‘Better late than never’: the interplay between green technology and age for firm growth
'Better late than never': the interplay between green technology and age for firm growth
Riccardo Leoncini 0 1 2 4 5 6 7 8
Alberto Marzucchi 0 1 2 4 5 6 7 8
Sandro Montresor 0 1 2 4 5 6 7 8
Francesco Rentocchini 0 1 2 4 5 6 7 8
Ugo Rizzo 0 1 2 4 5 6 7 8
0 R. Leoncini IRCrES-CNR , Milan , Italy
1 R. Leoncini Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg , Freiburg im Breisgau , Germany
2 R. Leoncini University of Bologna , Bologna , Italy
3 SPRU, Science Policy Research Unit, University of Sussex , Brighton , UK
4 U. Rizzo Department of Economics and Management, University of Ferrara , Ferrara , Italy
5 F. Rentocchini Southampton Business School, University of Southampton , Southampton , UK
6 F. Rentocchini Department of Economics, Management and Quantitative Methods, University of Milan , Milan , Italy
7 S. Montresor Faculty of Economics and Law, Kore University of Enna , Cittadella Universitaria, 94100 Enna, EN , Italy
8 A. Marzucchi (
This paper investigates the relationship between green/non-green technologies and firm growth. By combining the literature on eco-innovations, industrial organisation and entrepreneurial studies, we examine the dependence of this relationship on the pace at which firms grow and the age of the firm. From a dataset of 5498 manufacturing firms in Italy for the period of 2000-2008, longitudinal fixed effects quantile models are estimated, in which the firm's age is set to moderate the effects of green and non-green patents on employment growth. We find that the positive effect of green technologies on growth is greater than that of non-green technologies. However, this result does not apply to struggling and rapidly growing firms. With fast-growing (above the median) firms, age moderates the growth effect of green technologies. Inconsistent with the extant literature, this moderation effect is positive: firm experience appears important for the growth benefits of green technologies, possibly relative to the complexity of their management.
Green technology; Firm growth; Age; Quantile fixed effects
JEL Classification L26 . O33 . Q55
Following the ‘Porter hypothesis’ and the debate over
‘whether it pays to be green’, studies have shown that by
complying with environmental regulations, adopting
sustainable practices and eco-innovating, firms can
become more competitive
(Porter and Van der Linde 1995;
Ambec and Lanoie 2008; Ambec et al. 2013)
if not even
(Horváthová 2010; Ghisetti and
. However, the impact of green
technologies on firm growth has been minimally investigated,
especially given the abundant literature on ‘standard’
innovation as a growth driver
(Sutton 1998; Bottazzi
and Secchi 2006; Lotti et al. 2009; Coad and Holz
.1 Supportive evidence has been mainly obtained
by examining the relationship between eco-innovations
and firm growth through the lens of the technology–jobs
nexus, usually in a non-longitudinal setting
Gagliardi et al. 2016; Rennings and Zwick 2002)
However, with few exceptions, these analyses do not address
the ‘growth premium’ attached to green technologies
vis-à-vis the non-green ones, nor do they pay attention
to the inner complexity and dynamics of the
The present paper aims to close this gap by
addressing two research questions. We first draw on the idea
from the field of industrial organisation that the growth
effect of technology exploitation varies with the pace at
which a firm grows, given that growth opportunities and
threats change at different growth rates
(Coad and Rao
. Hence, we investigate whether the growth
outcome of eco-innovations depends on the firm’s pace of
growth and on whether the firm is struggling or rapidly
growing. To address this research question, we rely on a
novel methodological approach: a quantile regression
(e.g. Coad and Rao 2008; Coad and Rao 2010;
Coad et al. 2013)
performed by using a fixed effects
estimation technique (Canay 2011). This technique
captures the potentially heterogeneous effects of green (and
non-green) technologies on firm growth across different
growth rates, while controlling for unobserved
1 This issue is related to, but different from the relatively more studied
topic of the (mainly policy) drivers of ‘green growth’, for which see,
among the others,
Hallegatte et al. (2012)
The second research question examines whether the
firm’s age influences the growth impact of green
technologies. Again, we are informed by the industrial
(e.g. Barba Navaretti et al. 2014;
Distante et al. 2014)
: we consider age-dependent
mechanisms that characterise the firm’s capacity to exploit
(Coad et al. 2016)
and add to them specific
ones related to eco-innovations. By studying the
knowledge complexity implications of eco-innovations (e.g. in
terms of risk and financing) and the higher need for
technology experience to grasp it
et al. 2010)
, we investigate whether age moderates how
the firm’s growth benefits from green technologies.
