Green investment strategies and bank-firm relationship: A firm-level analysis

Abstract

This paper investigates the determinants of firm's green investment strategies in equipment for pollution control. It uses micro-data from a large survey of Italian manufacturing firms from 2001 to 2006. In particular, we test whether the length of the firm-bank relationship affects the firm's probability of investing in environmentally friendly equipment. Our findings show that a longer relationship with the main bank fosters firms' involvement in green investment strategies in order to reduce their environmental impact. Conversely, the presence of a multiple credit relationship could concretely hinder a firm's investments towards environmental innovations.

Full text

Volume 38, Issue 4
Green investment strategies and bank-firm relationship: a firm-level analysis
Pasquale Marcello Falcone
Bioeconomy i n Transiti on Research Group, IdEA, Unitelma Sapienza - University of Rome
Abstract
This paper investigates the determinants of firm's green investment strategies in equipment for pollution control. It
uses micro-data from a large survey of Italian manufacturing firms from 2001 to 2006. In particular, we test whether
the length of the firm-bank relationship affects the firm's probability of investing in environmentally friendly
equipment. Our findings show that a longer relationship with the main bank fosters firms' involvement in green
investment strategies in order to reduce their environmental impact. Conversely, the presence of a multiple credit
relationship could concretely hinder a firm's investments towards environmental innovations.
Citation: Pasquale Marcello Falcone, (2018) ''Green investment strategies and bank-firm relationship: a firm-level analysis'', Economics
Bulletin, Volume 38, Issue 4, pages 2225-2239
Contact: Pasquale Marcello Falcone - pasquale.falcone@unitelmasapienza.it.
Submitted: April 30, 2018. Published: December 02, 2018.

1. Introduction
Achieving a sustainable economy depends on investing sufficient capital to finance the
(possible) long-term transition of the real economy. It does this by focusing not only on a
restricted number of sectors (i.e. renewables, eco-innovations, bio-products, etc.), but also by
creating the basis for a sustainable financial system to finance and fuel this transition (Falcone
et al. 2018a).
Even though significant developments have been recorded in the last decade in greening the
economy, the amount of green investments still remains insufficient. The European
Environment Agency (2014) estimates that the amount of financial investments needed to attain
a low carbon economy ranges from US$300-400 billion per year up to 2020 for reducing gas
emissions. Under European regulations, Italy agreed to reduce greenhouse gas emissions to
20%, with respect to 1990 levels, by 2020. In view of these needs, and due to their intermediary
role in the economy, banks hold a unique position with regard to environmental goals. This is
because they might address or drive the economy through a sustainable allocation of funds. The
role played by finance and banking in boosting green investments in environmental innovations
(EI) is perhaps even more relevant than for traditional innovations, even though the literature
on EI adoptions has not taken into it account with adequate depth and breadth (Barbieri et al.
2016).
Our analysis tries to fill this gap by contributing to the sparse literature on the interactions
between the financial system and firms’ commitment on environmental goals. Specifically, the
novelty of this paper is to test whether the length of the firm-bank relationship affects the firm’s
probability of investing in environmentally friendly equipment. It uses micro-data from a large
survey of Italian manufacturing firms that matched information on firms and banks.
The rest of paper is organized as follows. Section 2 surveys the relevant literature. Section 3
describes our dataset, while Section 4 presents the empirical methodology. Section 5 reports
estimation results, and Section 6 concludes.
2. Related literature
Linking the ecosystem change with economic opportunities and social wellbeing has always
been a challenging work (Falcone and Imbert 2018; Cucchiella et al. 2017). While the relevance
and impact of environmental protection in producing environmental benefits is widely known
(Morone et al. 2019), discussions on the effects of environmental regulation on economic
performance have been a topic of debate among scholars for several years (Falcone 2014). A
commonly explored concern is up to what point endogenous and exogenous factors and
circumstances influence the relationship between firms’ environmental performance and their
economic outcomes. In contrast to the neoclassical wisdom, according to which environmental
aims and firm profitability are indeed incompatible, a new perspective, based on the assumption
that strict environmental protection may work as “win-win” policies, provides an innovation
opportunity for firms to gain long-term profitability and a competitive advantage (Porter and
Van der Linde 1995). This new perspective has stimulated a wide debate on the conditions
under which the Porter Hypothesis may emerge. A core part of the debate relies on the linkage
between environmental regulations and innovation; this linkage is recognised as a key
determinant that may lead to a positive effect on a firm’s economic performance (Costantini
and Mazzanti 2012; Lanoie et al. 2011).

