived based on the empirical evidence supporting a mix of energy and technology directions. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Since the early 90 s the EU has taken a number of initiatives, trying to cope with the climate change problem and the exhaustibility of energy resources. In this context, a range of policy measures has been introduced which aims at increasing energy saving and reducing energy usage and carbon dioxide emissions (Newell et al, 2006). Among the policy measures introduced are the incentives to adopt Energy Efficient Technologies (EETs), which primarily target manufacturing firms. However, the adoption rate of Energy Efficient Technologies is quite low1, especially in manufacturing firms, and this has led to the “Energy Efficiency gap” or in a more restricted notion, to the “Energy Efficiency Paradox” (Shama, 1983; DeCanio and Watkins, 1998). Among the several interpretations of the paradox some researchers have focused on the possible barriers which may be responsible for the low rate of EET adoption2. Surprisingly enough, one of the possible barriers, which has not been traced so far, is the impact that EETs may have on the firms' productive performance although in the framework of growth models ⁎ Corresponding author. Tel.:+30 261 0996376; fax:+30 261 0996130. E-mail address: [email protected] (K. Tsekouras). 1 The most recent pan-European survey of businesses, including SMEs and large enterprises, was carried out by the Observatory of European SMEs in 2006, the ending year of the 2000–2006 Action Plan for Energy Efficiency (Flash Eurobarometer, 2007). This survey revealed that close to two thirds of Small and Medium Enterprises (SMEs) operating in the EU do not even have simple rules or devices for saving energy while only 4% of EU SMEs have a comprehensive system in place for energy efficiency. Even more worrying is the fact that the manufacturing industry is not included among the top three energy-conscious sectors while the transport sector is only third, with the hospitality and healthcare sectors taking the first two places. Such results really question the efficiency of energy efficiency policies. 2 For a more detailed presentation of barriers of EET adoption see DeGroot et al. (2001), DeCanio (1993, 1998) and DeCanio and Watkins (1998). 0264-9993/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.econmod.2009.09.007

with energy conservation policies it has been shown that only under certain conditions the induced innovation can give rise to the win–win situation where the improvement of energy efficiency is associated with an increase in performance (Smulders and de Nooij, 2003). Even in the case where such a win–win situation is confirmed, in the framework of models with capital of different vintage and energy efficiency (Xepapadeas and Zeeuw, 1999; Boucekkine and Pommeret, 2004), some rather strict preconditions, concerning the downsizing and the modernization, have to be satisfied. In this direction, and in the context of the present paper, the main research question which is posed and examined is the identification and estimation of the impact, if any, of EETs on the manufacturing firms' productive performance. As the relative field research has shown, in the huge majority of cases the embodiment of EETs in the existing production technology is not for reasons related to productive performance improvements (DeGroot et al, 2001; Kounetas and Tsekouras, 2008). On the contrary, the firms' decision to adopt EETs resides in environmental regulations (DeGroot et al., 2001; Bjørner and Jensen, 2002), or in business strategies which depart from cost leadership behavior (Porter, 1990) and focus on differentiation strategies developed from factors such as the firms' social responsibility, customers' loyalty and rapid innovation (Lazonick, 2007). This does not mean that the adoption decision is irrational but rather that the contribution of EETs to the firms' overall performance comes from directions other than the improvement of their productive efficiency. However, when the impact of EETs on the firms' productive performance is potentially significantly negative, this impact itself may be considered as a barrier for the adoption of the specific technology. The economic rationale behind the possible negative impact of EET adoption on the firms' TFP growth may be traced to the internal cost of adjustment that the embodiment of any new technology may cause

K. Kounetas, K. Tsekouras / Economic Modelling 27 (2010) 274–283

(Jovanovic and Stolyarov, 2000; McHugh and Lane 1990). More specifically, the internal cost of adjustment includes factors such as the organizational disorders which are implied by the use of the new technology (Damanpour, 1996), the replacement of equipment of low vintage with the new one (Geroski, 2000), and the fact that learning by doing effects are practically zero compared to the corresponding positive and often significant effects that the previous technology exhibited (Levinthal and March, 1993; Jovanovic and Nyarko, 1996). We should also take into account the impact of the hidden costs (Rohdin and Thollander, 2006; Blok et al., 2004) which are realized mainly in the post-adoption period (Sorrell et al., 2004, p. 65). According to Sorrell et al. (2004, p. 65–71) and Blok et al. (2004, p. 14–15), the possible sources of hidden costs are (i) the general overhead costs of energy management, (ii) the costs which are specific to an individual investment in energy efficiency, or the choice of energy efficiency option and (iii) the potential loss in terms of performance associated with energy efficient choices. In any case, the hidden costs will be reflected on the firms' productive performance in the post-adoption period. The claim is that engineering– economic studies fail to account for either the reduction of the firms' productive performance associated with energy efficient technologies, or the additional costs associated with their use (Nichols, 1994). In both cases the impact on firms' productivity is apparent. Hence, the internal cost of adjustment and the hidden costs which may arise in the case of EET adoption affect the productive performance of firms along three main paths. EET adoption may affect (i) the frontier position and shape, (ii) the relative position of the adopting firm with respect to the frontier and (iii) both of these impacts may be present simultaneously. Hence, under the condition that EET adoption affects the productive performance of the firm we investigate, as a second research question, the manner through which such an effect takes place. Griliches (1995), reviewing the relevant literature, pointed out the existence of a significant relationship between new technologies at the firm level and productive performance. The in-house R&D expenditures or the number of patents are the measures used to capture the effects of new technologies on the output produced. However, capturing the effects of the new technology on the firms' productive performance by such innovation indices is not free of problems. Firstly, regarding EETs in the Greek manufacturing firms' case, which are examined in the present paper, the huge majority of the adopted new technologies are not an output of some in-house R&D activity. On the contrary, firms purchase such kind of new technologies from firm-suppliers which have developed and delivered them in the corresponding equipment market. Secondly, the level of R&D expenditure is endogenous to the firm's decision making process. Nevertheless, as previously mentioned, EET adoption, in many cases is imposed by environmental regulations and consequently its character becomes exogenous. Thirdly, EETs very often are characterized by indivisibilities, which of course is not the case with R&D expenditures. To the best of our knowledge, no rigorous approach, in the context of a production frontier, which permits the simultaneous test of hypotheses that a new technology, and EET in particular, may function as an additional input and/or as a productive inefficiency factor, has been carried out. In the present paper we develop an econometric methodology which allows us to do so. The rest of the paper is structured as follows. In the next section we present the econometric methodology which will be followed, while in Section 3 we present the data used and the variables definition. Section 4 discusses the estimation results. Finally, Section 5 concludes the paper. 2. Methodological issues The most prominent and influential approach to firms' productive performance measurement relies on the estimation of a parametric or non-parametric production or cost frontier, which directly links a productive efficiency notion to the notion of productive efficiency as