These two original research questions are addressed
by relying on a novel longitudinal dataset comprising
5498 Italian manufacturing firms studied over the period
of 2000–2008. In our econometric analysis, the impact
of green and non-green patents on the firm’s growth, as
measured by employment growth, is moderated by age.
Our main findings are as follows. First, green
technologies have a greater impact on firm growth than
nongreen ones, except in the case of struggling and rapidly
growing firms. Second, the firm’s age positively
moderates the growth effect of green technologies for
fastgrowing (above the median) firms. We discuss the
management and policy implications for both scenarios.
The remainder of the paper is structured as follows.
Section 2 reviews relevant background literature.
Section 3 presents the empirical application. Section 4
illustrates the results. Section 5 presents the conclusion.
2 Background literature and research questions
Surprisingly, despite the importance given to green and
sustainable growth in the current policy debate, the role
of eco-innovations in driving firm growth has scarcely
been investigated. Although firm growth can be
measured through different variables at the micro-level, like
job creation, assets and sales growth
contribution to the nascent literature on the growth
effects of green technologies focuses on employment.
While we empirically justify our choice in Section 3.3, it
is also motivated by our attempt to complement a recent
stream of literature about the effects of eco-innovations
on the dynamics of firm employment. These studies
investigate the possible positive (e.g. driven by product
demand or higher staff requirements for operating
environmental technologies) and negative (e.g. displacement
and substitution) employment effects of different types
(Rennings and Zwick 2002;
Rennings et al. 2004; Licht and Peters 2013; Horbach
and Rennings 2013)
. However, they generally rely on
cross-sectional, self-reported survey data, often using
binary variables for capturing employment growth
(Rennings and Zwick 2002; Rennings et al. 2004;
Horbach and Rennings 2013)
and/or focusing only on
(Rennings and Zwick 2002; Rennings
et al. 2004)
, thus preventing the distinction between
occasional and persistent effects.
More relevant to our analysis is the patent-based
study carried out in Italy (2001–2008) by
et al. (2016)
, showing that eco-innovator firms have
more employment growth than their non-green
Colombelli et al. (2015)
of more than 400,000 firms in Germany, France, Italy,
Spain and Sweden over the period of 2002–2011 and
found that eco-innovation capabilities drive sales
growth more than ‘generic’ ones.
On the strength of these previous contributions, we
argue that, despite their higher costs
(Gagliardi et al.
, new green technologies could provide firms with
‘extra returns’ compared to non-green technologies.
These extra returns can be exploited (e.g. re-invested)
and, as we posit, yield an employment growth premium
to eco-innovators. This argument finds support in three
different research streams. At the outset, by extending
the debate on Schumpeterian innovation regimes to the
(Malerba 2005; Oltra and Saint Jean 2009)
we argue that eco-innovators could make a more
effective ‘creative accumulation’ of knowledge (i.e.
Schumpeter Mark II) than standard ones and translate
economic returns into higher growth opportunities.
Given the irreversibility of complying with environmental
regulations, investing in green-specific assets and
acquiring internal/external green knowledge
Saint Jean 2009; Mazzanti and Rizzo 2017)
environmental technologies have actually been found to lead
to more persistent (eco-) innovation practices and
outcomes than standard technologies, with greater
opportunities of increasing returns
(see Sàez-Martínez et al.
2016; Chassagnon and Haned 2015)
A growth premium from green vs. non-green
technologies is also supported by the literature on the joint
improvements of environmental and economic/financial
performances of firms (i.e. their ‘win-win’ strategies).
The green-specific mechanisms that increase firm
revenues (e.g. green differentiation of products, access to
green demand segments and sale of environmental
control technologies) and reduce costs (e.g. material and
energy efficiency, and recycling initiatives)
and Lanoie 2008)
provide eco-innovators with
improved financial indicators
(e.g. Misani and Pogutz
, greater profits
(Ghisetti and Rennings 2014)
and, in general, extra economic returns to be turned into
Finally, the regulations and policy actions on which
eco-innovations depend (the so-called regulatory push/
pull effect) also represent an ‘extra’ driver of growth.
