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Environmental,investments,and,firm’s,productivity:,a,closer,look.!!

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The!Sustainability!Environmental!Economics!and!Dynamics!Studies!(SEEDS)!is!an!inter9university!research!centre!that!aims!at!developing!and!promote!research!and!higher!education!projects!in!the!fields! of! ecological! and! environmental! economics,! with! a! special! eye! to! the! role! of! policy! and!innovation!in!the!path!towards!a!sustainable!society,!in!economic!and!environmental!terms.!Main!fields!of!action!are!environmental!policy,!economics!of! innovation,!energy!economics!and!policy,!economic! evaluation! by! stated! preference! techniques,! waste! management! and! policy,! climate!change!and!development.!!

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SEEDS!Working!Paper!1/2014!January!2014!by!Roberto!Antonietti!and!Alberto!Marzucchi!!

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The!opinions!expressed!in!this!working!paper!do!not!necessarily!reflect!the!position!of!SEEDS!as!a!whole.!

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Environmental investments and firm’s

productivity: a closer look.

Roberto Antonietti1, Alberto Marzucchi2

Abstract

In this paper we investigate the relationship between investments in environmentally-oriented equipment and firms’ productivity. Drawing on Porter hypothesis, we estimate the impact of capital-embodied environmental innovations on the level of firm productive efficiency (TFP), distinguishing between investments aimed at reducing the environmental impact of production (Target 1) and investments aimed at reducing the use of raw materials (Target 2). Relying on a rich firm-level dataset on Italian manufacturing, and using a quantile regression approach, we show that Target 1 investments enhance the TFP level of low-performing firms, whereas the productivity effect of Target 2 investments concerns medium-high performing firms. When interacted, the two targets show an additive positive short-term effect on productivity for medium-low, medium and medium-high performing firms. This effect partially vanishes through time.

J.E.L. Classification:

Keywords: environmental investment; TFP; quantile regression

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 Corresponding Author. Department of Economics and Management “Marco Fanno”, University of Padova, via del Santo 33, 35123 Padova, Italy. Email: roberto.antonietti@unipd.it 2 Department of International Economics, Institutions and Development (DISEIS), Catholic University of Milan (Italy) and INGENIO (CSIC-UPV), Valencia (Spain). Email: alberto.marzucchi@unicatt.it !

2 !

1. Introduction

In the last 25 years, the traditional wisdom that environmental goals are not compatible with the sake

of fostering competitiveness has been challenged by an emerging strand of literature. This is based on

the idea that economic and environmental performance can be jointly improved (e.g. Porter, 1991;

Porter and van der Linde, 1995). In this perspective, which can be referred to the Porter Hypothesis

(PH), the trade-off between social benefit and private costs is challenged. PH postulates that “properly

designed environmental standards can trigger innovation that may partially or more than fully offset

the cost of complying with them” (Porter and van der Linde, 1995, p. 98). Actually, three different

specification of the PH can be identified in the literature (Jaffe and Palmer, 1997; Costantini and

Mazzanti, 2012). The strong version claims that environmental regulations enhance economic

performance and competitiveness of complying firms, of the sector they belong to and, eventually, of

the whole economy. Abandoning the profit-maximizing assumption, regulations are seen as a shock

that stimulates firms to look for new opportunities and drives a process of technological change. The

weak version of PH asserts that additional innovation stimulated by regulations implies both

opportunity costs and, possibly higher, gross benefits. Finally, the narrowly strong PH predicts that

environmental protection might impact only the green side of the economy.

Within the PH framework, the relation between environmental protection and economic

competitiveness has been investigated by several empirical contributions that have considered a broad

set of effects (e.g Iraldo et al., 2011): among these, the impact on productivity has received a fair

amount of attention.