275

introduced, in Farell's seminal paper (1957). The popularity of the production or cost frontiers approach to the productive performance measurement, is mainly established on the grounds of its ability to decompose the overall productive efficiency in components, which are mainly oriented either to the production mix itself, either to exogenous factors which are accounted as productive inefficiency factors. In addition, it is not worthless to mention that the approach of the parametric, which include the so-called stochastic, production and cost frontiers, allows us to test the hypothesis that (i) the EETs function affects the kernel of the frontier and thus are treated, in econometric terms, as an “additional input” in the production process or (ii) they are simply exogenous factors that may affect, in every possible direction, the firms' productive efficiency. Of course both of the aforementioned hypotheses may not be accepted and thus no impact of EETs on firm's productive performance is identified. 2.1. EETs affect the frontier Following Kumbhakar and Lovell (2000, p.262) let (x1,..., xN)be an input vector used to produce scalar output y ≥ 0. The stochastic production frontier may be written as: ln yit = ln f ðxit ; βÞ + vit  uit ; i = 1; :::; I; t = 1; :::; T

ð1Þ

where i indexes producers, t indexes time, ln f(xit;β) is the deterministic kernel of the stochastic production frontier [ln f(xit;β) +vit], vi ~iid N(0,σv2) captures the effect of random noise on the production process, ui ~ N(0,σu2) captures the effect of technical inefficiency and β is the parameter vector to be estimated. Hereafter, the subscript t is suppressed for simplicity reasons. Battese and Coelli (1992) show that the best predictor of the technical efficiency of each producer is TEi =exp(−ûi), where ûi =E(ui|(vi −ui)). In the above described model, the so-called Error Component Model (ECM), EETs may influence the productive performance through their inclusion in the input mix. Such being the case, EETs are econometrically treated as an additional input, and the corresponding stochastic production frontier can be written as: ln yi = ln f ðxi ; xE ; β; βE Þ + vi  ui ; i = 1; :::; I

ð2Þ

where xE is the employed EET which operates as a shifter of the deterministic part of the production frontier, βE is the vector of the additional parameters to be estimated and captures the alteration of the position and shape and the production frontier due to the inclusion of xE. 2.2. EETs as inefficiency factors In the next step we consider the case where a vector of exogenous variables (z1,...,zQ) influences the structure of the production process by which input x is converted to output y. The elements of z capture features of the environment in which the production takes place, and they are generally considered to be conditioning variables beyond the control of those who manage the production process. In this case, as Huang and Liu (1994) proposed, the stochastic production frontier of Eq. (1) is accompanied by the technical inefficiency relationship ui = gðzi ; δÞ + εi

ð3Þ

where δ is a vector of parameters which are associated to inefficiency factors, to be estimated. The requirement that ui = [g(zi;δ) + εi] ≥ 0is met by truncating εi from below such that εi ≥ −g(zi;δ), and by assigning a distribution to εi such that εi ~ N(0,σε2). This allows εi < 0 but enforces ui > 0. In the case in which the g function is a linear one, the above model is the so-called Technical Efficiency Effects Model (TEEM) which was introduced by Battese and Coelli (1995). The technical efficiency of the i-th producer is given by TE = exp{− ui} = exp {−δ'zi − εi}. In this paper we test the hypothesis that the EETs may

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have the character of a z variable which we name zE, and thus relationship (3) becomes: ui = gðzi ; zE ; δ; δE Þ + εi

ð4Þ

where δE are the additional parameters which have to be estimated since the EETs have been included among the other inefficiency factors. According to Eq. (4) EETs do not influence the structure of the production frontier, but they do influence the technical efficiency with which producers approach the production frontier. 2.3. EETs as an input and an inefficiency factor In order to test the hypothesis that EETs affect the production process through both the position and shape of the production frontier and the inefficiency term, Eqs. (2) and (4) should be combined and the following model arises: ln yi = ln f ðxi ; xE ; β; βE Þ + vi  ui ui = gðzi ; zE ; δ; δE Þ + εi :

ð5Þ

The essential novelty of the approach of Huang and Liu (1994) is that the function g(zi;δ) is allowed to include interactions between exogenous factors zi and production inputs xi (Battese and Broca, 1997). The incorporation of non-neutral effects of EETs in the production performance can be realized either through the consideration of EETs as a factor that affects the production frontier itself, or through the consideration of EETs as a technical efficiency factor. In the former case, the g(zi;δ) function for the i-th firm can be written as: Q

Q

N

Q

q

q

n

q

gðzi ; xni ; xEi ; δ; δEqi Þ = ∑δq zqi + ∑∑δqn zqi ln xi + ∑δEq zq ln xE : ð6Þ The last term of the right hand part of Eq. (6) depicts the nonneutral effects of the EETs on the inefficiency terms when they affect productive performance through the kernel of the stochastic production frontier. In the case where EETs are considered an inefficiency factor exhibiting non-neutral effects, the g(zi;δ) function for the i-th firm can be written as: Q

Q

N

N

q

q

n

n

gðzi ; zEi ; xi ; δ; δE Þ = ∑δq zqi + δE zE + ∑∑δqn zqi ln xni + ∑δnE zEi ln xni : ð7Þ

