Journal of Business Research 149 (2022) 161–177
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Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
Firms’ ESG reputational risk and market longevity: A firm-level analysis for
the United States
Irene Fafaliou a, b, d, Maria Giaka a, b, Dimitrios Konstantios a, Michael Polemis a, b, c, d, *
a
Department of Economics, University of Piraeus, 185 34 Piraeus, Greece
Entrepreneurship, Technology, and Economic Strategy Lab, University of Piraeus, Piraeus, Greece
c
Hellenic Competition Commission, Athens, Greece
d
Hellenic Open University, Patras, Greece
b
A R T I C L E I N F O
A B S T R A C T
JEL classification:
G1
G3
L2
M14
O1
This study examines the impact of environmental, social, and governance (ESG) reputational risk on a sample of
listed firms’ market longevity. Using a novel panel dataset consisting of US firms over the period 2007–2019, we
perform dynamic empirical analysis to quantify the underlying relationships between firms’ ESG reputational
risk and market longevity. We argue that ESG reputational risk has a negative impact on firm growth oppor­
tunities, mitigating thus market longevity. The empirical findings survive several robustness checks, providing
useful managerial implications for stakeholders and market participants..
Keywords:
Sustainability
ESG reputational risk
Longevity
Survival Analysis
1. Introduction
Profit is not the only element that takes a company further down the
road. Investors usually "oblige" firms to decouple their growth prospects
from environmental degradation, untransparent contributions to soci­
ety, and unethical governance, meeting thus profit and social goals
(Busch et al., 2016; Patel et al., 2021). Regulators, governments, and
Non-Governmental Organizations (NGOs) powered by social media
increasingly put the spotlight on firms’ operational excellence relating
to environmental, social, and governance (ESG) practices (Ahlström &
Monciardini, 2021)..
More specifically, consumers are now demanding high standards of
ESG and quality of employment from firms (Godfrey, 2005). Regulators
and policymakers are more interested in ESG because they need the
corporate sector to help them solve problems such as environmental
pollution and workplace diversity (Yan et al., 2019). The investor
community has also become much more interested in the ESG prospect
(Busch et al., 2016).
One can identify at least five sources of fundamental business value
behind the relationship between ESG risks and financial outcomes. The
first is top-line growth. Companies with a stronger ESG proposition tend
to attract customer loyalty and new customer segments (Albuquerque
et al., 2019). On the business-to-business side, lies an additional link.
Large companies are seeking to channel ESG through their value chain
(Drempetic et al., 2019). For example, suppliers to the world’s largest
retailers, display a strong sustainability proposition on plastics, pack­
aging, water use, and so on. The second source relates to cost; more
resource-efficient (e.g., more water-efficient) firms have generally had a
lower unit-cost structure (Derwall et al., 2005). The third relates to
regulatory relationships. Firms that are responsible for their assets’
environmental footprint decrease the chances of an adverse, punitive
regulatory outcome; therefore, there is potentially regulatory value here
(Ansari et al., 2013). The fourth source is talent. Nowdays, newer re­
cruits and millennials demand purposeful work and if companies can
meet that need, then they will be able to attract and retain that talent,
and likely enjoy higher productivity in the workplace (Greening &
Turban, 2000). Lastly, the fifth source is investment optimization. There
are enormous opportunities for ESG-related investments (Fatemi et al.,
2015; Ioannou & Serafeim, 2014). For example, there is a huge demand
for technology that could improve air quality (Wang, 2007); conversely,
there is a downside risk of holding assets that become stranded (e.g.,
coal assets and oil tankers).
* Corresponding author.
https://doi.org/10.1016/j.jbusres.2022.05.010
Received 13 April 2021; Received in revised form 3 May 2022; Accepted 7 May 2022
Available online 19 May 2022
0148-2963/© 2022 Elsevier Inc. All rights reserved.
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Stakeholders (consumers, employees, and investors) prefer trans­
parent firms, as economies move to more responsible accounting, con­
cerning firms’ ESG risks to avoid costly fines (Eccles et al., 2014), which
can have a major impact on firms’ performance and profits (McWilliams
& Siegel, 2001). In addition, the firm’s ESG reputation is of utmost
importance for its status (Brown & Dacin, 1997; Cornell & Shapiro,
1987; Hammond & Slocum, 1996), fundraising, and consequently in
fending competition pressures and survival in the market. Nevertheless,
there is a dearth of studies when it comes to a firm’s ESG reputational
risks and market longevity.
The present paper aims to investigate the relationship between firm’s
ESG reputational risk and market longevity.1 To our knowledge, this
study is the first attempt in the field. Guided by the agency theory , we
argue that a firm’s ESG reputational risk is a signal, that the market
evaluates and eventually responds to.2 If investors perceive ESG as an
agency-cost mitigating tool and a value-increasing attribute for a firm,
then we expect that firms with low ESG reputational risks will stay
longer in the market . In contrast, if investors perceive ESG as an agencycost aggravating tool and a value-decreasing attribute for a firm, then we
anticipate that firms with high ESG reputational risk stay shorter in the
market .
It is also noteworthy that ESG reputational risks may adversely affect
the equity raising and growth dynamics of the firm. This happens since
increased ESG reputational risks, lower the firm’s potential in raising
new equity due to the fact that investors may be reluctant to invest in
risky businesses. As a result, restricted financing options may impede the
growth opportunities of the firm. Overall, firms with ESG reputational
risks seem to have limited access to external financing and still have
reduced growth opportunities.
The effectiveness of ESG performance on corporate financial out­
comes has been thoroughly investigated by researchers and academi­
cians. However, a significant gap remains uncultivated. Specifically, the
existing studies have shed light on the relationship between ESG/CSR
performance and corporate innovation output or firm’s value neglecting
the impact on market longevity and growth dynamics of the firm (see
among others Tang, 2022; Gillan et al., 2021). Based on a panel of 1,528
US-listed firms over the period 2007–2019 we seek to contribute to this
nexus by employing the agency theory to address the following research
questions: a) Does an ESG-oriented firm is coupled with limited access to
external financing? b) Do firms with high ESG-reputational risks face
reduced growth opportunities? and c) Do firms’ ESG reputational risks
associate with less market longevity.
The motivation for studying US firms’ market longevity is the sub­
stantial decrease of the listed companies over the last 20 years.3 This
drop highlights two issues; first, the importance of this delisting phe­
nomenon and, second, the growing need to study the factors that may
contribute to its reduction. We conjecture that ESG reputational risk
could shape a firm’s market resilience and longevity.
Our study relates and contributes to two important strands of
research. We first add to a recent literature venue that studies ESG in the
context of the agency theory. This literature argues that responsible ESG
firms align the interests of managers, shareholders, and other stake­
holders (Montiel et al., 2020). ESG performance is linked with increased
firm value through a variety of channels, such as avoiding myopic de­
cisions and strengthening market positions (Benabou & Tirole, 2010),
attracting customers and providing employees with incentives for
greater productivity (Baron, 2001), valuable product market differen­
tiation and insurance against event risk in the long run (Albuquerque
et al., 2019). Firms with low ESG reputational risks achieve broader
societal goals, and profit (Dyck et al., 2019; Ferrell et al., 2016) miti­
gating thus investor concerns about value-decreasing, agency-driven
uses of ESG.
Second, we add to the literature that studies the impact of corporate
factors such as leverage level (Rajan & Zingales, 1998), capital structure
(Chung et al., 2013), specialist CEOs (Gounopoulos & Pham, 2018),
institutional quality (Baumöhl et al., 2019) on firms’ exiting from the
organized markets. However, little is known about the impact of a firm’s
ESG reputational risk - an important factor for transparency, account­
ability, and risk management - on the market longevity. Our study ex­
pands this literature by underling the importance of financial
materiality, in a non-financial context such as a firm’s ESG reputation
risk, in investors’ decisions in holding stocks of companies that comply
and excel in this aspect.
The rest of this paper unfolds as follows. Section 2 briefly discusses
the theoretical background and develops the testable research hypoth­
eses of the study. Section 3 presents our sample and data. Section 4
presents the methodological framework, while Section 5, discusses the
main empirical findings of the study. Section 6 conducts the necessary
robustness checks. Finally, Section 7 concludes the paper, providing
theoretical and managerial implications alongside the avenues for future
research.