Early contributions reviewed in Jaffe et al. (1995) point to a modest negative impact of

environmental regulation. Similarly, evidence provided by Gray and Shadbegian (2003) on U.S. paper

and pulp mills, and by Broberg et al. (2010) on a set of Swedish industrial sectors generally points to a

negative effect of environmental regulations on productivity. Nevertheless, there is a fair amount of

recent empirical investigations that find some support to PH. Berman and Bui (2001) highlight a

positive productivity effect on refineries located in the Los Angeles area (South Coast Air Basin),

with respect to other refineries not subject to the same stringent air pollution regulation. Findings

emerging from Alpay et al. (2002), on the productivity of Mexican and U.S. food processing industry,

lead to partial support to PH: increasingly stringent environmental control enhances productivity

growth in Mexico. Focusing on the Gulf of Mexico offshore oil and gas industry, Managi et al. (2005)

analyze the various components of TFP within a joint production model, which considers both market

(oil and gas production) and environmental outputs (water pollution and oil spill). Results support a

recast version of PH, which focuses on the productivity of joint production of market and

environmental outputs. However, no support is found for the standard formulation of the PH

regarding increased productivity of market outputs. Hamamoto (2006) focuses on five heavy polluting

sectors in Japan and adopts an indirect approach to estimate the impact of environmental regulation on

3 !

productivity. Empirical findings suggest that the increase in R&D investment, stimulated by

regulatory stringency, has a significant positive effect on the TFP growth. Following the approach of

Hamamoto (2006), Yang et al. (2012) investigate Taiwanese industrial sectors. Findings highlight a

positive effect of the R&D induced by regulations on industrial productivity. Lanoie et al. (2008) add

some further insight to the debate. Their analysis of the Quebec manufacturing industry leads to

support PH with some important specifications. A temporal lag is required for the environmental

protection to positively affect productivity; such a positive effect is also found to be higher for firms

exposed to international competition.

Despite the increasing amount of empirical evidence we believe that the literature on the

relation between environmental goals and firm’s productivity is still characterized by two major gaps,

which we try to fill with the present work.

The first, and possibly more general, gap is related to the scarce attention paid to the fact that

environmental investment may be the result of factors and motivations which are not confined in the

regulation-compliance sphere. Maxwell and Decker (2006) claim that voluntary environmental

actions can be stimulated by corporate image building, regulatory preemption and production cost

savings. Ambec and Lanoie (2008) stress that green investment may be aimed at both increasing the

revenues (e.g. by accessing new markets or differentiating products) and reducing the costs (e.g. by

easing the access to capital or increasing workers loyalty and commitment), increasing in turn firm’s

financial and economic performance. Similar points are raised by Portney (2008) who highlights that

firms may even adopt beyond-compliance strategies that increase their performances. Hence, taking

stock from this literature, we consider environmentally-oriented investments that are induced by

deliberate profit-seeking strategies of the firm.

The second gap pertains to the lack of overarching but fine-grained perspectives in the

analyses provided in the extant literature. Also due to data availability issues, relatively scarce is the

cross-sectoral evidence on the relation between environmental protection and productivity at the firm-

level. Moreover, all these studies focus on the average effect on the firm’s performance, assuming that

the impact of environmental investments on productivity is the same for all the firms. This impact can

be heterogeneous across firms and may depend on their actual level of productivity, i.e. whether there

is room to further improve the technical efficiency or whether the firm stands at the technological

frontier. The lack of unambiguous econometric evidence on the productivity effects of environmental

regulation and innovation may also lie in this heterogeneity. In the empirical analysis, we address this

issue by adopting a quantile regression approach, thanks to which we can observe the impact of

environmental investments on different points in the conditional distribution of productivity, here

given by Total factor Productivity (TFP).

The paper continues as follows. Section 2 describes the empirical strategy, with a focus on

quantile regression. Section 3 presents the dataset utilized in the analysis. Section 4 presents the OLS

(4.1.) and quantile regression (4.2) results. Section 5 concludes.

4 !