The last term of the right hand part of the above equation depicts the non-neutral effects of the EETs on the inefficiency terms when EETs are an inefficiency factor. The total effect of EETs on the technical inefficiency of the i-th firm is the sum of the second and fourth term of the right hand part of the above equation. Of course, combining Eqs. (6) and (7) we can explore the case where non-neutral effects arise from both the EETs as a factor that affects the production frontier as well as from EETs as an inefficiency factor. Thus, the multifaceted character of EET, as regards productive performance, may be based, in econometric terms, on the nonneutral effects that our model allows for. 2.4. The zero-value problem In the current paper the production frontier of the EET adopters is assumed to be described by the following translog functional form which is associated, through the inefficiency factor v to a linear inefficiency model. That is, we consider the following production frontier for the i-th firm with subscript t suppressed: ln yi = β0 + ∑ βn ln xni + n

+

1 ∑ ∑ β ln xni ln xmi + βT T 2 n m nm

ð8Þ

1 2 β T + ∑ βTn T ln xni + ui  vi 2 TT n

where n,m = K, L, E denote capital, labor and EET inputs respectively, while T is a time variable which captures technical change. The symmetry condition requires bnm = bmn ∀ n,m. As mentioned above in

this paper, we consider the case whether EETs affect productive performance through their inclusion in the input mix. In other words the EET adoption alters the position and the shape of the frontier itself. In such a case, the values of xe variable will be zero when considering the pre-adoption period and thus its logarithmic transformation is impossible. To cope with this zero-value problem we follow the procedure introduced by Battese (1997) for the Cobb–Douglas case and was further elaborated by Tsekouras et al. (2004) for the translog case. When using this approach, we replace values of the EET variable xe for the i-th firm in the t-th period with a transformed variable that is the outcome of the following rule:
where,

Dit =

x˜Eit = maxfxEit ; Dit g 1 if xEit = 0 ðpre  adoption periodÞ 0 if xEit > 0 ðpost  adoption periodÞ

ð9Þ

This transformation prohibits the inclusion of zero values in the EET's variable, replaces all zero values with 1, and leaves the positive values of EETs intact. Thus, the form of the production frontier to be estimated becomes: ln yi = β0 +

1 ∑ βn ln xni + 2 n

xE = x˜E

+ βT T +

1 2 β T + 2 TT

∑ n

xE = x˜E

∑ βnm ln xni ln xmi m

xE = x˜E

∑ βTn T ln xni + βD Di + ui  vi n

xE = x˜ E

2.5. Testing hypotheses procedures In order to provide a better illustration of our methodology, we devised Fig. 1, with four vertical flowcharts. Each of the first three charts depicts each of the three hypotheses regarding the impact that the embodiment of an EET may bear on the productive performance of a firm. More specifically: (i) it affects the deterministic part of the frontier, that is, the EET operates as an “additional input”; (ii) through the inefficiency term or in other words it has the character of an inefficiency factor; and (iii) both ways. The final chart depicts the hypothesis that this adoption has no effects. The full set of models that arise from these four distinct hypotheses is presented in Fig. 1 of the paper. To elaborate further, if we consider that the adoption of EETs is an input, the first vertical flow chart in Fig. 1 denotes that this may be approximated by an ECM specification (see Model B) or under a TEEM specification. The TEEM specification may be modeled with neutral (see Model D) or with non-neutral (see Model G) effects of inefficiency terms. The second from the left vertical flow chart indicates that the adoption of EETs acts as an inefficiency factor that can be approached by a TEEM model specification with neutral (see Model E) or nonneutral (see Model F) effects of inefficiency terms. Accordingly, the third vertical flow chart reveals that the impact of the adoption can be approximated by only a TEEM model with neutral (see Model H) or non-neutral (see Model I) effects of inefficiency terms. Finally the last vertical flow chart assumes that the adoption of EETs have nothing to do with the efficiency of firms. In that case, the ECM (see Model A) specification and the two versions of the TEEM (see Models C and J) specification are the ones that should be estimated3. 3 In the context of the non-neutral TEEM modeling procedure, two alternatives arise. The first alternative is the one which incorporates the non-neutral effects which are generated by the interaction of all the inputs with all the inefficiency factors. The second alternative is the one which is restricted to the inclusion in the inefficiency model only of those terms which are generated by the interaction of only a subset of inputs with the inefficiency factors. In the context of the present paper we have followed the second approach since the full version of the non-neutral TEEM approach incorporates thirty-two inefficiency factors and serious multicollinearity problems arise. Specifically, in all the cases where the modeling procedure considers EETs an additional factor, the inefficiency model encompasses the non-neutral effects of the x̃E input with all the inefficiency factors.

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277

Fig. 1. Model selection decision process.

As we can observe from the formulation of our models they are nested and their differences are in the number of restrictions employed in their estimation. Thus, we can use the generalized likelihood ratio to decide which identification is the most appropriate and thus to reveal the role of EETs on firms' productive efficiency.

3. Data and variable definition The records provided by the Ministry of Development and the Department of Energy and Natural Resources identify 396 Greek firms as EET adopters. 71 of those belong to the services sector and are

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Table 1 Time distribution of investment in EETs. Adoption years

Frequency

1993

3

1994

1

1995

4

1996

3

1997

31

1998

26

1999

6

2000

6

2001

10

2002

33

2003

34

2004

4

Total

161

Percent (observations percent) 1.86% (3.61%) 0.62% (1.16%) 2.48% (4.49%) 1.86% (3.23) 19.25% (31.96) 16.15% (26.53%) 3.73% (6.00%) 3.73% (5.83%) 6.21% (9.62%) 20.50% (31.73%) 21.12% (33.01%) 2.48% (3.88%) 100.0%