2. Theoretical underpinnings and hypotheses development
The value of an investment is not just about sheer returns. An
increasing number of corporate stakeholders prefer investments or funds
that are not only profitable over the long term but also reflective of their
social values and make a positive impact on the world at large (Dyck
et al., 2019; Ferrell et al., 2016; Hartzmark & Sussman, 2019; Starks
et al., 2017). A 2020 report from the US Sustainable Investment Foun­
dation (SIF) shows that sustainable investing assets in 2020 account for
$17.1 trillion of the total US assets under professional management; this
is a remarkable sum that amounts to an increase of more than 40% since
the start of 2018 ($12.0 trillion).4 Around the globe, a third of all pro­
fessionally managed assets are subject to environmental, social, and
governance ESG criteria.5
However, in the presence of information asymmetry, the agency
problem arises due to a conflict of interest between the principal (i.e.,
shareholders) and the agent (i.e., managers) (Jensen & Meckling, 1976).
Agency problems can manifest through non-value-maximizing in­
vestment strategies and managerial self-dealing (Shleifer and Vishny,
1989, 1997). Investments to improve a firm’s ESG performance could be
a signal of agency problems in a firm. Outsiders (e.g., public policy,
evolving legal requirements, key stakeholders) with no financial stake
and who do not bear the costs of such commitments press for im­
provements. Firms’ managers either care about these pressures and align
their firms’ interests with those of the stakeholders or obtain other
1
We consider as low ESG performance firms, those with high ESG reputa­
tional risks.
2
The relevant theory is based on the different incentives and consequently
the conflict of interest that is generated between principals (shareholders) and
their agents (managers) that do not usually act in the principals’ best interests.
A key assumption of the agency theory is that the agents (managers) attempt to
maximise their own utility function (e.g., turnover, personal wage, etc) against
their principals’ (shareholders) interest (e.g., profit maximization), increasing
thus the underlying costs that are associated with the information asymmetry
between the two parties (see among others, Shapiro, 2005; Jensen, 1994;
Eisenhardt, 1989).
3
In 1996, there were 8,000 listed firms in US markets, while in 2016 only
3,627 firms were listed. It is estimated that half of the U.S industries have lost
over 50% of their publicly traded firms during the period 1998–2016. See
Federal Reserve Bank of St. Louis, 1 Federal Reserve Bank Plaza, St. Louis, MO
63102.
4
Report on US Sustainable and Impact Investing Trends, The US Sustainable
Investment Foundation (SIF) Biennial Report. Washington D.C. November
2020.
5
Boffo and Patalano (2020), ESG Investing: Practices, Progress and Challenges,
OECD
Paris,
(https://www.oecd.
org/finance/ESG-Investing-Practices-Progress-and-Challenges.pdf).
162
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
private benefits by (over) investing in ESG activities.
A first explanation, based on the “stakeholder value maximization
view” of ESG, firms’ ESG activities can mitigate agency costs. This view
relies on the premise that the interests of managers, shareholders, and
other stakeholders are better aligned in firms that invest more in ESG
activities because of “good governance”. Companies may engage in ESG
investments for nonpecuniary “prosocial” reasons if such investments
reflect the preferences of their shareholders (Hart & Zingales, 2017).
Such investments, however, can increase firm value through a variety of
channels, namely avoiding myopic decisions and strengthening market
positions (Benabou & Tirole, 2010), attracting customers and providing
employees with incentives for greater productivity (Baron, 2001), and
allowing valuable product market differentiation and insurance against
event risk and litigation risks in the long-run (Albuquerque et al., 2019;
Eccles et al., 2014; Servaes & Tamayo, 2013). Therefore, under this
view, well-governed firms deliver both purpose and profit (Dyck et al.,
2019; Edmans, 2011; Ferrell et al., 2016), thereby mitigating investor
concerns about value-decreasing, agency-driven uses of future fund
equity raising (Vanhamme & Grobben, 2009).6 Additionally, the peck­
ing order theory predicts that firms with greater risk suffer larger value
losses in raising new capital (Lee & Masulis, 2009; Myers & Majluf,
1984).
A second explanation is based on the “shareholder expense view” of
sustainability practices. In contrast to the stakeholder value maximiza­
tion rationale, this view holds that ESG activities are a form of wasteful
spending with the primary goal of enhancing managers’ private benefits
at the expense of shareholders (Benabou & Tirole, 2010; Cheng et al.,
2013; Friedman, 1998; Jensen, 2001; Masulis & Reza, 2013). This
negative effect of ESG on firms’ perspectives is documented by research
that is focused on managers’ self-disclosures to achieve an advantage
over investors (Kim & Lyon, 2015), hide debatable practices (Delmas &
Burbano, 2011; Seele & Gatti, 2015), greenwashing (deceptive manip­
ulation) (Siano et al., 2017). Therefore, the market may perceive ESG
activities as a signal that a firm is suffering from high agency costs
(Jensen, 1986), which may increase investor concerns about valuedecreasing, agency-driven motives. Overall, due to information asym­
metries, a firm’s ESG performance may have important implications on a
firm’s financing, growth opportunities, and consequently market
longevity.
Hypothesis H1: ESG reputational risks are associated with limited
access to external financing.
2.2. ESG reputational risks and firm’s growth
ESG reputational risks may affect a firm’s equity raising and growth
perspectives. A strand of research has centered its attention on the po­
tential of increased firm’s idiosyncratic and systematic risk which is
associated with ESG reputational risk (Jo & Na, 2012; Luo & Bhatta­
charya, 2009; McWilliams & Siegel, 2001; Oikonomou et al., 2012;
Sharpe, 1964). Increased ESG reputational risks, however, may nega­
tively shape a firm’s potential in raising new equity as investors may be
reluctant to invest in risky businesses. This could be attributed to the
agency costs (e.g., bonding costs, residual costs, etc) generated as a
result of the conflict of interest between the principals (shareholders)
and the agents (managers) according to the agency theory.
Restricted financing options are also possible to hamper the growth
dynamics of a firm (Almeida & Campello, 2007). In contrast, lower ESG
reputational risks allow a firm to increase its corporate value (Broad­
stock et al., 2020; Carroll et al., 2012; Heal, 2004, 2008; Landier & Nair,
2009), portfolio performance (Kempf & Osthoff, 2007; Statman &
Glushkov, 2009), investment (Ioannou & Serafeim, 2014), capital
accumulation (Margolis & Walsh, 2003), shareholder wealth (Derwall
et al., 2005; Dowell et al., 2000; Edmans, 2011; Servaes & Tamayo,
2013) and enhance stakeholders’ commitment (Donaldson & Preston,
1995; Freeman, 1984; Godfrey, 2005; Godfrey et al., 2009; Kacperczyk,
2009; Wang et al., 2008). Overall, a large body of literature supports
that ESG-minded companies have higher returns, improved access to
capital, and other necessary resources to increase their growth dy­
namics. Consequently, we formulate the following hypothesis:
Hypothesis H2: ESG reputational risks are associated with restricted
firm growth opportunities.
2.3. ESG reputational risks and market longevity
As a derivative claim, based on the above discussion, ESG reputa­
tional risk that serves managerial self-interests and has an impact on
firms’ status, may increase a company the possibility of finding itself in
financial distress, attracting uncommitted investors, and having fewer
investment opportunities (Donaldson & Preston, 1995; Freeman, 1984;
Godfrey, 2005; Godfrey et al., 2009; Kacperczyk, 2009; Starks et al.,
2017; Wang et al., 2008).
Based on the agency theory, responsible firms with low ESG repu­
tational risks can build beneficial relationships between managers and
shareholders. This leads to an increase in firm value through a variety of
channels such as optimizing decisions making processes, employing
relationships, market position, and product market differentiation
(Albuquerque et al., 2019; Benabou & Tirole, 2010). In addition, ESG
activities can enhance trust between investors and managers (Kim et al.,
2014) and may reduce the level of information asymmetry and adverse
selection costs associated with equity raising. Moreover, firms with high
ESG performance tend to receive greater coverage from the media and
attract socially conscious investors (Becker-Olsen et al., 2006; Hong &
Kacperczyk, 2009; Hung et al., 2018). We expect that firms with low ESG
reputational risks would have reduced agency costs, lower information
asymmetry, valuation benefits, better market position, and consumer
loyalty. As a result, there is a high probability for a firm with ESG
reputational risks to exit from the organized markets. To test this
conjecture, we formulate the following hypothesis:
Hypothesis H3: Higher ESG reputational risk exposure is negatively
associated with market longevity.