2.1 Green investment determinants: firms’ specific characteristics vs. external factors
Despite the richness of the theoretical and empirical contributions, the analysis of the
relationship between environmental practices and a firm’s economic performance should be
improved to take the broad and diverse set of factors that affect the adoption of environmentally
oriented investments into account (Antonietti and Marzucchi 2014). In particular, in contexts
characterized by weak environmental regulatory frameworks, it is important to consider that
green investments might also be induced within the economic system (endogenously), rather
from regulatory institutions (Ghisetti and Quatraro 2013). Cucchiella et al. (2012) demonstrate
the relevance of making a portfolio risk explicit, and, through an optimal generation portfolio,
show as such risk can be mitigated by the diversification of investments in renewable energies.
Literature on corporate social responsibility (CSR) provides helpful tips for the identification
of endogenous drivers for firms to invest in green technologies. Specifically, Ambec and Lanoie
(2008) theoretically study how environmental practices contribute to a firm’s economic and
financial performance. First, a better environmental performance due to green investment
strategies can increase returns for firms by enabling: (i) a better access to “green” markets; and
(ii) a product differentiation strategy based on firm environmental reputation. Moreover, green
investment strategies can lead to a decrease in the cost of materials and energy use (e.g.
installation of PV), capital assets (e.g. by easing access to green or ethical mutual funds), and
cost of labour (e.g. by enhancing loyalty and commitment).
Firm’s’ investments in equipment for pollution control have gathered increasing attention from
policymakers and researchers. This led to a proliferation in the number of studies on the
determinants of the introduction of environmentally friendly technologies, mainly at the
microeconomic level. The empirical literature testing factors explaining a firm’s choice to
invest in environmental goals, has enlarged the analysis to the effects of environmental
practices that do not necessarily require environmental regulation. In this context, Antonietti
and Marzucchi (2014), focusing on the surveys of manufacturing firms conducted by Unicredit
bank covering the period 2001-06, empirically study the relationship between investments in
green tangible investments and firms’ export performance, relying on a rich firm-level dataset
on Italian manufacturing. They found that firms with higher productivity, induced among other
factors by green investments linking environmental and increased revenue aims, attain a higher
export performance. Haller and Murphy (2012) investigate the determinants of a firm’s current
environmental expenditure and their capital investment in equipment for pollution control in
Irish manufacturing industries. The main determinants for the two types of expenditure are
similar: larger, exporting and energy-intensive firms are more likely to invest in green
technologies for pollution abatement. Jaraitė et al. (2012), investigate the determinants of
environmental expenditures and investment in Swedish industrial firms. They found that larger,
more profitable and more energy intensive firms are more likely to spend and invest in the
environment. Aden et al. (1999), alongside firm-specific and regulatory factors, consider
community characteristics and related pressures in describing investments in pollution
abatement in Korean manufacturing plants. They find that plant specific factors are more
relevant than regulation and community characteristics in explaining green investment
strategies. Moreover, as emphasized by Heal (2008), there is growing evidence that firms
respond to other external pressures (e.g. local/interest group pressures, customer demand and
other social pressures) for voluntary over-compliance with environmental regulation by
investing more in green equipment than is required.
Collins and Harris (2002) investigated whether foreign-owned firms have a higher probability
of investing in pollution abatement than domestic firms in UK manufacturing industries. They
did not find a univocal relationship, but one that depended on the country of origin of the firm.