Total no of firms/ no of observations 83 86 89 93 97 98 100 103 104 104 103 103 1163

therefore excluded. Out of the 325 remaining firms only 298 proved after communication with their managers and chief-engineers to have actually accomplished the transition to energy saving technologies and 293 had been granted capital subsidies as an investment incentive for that purpose. The annual directories of ICAP, a private financial and business information service company, part of the AMADEUS database, provided key production, employment and financial information from the published balance sheets of almost all Public Limited and Limited Liability Companies operating in all sectors of economic activity in Greece. From the above sources we designed a database of Greek manufacturing firms identified as energy saving technology adopters for the period 1989–2004, based on the responses of 161 firms which took part in a survey conducted within the context of a research project named “IRAKLEITOS”4 .Thus, our data set is an unbalanced panel which contains information about 54% (161 out 298) of the Greek manufacturing EET adopters for a period of 16 years. The finally resulting data set contains 1163 observations. A view of our data set per year is depicted in Table 1. For our estimations, as an output variable (y) the deflated, using the wholesale price index, total value of shipments in thousands of Euros (based year 1990) was used. In the input side, the labor (xL) of each firm, based on annual full-time equivalents, and the deflated total value of total assets (xK) in thousands of Euros, were used to approximate the labor and capital inputs5. As a deflator of (xK) variable the price index of capital goods is used. More specifically the capital input of the examined firms 4 An analytical description of this survey process and outcomes is presented by Kounetas and Tsekouras (2008). 5 According to the OECD Productivity Manual (OECD, 2001) one of the most prominent approaches in order to construct the capital input variable is the perpetual inventory capital stocks method (PIM). However, applying the PIM method requires the values of parameters of (i) service life, (ii) discard pattern and (iii) depreciation method to be known. Such data are not available for the case of the Greek EET adopters. On the other hand we should mention that the book value approach which is used here, though much cruder than PIM, does recognize both the level of differences in the firms' capital, which are important in the pooled cross-section, and the fact that later additions to each firm's capital stock partly reflect general price level increases (Aw-Roberts, 2002). Finally, it is not worthless to mention that what is of greater interest is not the absolute magnitude of any bias of book value of assets but the distribution of this bias among the EET adopters. In a framework where the Accounting Standards are common, due to the National legislation, an assumption that the distribution of such a possible bias is almost identical among firms is rather plausible.

during the period 1990–2004 is measured, following the KLE approach (Coelli et al., 2005, p. 141), as the deflated total value of assets (base year 1990) without including the EET capital stock. In those cases where the impact of EETs on the deterministic part of the frontier is tested, and thus the EET is regarded as an additional input, the x̃E variable takes the value of the investment cost (again deflated by the price index of capital goods and base year 1990) which the EET adopters have undertaken in order to embody the corresponding EET in their production process. That is, the variable x̃E is a stock variable, as the capital input, but with different technological characteristics. However and in order to avoid double measurement of the assets which are associated to the EET investment projects, the corresponding values have been subtracted from the values of total assets which proxy the firms' capital input. The same approach is followed in all the cases where the xE variable is included in the inefficiency model. In the next few lines we present a brief description of the inefficiency model along with the underlying logic for the inclusion of specific variables. A more analytical presentation and discussion of the estimation results with respect to these variables is given in the next session. The variables which are incorporated in the inefficiency model may be grouped in three categories. The first category encompasses variables which depict the financial health of the firm and consequently its overall business performance. As such we have used the profitability variable (PROF) which is defined as the ratio of net profits to the firm's assets, and the leverage variable (LEV) which is defined as the equity to debt ratio. The second group of the inefficiency factors consists of variables that reflect the firm's attitude towards the new technology and the underlying knowledge conditions (Nelson and Winter, 1982) that are beginning to shape its dynamic capabilities (Teece and Pisano, 1994). In this category we have included the firm's age variable (AGE) as a proxy for the accumulated knowledge which entails learning by doing effects and thus may affect the productive performance. Ιn the same category of the inefficiency factors we have included the recent innovative activities of the firm which are captured by the (INNOV) variable which takes the value of 1 if the firm has introduced an innovation in its business process in the last 3 years and 0 otherwise. The third group of inefficiency variables reflects the firm's specific industry and technological characteristic especially concerning energy inputs. More specifically, as inefficiency factors that have been considered the firm's capital intensity captured by the ratio of capital to labor (CAPIN) and the (ENIN) variable which takes the value 1 if the firm has an energy consuming character and 0 otherwise. Finally we used the industry specific dummy variables (SE1) and (SE2). If the firm is activated in the primary metals industry (SE1) variable takes the value of 1 and 0, if otherwise. Similarly, (SE2) variable takes the value of 1 if the firm is activated in the chemicals and oil refining industry and 0, if otherwise. The definition of these two industry specific dummies are due to special idiosyncrasies of the industry and size distribution of the examined firms which are presented in Kounetas and Tsekouras (2008). At this point, it is not worthless to mention that the inclusion of the second and third group variables absorbs the technology heterogeneity of the examined firms which may bias the frontier estimation. This is due to the fact that in inter-industry studies technology heterogeneity may be reflected in increased white noise which is addressed to technical inefficiency (Tsekouras and Daskalopoulou, 2006). Table 2 provides descriptive statistics for all variables used in the estimation of the production frontier and the inefficiency model. 4. Results and discussion 4.1. Characteristics of the frontier and the inefficiency model All the models which are analytically presented in Fig. 1 have been estimated using Frontier 4.1 software (Coelli, 1996). It should be noted, that in all the estimated models the relevant tests indicate that the null hypothesis of no technical inefficiency effects (γ = 0) in the

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279

Table 2 Descriptive statistics of production frontier variables. xK a

xL

x̃Ea

PROF

Pre-EETs adoption period Meanb 54,576

12,554

326.726

–

0.321

Std. Dev Min Max

45,134 10.000 780,310

464.026 3 3289

– – –

2.232 − 1.577 48.579

Post-EETs adoption period Meanb 102,328

25,693

380.750

1998

0.203

Std. Dev Min Max

67,055 20.000 666,911

510.440 8.000 2617

5777 25.679 63,658

0.552 − 0.107 6.106

Ya

a b

254,352 13.415 4,529,772

380,910 195.598 3,734,995

CAPIN 0.921 2.718 0.411 81.733

0.955 1.403 0.252 11.125

LEV

ENIN

INNOV

AGE

SE1

SE2

0.399

0:62.63% 1:37.37% 0.483 0.000 1.000

0:61.00% 1:39.00% 0.487 0.000 1.000

33.459

0:70.77% 1:29.23% 0.455 0.000 1.000

0:85.94% 1:14.06% 0.347 0.000 1.000

0:62.63% 1:40.54% 0.492 0.000 1.000

0:61.49% 1:38.51% 0.488 0.000 1.000

0:71.63% 1:28.37% 0.452 0.000 1.000

0:86.50% 1:13.50% 0.343 0.000 1.000

0.740 − 9.001 2.173

0.382 0.538 − 3.305 0.977

24.137 1.000 141.000

32.533 25.969 1.000 141.000

Y, xK, x̃E are reported in thousands of Euros. Frequencies are reported for dummy variables.