‘Polluting’ firms at the end of the value chain are legally
forced to improve their environmental performances
and, in so doing, ‘induce’ an additional element of
‘derived demand’ in the upstream producers of green
technologies that fuels the latter’s growth
et al. 2015; Ghisetti and Quatraro 2013)
In summary, the extant literature seems to imply a
growth effect of green technologies vs. non-green ones,
which can depend on two scarcely analysed aspects: (i)
the pace at which firms grow and (ii) the firm age.
The pace aspect has been confirmed through the
use of quantile regressions for standard innovations.
Coad et al. (2016)
, for example, showed that only
the fastest-growing firms benefit from standard
innovation in terms of employment growth, while this
return is actually negative for the slowest-growing
ones. In general, fast-growing firms have been
shown to have crucial advantages in the ‘job
(for a review see Almus 2002)
they are generally smaller, and thus more prone to
commercialising their innovations, and younger, and
accordingly more in need of investing in the
knowledge they miss at the beginning of their businesses.
Second, they often operate in technology-intensive
sectors and are thus endowed with a larger
knowledge base, qualified human capital and technological
skills and experience. They also usually have a
limited liability legal form, thus showing greater
incentives for riskier but also more rewarding
innovations. Finally, their close connection to suppliers,
customers and competitors enables them to benefit
from an open innovation approach.
As these aspects do not vary by the nature of the
relevant technologies and given the absence of
specific literature on green technologies, we maintain
that the distinction between rapidly and slowly
growing firms could be a relevant factor for
ecoinnovations too. Although in a non-quantile
framework, but rather in a dynamic parametric
estimation of Gibrat’s law
(Gibrat 1931, 2003)
, this is
confirmed by Colombelli et al. (2015). They find
that the growth differential between green and
generic technologies is actually greater for firms
growing at more than ‘the average’ rate. We
interpret this in light of the ‘induced’ innovation, which
is the ‘derived demand’ from environmental
regulations that fuels green sectors.
With regard to the second aspect of our analysis, in
the industrial organisational literature, age has a twofold
effect on growth. On the one hand, it is (along with size)
an important determinant of a firm’s growth potential,
with a large (although not yet conclusive) body of
evidence favouring younger firms
(Haltiwanger et al.
2013; Lawless 2014)
. On the other hand, age (along
with other characteristics) is a crucial moderating factor
of the impact of innovative activity on firms’ growth
(Audretsch et al. 2014) and on employment growth, in
(Coad et al. 2016)
However, the role of age in the relationship between
green technologies and growth has received little
emphasis. A sort of ‘sin of youth’ seems to emerge from the
literature on ‘(eco-)sustainable entrepreneurship’
and McMullen 2007)
, in which the comparative
analyses of start-ups (young firms) vs. incumbent (old) firms
have been very rare, so far, and specific to some sectors
(e.g. green electricity and microfinance)
Wustenhagen, Hockerts and Wüstenhagen 2010)
‘emerging green Davids’ usually show higher
environmental commitment and attractiveness to
sustainable consumers, they often fail to translate their niche
market potential into a broad mass market, mainly
because of the competition from incumbent ‘greening
Goliaths’, through their ‘inner’ form of corporate
(e.g. Bird et al. 2002;
Stenzel and Frenzel 2008)
Other and more general age-related insights
emerge from environmental and eco-innovation
studies, all suggesting a greater growth potential of
mature eco-innovators. First, an older firm can be
expected to have an advantage in terms of learning
experience against the multidimensionality and complexity
that characterises green knowledge
et al. 2010)
and new green product development
. Second, younger firms may be more
averse to the growth exploitation of green technologies,
as these are often in the early stage of their life cycles
and thus marked by greater uncertainty than non-green
(Consoli et al. 2016)
. Similarly, young firms could
be disfavoured in benefiting from policy instruments for
the adoption of green-tech—such as new practices of
green public procurement
these are still marked by uncertainty and require
experience in managing demand-pull policy. Third, given the
hard collaterisation and information signalling of green
investment projects, older firms could be expected to
have better access to financing
and be better prepared to cope with
the higher cost of eco-innovations without crowding out
other growth-driving investments
(Hall et al. 2016)
Last, but not the least, older firms may have an
advantage in strengthening their available resources to
increase their economic green returns (e.g. through
economies of scale) as well as in forming alliances to tap into
external resources (e.g. through reputation and market
(e.g. Cainelli et al. 2015)
. Similarly, firm
maturity could be beneficial for searching, absorbing and
transforming external knowledge
(Franco et al. 2014)
towards adopting the open eco-innovation mode
(Ghisetti et al. 2015)
, particularly when accessing
new and foreign markets
(e.g. Autio et al. 2000;
In light of the above aspects, our study attempts
to address the firm growth potential of green vs.