Later (2005), they analysed whether foreign-owned firms invested more than domestic firms
on pollution abatement in the UK chemical industry. Controlling for other firm characteristics,
they find that the effects may go in either direction depending on the type of pollution abatement
expenditure. Horbach (2008) found that improved “knowledge capital”, induced among others
by R&D and further education measures, triggers environmental innovation adoption.
Moreover, environmentally innovative firms in the past are also more likely to invest in
environmental innovations in the present. Given these specificities, Horbach et al. (2012)
extend the analysis to consider “Market-pull factors” to explain EI, including turnover
expectations, new demand for eco-products, past economic performance and customer benefits.
2.2. Green investments and finance
In this paper, instead, we investigate the impact of the firm-bank relationship on environmental
investment strategies. To this end, we consider green investments not derived solely from
external regulations or by other single specific determinants. In doing so, we focus on a broad
and interrelated set of economic and financial factors; this might not only involve regulations
and policy instruments, but also other firm characteristics and strategies.
Classical investment theories emphasized the key role of financial institutions as the optimal
mechanism for flowing funds from investors to businesses when information asymmetries
occur between them. The empirical literature that tests what hinders innovation adoption by
firms has extended the analysis to the bank-firm relationship. A particular strand of literature
on banks and innovation investigated the effect of banking development and its relationship to
lending on the adoption of innovation by Italian manufacturing firms (Brancati 2015;
Alessandrini et al. 2010; Benfratello et al. 2008). From these contributions, the importance of
the relationship lending on the adoption of innovations clearly emerges.
In their seminal paper, Petersen and Rajan (1994) point out that the length of bank-firm
relationships (i.e. number of years) positively affects the availability and cost of funds for the
firm. The availability of financial resources is particularly important to trigger green
investments (Falcone et al. 2017) and, thus, the adoption of environmental innovations giving
rise to a transition towards sustainability (Falcone et al. 2018b). However, green investments
are often hampered because potential investors struggle with imperfect information and thus,
the financial sector tends to constrain credit in markets characterized by such failure (Stiglitz
and Weiss 1981). Consequently, financiers show stricter lending policies for green investment
with respect to cases where relevant information is easily available. In this context, Ghisetti et
al. (2017), by focusing on SMEs and using a survey data at the EU level, investigate whether
financial barriers affect the adoption of environmental innovations by firms. Their main result
is that perceived financial barriers concretely hinder firm’s investments towards environmental
innovations. However, still lacking to our knowledge, the impact of relationship lending on the
firm’s likelihood to invest in environmental innovations still remains uninvestigated.
3. Data and Variable Definitions
3.1. Data sources
Our empirical investigation rests on a single dataset that was built from two different sources:
(i) the Survey on Manufacturing Firms (SMF) carried out by the Unicredit corporate bank; (ii)
the AIDA-Bureau van Dijk database (AIDA-BvD). While the latter (AIDA-BvD) provides
balance sheet information, the former (SMF) covers different areas of interests and gives
information on ownership structure, labour force, investments, R&D and innovation, export,
bank-firm relationship, etc.

To extract our data, we combined the surveys (IX) carried out in 2004, concerning the period
2001-2003, and in 2007 (X) with reference to the period 2004-2006
1
. The two surveys provide
information on representative samples of 4,289 and 5,137 Italian manufacturing firms
respectively. The SMF considers the whole universe of firms with more than 500 employees,
while firms with 11-500 employees were selected looking at the geographical area of location,
firm size, and sector of economic activity (Pavitt classification). After merging the two
datasets, the pooling sample has information on 9,426 firms. Then, we excluded firms with
missing values, or those with inconsistencies or negative values for some firm specific variables
(i.e. value added, labour cost, etc.).
3.2. Dependent variable
Our dependent variable concerns the firm’s green investment strategies in equipment for
pollution control. Specifically, we employ the following qualitative question included in SMF:
“Which are the main objectives pursued by firm’s investments in new machinery and
equipment during the time-period 2001-2003 and 2004-2006?”
Particularly, the X survey (2004-2006) asked firms to evaluate the importance of the investment
strategies pursued by identifying the three most important objectives, in decreasing order,
related to their investment decision among seven alternatives: (1) improving the quality of
existing products; (2) increasing the production of existing products; (3) introducing new
products; (4) lowering the environmental impact; (5) lowering the use of raw materials; (6)
reducing the employment of labour inputs; and (7) others. We then define the dummy
HIGHMED_GINV, equal to 1 if the firm recognize the 4th investment decision (i.e. lowering
environmental impact) as one of the top two alternatives (e.g. medium-high importance), 0
otherwise. It is worth nothing that, different from the X survey, the previous one (2001-2003)
asked the question in a slightly different way. Specifically, while the X survey forced the
respondents to provide a hierarchy of investment strategies, the IX survey allowed them to
indicate for each of the seven alternatives of investment the level of importance (i.e. high,
medium and low), creating a potential overlapping issue among investment variables. However,
as Table 1 shows, this does not seem to be a problem for our empirical investigation because
the level of pairwise correlation is not of concern.
Table 1: Correlation among investment variables (IX Survey)
1
2
3
4
5
6
Object
1. Product quality improvement
1
2. Increasing existing production
0.3005***
1
3. Introduction of new products
0.2047***
0.1508***
1
4. Lower environmental impact
0.1288***
0.1727***
0.1524***
1
5. Less raw material
0.1915***
0.1442***
0.1608***
0.3050***
1
6. Less employment
0.0710***
0.1643***
0.1154***
0.2208***
0.2156***
1
7. Other
- 0.2786***
- 0.2024***
- 0.1599***
- 0.2232***
- 0.2250***
- 0.1299***
Source: author’s elaboration
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1
The main reasons for using this limited and dated time span is that, since we aim at investigating the determinants
of firm’s green investment strategies in equipment for pollution control, no data are available for a more recent-
crisis period. In fact, the current literature (see among the others D’Aurizio et al. 2015 and Bolton et al. 2016),
mainly concentrating on the last financial crisis and the mechanisms affecting the access to credit of businesses,
does not consider green investments strategies.