2. Empirical strategy

The starting point of our empirical strategy is the estimation of the TFP. To do this, we employ a

Cobb Douglas production function, like in Equation 1:

(1) ititKitLit akly ++= ββ

where y is the log of value added (deflated by a 2-digit price index), l is the (log-transformed)

labour cost (deflated by a wage index) and k is the (log-transformed) net tangible assets (deflated by a

capital price index). The residual a represents the TFP.

TFP is estimated using the semi-parametric method provided by Levinsohn and Petrin (2003),

which employs raw materials and the cost of services (all deflated by proper price indexes) as

instruments. This reduces the simultaneity bias between inputs and output. Since TFP cannot be

measured in any meaningful unit, we compute it as averages of the exponential transformations of

ita divided by the industry means. These scaled values are then log-transformed. Hence, our measure

refers to how firm-specific TFP differ from the industry mean in the year considered.

Secondly, we regress the TFP on two main vectors of independent variables, as in Equation 2:

(2) ititZitIita εγγγ +++= −− 110 ZI .

The vector Z includes the following set of control variables. First, we control whether the firm

is part of a business group, either as a leader (Group leader) or as an affiliate (Group affiliate), with

the intermediate case of an affiliate which controls other firms in the group taken as the reference

term. Size effects are captured by three dummies, denoting if the firm is a small (reference), medium

or large, respectively. Area and industry dummies control for sector and geographic specificities. The

internationalization of the firm is captured by a dummy equal to 1 if it is engaged in export activities

(Export), and a dummy equal to 1 if it is foreign owned (Foreign). We also control for firm’s age

(Age, in natural log), computed as 2001 minus the start-up year, as reported by the questionnaire.

Finally, we account for human capital and innovation capabilities. The former (HC) measures the

average 2001-03 share of white collars (i.e. top and middle managers, executives and clerks, log

transformed). The latter are captured by the log of total 2001-03 R&D expenditure (R&D) and its

squared term (R&D2).

The second vector (I) includes the main variables of interest, i.e. investments targeted to

environmental goals. In building these variables, we interacted the total amount of fixed investment in

5 !

2001-033 with two target dummies which take value 1 if the firm assigned high importance to the

reduction of the environmental impact of the production (Target 1) and the reduction in the use of raw

materials (Target 2). Therefore, these two variables capture both the objectives and the amount of the

investment. Since these two objective dummies overlap, we adopt a second econometric specification

in which we isolate the investments related exclusively to the mitigation of the environmental impact.

We do this by interacting the amount of fixed investments in 2001-03 with other two dummy

variables: one (Target 1 only) captures investments exclusively aimed at reducing the environmental

impact of the production, while the other (Target 1+2) captures a joint and “more pervasive” effort

towards the reduction of the environmental impact and the use of raw materials4. In this way, we can

also try distinguishing between ‘end-of-pipe’ and ‘cleaner production’ technologies. The former (e.g.

air pollution filters) refer to solutions that do not alter production methods and techniques, but are

designed to reduce the environmental impact in order to comply with norms and regulations. The

latter (e.g. new low waste processes and technologies) are designed to reduce the environmental

impact by integrating the production process, and substituting for, or improving, existing technologies

with the addition of cleaner ones (Frondel et al., 2007).

In order to mitigate potential simultaneity and reverse causality issues, we introduce a one to

three year lag between the dependent and the explanatory variables: the former refers to year 2004

(TFP2004) and to the entire 2004-06 period (TFP2004-06), while the latter refer to the 2001-03 period.

However, although we partially mitigate the simultaneity bias by introducing such a time lag, we do

not formally test for the presence of endogeneity. Therefore, we interpret our results as robust

correlations rather than true causal effects.