estimated production frontier is not accepted6. In addition, a range of specification tests was carried out for all the estimated production frontier, including a test for the specification of the production frontier as Cobb–Douglas, a test examining the hypothesis of Constant Returns to Scale (CRS), three tests related to the presence and nature of technical change, and, finally, a test indicating that the joint influence of the inefficiency factors is zero. In all the cases the hypotheses that the functional form of the production frontier is of the Cobb– Douglas type and that the technology exhibits Constant Returns to Scale were not accepted. Furthermore, the relevant tests indicate the presence of a two-dimensional technical change, one concerning neutral technical change and the other biased technical change. The results of the model selection procedure through which we test our hypotheses are presented in Table 3. From the vertical decision making process the outcome is that in the case that the initial assumption is that the EETs affect productive efficiency through the deterministic part of the frontier the preferable model is the one denoted as Model D (first column, Fig. 1). Under the assumption that the EETs exert significant effects on productive performance, as an inefficiency factor, the model that fits best is Model F (second column, Fig. 1). When the impact of EETs is considered to be a resultant of both the above effects, the superior model proves to be Model I (third column, Fig. 1). Finally, Model C is the one which fits best when the decision making process is based on the assumption that EETs exert no effects on the firms' productive performance (fourth column, Fig. 1). Maximum likelihood estimates of these four models are given in Table 4. At this point the decision making process turns to the horizontal direction instead of the vertical which we followed until now. Models D, F and C are nested to Model I and simple likelihood ratio tests indicate that the last is superior in econometric terms (Table 3). Thus it can be argued that EETs affect the firms' productive performance both through the position and shape of the frontier and the inefficiency term. Thus and hereafter the discussion will be focused on the estimation results of Model I. Considering the deterministic part of the frontier which corresponds to Model I we should mention that regarding the capital (xK) and labor (xL) input variables, the corresponding frontier is well behaved in terms of the neoclassical production theory. That is, the first and second order conditions of the classical maximization problem are fulfilled. In addition, technical change appears to be twofold, neutral and biased, and the non-neutral part is revealed to be labor saving and capital intensive. The estimates of the inefficiency model which are summarized in the lower part of Table 4. The null hypothesis that the coefficients of the inefficiency factors are jointly zero is not accepted. Technical efficiency is positively affected by the firm's financial health as it is captured by the

6 This test is carried out in the form of the likelihood ratio test. The critical value for testing the hypothesis γ = 0 is derived from Kodde and Palm (1986) with degrees of freedom equal to the number of inefficiency variables included each time.

variables of profitability and leverage. These results are in accordance with the two of the three below approaches, based on financial theory, of the free cash flow, agency costs and credit evaluation which have been used to study the relationship between technical efficiency and the financial structure of the firm (Nasr et al., 1998; Davidova and Latruffe, 2007). The attitude of the firms towards the new technology and knowledge accumulation, affects the technical efficiency in a rather interesting way. The results suggest that innovative firms are revealed to be more efficient. The same applies to the firms which are relatively young. According to the “resource-based view” of the firm, these two findings jointly indicate that firms with particular managerial and technological capabilities at their early stage of industrial life present characteristics that do not stem from learning by doing effects (Teece and Pisano, 1994) and which allow for a significant reduction of the technical inefficiency. The role of organizational flexibility, as opposed to bureaucratic routines (Teece, 1996; Nelson and Winter, 1982), may be of major significance for the mere existence and effects of technical inefficiency. In the same direction we can interpret the influence of the capital intensity variable (CAPIN). In addition, significant technological heterogeneity and size effects, with respect to the firms' technical efficiency, are depicted in the estimated coefficients, t-ratios of the energy intensity (ENIN) and industry specific variables (SE1) and (SE2). Finally, no significant time effects on firms' productive inefficiency have been identified, although we have used two alternative groups of three- and five-year time-period dummies7. 4.2. The impact of EETs on productive efficiency Regarding the impact of EETs on the firms' productive performance, the first thing to notice is the statistically significant value of the estimated βD coefficient which indicates that the specific dummy variable (D of Eq. (9)) was correctly included in the estimation of the production frontier. Had the dummy variable D been omitted, the estimation of the production frontier would have been incorrectly specified because the changes in output would have been attributed to capital and labor only and not to the EETs as well. In Model I, which as we have already mentioned is the one fitting best to the data, the impact of EETs on firms' productive performance is traced in both the deterministic kernel of the stochastic frontier and the inefficiency model. Regarding the deterministic part of the model, it is evident that EETs exert a mixed direct influence on the produced output since the coefficient of the (x̃E) variable is negative while the coefficient of the (x̃E)2 variable is positive. In addition, EETs affect negatively the produced output when they interact with labor input and the time trend variable which captures technological change. The interaction of EETs with 7 The corresponding estimation results are not presented for space limit reasons but are available upon request. We owe this to an anonymous referee's comment.

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Table 3 Model selection decisions. No. of X20.05 restriction

λ

H0 hypothesis

Restricted Unrestricted L(H0) model model

EET affects the kernel of the frontier. The ECM is a valid assumption δprof = ... = δse2 = 0 The neutral TEEM is a valid assumption δprof_E = ... = δse2_E = 0 EET affects the inefficiency model. The neutral TEEM is a valid assumption δEK = δEL = 0 EET affects both the Kernel of the frontier and the inefficiency model. The neutral TEEM is a valid assumption δprof_E = ... = δse2_E = 0 EET doesn't affect either the Kernel of the frontier or the inefficiency model. The ECM is a valid assumption δprof = ... = δse2 = 0 The neutral TEEM is a valid assumption δprof_E = ... = δse2_E = 0 Horizontal decisions δE = δprof_E = ... = δse2_E = 0 δE = δprof_E = ... = δse2_E = 0 δprof_E = ... = δse2_E = 0 δEK = δEL = 0

(B)

(D)