In order to standardize the information concerning our dependent variable between the two
surveys, selection work has been carried out. In particular, following Antonietti and Marzucchi
(2014) we construct our HIGHMED_GINV dummy variable; this is equal to 1 if the firm
assigns a high level of importance to the 4th investment decision (i.e. lowering environmental
impact), and does the same with at most one of the other six objectives. The rationale behind
our empirical strategy is given by the need to better capture the firm’s attitude towards green
investments, disregarding those “enlarged” investment choices (e.g. the case in which firms
assign high importance to several investment objectives).
3.3. Potential determinants of firm’s green investment strategies
Based on earlier empirical and theoretical contributions, we consider the length of the credit
relationship between the firm and its main bank as a key explanatory variable. Those firms who
missed reporting such information are not considered for our empirical investigation.
Additionally, we focus also on another indicator of the bank-firm relationship, namely the
number of banks involved in a credit relationship with the firm. Moreover, we take into account
firm-specific control variables such as size (number of employees), age (number of the years
from its foundation), labour productivity, and return on investment (ROI). We also control for
the firm’s attitude towards research and development activities, and some financial indicators
such as leverage, membership of a credit consortium and the firm’s credit rationing. Other
control variables refer to the geographical location of firm to better capture regional
peculiarities (i.e. south, north-east and north-west), and sector specific effects for technological
characteristics of the firms. Table 2 below displays the whole set of considered variables with
related description and source.
Table 2: Variables: description and source
Variables
Description
Source
Dependent variable
Green investments
Dummy that takes the value 1 if the firm has declared
to pay a medium-high attention towards investment
strategies aimed at reduce the environmental impact of
the firm activities; 0 otherwise
Capitalia Survey IX, X
Endogenous variables:
Relationship length
Log of the number of years of the relationship between
the firm and its main bank
Capitalia Survey IX, X
Instrumental variable
Saving banks in 1936
Number of saving banks per 10,000 citizens in the
region in 1936
Guiso et al. (2004)
Exogenous variables
Credit rationed
Dummy that take the value 1 if the firms demanded
more credit without obtaining it.
Capitalia Survey IX, X
Number of banks
Log of the number of banks with which the firm
entertains credit relationships
Capitalia Survey IX, X
Incentives and tax relief
Dummy that take the value 1 if the firms received
incentives and tax relief
Capitalia Survey IX
Labour productivity
Log of value added per worker
Aida and Capitalia
Survey IX, X
Roi
Return on Investment
Aida
Roe
Return on Equity
Aida
Value added
Average of the firm’s value added in the period 2001-
2003
Aida
Age
Log of the number of years of firm from its foundation
Capitalia Survey IX, X
Employment
Log of the number of workers
Capitalia Survey IX, X
Firm size
Variable that take value 1 (11-20 workers); 2 (21-50);
3; (51-250); 4 (>250)
Capitalia Survey IX, X