We first estimate Equation 2 via OLS. However, in order to analyse if the effect of investment

in environmental technologies varies across the TFP distribution, we also adopt a quantile regression

approach. Unlike the standard OLS regression, the quantile regression estimates are less sensitive to

outliers, provide a richer interpretation and characterization of the data, and do not require existence

of the conditional mean for consistency. Let q denote a specific quantile, the qth estimator qβ

minimizes over βq the objective function

(3) qii

N

y

N

yiqiiq yqyqQ

iiii

βββββ

xxxx

"−−+"−= ∑∑"<"≥ ::

)1()(

where 0<q<1, y is the dependent variable, x is a vector of independent variables, and where the

solution is found by applying the simplex method. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!3 This is properly deflated by a business investment price index, and log-transformed. 4 With our data, only two firms invested exclusively in the reduction of raw materials, so we do not consider this option in our econometric exercise.

6 !

Figure 1 plots the conditional density functions of the two estimated TFP, i.e. TFP2004 and

TFP2004-06. We can observe that the distributions appear to be reasonably symmetric, at least for

0.05<q<0.95, and that the median value of the TFP is around zero (i.e. 0.047 in 2004 and -0.066 in

2004-06).

Figure 1. Quantiles of the TFP

0

20

40

60

80

100

1°(Trim. 2°(Trim. 3°(Trim. 4°(Trim.

Est

Ovest

Nord

-20

24

quan

tiles

of T

FPLP

2004

0 .2 .4 .6 .8 1fraction of the data

0

20

40

60

80

100

1°(Trim. 2°(Trim. 3°(Trim. 4°(Trim.

Est

Ovest

Nord

-2-1

01

2

quan

tiles

of T

FPLP

2004

-06

0 .2 .4 .6 .8 1fraction of the data

The values of TFP2004 and TFP2004-06 across the main quantiles are also shown in Table 1.

Table 1. Average TFP level across the main quantiles

Q10 Q25 Q50 Q75 Q90

TFP2004 -0.3792 -0.2012 -0.0473 0.1860 0.4679

TFP2004-06 -0.3911 -0.2335 -0.0666 0.1618 0.4257

3. Data

The data we use are extracted from the merge of the 9th and 10th wave of the Unicredit Survey on

manufacturing firms, which allows to measure our variables over the period 2001-2006. The two

waves provide information on a representative sample of 4,289 and 5,137 Italian manufacturing firms

respectively. Large firms (i.e. with more than 500 employees) are fully represented; while small and

medium sized (i.e. 11-500 employees) firms are included according to a sampling procedure based on

industry, NUTS-1 area and employment size.

Unicredit survey data represents a unique source of information, including aspects related to

provide information on firms’ innovative activities, labour force composition, internationalization,

7 !

and market relationships between firms, banks, customers and competitors. After the merge, we

dropped firms with missing values in the variables of interest, and with inconsistencies or negative

values for value added, labour costs or capital. The final sample consists of 830 firms. Table 2 shows

the structure of the sample with respect to employment size, macro-area and à la Pavitt industry.

Table 2. Sample distribution Employment size N. % 11-20 91 10.96 21-50 196 23.61 51-250 423 50.96 251-500 57 6.87 500+ 63 7.59 Area N. % North West 302 36.39 North East 272 32.77 Centre 152 18.31 South 104 12.53 Industry (Pavitt classification) N. % Supplier dominated 410 48.18 Scale intensive 155 18.21 Specialized supplier 265 31.14 Science based 21 2.47 Total 830 100.0

Despite the loss of observations with respect to the original samples, we observe that the

majority of firms are small and medium sized, located in the North of the country and belong to

traditional and specialized industries. This picture is in line with that provided by Census data and

official statistics on the Italian industrial composition.