non-green technologies, by providing new empirical
evidence for two original research questions: (1) To
what extent does the association between green
technologies and firm growth vary along the conditional
distribution of growth rates? (2) What is the role that
a firm’s age plays in the relationship between green
technologies and growth?
3 Empirical application
The empirical analysis is based on a dataset that has
been obtained by combining three different sources (see
2 The arguments about the growth potential of mature (young)
companies that we have just presented refer to green technologies in general
terms (i.e. without distinguishing specific environmental targets or
technological realms). In the absence of theoretical backing and/or
prior empirical findings on the existence of differences for different
green technological realms, we distinguish between green and
nongreen technologies only, without focusing on specific green
Online Appendix A1 for details): (i) the ASIA database
of the Italian National Statistical Office (ISTAT); (ii) the
Bureau van Dijk AIDA database; (iii) and the
Worldwide Patent Statistical Database (PATSTAT).
By restricting our sample to manufacturing
companies (Section D of NACE Rev. 1.1) that filed at least one
patent application in the period of 1977–2008 and
because of the availability of the other relevant data
sources (see Online Appendix A1), we ended up
with an unbalanced panel comprising 5498 firms
observed over the period of 2000–2008. The focus
on patenting firms allows us to minimise unobserved
heterogeneity in terms of innovative capabilities
across firms. Given that the objective here is to
investigate the growth premium, if any, offered by
green vs. non-green innovations and the moderating
effect of age in the relationship between innovative
activity and growth, our implications will be valid
for innovative firms only.3
3.2 Methodology To address our research questions (see Section 2), we investigate the following relationship:
Growthit ¼ α þ β1Pat Greeni;t−1
þ β2Pat Nongreeni;t−1 þ β3Agei;t−1
þ β4ðPat Green X AgeÞi;t−1
þ β5ðPat Nongreen X AgeÞi;t−1
þ z0i;t−1γ þ δt þ μi þ εit
where δt indicates a series of time dummies; z′i,t − 1 is a
vector of firm-specific control variables; μi denotes the
unobserved firm-specific effects; and εit is the error
Building upon previous empirical works on the
relationship between growth and innovation, and given our
focal interest in the role of the pace at which firms grow,
we employ a quantile regression approach
Rao 2008; Kesidou and Demirel 2012)
. As is well
3 Table A1 in the Online Appendix reports the difference in 1-year
employment growth, employment and age between our sample and the
overall population of Italian companies (source: ASIA-ISTAT). We test
whether the means for the variables above are statistically different
between the two groups. In line with the approach of
Gagliardi et al.
, who employed similar data, firms in our sample are older and
bigger, while there is no significant difference in terms of employment
known, this approach allows for a richer characterisation
of the data: it disentangles the relationships between our
independent variables and firm growth at different
quantiles of the distribution of the growth rates, rather
than at the conditional mean only. Further, as is
normally the case when investigating firm growth
1998; Bottazzi and Secchi 2003)
, quantile analysis is
preferable over standard least squares for different
reasons linked to the distribution of the growth rates in our
sample (see Online Appendix A2).