Traditional sector
Dummy that takes the value 1 if the firm belongs to
the traditional sector (Pavitt taxonomy); 0 otherwise
Capitalia Survey IX, X
Scale intensive sector
Dummy that takes the value 1 if the firm belongs to
the scale intensive sector (Pavitt taxonomy); 0
otherwise
Capitalia Survey IX, X
ISO9000 certified
Dummy that takes the value 1 if the firm is ISO9000
certified; 0 otherwise
Capitalia Survey IX, X
Export
Dummy that takes the value 1 if the firm exported; 0
otherwise
Capitalia Survey IX, X
Consortium
Dummy that takes the value 1 if the firm belong to a
consortium; 0 otherwise
Capitalia Survey IX, X
Group
Dummy that takes the value 1 if the firm belong to a
group; 0 otherwise
Capitalia Survey IX, X
Research and Development
Dummy that takes the value 1 if the firm has
incurreded expenditure on Research and development;
0 otherwise
Capitalia Survey IX, X
Science Based
Dummy that takes the value 1 if the firm belongs to
the science based category (Pavitt taxonomy); 0
otherwise
Capitalia Survey IX, X
Located in an industrial
district
Dummy that takes the value 1 if the firm is located in
an industrial district; 0 otherwise
Capitalia Survey IX, X
South
Dummy that takes the value 1 if the firm is located in
a region South of Rome, with Lazio excluded; 0
otherwise
Capitalia Survey IX
Northwest
Dummy that takes the value 1 if the firm is located in
the regions Emilia Romagna, Veneto, Friuli, Trentino
Alto Adige; 0 otherwise
Capitalia Survey IX, X
Northeast
Dummy that takes the value 1 if the firm is located in
the regions Lombardia, Piemonte, Liguria, Valle
d’aosta; 0 otherwise
Capitalia Survey IX, X
Leverage
Ratio of the financial debt to debt plus net capital
Aida
Source: author’s elaboration
3.4. Descriptive statistics
Table 3 shows the descriptive statistics for the variables used in this analysis. Separately, the
table also provides the summary statistics for the firms involved in green investment strategies.

Table 3: Descriptive statistics
All observations
If highmed_ginv = 1
Label
Variable description
Obs.
1st
percentile
99th
percentile
Mean
S.D.
Obs.
1st
percentile
99th
percentile
Mean
S.D.
high_med_ginv
Medium high attention towards green
investments
8589
0
1
0.099
0.299
855
1
1
1
0
num_banks
Log of the number of banks involved in
credit relation with the firm
8188
0
3.02
1.51
0.61
844
0
3.22
1.77
0.64
relat_lenght
Log of the number of years of
relationship
7048
1.12
4.12
2.72
0.68
766
0
4.18
2.29
0.82
cred_ration
Firm credit rationed
7771
0
1
0.057
0.22
819
0
1
0.098
0.18
ReS
Research and Development
7984
0
1
0.61
0.48
855
0
1
0.59
0.49
Employment
Log of the number of workers
7441
2.34
7.26
3.52
1.08
795
2.88
7.24
4.11
0.92
Age
Log of the number of years from the
firm’s foundation
8334
1.33
5.58
3.18
0.64
834
1.46
5.58
3.68
0.74
Lab_prod
Log of the output per worker
8178
5.56
9.38
6.814
0.18
806
5.64
9.20
6.643
0.25
Consortium
Firm belong to a consortium
8549
0
1
0.042
0.11
852
0
1
0.06
0.17
Roi
Return on investment
8129
-8.26
38.40
11.31
7.58
799
-6.02
37.83
12.54
7.40
Leverage
Ratio of financial debt to financial debt
plus net capital
8261
0.47
0.99
0.88
0.12
796
0.46
0.96
0.85
0.14
Ind_dist
Firm is located in an industrial district
8233
0
1
0.47
0.49
812
0
1
0.49
0.44
South
Firm is located in a region south of
Rome (Lazio excluded)
8589
0
1
0.15
0.33
855
0
1
0.12
0.31
North-west
Firm is located in the regions
Lombardia, Piemonte, Liguria, Valle
d’aosta
8589
0
1
0.37
0.45
855
0
1
0.39
0.48
North-east
Firm is located in the regions Emilia
Romagna, Veneto, Friuli, Trentino Alto
Adige
8589
0
1
0.29
0.41
855
0
1
0.35
0.46
Trad_sect
Supplier-dominated (Pavitt)
8589
0
1
0.37
0.48
855
0
1
0.42
0.48
Hightech_sect
Science based sector (Pavitt)
8589
0
1
0.05
0.17
855
0
1
0.09
0.06
Scale_sect
Scale intensive sector (Pavitt)
8589
0
1
0.17
0.34
855
0
1
0.22
0.23
Source: author’s elaboration