− 1843

− 1833

19.578 8

15.507 Not accepted

(D)

(D)

(G)

− 1833

− 1750 166.146 8

15.507 Not accepted

(G)

(E)

(F)

− 1804

− 1794

2

5.991 Not accepted

(F)

(H)

(I)

− 1825

− 1749 152.274 8

15.507 Not accepted

(I)

(A)

(C)

− 1846

− 1812

67.634 8

15.507 Not accepted

(C)

(C)

(J)

− 1812

− 1804

16.854 8

15.507 Not rejected

(C)

(D)

(I)

− 1831

− 1749 168.921 9

16.919 Not accepted

(I)

(C) (F) (I)

(I) (I') (I')

− 1833 − 1749 166.916 9 − 1794 − 1747 89.194 6 − 1749 − 1747 4.62 2

19.675 Not accepted 12.592 Not accepted 5.991 Not rejected

(I) (I') (I)

capital input does not reveal any statistically significant influence on the produced output. Apparently, a non-monotonic relationship between the EETs and the firms' productive performance is in place. Further elaboration of this relationship is presented below in this section. Turning to the inefficiency model, EETs reduce technical inefficiency when no non-neutral effects are taken into account. When the latter appear we can identify the positive influence of EETs on the firms' technical efficiency, when they are combined with the age variable and the industry specific variables of (SE1) and (SE2). Thus, we can argue that although the learning by doing effects in general are not technical inefficiency reducing, as we have already seen above, in the case of EETs the older firms attempt to embody these technologies in their productive processes more efficiently than the younger. Apparently the accumulated knowledge, which is due to learning by doing effects, in handling new technologies which are of the energy saving type is valuable in this case. At this point it should be noted that it is quite reasonable to consider that the investment in EET is, among others, a replacement mechanism of older to newer capital equipment and thus is expected to be beneficiary for capital vintage and consequently for the productive efficiency of the EET adopters. On the other hand we should point out that the variable Age captures the firm's age and not the age of the capital equipment. Although it is also reasonable to assume that the firm's age is positively correlated to firm's capital vintage it is also reasonable to assume that older firms exhibit higher levels of accumulated knowledge, arising from learning by doing effects, which reduces the adjustment costs associated with EET adoption. Thus the specific variable captures several interrelated issues and unfortunately the available data do not allow us to disentangle the above components and to test rigorously what exactly the situation is. Apparently, further research is needed here8. Concerning, the interaction of the EETs with the industry specific variables of (SE1) and (SE2), the identified positive influence on the firms' technical efficiency is rather expected, since these two dummies capture production technologies which are vastly energy consuming, as the primal metal and chemical industries. The same applies to the interaction of the EETs with the (ENIN) variable. Finally, the non-neutral effects that arise from the interaction of EETs with the financial structure of the firm and its technological progressiveness are statistically non-significant.

8

We owe this to an anonymous referee's comment.

L(H1)

21.08

Decision

Preferable model

From all the empirical results presented and discussed above, it is evident that in order to determine the influence of the EETs on the firms' productive performance we have to use a composite measure which will take into account the several segmental influences. Following Battese and Broca (1997), we estimate the output elasticity with respect to EETs εqE, taking into account that EETs affect both the deterministic part of the frontier as well as the inefficiency model. Thus the output elasticity is defined as the sum of the frontier elasticity with respect to EETs (εEf) and the technical inefficiency elasticity with respect to EETs (εuE). Hence, in the case of the translog non-neutral stochastic frontier which corresponds to Model I the output elasticity with respect to EETs is: 

∂μi ∂ lnEðqi Þ = βE + 2βEE ln x˜E + ∑ βjE ln xji + βET T  Ci x˜ ∂ ln x˜ E ∂ ln E j = K;L

 ð10Þ

The term εEf = ðβE + 2βEE ln x˜E + ∑ βjE ln xj + βET TÞdepicts the j = K;L

elasticity of the frontier output with respect to EETs. In other words, the specific term is the measure for the EET's influence on the position and the shape of the production frontier. On the other hand the term εuE = Ci ð∂μi = ∂ ln x˜E Þ, the elasticity of technical inefficiency with respect to EETs, captures their influence on the firm's technical efficiency, where μit is the mean of the distribution of the inefficiency model (6). That is, Q

Q

q

q

μ i = δ0 + ∑ δq zqi + ∑ δeq zq ln xe In addition, it can be shown (Battese and Broca, 1997), that Ci = 1 

1 σ

( μ   ) ϕ σit  σ ϕ μ it μ it   μσit  Φ σ σ Φ σ

where ϕ and Φ represent the density and distribution functions of the standard normal random variable, respectively. Essentially, the term Ci disentangles the influence of the EETs' change in two parts. The first regards the inefficiency term and the second the white noise of the stochastic frontier. Using the above described approach we have computed the impact of EETs on the firms' productive performance. The results are presented in Table 5. All the elasticities have been estimated at the mean values of

K. Kounetas, K. Tsekouras / Economic Modelling 27 (2010) 274–283

281

Table 4 Maximum likelihood estimators for Models C, D, G, F and I. Coefficient

Variable

Model C

Model D

Model G

Model F

Model I

β0

Constant

βK

ln xK

βL

ln xL

βE

ln x̃E

− 0.683 (− 2.858)* 0.862 (2.213)* 0.266 (0.330) −

(ln xK)2

βLL

(ln xL)2

βEE

(ln x̃E)2

βKL

(ln xK)(ln xL)

βKE

(ln xK)(ln x̃E)

0.026 (3.170)* –

βLE

(ln xL)(ln x̃E)

–

βt

T

βtt

T2

βtK

T ln xK

βtL

T ln xL

βtE

T ln x̃E

0.002 (1.908)** 0.002 (1.098) − 0.007 (− 1.862)** 0.003 (0.669) –

βD

D

–

− 0.590 (− 3.364)* 0.858 (2.211)* 0.294 (0.367) − 7.962 (− 7.224)* − 0.169 (− 0.867) 0.218 (0.542) 34.748 (34.075)* 0.022 (2.747)* 0.155 (0.333) − 0.987 (− 1.426)** 0.025 (1.152) 0.001 (1.363)** − 0.007 (− 1.974)* 0.003 (0.723) − 0.250 (− 1.644)** 0.125 (1.719)**