Most of the applied literature adopting a quantile
regression approach stems from cross-sectional settings,
and for this reason, controlling for problems of
endogeneity arising from unobserved heterogeneity has
been difficult. Conversely, we follow recent
developments in a stream of the applied econometrics literature
that has attempted to overcome this major limitation
(Koenker 2004; Galvao 2011; Canay 2011)
we implement the procedure suggested by
who developed a method to estimate fixed effects
quantile regressions for panel data. The solution proposed
consists of a two-step estimator. In the first step, we
estimate our previous equation (1) as a standard linear
panel regression model via the within estimator
. From this model, we obtain the
predicted value depurated from the unobserved
^yit ¼ Growthit−μ^i
where μ^i ¼ E Growthit−Grdowthit is an estimate of the
unobserved heterogeneity term. In the second step, a
standard quantile regression model is implemented in
which the transformed dependent variable above (^yit) is
regressed on our relevant independent variables
and Hallock 2001)
Our dependent variable is the growth rate of employees
(Growthit), calculated as the difference between the
logarithm of firm i’s employees in year t and the
logarithm of employees in year t − 1
(Coad and Rao 2006;
(see Online Appendix A3 for details). In
addition to theoretical reasons (see Section 2), this
choice has also empirical motivations. Unlike other
(Delmar et al. 2003)
such as sales growth,
employment growth can capture growth performance
in recently constituted firms
(Clarysse et al. 2011)
Pat Greenit − 1
Pat Nongreenit − 1
Ageit − 1
Empit − 1
Inv Tangit − 1
Inv Intangit − 1
Growth of employees of firm
i in year t
(lnsizeit − lnsizeit − 1)
Stock of green patents of
firm i in year t − 1 (log
Stock of non-green patents of
firm i in year t − 1 (log
Number of years since
constitution of firm i in
year t − 1 (log
Number of employees of
firm i in year t − 1 (log
Investment in physical
capital of firm i in year
t − 1 (log transformed)
Investment in intangible
capital of firm i in year
t − 1 (log transformed)
of industry concentration
for industry j at time t
We have three main independent variables: (i) Pat
Greeni,t − 1, which is the logarithm of the stock (at time
t − 1) of environmentally friendly technologies (plus 1),
filed by firm i; (ii) Pat Nongreeni,t − 1 is the logarithm of
the stock of non-environmentally friendly technologies
(plus 1); (iii) Agei,t − 1 which measures the (log
transformed) age of company i at time t − 1, with the
difference between the current and its constitution date.
Despite its limitations as an innovation proxy, patent
data has been used by most of the recent research on
ecoinnovations because they are, on the one hand, more
widely available and more informative than R&D about
their environmental nature and, on the other hand, a more
robust indicator than questionnaire-based measures
(Arundel and Kemp 2009; Berrone et al. 2013)
. For the
identification of ‘green patents’ in particular, we have
relied on Marin and Lotti (2016) (see Online Appendix
A3). Both green and non-green technological variables are
defined as stocks, rather than flows (see Online Appendix
A3 for details). We do so because we expect a firm’s rate of
growth to be affected by the knowledge cumulated over
time and not only by its variation added in the recent and/or
(Bloom and Van Reenen 2002; Hall et al.
. This also helps reduce, at least partially, the possible
confounding effect of the persistency in technological
leadership (Denicolò, 2001) on firm growth, which cannot
be addressed by controlling for unobserved heterogeneity
We control for a set of variables that are often
included in growth rate regression models: (i) investments in
tangible (Inv Tangi,t − 1) and intangible (Inv Intangi,t − 1)
assets (at time t − 1), recognised by the literature to have
an important role in ‘accounting’ for the firm’s capacity to
; (ii) a measurement of size in terms of
number of employees of firm i at time t − 1 (Emp,t − 1),
used to retain the implications of the Gibrat’s law
Evans 1987; Hall 1987; Calvo 2006)
; (iii) an
HerfindahlHirschman index of industry concentration (Herfindahl
indexjt), which has been often found to play a relevant
role with respect to firms’ performance
(see Online Appendix A3 for details).
Finally, we include a set of eight dummy variables to
control for year effects. Table 1 shows the variables
included in the analysis and their sources.
4 The empirical literature does not point to unambiguous evidence on
the relevance of persistence in technological innovation, especially
when it comes to major innovation or patents
(Raymond et al. 2010)
In these cases, partial support for the presence of persistence emerges
when considering top innovators
(Geroski et al. 1997; Cefis 2003)
Descriptive statistics of the variables employed in the
empirical exercise are reported in Table 2. Table 3 reports
the bivariate correlations of the variables considered in the
analysis. No indication of significant multi-collinearity
among the independent variables was found (i.e. the
variance inflation factor ranges from 1.02 to 2.62, well below
the threshold level of 5).