Looking at the whole sample, Table 3 reveals that, during the period of our investigation (2001-
2006), on average, firms are in business for 24 years with a number of employees equal to 34
2
.
More than 60% of firms are involved (directly or not) in R&D activities, and 5% of them belong
to a science-based sector (Pavitt taxonomy); 29% are located in the more developed northeast
of Italy. Of the total, only 4% adhere to a credit consortium, and around 6% of firms reported
to be credit rationed (i.e. they demanded more credit without obtaining it). Regarding our key
explanatory variable, the average length of the firm-bank credit relationship is 15 years and
ranges from 3 years (1st percentile) to 62 (99th percentile), while the number of banks involved
in a credit relationship with each firm is, on average, 5 and ranges from 1 to 20.
Looking at the right hand side of the above table, we can observe that about 10% of the firms
identified green investment in equipment for pollution control as one of the most important
investment strategies. It is evident, from the table above, that firms involved in environmental
investments are bigger and older than the whole universe of firms, with an average of 61
employees and 39 years respectively. They are prevalently located in the northwest of the
country (40%) and almost 10% of them stated that they were credit rationed. The sectorial
allocation reveals that firms involved in supplier-dominated sectors (e.g. textile and agriculture)
and scale intensive sectors (e.g. automotive sector), report a greater pursuit of environmental
objectives in their investment strategies, respectively 48% and 23% on average. As to our key
explanatory variables, data show that the average duration of the credit relationship between
the main bank and firm decreases to 10 years, while the average number of banks with whom
each firm has established a credit relationship increases to 6.
4. Empirical methodology
The question we address in this analysis is: does the length of a bank-firm relationship affect
firms’ attitude to invest in order to reduce their environmental impact?
To answer this question, we estimate a pooled probit model in which the probability that a firm
i undertakes investment strategies aimed at lowering their environmental impact can be
expressed as:
!"#$
%
&'( )
*
( +
%
,-.-/ 0-1-/ 2-3 4
* (1)
where
+
is the normal distribution function,
&'
is a binary variable (that is equal to 1 if the firm
undertakes environmental investments, 0 otherwise),
.-
is the length of the bank-firm credit
relationship,
0-
denotes a vector of controls, and
2-
is the residual in the “green investment
equation” (1).
The length of the firm-bank relationship represents our most relevant concern because it could
be endogenous to the borrower’s green investment choices. Basically, because of the relative
newness of some environmental innovations, the classical financial sector often lacks adequate
experience and information to efficiently appraise the technical and market performance of new
green projects (Volz et al. 2015). In this vein, banks might be motivated to have a long
relationship with a more profitable firm: accordingly, the increase of the probability to green
invest could be interpreted as the cause rather than the effect of relationship lending. To deal
with potential endogeneity of the regressors, we consider a set of firm specific controls
including both economic and financial indicators (e.g. roi, leverage, etc.), and then some firm’s
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2
For the variables expressed in logarithmic we took the antilog of the number reported in table.

qualitative characteristics (e.g. age, size, sector, etc.). Moreover, in order to check for possible
endogeneity between dependent and independent variables, we employ the instrumental
variable (IV) method. Therefore, we look for a variable that could affect the duration of the
credit relationship between bank and firm, but does not influence the firm investment strategies;
therefore, it is not correlated with the residual of the “green investment equation” (1).
As argued in Herrera and Minetti (2007), the length of the credit relationship could partially
explain the strength of the relationship because of the presence of multi-credit relationships.
Hence, a firm can borrow from other banks even if it has a privileged relationship with a
particular bank. In this perspective, and following De Bonis et al. (2015), we check for a further
robustness analysis accounting for the number of bank relationships a firm enjoys. We also
present linear regression results.
5. Results
In Table 4 we report estimation results without accounting for endogeneity. Specifically, the
first two columns list the value of the coefficients and standard errors for the probit model
specification, while the other two columns present OLS estimated coefficients and standard
errors. Geographical, industry and survey dummies are taken into account while estimating the
models.
Notes: Three, two and one star (*) means, respectively, a 99%, 95% and 90% level of significance. The dependent
variable Pr(highmed_ginv) is a dummy variable, equal to 1 if the firm undertakes environmental investments, and
0 otherwise. All regressions include geographical, industry and survey dummies and a constant.
Source: own elaboration
In Table 4, regressions present the estimations for the determinants of the firm’s green
investment strategies. Our main interest is in the length of the firm–bank relationship. This
variable is statistically significant at 10% of significance level for both model estimations.
Therefore, the duration of the firm-bank relationship is associated with a higher probability of
firms realizing green investments.
Table 4: Determinant of the firm’s green investment strategies
Pr(highmed_ginv)
OLS
Probit
Coeff.
S.E.
Coeff.
S.E.
relat_lenght
.0715*
.0019
.0625*
.0321
cred_ration
- .2855***
.0002
- .3212**
.0004
ReS
.0923**
.0231
.0876***
.0228
Employment
.0002***
.0002
.0322*
.0212
Age
.0019***
.0031
.0218**
.0123
Lab_prod
0.3758*
.0245
0.4328**
.0449
Consortium
0.2123
.0002
0.1872*
.0009
Roi
.0425
.0412
.0216
.0543
Leverage
-.3675**
.0534
-.4123*
.0579
Ind_dist
.0324
Geographical dummies
yes
yes
Sectorial dummies
yes
yes
Survey dummies
yes
yes
R2
0.09
0.02
Observation
5732
5736