4. Results

4.1. Baseline OLS results

Baseline OLS results are shown in Table 3. In Models 1 and 3, the dependent variable is TFP2004,

while in Models 2 and 4 TFP is averaged over 2004-06. Among the environmental investment

covariates, Models 1 and 2 include Target 1 and Target 2, while Models 3 and 4 include Target 1 only

and Target 1+2. Looking at control variables, we note that size matters, since medium and large

firms benefit from a significant productivity premium with respect to the small ones. Belonging to a

business group in 2001-03 increases TFP only if the firm is the leader and only in the very short run

(i.e. 2004), whereas no effect is registered for being an affiliate and over the period 2004-06. On the

8 !

contrary, foreign ownership materializes its productivity effect only over time (i.e. 2004-06), when

probably the firm is able to adopt new technologies and/or organizational practices that increase its

efficiency. Engagement in export activities also increases firm’s productivity. This can be a sign of

the ‘learning-by-exporting’ hypothesis (e.g. Salomon and Shaver, 2005), according to which firms

benefit from productivity enhancements due to higher experience on international markets.

Our regression analysis identifies two other important sources of higher productivity: human

capital and R&D. A 1% increase in the share of white collars is related to an average 0.23-0.26

productivity premium. The positive effect of lagged R&D on TFP, instead, materializes only after

achieving a threshold amount of R&D investments, as shown by the negative coefficient of the linear

R&D term and the positive coefficient of R&D2.

The core of our analysis is related to the productivity effect of investments in environmentally-

oriented technologies. In Model 1 and 2 we notice that targeting fixed investments to reduce the

environmental impact of production (Target 1) does not have any productivity return, both in 2004

and in 2004-06. On the contrary, when investments are targeted to technologies that reduce the use of

raw materials, an average 0.005-0.006 productivity premium is found.

Models 3 and 4 provide a closer look at the productivity impact of different green investment

strategies. We here disentangle the effect of investing in end-of-pipe technologies (i.e. Target 1 only)

and cleaner production technologies (Target 1+2). OLS estimates show that investing in Target 1

only-technologies at time t does not affect productivity at time t+1. However, when the environmental

target joins that of reducing the use of raw materials, we find a statistically significant 0.010

productivity return.

This latter result is consistent with the strong version of the PH, according to which

environmental protection can act as a stimulus for business performance, but also provides an

interesting alternative indication. Target 1 investments increase firm productivity only when they are

integrated with Target 2 investments, i.e. profit-oriented strategies aimed at reducing inputs, as in the

case of cleaner production technologies.

9 !

Table 3. The impact of green investments on TFP: OLS results

(1) TFP2004

(2) TFP2004-06

(3) TFP2004

(4) TFP2004-06

Group leader 0.184*** (0.068)

0.088 (0.067)

0.186*** (0.067)

0.091 (0.068)

Group affiliate -0.032 (0.030)

-0.039 (0.029)

-0.033 (0.030)

-0.039 (0.029)

Small Ref. Ref. Ref. Ref. Medium 0.128***

(0.026) 0.118*** (0.026)

0.130*** (0.026)

0.120*** (0.026)

Large 0.425*** (0.053)

0.355*** (0.057)

0.427*** (0.052)

0.357*** (0.056)

Foreign 0.020 (0.047)

0.081* (0.044)

0.020 (0.047)

0.082* (0.044)

Export 0.050* (0.030)

0.064** (0.031)

0.051* (0.030)

0.065** (0.031)

Age 0.005 (0.019)

0.009 (0.020)

0.003 (0.019)

0.008 (0.020)

HC 0.233** (0.105)

0.261** (0.104)

0.242** (0.105)

0.261** (0.104)

R&D -0.039*** (0.011)

-0.030** (0.012)

-0.038*** (0.011)

-0.030** (0.012)

R&D2 0.003*** (0.001)

0.002** (0.001)

0.003*** (0.001)

0.002** (0.001)

Target 1 0.002 (0.002)

0.003 (0.002)

Target 2 0.006* (0.003)

0.005** (0.002)

Target 1 only 0.001 (0.005)

0.005 (0.005)

Target 1+2 0.010** (0.004)

0.010** (0.004)

Industry dummies Yes Yes Yes Yes Area dummies Yes Yes Yes Yes N 830 830 830 830 R2 0.250 0.191 0.250 0.190 Notes: all the estimates include a constant term. Standard errors are robust to heteroskedasticity. *** significant at 1% level; ** significant at 5% level; * significant at 10% level.