2.1. ESG reputational risks and external financing
Agency theory dictates that information asymmetry between man­
agers (agents) and shareholders (principals), increases the cost of
external financing compared to internal capital (equity funds), acceler­
ating the problem of financing constraints (Tang, 2022). The two
opposing views on ESG activities (i.e., stakeholder value maximization
rationale vs. shareholder expense view) may lead to different finance
patterns. If ESG is indeed a waste for the firm and is channeled for
managers’ private profit, it will probably reduce the chance of external
finance as the firm inefficiently uses its valuable resources. Alterna­
tively, if ESG is the outcome of aligned interest of share- and stake­
holders then increases the efficiency (and productivity) of the firm and
could also be a contributor to raising external finance.
As managers’ ESG motives are latent, higher ESG reputational risk
scores (provided by external firm evaluators) would release a negative
signal to the capital market. As a result, the stakeholders (i.e., share­
holders, institutional investors, funds, venture capitalists, etc), maybe
reluctant to provide capital and facilitate financially constrained firms
to gain access to external financing driving thus the cost of the financing
process upwards (Qiu & Yin, 2019). Based on the above, we hypothesize
the following:
6
High ESG performance is found to reduce the negative outcomes of
corporate scandals (Vanhamme & Grobben, 2009).
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I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Fig. 1. RepRisk’s Research Scope – 28 ESG reputational risks Note: The 28 RepRisk ESG reputational risks are mapped to the UN Global Compact Principles and the
SASB Materiality Map. Source: RepRisk Methodology Overview, 2021.
3. Sample and data
index, SA index, or WW index are more financially constrained. In
addition, we use Tobin’s Q to capture firms’ growth opportunities (Dixit
& Pindyck, 1994). An increased Tobin’s Q indicates greater market to
book value which reflects higher growth opportunities.
3.1. Sample Construction
For the empirical analysis, we construct a sample based on annual
financial and stock price data for US publicly listed firms, drawn from
the Center for Research in Security Prices (CRSP) and Compustat. In­
formation for patents and trademarks has been obtained by the Com­
pustat database and the USPTO office. We collect ESG information from
the RepRisk database. We retrieve from CRSP delisting codes to cate­
gorize the reasons for firms’ delisting7. Our analysis focuses on com­
panies that do not meet listing requirements or declare bankruptcy
(codes 500 to 591), so we exclude mergers. Finally, we construct an
unbalanced panel dataset consisting of 1,528 US publicly listed com­
panies over the period 2007–2019. The sample period has been dictated
by data availability.
The key constructs of the relevant study were borrowed from pre­
vious works. Specifically, we constructed the Kaplan and Zingales
(1997) index using Baker et al., (2003) modification version. Moreover,
we measure financial constraints with a relevant index (WW index)
developed in Whited and Wu (2006) based on the GMM econometric
estimation of an investment Euler equation. The financial status
construct was based on the firm’s age and size and is proxied by the
Hadlock and Pierce (2010) indicator (SA index). Finally, we used
Tobin’s Q indicator estimated as the ratio of the market value of a firm’s
assets divided by the book value of assets (see among others Tobin,
1969).
3.2.2. ESG reputational risks and predictors
The variable of interest is a firm’s ESG reputation risks based on the
RepRisk Index (RRI), which is a risk metric that quantifies a company’s
exposure to environmental, social, and governance issues9. The former
database follows a strict, rules-based research process to ensure data
precision and accuracy. The database identifies and retrieves informa­
tion about any company (regardless of the size, industry, country, pri­
vate or public) that is exposed to ESG reputational risks. Every ESG
reputational risk is verified from different external sources and analyzed
according to its severity, reach, and novelty10 and then checked for
quality assurance. Then RepRisk quantifies the ESG reputational risks,
using data science through RepRisk Index (RRI). The (RRI) score is
calculated according to several factors, including the credibility of the
information source, the frequency and timing of criticisms, the novelty
and severity of the criticism, and the company’s exposure to reputa­
tional risk relating to ESG.
The RRI score ranges from 0 to 100, with a high score corresponding
to high-risk exposure and vice versa. Higher RRI corresponds to lower
ESG performance. RRI is calibrated by RepRisk in a specific way that
denotes low ESG reputational risks exposure from 0 to 24, medium ESG
reputational risks exposure from 25 to 49, high ESG reputational risks
9
RepRisk database is the pioneer in conducting quantitative business risk
research using ESG data. It uses human intelligence and advanced machine
learning to transform ESG risks into quantitated measurements. The database
delivers transparency that drive companies to better operation, decisions and
strategies by analyzing information from public sources and stakeholders,
without considering the information published by the companies (self-disclo­
sures), as they often provide unreliable data. The database uses a combination
of daily searches and client feedback.
10
Severity (harshness) of the risk exposure. It is determined as a function of
three parts; the consequences of the risk exposure; the extent of the impact; and
the risk exposure’s cause. It can be categorized as low severity, severe, and high
severity. Reach (influence based on readership/circulation as well as by
importance in specific country) of the information source. The external sources
are pre-classified by reach: limited reach (local media, smaller NGOs, local
governmental bodies, and social media), medium reach (national and regional
media, international NGOs, and state, national, and international governmental
bodies), and high reach (global media outlets). Novelty (newness) of the issues
addressed for the company: the first time a company is exposed to a specific
ESG reputational risk.
3.2. Data description and research design
3.2.1. Firms’ financial constraints and growth opportunities.
We follow Schauer et al. (2019) to distinguish firms according to
their degree of financial constraints. We employ the three most
commonly used financial constraint indices8, namely the KZ index
(Baker et al., 2002; Kaplan & Zingales, 1997), the SA index (Hadlock &
Pierce, 2010), WW index (Whited & Wu, 2006). Firms with a higher KZ
7
Firms delisting from the stock exchange can be voluntary or involuntary.
Issue codes equal to 100 indicate that firms at the end of our sampling period
are still trading, while those with delisting codes from 200 to 299 have been
acquired, with codes from 300 to 390 are exchanges and from 400 to 490 are
liquidations. Codes from 500 to 591 are drops, from 600 to 610 are expirations
and 900 to 903 are domestics firms that became Foreign.
8
We compute financial constraint indices, in the same way as Schauer et al.
(2019).
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I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Fig. 2. RepRisk’s 67 ESG Topic Tags. Source: RepRisk Methodology Overview, 2021.
Table 1
Summary statistics.
Panel A: Descriptive statistics for firms 2007–2019
Variables
Observations
Mean
Std. Dev.
Min
Max
ESG reputational risks (RRI)
ESG reputational risks low
ESG reputational risks medium
ESG reputational risks high
Market Value
Sales
Sales growth
FirmAge
Patents
TM
Advertising
Leverage
13,718
13,718
13,718
13,718
13,718
13,718
13,718
13,718
13,718
13,718
13,718
13,718
8.470
0.920
0.059
0.009
11754.268
8197.063
− 0.012
36.458
46.432
2.519
217.212
0.302
11.356
0.271
0.235
0.093
39887.057
26111.35
1.721
32.804
365.308
12.249
682.042
1.060
0
0
0
0
0.018
0.010
− 141.900
2
0
0
0
0
71.333
1
1
1
1073390.500
521,426
3.474
229
10,595
370
11,000
77.757
Panel B: Failed and survived firms
From 2007 to November 2019—Delisting firms (CRISP delisting codes 500 to 591)
Failed
Survived
Total
N
%
191
1,337
1,528
12,50%
87.50%
100.00
exposure from 50 to 59, very high ESG reputational risks exposure from
60 to 74, and extremely high ESG reputational risks exposure from 75 to
100 providing the necessary information to identify the exposed com­
panies. Fig. 1 illustrates the 28 ESG reputational risks, while the 67
specific and thematic ESG “hot topics” and “themes” that the database
focuses on to build the RRI index are portrayed in Fig. 2.11
Following the existing literature (Chemmanur et al., 2019; Wagner,
2010) we use a rich set of control variables. We retrieve information for
a firm’s sales (Log (Sales), advertising (Advertising), market value (Market
Value), in millions of dollars, from the Compustat database. To control
for a firm’s innovation and market establishment, we use the natural
logarithm of a firm’s patents (Log (Patents + 1)) and trademarks (Log
(TM + 1)). Data for both former measures, as well as for firm age (Log
(Firm Age)), are obtained from Orbis Intellectual Property: a global
company database produced by Bureau van Dijk (2005). Information
about firms’ duration on market, as well as delisting codes to indicate
the status of the issuing firm came from the CRSP database. Table 1
Firms’ ESG reputational risks (ESG reputational risks (RRI)) in our
sample is, on average, quite low at about 8.47 (out of 100) and, on
average the sales growth was − 1.2%. Also, in our sample, firms’ age and
sales are 36.45 years and 8197.063 million dollars respectively.