With regard to the firm-specific characteristics, as predictable, we found that bigger, older,
more profitable and more innovative firms are more likely to invest in environmentally friendly
equipment (the estimated coefficients of employment, labour productivity, age and R&D are
positive and statistically significant), while credit constrained and indebted firms encounter
more difficulties in pursuing green investment strategies.
As a further analysis we check for the presence of potential endogeneity issues. We account for
the possible existence of endogeneity in the relationship between the choice of green investment
strategies and the length of the firm–bank relationship. The idea is to find a variable which
could influence the bank-firm relationship length but is not able to affect the choice of firms to
green invest; therefore, is not correlated with the residuals of our equations. In particular,
following Guiso et al. (2004), we take the regional banking structure in Italy in 1936 as a
reliable exogenous factor since it is not correlated with the historical development of the Italian
banking system (as it was due to regulation). In Table 5 we report the first stage regression for
the length of the firm–bank relationship.
Notes: Three, two and one star (*) means, respectively, a 99%, 95% and 90% level of significance. The dependent
variable relat_lenght is the Log of the number of years of relationship between firm and its main bank. All
regressions include geographical, industry and survey dummies and F-statistic for the Wald test of excluded
instrument.
Source: own elaboration
The null hypothesis of excluded instruments is rejected at 5% confidence level. The F-statistic
exceeds the conventional critical values, therefore the identified instrument is relevant
3
. The
presence of savings banks in the region in 1936 has a positive link with the length of the firm–
bank relationship. The estimates obtained also suggest a strong and positive link between the
R&D carried out by firm and the relationship length with its main bank. One possible
interpretation of this finding is that a higher R&D increases the main bank's power and firm's
switching costs. Thus, more R&D implies that firms have a lower incentive to change the main
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!
3
Staiger and Stock (1997: 557-586) proposed a «rule of thumb» when we have only one endogenous variable on
the right-hand side of the equation. If the F-statistic is greater than ten, the instrument is strong.
Table 5: Determinant of the relationship length
relat_lenght
OLS
Coeff.
S.E.
Saving banks
.0625**
.0321
Cred_ration
- .3212*
.0019
ReS
.0536***
.0002
Employment
- .0022*
.0021
Age
.0218**
.0202
Lab_prod
0.2328*
.0031
Consortium
0.1872*
.0245
Roi
.0216
.0002
Leverage
-.0415*
.0412
Geographical dummies
yes
Sectorial dummies
yes
Survey dummies
yes
R2
0.2566
Observation
5087
Wald test of excluded
instruments, F-statistic
10.22**