The results of the quantile fixed effect estimations are
presented in Table 4, which shows the baseline model,
and in Table 5, which incorporates the role of the firm’s age
as a moderating factor in the relationship between
environmental (and non-environmental) patents and firm’s
5 Both tables report results of the
Parente and Santos Silva (2016
test to determine whether intra-industry correlation affects the
standard errors in our estimates. Results show that, apart from
the 50th percentile, all other percentiles (10th, 25th, 75th and
90th) are affected by intra-cluster correlation. Results reported in
the tables therefore use cluster-robust standard errors at industry
level (NACE rev. 1.1 2 digit codes).
Starting with the controls, as expected
(e.g. Coad and
, both tangible and intangible investments
significantly drive firm growth. As for size, smaller
companies show greater growth opportunities and
capacities, in agreement with the entrepreneurship
literature (Acs and Audretsch 2006). An increase in market
concentration (Herfindahl index) seems to favour firm
growth, although the effect is significant—and
positive—only at the 50th and 90th percentiles. This result
resonates well with the characterisation of the
‘Schumpeter Mark II’ pattern of innovation
and Orsenigo 1995)
—marked by an oligopolistic
context with high technological opportunities and
appropriability—which the former literature actually
identified in a section of the Italian national system of
, and that here appears to be
represented by fast-growing companies.
As far as firm’s age is concerned, the results of the
standard literature on the growth advantages of newly
(Coad et al. 2013; Barba Navaretti
et al. 2014)
appeared reversed across all quantiles in
Table 4: unexpectedly, older companies grow more than
younger ones. This result can be only be partially
explained by the specificity of our quantile
methodology. Most likely, its explanation lies in the characteristics
of our sample. Our sample consists of
innovationoriented manufacturing firms operating in a national
context, where new-born firms face structural
difficulties in taking off and surviving (Audretsch et al. 1999),
and where established incumbents usually obtain the
most radical innovation outcomes
i m p o r t an c e of ba n k s i n f i na n c i n g i n n o v a t i o n
(Benfratello et al. 2008)
also plays a key role in the
Italian context, and mature firms are more capable (e.g.
by reputation) of developing borrowing relationships for
(Gregory et al. 2005; Hartarska and
Gonzalez-Vega 2006; Carpenter and Rondi 2000; Magri
. In the same context, firm internationalisation and
innovation often entails a strong increase of competitive
pressure and failure risk (Giovannetti et al. 2013), and
maturity and foreign market experience increase the
chance of post-internationalisation survival
et al. 2000; Carr et al. 2010)
. Finally, the regime of
‘creative accumulation’ (Schumpeter Mark II) that
characterises the most competitive Italian industries
(e.g. motor vehicles and non-electrical machinery)
Year dummy variables have been included in all of the models. Bootstrapped standard errors are reported in parentheses. They are based on
1000 replications of the data
*p < 0.10, **p < 0.05, *** p < 0.01
(Malerba and Orsenigo 1995)
knowledge accumulation and innovation persistence
experienced by mature firms a larger impact, also on
the growth performance of sectoral systems of
(Chassagnon and Haned
2015; Oltra and Saint Jean 2009)
We now come to the core of our analysis. As Table 4
shows, the positive and significant coefficients of both
Pat Nongreen and Pat Green across the whole set of
percentiles confirm the role of green technology as a
driver of firm growth. This finding supports and extends
the emerging evidence on the business environmental
win-win situations enhanced by environmental
practices. Indeed, as we expected, the increase of product
value and the reduction of production costs they entail
(Ambec and Lanoie 2008)
actually seem to translate into
A more relevant aspect to consider is the comparison
between the coefficients of Pat Green and Pat Nongreen
across the quantiles of the distribution. This comparison
is crucial for assessing whether green technologies
provide a growth premium with respect to non-green
technologies or whether, instead, the effects of green
and non-green patents are not different. By running
appropriate statistical tests on the difference between
the two coefficients (tests are reported in Table 4), we
found that the difference between Pat Green and Pat
Nongreen is not homogeneous across the quantiles.