Considering firms’ innovation activity, the average in our sample is 46
patents and 2.5 trademarks. Lastly, the firm’s market value and adver­
tising expenses are 11754.26 and 217.21 million respectively. Panel B
reports the number and shares of delisted firms in our sample. On
average, in our study period, 87,5 % of firms survived, while 12,5% of
firms were delisted due to bankruptcy or inability to comply with listing
11
The Issues are selected and categorized in align with the World Bank Group
Environmental, Health, and Safety Guidelines, the IFC Performance Standards,
the Equator Principles, the OECD Guidelines for Multinational Enterprises, the
ILO Conventions, and more. Moreover, the Ten principles of the UN Global
Compact can be specifically mapped to RepRisk’s 28 Issues.
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I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
requirements (codes 500 to 591).
logarithm of total assets and Age as the total number of years that a firm
exists in Compustat. The higher the value of the SA index, the more
financially constrained a firm is. We construct the SA index following
equation:
4. Empirical methodology
4.1. Fixed effects estimator
SAIndexit = − 0.737 × Sizeit + 0.043 × Size2it − 0.040 × Ageit
To test our hypotheses regarding whether ESG reputation risks are
associated with restricted access to external financing and reduced
growth opportunities (Tobin’s Q), we estimate the following equations:
(FinancialConstrains)i,t =b0 +b1 ESGReputationalRisksi,t +b2 Constrolsi,t +ei,t
(1)
′
(Tobin s Q)i,t = a0 + a1 ESGReputationalRisksi,t + a2 Constrolsi,t + ei,t
(2)
Where ESGReputationalRisks measures firms’ ESG reputation risks
and i and t refer to firm, and year, respectively. The variable Controls is a
vector that includes a rich set of control variables such as sales, sales
growth, advertising, firm’s age, patents, trademarks, and market value.
We perform the Hausman test to check whether a fixed or randomeffects regression is appropriate for our panel data analysis. The Haus­
man test indicates the use of a fixed-effects estimator so, all the re­
gressions include firm, year, and state12 fixed effects. Moreover, in our
analysis, the structure of variation is unknown, so we use robust stan­
dard errors.
Tobin sQit = ((PRCC Fit *CSHOit ) + ATit −
′
CEQit )/ATit
(6)
4.2. Semiparametric and nonparametric survival methods
Survival analysis is a statistical method that has been used exten­
sively by the literature to examine firms’ market survivability (e.g.,
Alhadab et al., 2015; Carpentier & Suret, 2011; Espenlaub et al., 2012;
Fama & French, 2004; Gerakos et al., 2013; Hensler et al., 1997; Jain &
Kini, 2000; Jain & Martin, 2005). The main reason for using this method
is its advantage over regression analysis (such as probit and logit
models) to account for both event occurrence and time to the event.
Moreover, this kind of technique works well for censored data and
events with different time horizons (LeClere, 2000; Shumway, 2001).
In our analysis, we apply nonparametric and semiparametric ap­
proaches to study the association between ESG reputational risks and
firms’ market survival. By using nonparametric estimates of hazard and
survival functions, we compare the failure risks and survival rates of
firms with ESG reputational risks and those without, to determine
whether ESG had an impact on firms’ survival. The hazard function
expresses a conditional probability of failure, considering the time that
the firm existed in the market. We use the Nelson–Aalen estimator to
compute the hazard functions for the two groups as expressed by the
following equation:
4.1.1. Financial constraints and growth opportunities measures
In this section, we briefly present the four indices we use in this study
to measure firms’ financial constraints and growth opportunities.
1. KZ index: We construct Kaplan and Zingales (1997) index using
Baker et al. (2002) modification version which does not contain Tobin’s
Q as it might capture mispricing of stocks. This index categorizes firms
as financial constrained based on the operating income and depreciation
divided by a firm’s beginning-of-year total assets (Cashflow), total debt
divided over last year’s total assets (Leverage), total dividends payments
(Dividends), and cash holdings plus marketable securities, both scaled by
beginning-of-year total assets (Cash). A higher score of the KZ index
indicates more financial constraints. To compute the KZ index, we
follow equation (3).
KZIndex = − 1.002 × Cashflowit + 3.139 × Leverageit − 39.368
× Dividendsit − 1.315 × Cashit
(5)
4. Tobin’s Q: Tobin and Brainard (1968) and Tobin (1969) introduced
the ratio of the market value of a firm’s assets ((PRCC_F*CSHO) + AT –
CEQ)) divided by the book value of assets (AT). This index expresses the
relationship between market and book value and measures a firm’s
growth opportunities. Tobin’s premise is that firms should be worth
what their assets are worth, so anything above 1.0 theoretically in­
dicates that a company is overvalued because of the discounted growth
opportunities. Typically, Tobin’s Q values between zero and one indicate
that the stock is undervalued (lower growth opportunities) while a value
greater than one means that is overvalued (higher growth opportu­
nities). We construct Tobin’s Q index following equation:
(3)
2. WW index: Based on the structural investment model, Whited and
Wu (2006) construct an index of financial constraints by estimating the
investment Euler equation with generalized methods of moments
(GMM). The constructed index categorizes firms as financial constrained
based on their (Cashflow) which is defined as operating income and
depreciation over the firm’s beginning-of-year total assets; a dividend
dummy (DividendDummy) that takes the value of one of the firms pays
cash dividends), and zero if the firm does not distribute cash with div­
idends, the share of long-term debt over total assets (Leverage), a firm’s
size (Size) proxied by the natural logarithm of total assets, a three-digit
industry’s sales growth (ISG), and finally, a firm’s sales growth (SG).
The higher the value of the WW index, the more financially constrained
a firm is. We compute the WW index following equation:
̂
H(t)
=
∑di
n
ti ≤t i
(7)
We define di as the number of failed firms at time ti, and ni as the
number of firms with possible risk at time ti, then we compute with the
survival function the probability of firms’ survival at a particular time.
We expect the survival function curve for firms with high ESG reputa­
tional risks to be above those with low ESG reputational risks. We use
the Kaplan–Meier methodology to estimate the survival functions:
̂
S(t) =
∏ni − di
ni
ti ≤t
(8)
Finally, we use a log-rank test to examine the difference in survival
curves between firms with high ESG intensity and those with low ESG
intensity. We use a semi-parametric approach to determine fit, via
maximum likelihood proportional hazards for a panel with multiple
records, using the Cox proportional hazards model (Cox, 1972), which
extends our analysis by taking into account the simultaneous impact of
several risk factors on survival time. One of the advantages of the Cox
proportional hazards model is that it works with no pre-specified
baseline hazard function and can thus take any functional form (Alli­
son, 2000). The reason for relying on a semiparametric estimation can
be justified by the fact that only in rare cases, the theory implies a
particular functional form for an empirical model specification (see
WWIndexit = − 0.091 × Cashflowit − 0.062 × DividendDummyit + 0.021
× Leverageit − 0.044 × Sizeit + 0.102 × ISGit − 0.035 × SGit
(4)
3. SA index: Hadlock and Pierce (2010) measure a firm’s financial
status based on firm age and size, where Size is expressed by the natural
12
We include state dummies in our estimations to control for time invariant
local characteristics and regional disparities that may affect ESG.
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Journal of Business Research 149 (2022) 161–177
that the mean, variance, and skewness of all covariates are balanced
across the treatment and control groups. In doing so, we reduce random
and systematic inequalities in the variable distributions between the
treatment and control (Hainmueller, 2012). Then, we re-estimate the
Cox proportional hazards model using the entropy balanced weights.
The results are reported in Table 5.
Table 2
ESG reputational risks external financing and growth opportunities This table
provides the estimates of the impact of ESG reputational risks on firms financing
constraints and growth opportunities. All estimations include firm, year, and
state fixed effects. The standard errors are shown in parentheses below the
estimated coefficients. One, two, and three asterisks indicate statistical signifi­
cance at the 10%, 5%, and 1% levels, respectively. *** p < 0.01, ** p < 0.05, * p
< 0.1.