− 1.140 (− 7.646)* 0.812 (2.094)* 0.364 (0.454) −

βKK

− 1.141 (− 1.086) 0.852 (2.184)* 0.306 (0.380) − 5.034 (− 5.006)* − 0.178 (− 0.824) 0.213 (0.515) 34.653 (34.650)* 0.025 (0.792) − 0.417 (− 0.432) 0.077 (0.078) 0.001 (0.021) 0.002 (0.301) − 0.007 (− 0.613) − 0.001 (− 0.065) 0.050 (0.066) 0.130 (0.138)

− 0.476 (− 2.074)* 0.854 (2.199)* 0.301 (0.375) − 7.908 (− 5.529)* − 0.166 (− 0.852) 0.212 (0.527) 36.132 (31.848)* 0.022 (2.688)* 0.068 (0.141) − 0.949 (− 1.382)** 0.026 (1.209) 0.001 (1.366)** − 0.007 (− 1.868)** 0.003 (0.680) − 0.235 (− 1.638)** 0.096 (1.303)**

Inefficiency model δ0

Constant

δprof

PROF

δcapin

CAPIN

δlever

LEV

δenin

ENIN

δinnov

INNOV

δage

AGE

δse1

SE1

δse2

SE2

δE

x̃E

0.523 (4.224)* − 0.093 (− 0.854) 0.139 (12.346)* − 0.077 (− 1.768)** − 0.423 (− 4.286)* − 0.213 (− 2.062)* 0.007 (4.161)* − 0.146 (− 1.307)** − 0.118 (− 1.044) –

− 0.049 (− 0.029) − 0.111 (− 0.057) 0.118 (2.545)* − 0.039 (− 0.134) − 0.405 (− 0.444) − 0.275 (− 0.292) 0.006 (0.682) − 0.192 (− 0.168) − 0.025 (− 0.026) –

0.841 (9.008)* − 0.045 (− 10.349)* 0.110 (11.499)* − 0.052 (− 1.627)** − 0.274 (− 4.663)* − 0.124 (− 2.332)* 0.003 (3.285)* − 0.130 (− 2.078)* − 0.110 (− 1.607)** –

δEK

x̃E ln xK

–

–

–

δEL

x̃E ln xL

–

–

–

δprof_E

(PROF)(ln x̃E)

–

–

δcapin_E

(CAPIN)(ln x̃E)

–

–

δlev_E

(LEV)(ln x̃E)

–

–

δenin_E

(ENIN)(ln x̃E)

–

–

δinnov_E

(INNOV)(ln x̃E)

–

–

δage_E

(AGE)(ln x̃E)

–

–

δse1_E

(SE1)(ln x̃E)

–

–

δse2_E

(SE2)(ln x̃E)

–

–

1.336 (1.300)** 1.263 (2.685)* − 1.587 (− 1.618)** 1.723 (1.683)** − 0.251 (− 0.239) − 0.061 (− 2.448)* − 0.696 (− 0.683) − 5.257 (− 3.582)*

0.376 (5.338)* − 0.012 (− 1.193) 0.115 (13.849)* − 0.061 (− 1.883)** − 0.275 (− 5.141)* − 0.117 (− 2.428)* 0.002 (3.556)* − 0.129 (− 2.370)* − 0.164 (− 2.499)* − 0.066 (− 1.552)** − 0.021 (− 2.100)* 0.029 (1.953)** –

− 0.157 (− 0.800) 0.026 (0.592) –

− 0.151 (− 0.779) 0.181 (0.451) – 0.021 (2.731)* – – 0.025 (1.209) 0.001 (1.100) − 0.005 (− 1.451)** 0.001 (0.249) – –

– – – – – – –

0.987 (6.109)* − 0.047 (− 8.445)* 0.110 (12.010)* − 0.054 (− 1.794)** − 0.277 (− 4.951)* − 0.124 (− 2.378)* 0.003 (3.568)* − 0.138 (− 2.360)* − 0.125 (− 1.663)** − 0.034 (− 8.298)* – – 1.198 (1.185) 1.233 (2.790)* − 0.985 (− 1.003) 1.025 (1.014) 0.125 (0.117) − 0.055 (− 2.392)* − 0.244 (− 0.236) − 2.349 (− 1.506)** (continued on next page)

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Table 4 (continued) Coefficient

Variable

Model C

Model D

Model G

Model F

Model I

LogL σ2

– –

γ

– –

− 1832.800 0.996 (2.498)* 0.434 (1.491)** 145.161

− 1749.727 0.604 (23.637)* 0.446 (2.714)* 311.308

− 1793.939 0.615 (27.703)* 0.142

LR test

− 1812.226 0.755 (8.636)* 0.307 (2.817)* 194.148

− 1749.342 0.603 (25.691)* 0.457 (3.603)* 312.078

292.722

*Numbers in parentheses are the ratios of estimated coefficients to their standard errors. **One and two asterisks denote statistical significance at 5% and 10% respectively.

the inputs and the inefficiency factors. The first noticeable thing is that if we do not consider the firms' size and industry characteristics, the total impact of the EETs on the produced output is negative since the total estimated value of the total elasticity is negative (−0.053). However, this negative overall effect is actually the outcome of two opposite direction effects. The first is referred to the negative impact of EETs on the deterministic part of the frontier (εEf) and the second to the influence of EETs on the firms' technical efficiency (εEu). The whole picture becomes more puzzling if we introduce into the analysis the size and industry characteristics of the firms. At first the impact of the EETs on the frontier remains negative for all the size classes and examined industries. However, the most influential effects appear in the cases of medium size firms (−0.156), chemicals (−0.271) and food–beverages (−0.229) industries. On the other hand, the negative impact of EETs on the frontier is substantially smaller in the cases of large firms in the nonmetallic mineral products (glass, cement, etc.) industry. Although EETs appear to be beneficial for all firms in terms of the reduction of their technical inefficiency, significant variations are identified among different industries and size classes. In particular, the industries of basic metals (–0.168) as well as of the chemicals and oil refining (–0.173) appear to exploit more the potentials that the EETs may offer in order to improve their technical efficiency. The same but in a rather smaller extent, holds for the medium size enterprises (–0.077). In three cases, that is, the small firms, the industry of basic metals and the industry of non-metallic mineral products the positive impact of EETs on firms' technical efficiency is quite large in order to compensate for the aforementioned negative impact of EETs on the frontier. In terms of the EETs overall impact on productive performance the most negatively affected size group is that of medium size enterprises (–0.079), while the industries of food and beverages (–0.172) and chemicals and oil refining (–0.098) are the corresponding negative extreme cases when the industry distribution of the εEq is examined.