4.2. Quantile regression results

As explained in Section 2, the OLS regressions return the average effect of the investment in

environmental technologies, disregarding the fact that this impact can be heterogeneous across firms.

We address this issue by adopting a quantile regression approach, and we show the results in Tables 4

and 5, where we only report the coefficients of the variables related to the green investment strategies.

Table 4 reports the effects of green investments on the distribution of TFP in 2004.

10 !

Table 4. The impact of green investments on TFP2004: quantile regression results DEP. VAR. TFP2004 Q10 Q25 Q50 Q75 Q90 Target 1 (Model 1) 0.007***

(0.003) 0.003

(0.003) 0.001

(0.003) 0.003

(0.002) -0.001 (0.004)

Target 2 (Model 1) -0.002 (0.004)

0.002 (0.004)

0.007** (0.003)

0.006* (0.003)

0.005 (0.005)

Target 1 only (Model 2) 0.010 (0.008)

0.003 (0.006)

0.001 (0.008)

0.001 (0.007)

0.006 (0.009)

Target 1+2 (Model 2) 0.007 (0.006)

0.007** (0.003)

0.011** (0.006)

0.007** (0.003)

0.002 (0.008)

Model 1 2 N 830 830 R2 Q10 R2 Q25 R2 Q50 R2 Q75 R2 Q90

0.136 0.127 0.146 0.179 0.221

0.131 0.126 0.147 0.176 0.221

Notes: all the estimates include a constant term and the following variables: group leader, group affiliate, small (ref.), medium, large, MNE, export, age, HC, R&D, and R&D2. Estimates also include industry and area dummies. Standard errors are boostrapped. *** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Interestingly, in Model 1 we find that the productivity effect of Target 1 investments is limited

only to firms with the lowest performance (Q10). Thus, a strict environmental investment strategy

rewards only firms with a large potential room for improvement or firms that strongly need for new

business strategies for launching their products and improve their efficiency. For instance, these least

productive firms may benefit from the possibility to signal themselves as green producers. This would

allow them to enter new markets or take advantage from the reduced input costs (see Section 1).

Different is the effect of investment strategies aimed at reducing the use of raw materials

(Target 2). These become particularly suitable for firms already characterized by a medium-high

productivity level (50th and 75th percentile), but not for the very most productive ones, the

performance of which is only affected by R&D investments (not reported).

Further details emerge when we account for the difference between end-of-pipe and clean

production technologies (Model 2). In this case, we notice how investment strategies exclusively

aimed at reducing the environmental impact of production do not show any significant effect over the

entire TFP distribution. On the contrary, investing in integrated technologies pervasively rewards a

large spectrum of firms, i.e. those characterized by a medium-low to medium-high productivity levels

(25th, 50th and 75th percentiles).

These results largely hold when we measure the TFP on the entire 2004-06 period (Table 5),

but with some specifications.

11 !

Table 5. The impact of green investments on TFP2004-06: quantile regression results DEP. VAR. TFP2004-06 Q10 Q25 Q50 Q75 Q90 Target 1 (Model 1) 0.007***

(0.002) 0.003* (0.002)

-0.000 (0.002)

-0.001 (0.003)

-0.001 (0.005)

Target 2 (Model 1) -0.004 (0.003)

0.000 (0.003)

0.006** (0.002)

0.006* (0.003)

-0.001 (0.006)

Target 1 only (Model 2) 0.008 (0.009)

0.003 (0.006)

0.003 (0.007)

-0.002 (0.008)

0.011 (0.009)

Target 1+2 (Model 2) 0.011** (0.005)

0.007* (0.004)

0.005 (0.006)

0.008 (0.006)