VARIABLES
ESG reputational risks
Log (Market Value)
Log (Sales)
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
leverage
Observations
R-squared
Firm FE
State FE
Year FE
(1)
KZ Index
(2)
WW Index
(3)
SA Index
(4)
Tobin’s Q
0.133**
(0.053)
− 1.486***
(0.067)
1.427***
(0.071)
− 0.037
(0.065)
− 0.132*
(0.079)
− 0.259***
(0.047)
− 0.514***
(0.079)
− 0.274***
(0.032)
0.755
(0.473)
13,718
0.222
YES
YES
YES
0.02***
(0.01)
− 0.027***
(0.001)
− 0.021***
(0.001)
− 0.002**
(0.001)
− 0.004***
(0.001)
0.005***
(0.001)
− 0.002**
(0.001)
0.004***
(0.000)
0.000
(0.000)
13,718
0.490
YES
YES
YES
0.040***
(0.002)
− 0.033***
(0.002)
− 0.079***
(0.003)
− 0.005
(0.003)
− 0.036***
(0.003)
0.020***
(0.002)
0.012***
(0.003)
0.010***
(0.001)
0.070***
(0.012)
13,718
0.412
YES
YES
YES
− 0.013***
(0.005)
5. Results
5.1. The association between a firm’s ESG reputational risks, external
financing, and growth opportunities.
In this subsection, our objective is to investigate the relationship
between firms’ ESG reputational risks, external financing, and growth
opportunities regarding Hypothesis (H1) and (H2). In doing so, we
regress ESG reputational risks on the three most used firms’ financial
constraints induce namely (KZ index, SA index, WW index) (equation
(1)), and growth opportunities (Tobin’s Q) (equation (2)) including a
vector of control variables. As we are interested in the over-time ESG
financial constraints and growth opportunities relationship for any
given firm, we use fixed effects at the firm level restricting the variation
within the firm. In Table 2, we present our estimates considering firms’
financial constraints and growth opportunities. As columns (1) to (3)
show, firms with a one percent increase in ESG reputational risks related
to a rise of their financial constraints by 2% to 13,3% according to the
index we use. In addition, in column (4), we document that a one
percent increase in ESG reputational risks is associated with a 1,3%
reduction in firms’ growth opportunities. In sum, our results presented
in this section suggest that ESG reputational risks are associated with
difficulties in external financing and negative growth opportunities,
leading to the validity of Hypotheses H1 and H2..
− 0.100***
(0.015)
0.005
(0.006)
− 0.235***
(0.038)
− 0.047***
(0.009)
− 0.040***
(0.009)
− 0.011
(0.010)
0.008**
(0.004)
13,718
0.762
YES
YES
YES
among others Lokshin, 2006). Besides an incorrect parameterization of
the regression equation might result in inconsistent estimates (Tran &
Tsionas, 2010). By adopting this semiparametric approach, we let the
data reveal the structural relationship between the sample variables
without specifying the functional form of the hazard function (Kasioumi
& Stengos, 2020; Yang & Pickford, 2016). This variant of the model has
been widely used, in the business literature (see among others Yang &
Pickford, 2016).
We estimate the following model by applying a Cox proportional
hazard model to the panel data:
hExit(t, ESGi ,Controlsi )=h0 (t)exp(β0 +β1 ESGReputationalRisks +β2Controls )
i
i
5.2. Hazard and survival curves
We provide below a visual representation of the cumulative hazard
and survival functions for firms with ESG reputational risks and those
without. Figs. 3 and 4 show Nelson–Aalen cumulative hazard functions
and the Kaplan–Meier survival functions.
Hazard function (Fig. 3) documents that throughout the entire
period, low ESG reputational risks firms exhibit a lower hazard rate for
exiting the market compared with high ESG reputational risks firms
(above the sample median), and the gap widened over the length of
time.
By contrast, as shown in Fig. 4, the survival function of firms with
high ESG reputational risks is lower than that of firms with low ESG
reputational risks. The graphical analysis indicates that the probability
of delisting was greater for firms with higher ESG reputational risks
throughout the entire period of our study. Additionally, the long-run test
shows that the survival distributions of the two samples differ at a 1%
level of significance and provided evidence that the comparison is effi­
cient. In summary, the hazard and survival functions demonstrate that
firms with higher ESG reputational risks are more likely to exit the
market compared with those with lower ESG reputational risks. The
results suggest that ESG reputational risks matters and sustainable
companies tend to have better survival profiles.
(9)
We define h(t) as the baseline hazard function with t as the time to
exit from the market (failure). The dependent variable captures the risk
of exiting the market. The hazard ratios measure the increase in failure
risk for a unit increase of the independent variables. For the continuous
variables, the hazard rate for a one-unit increase was 100*(hazard ratio
– 1) (Allison, 2000). Our variable of interest is the firms’ ESG reputa­
tional risks. Following the literature (Chemmanur et al., 2019; Hensler
et al., 1997; Wagner, 2010), we control for firms’ characteristics, such as
sales, sales growth, advertising, patents, trademarks, and market value.
Following Klein and Moeschberger (2003), and Gounopoulos and Pham
(2018) we include a firm’s age in the covariates to control for a
dimension of information asymmetry and growth options since younger
firms may be characterized by a larger extent of information asymmetry
and growth options.
5.3. Cox proportional hazards model
In this section, we study the relation between ESG reputational risks
and market longevity, which corresponds to our hypotheses (H3). We,
therefore, estimate (equation (9)) with Cox proportional hazards model
and present our findings in Table 2.
In column 1, we focus on the relationship between ESG reputational
risks and firms’ market longevity. To further explore our findings in
columns (2) to (4), we expand our analysis and estimate the impact of
three major ESG reputational risks components (environmental
4.3. Entropy balancing method
To further alleviate endogeneity concerns, we use the entropy
balancing method (Arifin et al., 2020; Ashraf et al., 2020; Hasan et al.,
2021). In doing so, we split firm-year observations into treatment (High
ESG Reputation Risk) and control (Low ESG Reputation Risk) groups
based on the sample median of firms (RRI) activity. Using the entropy
balancing score we re-weight the observations of the control group so
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I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Table 3
Cox proportional Hazard estimates of firms’ ESG reputational risk on market
longevity.
VARIABLES
(1)
(2)
(3)
(4)
Cox-Model
Log (ESG reputational
risks)
Log (Environmental
reputational risks)
0.099***
(0.017)
0.045***
(0.016)
Log (Social reputational
risks)
(0.014)
Log (Governance
reputational risks)
Log (Market Value)
Log (Sales)
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
leverage
Observations
Year FE
State FE
Industry FE
0.028**
− 0.388***
(0.024)
− 0.092***
(0.019)
− 0.002
(0.012)
− 0.452***
(0.061)
− 0.252***
(0.032)
− 0.300***
(0.051)
− 0.015
(0.012)
0.016***
(0.003)
13,718
YES
YES
YES
− 0.384***
(0.023)
− 0.085***
(0.019)
− 0.003
(0.012)
− 0.453***
(0.063)
− 0.253***
(0.033)
− 0.296***
(0.053)
− 0.010
(0.012)
0.016***
(0.003)
13,718
YES
YES
YES
− 0.381***
(0.024)
− 0.086***
(0.018)
− 0.003
(0.012)
− 0.451***
(0.062)
− 0.253***
(0.034)
− 0.300***
(0.052)
− 0.013
(0.012)
0.016***
(0.004)
13,718
YES
YES
YES
0.047**
(0.020)
− 0.384***
(0.025)
− 0.082***
(0.019)
− 0.003
(0.011)
− 0.448***
(0.060)
− 0.256***
(0.035)
− 0.299***
(0.052)
− 0.014
(0.012)
0.016***
(0.004)
13,718
YES
YES
YES
Fig. 4. Survival functions—firms with low and high ESG reputational risks.
activity, proxied by patents and trademarks.
As it is evident from the relevant table, there is a negative association
between the former innovation indicators and market delisting, which is
expected since firms, through patenting and trademark activity, protect
themselves in the market, reduce risk, and acquire a competitive
advantage. Moreover, we control for firms’ advertising expenses to
capture adjustments in consumers’ preferences and, hence, public sup­
port for companies. The results indicated that an increase of ESG repu­
tational risks by 1% leads to higher probability of delisting, of 9,9%. In
addition, our analysis is extended concerning the main components of
ESG reputational risks. We find that firms with a 1% increase in Envi­
ronmental, Social, and Governance reputational risks are more likely to
exit the organized market by 4,5%, 2,8%, and 4,7% respectively.