Table 5 Elasticity of mean output with respect to EETs.

All firms

Elasticity of the frontier with respect to EETs

Elasticity of technical efficiency with respect to EETs

Total elasticity

− 0.129

− 0.076

− 0.053

− 0.075

0.003

− 0.077

− 0.079

− 0.051

− 0.019

− 0.145 − 0.271

− 0.168 − 0.173

0.023 − 0.098

− 0.048

− 0.058

0.010

− 0.229 − 0.097

− 0.057 - 0.084

− 0.172 − 0.013

Size distribution Small firms − 0.072 (< 50 employees) Medium size firms − 0.156 (51–250 employees) Large firms − 0.071 (> 250 employees) Industry distribution Basic metals Chemicals and oil refining Non-metallic mineral products Food and beverages Other

5. Conclusions Energy Efficiency Technologies' impact on the manufacturing firms' productive performance is a rather neglected issue although it may be a crucial factor that accounts for the rather low rate of adoption of such technologies. In this paper we develop an econometric framework using data from a sample of Greek manufacturing firms which have embodied in their production process, EETs during a period of 15 years which in turn allows us to estimate not only the overall influence of EETs on firms' productive performance, but also the decomposition of this total effect. More specifically, our empirical results indicate that the EETs negatively affect the kernel of the production frontier or in other words they can be characterized as “bad or low quality input” in terms of productive performance. On the contrary, EETs affect positively the firms' technical efficiency. The overall influence depends on the relative magnitude of these two partial effects. In the examined industries and time period the overall influence appears to be negative. Although this negative impact of EETs on the kernel of the frontier is well persistent across industry and size characteristics, significant variations are present both across industries and between size groups. The approach presented in this paper is only indicative of the specific country and period. Nevertheless, our results serve as a starting point for intuitive discussions regarding the relationship between EET adoption and firms' productive performance. Although no strict policy recommendations may be based on the interpretation of our results, the above presented empirical evidence may induce significant policy implications. At present, policy incentives, regarding the adoption of EETs, are homogeneous both at the industry and firm level, and do not take into account the impact of EETs on productive performance. The empirical evidence presented above reveals, however, that in terms of Productive Efficiency (Productive Performance), EETs have differential effects with respect to various industries and firm size. The authority responsible for the design and implementation of policies that aim at the reduction of energy consumption by manufacturing firms should have taken into account two factors which lead to differential policy initiatives. More specifically, efficient policy initiatives should have been twofold, giving emphasis on the firms' motives to adopt EETs on one hand, and producing EETs which fit (suit) to technological and size characteristics of the targeted manufacturing firms on the other. Regarding the incentives for the EET adoption policy makers ought to consider, the differential effect that these incentives may have on firms' attitude towards the EETs embodiment due to heterogeneous effects that EETs exhibit on firms' productive performance. Furthermore, a strategy of policy differentiation should also be examined regarding the funding of the research conducted which is related to the development of EETs, since the adoption of the existing EETs by low energy consuming manufacturing firms appear to have a significantly negative impact. Hence, the authorities that design and promote policies aiming at the acceleration of the adoption rate of EETs, should primarily focus on those firms that due to their technological characteristics and size, undergo the greatest negative impact by the EET adoption on their productive performance and partially corresponds to Verhoef and Nijkamp (2003) that EET recommendation by subsidies may be counterproductive in the case of heterogeneous firms.

K. Kounetas, K. Tsekouras / Economic Modelling 27 (2010) 274–283

Finally, empirical findings suggest that for those firms with a technological frontier characterized as low energy intense, EETs have a particularly negative influence on their productive performance in contrast to those firms with a technology that is characterized as high energy intense. In other words, the existing EETs are adjusted to the specificities of those firms that are considered as high energy intensive and are characterized by inflexibilities or/and indivisibilities with respect to energy input. Thus, in terms of the supply of EETs, policy incentives should promote the development of technologies (i.e. University Research, in-house R&D) that would be suitable for the low energy consumer firms. Overall, following Smulders and de Nooij (2003) we could argue that “energy policy is a second-best policy unless it is combined with the appropriate technology policy and product market regulations” .One aspect of technology policy is providing subsidies to the type of research that suffers most from approprability problems. The optimal policy should increase the share of returns to innovation that accrue to the inventing firm, which is an important determinant of how growth reacts to energy conservation policies. For that reason, the energy conservation policies crucially depend on how they are combined with technology policies. Finally, the above discussion draws attention to the issue of the interrelationship among the EET adoption rate, the EET adopters' productive performance and the incentive schemes structure. The evidence provided by the relevant literature is contradictory. On the one hand, some scholars argue that the use of energy taxes prevent firms from undertaking investments in energy saving technologies and thus, reducing EETs' attractiveness (Verhoef and Nijkamp, 2003; Van Soest, 2005). On the other hand, some scholars argue that tax policy is an effective tool for EET adoption although inferior, compared to voluntary agreements (Bjørner and Jensen, 2002). In any case, the links among EET adoption, EET adopters' productive performance and incentive scheme structures remain quite vague. Therefore, it would be quite interesting for one to explore whether a combined policy providing subsidies for encouraging the EET adoption, and enforcing taxes, at the same time, if the firms decide not to proceed in the adoption of an EET, exerts any impact not only on the adoption rate of EETs but also on firms' productive performance.

Acknowledgments The authors are grateful to two anonymous referees for their helpful comments and suggestions. This research was funded by European Social Fund (ESF), Operational Program for Educational and Vocational Training II (EPEAEK II), and particularly the Research Program HERAKLITOS. The usual caveats apply.

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