-0.004 (0.008)

Model 1 2 N 830 830 R2 Q10 R2 Q25 R2 Q50 R2 Q75 R2 Q90

0.127 0.132 0.150 0.166 0.187

0.116 0.132 0.148 0.165 0.189

Notes: all the estimates include a constant term and the following variables: group leader, group affiliate, small (ref.), medium, large, MNE, export, age, HC, R&D, and R&D2. Estimates also include industry and area dummies. Standard errors are boostrapped. *** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Model 1 confirms that Target 1-type of investments helps the least performing firms to increase

their level of productivity. Target 2-type of investment, instead, is confirmed to be a profitable

strategy for firms with medium and medium-high levels of productivity (50th and 75th percentile). As

before, Model 2 allows us to distinguish between end-of-pipe and cleaner production technologies.

As before, the productivity effect of end-of-pipe technologies (Target 1 only) is not statistically

different from zero. When we consider the effect of Target 1+2-type of investments, we notice a

positive effect on firms with a low or medium-low productivity level only (10th and 25th percentile).

Only for this type of firms, there is a combined impact of the increase in the production efficiency -

due to the reduction in the use of raw materials - and of the “green signalling” – due to reduction of

the environmental impact. Interestingly, this effect does not appear in 2004, but materializes over

time. At the same time, we also register that this effect vanishes along time for firms with medium

and medium-high productivity levels (50th and 75th percentile), that is the effect turns not statistically

significant from 2004 (see Table 3) to 2004-06.

All in all, investing in environmental practices does not increase per se the productivity level of

firms. This effect occurs when the firm adopts a strategy that pools the lower environmental impact

objective with that of reducing the use of raw materials. However, the effect is not homogenous. It

rather occurs only in presence of room for improving the productivity of the firm, especially when we

consider the effect over the entire period 2004-2006. In other terms, for companies that already

achieved a high level of productivity, investing in integrated environmental practices does not seem to

represent a suitable investment strategy.

12 !

5. Conclusions

In this paper we analysed the relation between the investment in green technologies and firm’s

productivity, here given by the level of TFP. With respect to the extant literature we provided two

main contributions. First, we considered different types of investment strategies. Second, we

scrutinized the heterogeneity of the effect on the firm’s productivity. We based our empirical analysis

on a rich dataset of Italian manufacturing firms, and we adopted a non-parametric approach based on

quantile regression in order to capture potential non-linearities in the environmental investment-TFP

relationship.

When we consider the average productivity effect, both in 2004 and 2004-06, we find that

strategies aimed at reducing the use of raw materials, or at integrating this reduction with more

explicit environmental aims, are related to significant productivity gains. In this sense, we find

support to the strong version of the PH. However, this support is limited to the implementation of

cleaner production technologies, which include the reduction in the use raw materials among the aims.

The importance of policies and business practices that support this type of technologies emerge thus

as primary implication from our evidence.

The picture becomes more complex when we allow the productivity effect of environmental

investments to be heterogeneous across firms. Investment aimed at reducing the environmental impact

of the production has an effect on the least productive firms only. Investments that reduce the

environmental harm of production might represent for these firms a sort of opportunity for green

signalling, which allows the companies to enter green markets and obtain production inputs (capital

and labour) at lower costs. On the contrary, investing in technologies that reduce the use of raw

materials represents a suitable strategy for firms characterized by medium-high levels of productivity.

The quantile regressions confirm that end-of-pipe technologies do not increase firm’s productivity.

Such an effect takes place only for cleaner production technologies that combine the reduction of the

environmental impact and the use of raw materials. All in all, none of the strategies that we consider

appear able to increase the productivity of the top-productive firms, whose TFP level is improved

primarily by R&D investments. Such a complex picture helps understand that investing in green

technologies does not always lead to win-win situations. This gives rise to the need of proper policies

that compensate specific types of firms for the absence of productivity gains.

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13 !

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