In summary, we provide evidence that firms with greater ESG
reputational risks face a higher probability to exit the market. This
finding leads to the acceptance of Hypothesis H3, indicating that higher
ESG reputational risk exposure is negatively associated with market
longevity.Table 3
This table provides the Cox estimations of proportional hazards for the proba­
bility of failure and time-to-failure for the ESG reputational risk and its three
major components. In all the regressions, we include industry and year fixed
effects. All the variables are defined in the Appendix. The standard errors are
shown in parentheses below the estimated coefficients. Robust standard errors
are in parentheses. One, two, and three asterisks indicate statistical significance
at the 10%, 5%, and 1% levels, respectively. *** p < 0.01, ** p < 0.05, * p < 0.1
5.4. Robustness checks
5.4.1. Controlling for service and manufacturing characteristics
Service and manufacturing industries may comprise firms with
different characteristics, in terms of growth, innovation, and risk
exposure (Ortiz-Villajos & Sotoca, 2018; Patel & Pearce, 2018). We,
therefore, study the impact of ESG reputational risks on firms’ proba­
bility of exiting the market in these two industries. We separate our
sample into two subsamples using the first digit of their SIC classifica­
tion. The first includes all the firms assigned to the service sectors
(Transportation, Communications, Electric, Gas and Sanitary service,
Retail Trade, Finance, Insurance, Real Estate, General Services, and
Public Administration), while the second contains those assigned to
manufacturing (Agriculture, Forestry and Fishing, Mining, and Con­
struction). Our analysis considers as failed firms all those exiting the
market.
In Table 4, we perform a semi-parametric analysis using the Cox
proportional hazards model. We control for a firm’s market value and
expect to find a negative association with firms’ probability of exiting
the market. We control for sales and growth in sales (Sales growth)
(Hirsch, 1990) and innovation activity, proxied by patents and trade­
marks. We found a negative and statistical sign association between
firms’ patents, trademarks, and market delisting. This is expected,
because firms’ patent and trademark investments, increase protection,
market establishment, and consumer loyalty, reduce risks, and enable
them to acquire a competitive advantage. We control for firms’ adver­
tising expenses to capture adjustments in consumers’ preferences and,
Fig. 3. Nelson – Aalen cumulative hazard functions—firms with low and high
ESG reputational risks.
reputational risks (ERR), social reputational risks (SRR), and governance
reputational risks (GRR) on the probability of a firm exiting the market.
We control for the firm’s market value and expect to find a negative
association with firms’ probability of exiting the market. We also include
in our model sales, growth in sales (Hirsch, 1990), and innovation
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I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
further ensure the robustness of the results (column 1) we re-estimate
the former model by applying the entropy balanced method (column
2). For both specifications, the results were consistent and in line with
the previous findings. Our analysis indicates that while for low ESG
reputational risks there is no statistically significant effect on a firm’s
delisting activity, medium, and high-very high ESG reputational risks
have a positive and statistically significant effect on the probability of
exiting the market. The results imply that the more the ESG reputational
risks the higher the probability of a firm exiting the market and the
lower the survival time. Overall, these estimates support our main
analysis and the importance of our findings by focusing on the impact of
the different levels of ESG reputational risks on market longevity.
In Fig. 5 (see left panel), we plot the survival functions of firms over
time for different levels of ESG reputational risks. The magnitude of this
exposure is calibrated by the RepRisk database in a specific way that
denotes low-risk exposure from 0 to 24, medium risk from 25 to 49, and
high risk to extremely high-risk exposure for more than 50, providing
the necessary information to categorize the ESG reputational risks of the
companies. We find that the one with the highest probability of sur­
viving is the blue line which corresponds to firms with low exposure to
ESG reputational risks. The graphical representation documents that
these firms are more likely to remain listed in comparison with those
having medium and high ESG reputational risks. Accordingly,
comparing the red with the green plots, we can see that for firms with
medium ESG reputational risks the probability to remain listed is greater
than for those with high-very high ESG reputational risks.
In Fig. 6 (see right panel), we plot the hazard functions for the
different levels of ESG exposure. The green, red and blue lines corre­
spond to the group with high - very high, medium, and low ESG repu­
tational risks exposure respectively. We find quite a drastic difference
between the groups. The blue plot indicates that firms with low ESG
reputational risks exposure face a reduced hazard of exiting the market
compared with those with medium and high-very high ESG reputational
risk exposure. Besides, as one can see firms with medium ESG reputa­
tional risks exposure have a reduced hazard of exiting the market
compared with those with high-very high ESG reputational risk expo­
sure. The findings of the threshold analysis are in line with the estima­
tions of Table 6 and provide evidence that firms’ ESG reputational risk
exposure is associated with a higher probability of exiting the organized
markets.
Table 4
Cox estimates of ESG’s impact on firms exiting the market, controlling for ser­
vice and manufacturing.
(1)
(2)
VARIABLES
Service
ESG reputational risks
Manufacturing
ESG reputational risks
Log (ESG reputational risks)
0.078*
(0.042)
− 0.434***
(0.037)
0.083*
(0.048)
− 0.037***
(0.009)
− 0.489***
(0.037)
− 0.356**
(0.161)
− 0.412***
(0.100)
− 0.034
(0.028)
0.167***
(0.035)
3,746
YES
YES
YES
0.064**
(0.026)
− 0.369***
(0.029)
− 0.172***
(0.028)
0.026
(0.037)
− 0.372***
(0.081)
− 0.226***
(0.035)
− 0.228***
(0.069)
0.064***
(0.022)
0.011***
(0.002)
9,972
YES
YES
YES
Log (Market Value)
Log (Sales)
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
leverage
Observations
Year FE
State FE
Industry FE
This table provides Cox estimations of proportional hazards for the probability
of failure and time-to-failure for service and manufacturing industries, consid­
ering firms’ ESG reputational risk. In all the regressions, we include industry and
year fixed effects. All the variables are defined in the Appendix. The standard
errors are shown in parentheses below the estimated coefficients. One, two, and
three asterisks indicate statistical significance at the 10%, 5%, and 1% levels,
respectively. *** p < 0.01, ** p < 0.05, * p < 0.1.
hence, public support for companies. By extending our initial findings to
the industry level, we found that ESG reputational risks increase the
probability of firms delisting in both the manufacturing and service
sector. Our results document that an increase of 1% in ESG reputational
risk is associated with a higher probability of delisting, from 7,8% in the
service sector to 6.4%, in the manufacturing sector. These findings were
in line with those of Chemmanur et al. (2019). Overall, in this section,
we show that our initial analysis is robust and support the hypotheses
that ESG reputational risks have a negative and significant impact on
firms’ market longevity.
6. Additional robustness tests
6.1. Robustness with Probit and IV- Probit models
5.4.2. Entropy balancing method
In this sub-section, to further alleviate endogeneity concerns and
provide robustness in our findings we apply the entropy balancing
approach. In doing so, we split firm-year observations into treatment
(High ESG reputational risks) and control (Low ESG reputational risks)
groups based on the median ESG reputational risks in each year. In
Table 5 panel A, we present a comparison of the mean, variance, and
skewness of the variables between treated and control groups. In Table 5
panel B and panel C, we re-estimate equations (1), (2), and (9) with the
entropy balancing method respectively. Our findings are in line with
those of the baseline model and support that ESG reputational risk is
positively associated with restricted firm growth opportunities and
limited access to external financing. We also continue to find a positive
and statistically significant relationship between ESG reputational risks
and a firm’s market exiting which confirms our previous findings.
In this section, we perform an additional robustness analysis with a
probit model. This model has a binary dependent variable that takes the
value one when a firm exits the market and zero otherwise. In addition,
to further address endogeneity concerns, we instrument ESG reputa­
tional risks by using an IV-probit model. This model fits for a binary
dependent variable with one or more endogenous covariates and nor­
mally distributed errors. The instruments, we use are the industry’s
average ESG reputational risk that a firm belongs to, as well as the religion
and the political beliefs of a firm’s headquarters’ state (Deng et al., 2013;
Dutordoir et al., 2018; Hoi et al., 2013). Considering the first instrument,
industry means are used quite often in the literature as instruments
(Marwick et al., 2020). The second instrument, Religion, is motivated by
the observation that firms’ ESG activity tends to be affected by the de­
gree of religiosity in the state of their headquarters (Angelidis & Ibra­
him, 2004). The third, political beliefs, is motivated by the observation
that firms with headquarters in states, where the Democratic party is
elected, are typically more engaged in ESG activities (Rubin, 2008). In
Table 7, in columns (1) and (2), we present our estimates from the probit
and the IV-probit models respectively. In both specifications, we
continue to find that the effect of ESG reputational risk remains signif­
icant and has a negative impact on market longevity.