Specifically, it emerges that for the 25th, 50th and 75th
percentiles, green technologies have a significantly
larger effect (at a 99% level of confidence) on growth than
standard technologies, while for the 10th and the 90th
percentiles, green and non-green patents have
statistically similar effects on firm growth. In brief, the growth
premium of green over non-green technologies is not
unlimited and weakens when innovation efforts are
pursued either to survive (struggling firms) or to remain
among the growth ‘superstars’ (gazelles).
The picture becomes more nuanced when we
introduce interaction terms to capture the moderating role of
age (Table 5). While Pat Nongreen remains positive and
significant, except for the 10th and 25th percentiles, Pat
Green, per se, is not positive and becomes negative and
significant for the 75th and 90th percentiles. However,
Year dummy variables have been included in all of the models. Bootstrapped standard errors are reported in parentheses. They are based on
1000 replications of the data
*p < 0.10, **p < 0.05, ***p < 0.01
the effect of Pat Green, as shown in Table 5, should be
understood in relation to the age of the company, given
the contribution of the interaction between Pat Green
and Age, which is always positive and significant,
except for the 10th and 25th percentiles.
In other words, we find an apparently exclusive
capacity of older firms to translate eco-innovation into
growth. This is the second important result of our
analysis, which is possibly linked to the points of firm
maturity and eco-innovations discussed in Section 2.
First, as we said, older firms may be better equipped to
evaluate the uncertainty/risk and the actual marketability
of their eco-innovations, irrespective of their likely
disadvantages in terms of organisational inertia and
(Majumdar 1997; Sorensen and Stuart
2000; Criscuolo et al. 2012)
. Second, owing to better
access to finance
(Schneider and Veugelers 2010)
firms can have a higher capacity to cope with the cost of
(Gagliardi et al. 2016)
and with the
resources needed to engage in signalling, labelling and
certification efforts, which are often required to extract
value from investment in green innovations
. Third, older firms might have greater
pressures and incentives for renewing their older capital
vintages in an eco-sustainable manner—for example in
responding to a policy constraint
(Ruth et al. 2004)
also in light of their greater capacity to exploit internal
economies of scale and external knowledge sourcing
(Herriott et al. 1985; Levitt and March 1988; Ghisetti
et al. 2015)
. Finally, the persistence of the learning and
innovation patterns that characterises green technologies
(Sàez-Martínez et al. 2016, Chassagnon and Haned
can ‘reserve’ the growth impact to firms that are
capable of reaping the benefits of their path dependence.
While favouring older firms, the implications of
our results for entrepreneurial growth are quite
discouraging. When attempting to pursue the
heavily uncertain path of growth
(e.g. Coad et al.
, young companies are able to obtain
shortterm gains only from standard innovations, which
do not target external benefits associated to
environmental protection and are arguably less distant
from the traditional industrial knowledge base
(Ghisetti et al. 2015). Interestingly, these gains
occur for the central quantiles of the distribution,
as can be noticed from the negative and significant
coefficients on the interaction term Pat Nongreen
X Age in the 50th and 75th percentiles. For
rapidly or slowly growing companies, age does not
moderate the growth-driving effects of non-green
We further qualify the additional effect of green
technologies compared to non-green technologies for
the quantiles where the interaction between Pat Green
and Age is significant (Table 5, percentiles 50th to 90th).
For young firms (those with less than 5 years for the
50th and 75th percentiles of growth rate and below
10 years for the 90th percentile), a stronger association
can be determined between non-green technologies and
firm growth vs. green technologies and growth. On the
contrary, for more mature firms (i.e. those with more
than 20 years for the 50th and 75th percentiles of growth
rate and above 30 years for the 90th percentile), green
technologies exert a higher effect on firm growth
compared to non-green technologies (Figure A3 in the
Online Appendix provides a graphical representation).
These quantile-specific effects further confirm the
choice of a quantile approach as the most suitable to
identifying the different effects of the interplay between
green technology and age on firm growth.