bank. Table 6 presents the IV results, corresponding to our main estimation (i.e.
Pr(highmed_ginv)) instrumenting for our endogenous variable (i.e. relat_lenght).
Notes: Three, two and one star (*) means, respectively, a 99%, 95% and 90% level of significance. The dependent
variable Pr(highmed_ginv) is a dummy variable, equal to 1 if the firm undertakes environmental investments, and
0 otherwise. All regressions include geographical, industry and survey dummies, F-statistic for the Wald test.
Source: own elaboration
The objective here is to demonstrate how results might change as we move from an OLS and
Probit estimations to IV estimations. Although the sign of the coefficient of the main variable,
(i.e. relationship length) does not change, the level of significance increase considerably,
moving from 10% to 1%, when IV is employed for both model estimations. Moreover, the
estimated coefficient increase to (0.1722) and, since we have instrumented for it, it represents
now the causal effect. This result is consistent with the idea that long term relationship between
banks and firms promotes a sense of trust among parties, which would have a positive effect on
green investing. At a glance, with regard to firm-specific characteristics, the main determinants
of green investment strategies are: credit constraints, age, R&D, return on investment, leverage
and credit consortium. In particular, being part of a credit consortium has now a positive and
significant relation with the probability for a firm to green invest. This is in line with the objects
of the mutual guarantee consortia (MGC), a financial institution well developed in Italy,
established to alleviate the difficulties that SMEs face when they ask for a bank loan.
In order to test whether the above-mentioned findings are heightened or attenuated by other
relevant firm-specific characteristics, and following De Bonis et al. (2015), we check for the
impact of the presence of multiple credit relationships. Indeed, a firm can borrow from different
banks even if it could maintain a privileged relationship with a specific one. This entails that
the duration of credit relationships could not fully capture the degree of informational closeness
between the firm and its main bank. Table 5 confirms our previous findings on the impact of
the credit relationship on the green investment strategies pursued by firms
Table 6. Determinant of the firm’s green investment strategies: IV estimations
Pr(highmed_ginv)
IV
IV- Probit
Coeff.
S.E.
Coeff.
S.E.
relat_lenght
.1722***
.0021
.1625***
.0128
cred_ration
- .1155***
.0004
- .1112**
.0009
ReS
.1322**
.0342
.1287***
.0173
Employment
.0021
.0005
.0654
.0279
Age
.0024***
.0032
.0412**
.0876
Lab_prod
0.3758
.0432
0.5328**
.0234
Consortium
0.2123***
.0023
0.1872*
.0008
Roi
.0428**
.0234
.0519**
.0278
Leverage
-.1674**
.0238
-.2124*
.0527
Geographical dummies
yes
yes
Sectorial dummies
yes
yes
Survey dummies
yes
yes
Observation
5732
5736
Wald test of excluded
instruments, F-statistic
11.40**
10.23**

Table 7: Determinant of the firm’s green investment strategies
Pr(highmed_ginv)
Probit
OLS
Coeff.
S.E.
Coeff.
S.E.
relat_lenght
.0928*
.0023
.0967*
.0028
num_banks
-.0826**
.0012
.0754**
.0122
cred_ration
- .2215***
.0202
- .1112**
.2004
ReS
.0236*
.0235
.0367*
.0268
Employment
.0653
.0032
.0237*
.0421
Age
.0311**
.0123
.0226**
.0129
Lab_prod
0.0322
.0011
0.0265
.0021
Consortium
0.1150***
.0987
0.0641***
.0032
Roi
.0825*
.0412
.0212
.0544
Leverage
-.1834
.0855
-.1277
.0657
Ind_dist
-.0342*
.0012
-.0287
.0034
Geographical dummies
yes
yes
Sectorial dummies
yes
yes
Survey dummies
yes
yes
R2
0.09
0.07
Observation
5453
5464
Notes: Three, two and one star (*) means, respectively, a 99%, 95% and 90% level of significance. The dependent
variable Pr(highmed_ginv) is a dummy variable equal to 1 if the firm undertakes environmental investments, and
0 otherwise. All regressions include geographical, industry and survey dummies and a constant.
Source: own elaboration
Specifically, the number of banks is statistically significant in the Probit estimations, and has a
negative effect on green investments. One possible interpretation is that green investment is
often hampered because potential investors struggle with imperfect information (Volz et al.
2015). As the number of financing banks increases, the information tightness between firm and
banks, decreases. Therefore, the presence of a multiple credit relationship could concretely
hinder a firm’s investments towards environmental innovations.
4. Conclusions
Using data on Italian manufacturing firms, this study addressed the role of the duration of the
credit relationship between the firm and its main bank in affecting the decision of the firm to
invest in environmentally friendly equipment. Estimation results show that the duration of the
firm-bank relationship is associated with a higher probability of a firm’s green investment
strategies. Conversely, the presence of a multiple credit relationship could concretely hinder
firm’s investments towards environmental innovations. With regard to the firm-specific
characteristics, bigger, older, more profitable and more innovative firms are more likely to
invest in environmentally friendly equipment, while credit constrained and indebted firms
encounter more difficulties in pursuing green investment strategies. Overall, these findings
seem to endorse the idea concerning the importance of the relationship lending on the adoption
of environmental innovations.
The main limitation of this paper rests on restricted and dated timeframe of available data.
Indeed, it does not allow investigating whether the recent financial crisis, that heavily impacted
the entire bank system and, because of the credit crunch, have affected the firm-bank
relationship. Moreover, the cross-sectional and survey nature of our data does not permit
generalizing our findings.
Further lines of research could aim at extending our investigation by focusing on more
representative longitudinal data, which would better account for more recent exogenous shocks
(e.g. financial crisis).

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