5.4.3. Threshold analysis of the impact of ESG reputational risks on firms
exiting mechanism
In Table 6, we apply the Cox proportional model for low, medium,
and high - very high ESG reputational risks. We split our variable of
interest according to the RepRisk database into three levels that repre­
sent low, medium, and high - very high ESG reputational risks. To
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Journal of Business Research 149 (2022) 161–177
Table 5
Entropy Balancing.
Panel A: Entropy Balancing Weighting
Before: without weighting
Log (Market Value)
Log (Sales)
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
Leverage
After: Weighting variables
Log (Market Value)
Log (Sales)
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
Leverage
Treat
mean
variance
skewness
Control
mean
variance
skewness
8.443
8.208
− 0.005
3.375
1.138
0.531
1.999
0.306
3.491
3.389
0.886
0.677
3.876
0.994
6.429
0.068
− 0.325
− 0.687
− 57.130
0.111
1.725
2.109
0.860
4.091
6.799
6.502
− 0.018
3.049
0.688
0.435
1.051
0.297
2.896
3.294
5.018
0.718
1.777
0.599
2.522
2.167
− 0.528
− 0.964
− 45.600
− 0.053
2.017
1.908
1.358
41.940
Treat
mean
8.443
8.208
− 0.005
3.375
1.138
0.531
1.999
0.306
variance
3.491
3.389
0.886
0.677
3.876
0.994
6.429
0.068
skewness
− 0.325
− 0.687
− 57.130
0.111
1.725
2.109
0.860
4.091
Control
mean
8.443
8.208
− 0.005
3.375
1.138
0.531
1.999
0.306
variance
2.010
1.803
9.475
0.674
3.654
0.825
5.340
2.326
skewness
− 0.103
− 0.142
− 45.490
− 0.070
1.570
1.839
0.707
48.070
Panel B: Regressions results on firm’s external financing and growth opportunities based on the entropy balanced sample
(1)
(2)
(3)
VARIABLES
KZ Index
WW Index
SA Index
(4)
Tobin’s Q
Log (ESG reputational risks)
0.107*
(0.057)
0.003***
(0.001)
0.028***
(0.003)
− 0.028***
(0.005)
Other Controls
Observations
R-squared
Year FE
State FE
Industry FE
YES
13,718
0.222
YES
YES
YES
YES
13,718
0.409
YES
YES
YES
YES
13,718
0.199
YES
YES
YES
YES
13,718
0.812
YES
YES
YES
Panel C: Cox estimates of the impact ESG and its major components based on the entropy balanced sample
VARIABLES
(1)
(2)
(3)
Log (ESG reputational risks)
Log (Environmental reputational risks)
0.136***
(0.032)
Log (Social reputational risks)
0.049*
(0.026)
0.036**
(0.016)
Log (Governance reputational risks)
Other Controls
Observations
Year FE
State FE
Industry FE
YES
13,718
YES
YES
YES
YES
13,718
YES
YES
YES
YES
13,718
YES
YES
YES
(4)
0.057**
(0.023)
YES
13,718
YES
YES
YES
This table documents the results from the entropy balancing approach. Panel A presents the mean, variance, and skewness between the treated and control groups
before and after weighting. Panel B reports the entropy balancing estimates on a firm’s external financing and growth opportunities while panel C presents the Cox
estimates of the impact of ESG reputational risk and it is major components on firms’ market survival. Standard errors are reported in parentheses. Variable definitions
are reported in Table A1, appendix. *** p < 0.01, ** p < 0.05, * p < 0.1
Robust standard errors in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.1.
170
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Table 6
Performing threshold analysis of the impact of ESG reputational risk on firms’
market exiting.
VARIABLES
Low ESG reputational risks exposure
Medium ESG reputational risks exposure
High and very high ESG reputational risks
exposure
Log (Market Value)
Log (Sales)
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
Leverage
Observations
Year FE
State FE
Industry FE
(1)
Cox-Model
(2)
Cox-Model using
entropy balanced
weights
0.686
(0.583)
1.336**
(0.600)
2.865***
0.620
(0.583)
1.307**
(0.599)
2.790***
(0.661)
− 0.349***
(0.025)
− 0.078***
(0.023)
0.000
(0.009)
− 0.304***
(0.036)
− 0.118***
(0.030)
− 0.103**
(0.043)
− 0.012
(0.022)
0.021***
(0.006)
13,718
YES
YES
YES
(0.672)
− 0.432***
(0.035)
− 0.018
(0.040)
0.012
(0.011)
− 0.426***
(0.051)
0.005
(0.032)
− 0.041
(0.058)
− 0.081***
(0.027)
0.016***
(0.005)
13,718
YES
YES
YES
Fig. 6. Hazard functions.
Table 7
Estimates of the probability of exiting the market.
VARIABLES
Log (ESG reputational risks)
Log (Market Value)
Log (Sales)
This table provides Cox proportional hazards column (1), for the probability of
failure and time-to-failure considering threshold in ESG reputational risk with
Cox-Model. In column (2), we apply an entropy Balanced weight Cox-Model. In
all the regressions, we include industry and year fixed effects. All the variables
are defined in the Appendix. The standard errors are shown in parentheses below
the estimated coefficients. One, two, and three asterisks indicate statistical sig­
nificance at the 10%, 5%, and 1% levels, respectively.
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
leverage
Observations
Year FE
State FE
Industry FE
(1)
Probit
(2)
IV - Probit
0.041**
(0.016)
− 0.262***
(0.016)
− 0.041***
(0.016)
0.002
(0.006)
− 0.109***
(0.023)
− 0.097***
(0.021)
− 0.127***
(0.030)
0.000
(0.014)
0.516***
(0.067)
13,718
YES
YES
YES
0.033*
(0.018)
− 0.226***
(0.014)
− 0.065***
(0.014)
0.003
(0.008)
− 0.155***
(0.022)
− 0.080***
(0.019)
− 0.133***
(0.030)
− 0.015
(0.012)
0.466***
(0.055)
13,718
YES
YES
YES
This table provides the estimates of the impact of ESG reputational risk on the
probability of exiting the market. In columns (1) and (2), we apply a Probit and
an IV-Probit Model. In all the regressions, we include industry and year fixed
effects. All the variables are defined in the Appendix. The standard errors are
shown in parentheses below the estimated coefficients. One, two, and three
asterisks indicate statistical significance at the 10%, 5%, and 1% levels,
respectively.
argue that defenders are expected to exhibit higher SG&A expenses in
contrast with the prospectors who are innovative and invest in R&D
activities. Ballas et al. (2020) argue that defenders and prospectors have
significant differences in strategies regarding risk management and
operating efficiency. We expect, prospectors and defenders tο follow
different ESG strategies to manage their possible ESG reputational risks.
To deal with this type of selection bias between ESG reputational
risks and firms’ ESG strategies we apply a Probit model with sample
selection (Van de Ven and Van Praag, 1981) where we control for
possible selection considering, SG&A expenses and R&D activities. We
also control for firm efficiency (Firm Efficiency) constructed using data
envelope analysis following Demerjian et al. (2012), as efficient firms
are more skilled at addressing ESG concerns (Erhemjamts et al., 2013).
In Table 8 we present our findings. In columns (1) and (2) we provide
Fig. 5. Survival functions.
6.2. Robustness considering firms ESG management strategies
Firms can form strategies to mitigate the impact of their ESG expo­
sures. Bentley et al. (2013), categorize firms as either prospectors or
defenders. Defenders’ strategic mission focus on maximizing the shortterm earnings at the expense of the long-run economic benefits. Alter­
natively, prospectors follow strategies that focus on maximizing longterm economic benefits (Langfield-Smith, 2007). Miles et al. (1978),
171
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Table 8
Probit model with sample selection.