In this paper, we examined the capacity of green
technologies to sustain firm growth, building upon the idea
that a firm’s capacity to grow is closely linked to its
ability to master technological knowledge and capture
the value of the innovation
(Mansfield 1962; Scherer
. While an extensive body of industrial
organisation and innovation literature has addressed the growth
impact of technology
(e.g. Audretsch et al. 2014)
, only a
few studies have examined the relationship between
green technologies and firm growth. Our contribution
is novel for two reasons. First, we assessed whether
green technologies, compared to non-green
technologies, affect the growth of firms with different growth
paces (e.g. struggling or rapidly growing). Second, we
considered whether green-based growth is affected by a
We adopted a novel econometric approach,
combining panel fixed effects with quantile regression
estimations. We thus simultaneously controlled for unobserved
heterogeneity (which is likely to affect firm growth) and
for the heterogeneity of the growth process, along the
distribution of growth rates.
Our analysis of a large sample of Italian firms
between 2000 and 2008 confirms the vital role of green
and non-green technologies in fostering firm growth, as
measured by the growth of employment. Moreover, the
results indicate a ‘win-win’ situation as green
technologies exert superior effects on growth than non-green
ones. The possibility to enter green markets, to decrease
production costs because of greater resource efficiency
(e.g. reduced material and energy use) and to reinvest
the relative extra returns from eco-innovating can justify
this result. However, our analysis shows that the
superior effect of green technologies does not extend to the
extreme percentiles of the growth rate distribution.
The second contribution of the paper pertains to the
moderating effects of age: the green growth path is
mainly taken by mature firms (age higher than 20 or
30 years), with the exception of the slow-growing ones.
Hence, more mature companies seem to be better
equipped to transform green technology into growth.
Although further research is required, we contend that
greater experience, fewer financial constraints and
exemption from issues related to the liability of newness
(e.g. Freeman et al. 1993)
—a set of aspects that are
particularly relevant in the Italian context—allow older
firms to engage successfully in complex and uncertain
technological projects, such as environmentally related
ones. These results are partially balanced by the positive
effects on young companies of non-green technologies,
which trigger short-term firm growth (for the central
quantiles), possibly because of their less complex and
These results hold important implications both for
management and for policy. Extracting value from green
technology and transforming it into higher growth is not
a ‘one-size-fits-all’ strategy. On the one hand, struggling
firms might not find it viable to engage in more complex
and costly green technological projects. On the other
hand, for the elite group of fast-growing companies, a
green orientation might not add to their portfolios of
already outperforming and possibly unique—compared
to their competitors—technological capabilities. As
said, our results suggest that the process of green-led
growth is a complex and costly one: only older
companies are sufficiently broad shouldered to pursue a growth
path based on environmental technology.
Building on our evidence, we also believe that our
results have relevant implications for policy makers. If
their short-run objective is to maximise the social impact
of public resources in supporting the transition towards
green forms of production, the main beneficiary group
should be made of relatively established firms, rather
than start-ups. This aspect should be considered when
implementing policies favouring innovative start-ups
(e.g. Mason and Brown 2013; European Commission
In conclusion, this is a first attempt at providing
empirical evidence for the relation between firm growth
and green technology. From a policy implications’
perspective, future research should investigate the
mechanisms that make growth particularly problematic for
young companies. Further research should also go
beyond patenting firms: patent data, although the most
diffused source of information for defining continuous
firm-level innovation variables
(e.g. Gagliardi et al.
does not capture all the innovations introduced
Acknowledgements Previous versions of this paper have been
presented at the following: workshop BBorn to be Green. The
Economics and Management of Green Start-Ups^, Southampton
Business School (UK) 21–22 May 2015; 2016 Italian Economic
Association Conference, Bocconi University of Milan (IT), 20–22
October 2016; 13th European Network on the Economics of the
Firm (ENEF) Meeting, University of Turin (IT), 13–14
; SPRU 50th Anniversary Conference, University of Sussex
; 2016 Conference of the Governance
of a Complex World (GCW) BInnovation, Employment and the
Environment^, INGENIO (CSIS-UPV), Valencia (ES), 22–24
June 2016; 2016 DRUID Conference, Copenhagen Business
School (DK) 13–15 June 2016. We are grateful to the discussants
and participants of these conferences, as well as to two anonymous
referees for their precious comments. We also acknowledge the
help and suggestions of Giovanni Marin and Alex Coad on data
and methods, respectively. Usual caveats apply.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided
you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons license, and indicate if
changes were made.
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