(1)
(2)
(4)
(5)
Probit model with sample selection
Probit model with sample selection using entropy balanced weights
VARIABLES
Second Stage
Second Stage
Log (ESG Reputational Risks)
0.194*
(0.108)
R&D
SG&A
Firm Efficiency
Log (Market Value)
Log (Sales)
Log (Sales growth)
Log (FirmAge)
Log (Patents + 1)
Log (TM + 1)
Log (Advertising)
leverage
athrho
Observations
Year FE
Industry FE
State FE
− 0.267***
(0.037)
0.052
(0.034)
− 0.023
(0.021)
− 0.187***
(0.046)
− 0.075**
(0.031)
− 0.029
(0.052)
− 0.051***
(0.019)
0.519***
(0.090)
0.255
(0.196)
13,718
YES
YES
YES
First
Stage
0.197*
(0.106)
0.000***
(0.000)
0.000***
(0.000)
− 1.601***
(0.139)
0.142***
(0.011)
0.229***
(0.012)
− 0.014**
(0.006)
0.080***
(0.015)
− 0.011
(0.009)
− 0.084***
(0.016)
0.041***
(0.007)
0.026**
(0.011)
− 0.282***
(0.029)
0.005
(0.028)
− 0.019
(0.012)
− 0.193***
(0.042)
− 0.076***
(0.028)
− 0.011
(0.045)
− 0.060***
(0.016)
0.491***
(0.121)
0.505**
(0.201)
13,718
YES
YES
YES
13,718
YES
YES
YES
First
Stage
0.000**
(0.000)
0.000
(0.000)
− 1.637***
(0.226)
− 0.006
(0.016)
− 0.016
(0.020)
0.012*
(0.007)
− 0.007
(0.025)
− 0.028**
(0.014)
0.023
(0.025)
− 0.016*
(0.009)
− 0.013
(0.012)
13,718
YES
YES
YES
This table provides a Probit model with sample selection with the dependent variable the probability of a firm exiting the market. In the first step of this model, we
correct possible selection bias between ESG reputational risks and firms’ ESG strategies controlling for S&A, R&D, and firms’ efficiency. In all the regressions, we
include industry state and year fixed effects. The robust standard errors are shown in parentheses below the estimated coefficients. One, two, and three asterisks
indicate statistical significance at the 10%, 5%, and 1% levels, respectively. *** p < 0.01, ** p < 0.05, * p < 0.1. All the variables are defined in Appendix A1.
our estimates of probit with sample selection while in columns (3) and
(4) we re-estimate the former model using the entropy balanced weights.
Overall, our findings remain in the same direction with the baseline
model providing additional robustness considering firms’ ESG man­
agement strategies.
7. Discussion
7.1. Theoretical implications
This study makes the following specific contributions to theory. First,
our findings add to the ongoing discussion on the impact of ESG repu­
tational risks on corporate innovation output. We document evidence
that ESG reputational risks amplify capital constraints, reduce firms’
growth opportunities, and increase the firm probability of exiting the
market. Our findings highlight that firms have strong incentives to do
good by serving a social purpose, as they not only perform financially
well but they also manage to stay longer in the market. This justifies
initiatives for responsible investment and sustainable growth such as
those undertaken by international organizations (e.g., United Nations)
to develop practices and strategies that incorporate ESG performance in
firms’ investments.
Second, previous studies have focused on the impact of financial
performance on the value of firms that invest in ESG (Cho et al., 2010;
Clark & Viehs, 2014; Fatemi et al., 2015; Lu et al., 2021; Malik, 2014;
Margolis et al., 2009; Porter & Kramer, 2011; Porter & van der Linde,
1995; Porter, 1991). Those studies link low ESG risks with better pros­
pects in terms of value and financial performance. However, largely
absent in these research works is the role of ESG reputational risks on
market longevity. We document that exposure to ESG risks can play a
catalytic role in market longevity, raising serious concerns about the
financial stability of the firm. Therefore, our framework provides an indepth evaluation of the ongoing debate on ESG since it includes a
dimension for considering the role of industry-level ESG reputational
risk dynamics.
6.3. Additional sensitivity analysis
Lastly, for further robustness tests, we examine the relationship be­
tween ESG reputational risks and firms exiting the market following the
Weibull distribution and fit a parametric survival model with panel data.
We cluster panel surviving data (Gutierrez et al., 2001) and fit a mixed
effect13 survival model containing both fixed and random effects. With
the inclusion of random effects, we control possible potential biases that
could arise from intra-cluster correlations. In all tests, our findings
remain qualitatively like our baseline analysis and, in the interest of
brevity, are suppressed14.
13
Mixed-effects survival models contain both fixed effects and random effects.
In longitudinal and panel data, random effects are useful for modelling intra
cluster correlations: that is, observations in the same cluster that are correlated
because they share common cluster-level random effects.
14
Due to space constraints the empirical results are available from the authors
upon request.
172
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
the information to be collected under specific regulatory frameworks. In
the last two years, there has been an effort in Congress to pass five
regulations covering ESG disclosures: climate risk disclosure, tax pay­
ment disclosures, human rights, and shareholder protection. While none
of these bills were passed, they signal growing regulatory interest in
ESG.
Lastly, there are interesting opportunities for future research. The
number of investors that integrate ESG reputational risk considerations
into their investment – decision-making process is significantly growing.
Future research may expand our findings by further addressing possible
strategies that companies may use to control their ESG reputational
risks. More important, there is still much to understand about ESG dy­
namics and how they affect firms’ market longevity. Future research
may extend the findings of this study.
Third, the ongoing business research literature has highlighted the
value of the firm’s exposure to ESG reputational risks (Patel et al., 2021).
Given the differences in ESG across industries (services and
manufacturing), the constructs of this study, provide an additional
explanatory tool for testing the added value of ESG reputational risks,
consistent with the agency theory. Our empirical analysis unravels an
important finding justifying that ESG reputational risks are associated
with limited access to external financing and growth dynamics of the
firm, highlighting the agency costs driven by the limited information
dissemination of the management.
Finally, to the extent that having a longer-term-oriented shareholder
base is desirable, companies may have strong incentives to invest in ESG
independently of legislation stringency. Our results have also important
implications for the future economy if one believes that the shorttermism of some institutional investors holds back corporate in­
novations and investments and has social costs for financial markets and
the economy as argued by several economists (Kay, 2012; Keynes, 1935;
Lipton, 1979).
7.4. Conclusion
Our findings have direct managerial implications. First, our research
suggests that ESG reputation risk plays a significant role in firms’ capital
raising and growth opportunities. Second, we document clearly that ESG
reputation risk is an important factor with a negative impact on firms’
market longevity.
Third, our results, have implications for asset managers who have
committed to the integration of sustainability factors in their capital
allocation decisions. Firms’ ESG reputational risks could be informative
for investors as it signals financial and growth opportunities and market
longevity. ESG reputational risk depends to a large extent on managerial
decisions and therefore our findings could help executives to form
strategies for improving firms’ market longevity.
All in all, this study provides managers a comprehensive view of the
consequences that ESG reputation risks have on firms’ market longevity
and calls for actions on operational as well as executive levels.
The underline effect of ESG reputational risks on firms’ market
longevity is an important element in shaping modern firms’ strategies.
The empirical findings of this study indicate that ESG reputational risks
are associated with difficulties in external financing and reduced growth
opportunities, leading to the validity of Hypotheses H1 and H2. More­
over, firms with higher ESG reputational risks seem to have mitigated
market longevity. This finding remains robust even when we split the
sample to include only service and manufacturing industries. Following
threshold analysis, we provide evidence that the magnitude of firms’
ESG reputational risk has a negative and statistically significant impact
on market longevity, thus leading to the validity of Hypothesis H3.
Overall, the empirical results show how listed firms may incorporate
the benefits stemming from their ESG performance and suggest how
such incorporations may contribute to reducing the number of firms
exiting the US market. These findings serve as a useful tool to explain
why firms’ ESG reputational risk does matter in justifying market
longevity. Finally, our results, could increase the awareness of policy
makers toward the provision of the ESG best practices , which has major
managerial implications in the long run.
7.3. Limitations and future research
CRediT authorship contribution statement
Although we have gone beyond the existing literature by showing
that ESG reputational risk is an important driving force for market
longevity, our study does not come without limitations. First, our esti­
mations are critically dependent on the rating methodology that Rep­
Risk uses to dynamically capture and quantify ESG reputational risks.
For example, the fossil fuels industry is still churning out emissions but
ranked with quite satisfactory ESG rating because they have developed
the right tools to manage their ESG reputational risks. These companies
have changed tactics and while once trying to avoid discussion now
devote significant time to discussing and managing their ESG profiles.
Second, although RepRisk is one of the largest databases, it does not
provide coverage for all the listed companies. Moreover, in the US, ESG
disclosures are not mandatory which makes it even more difficult to
obtain information on a large scale. This significantly reduces the
available sample in our study and weakens the findings. Future studies
could use increased samples and for larger periods to further expand our
findings.
Third, we must take into consideration, the absence in the US of a
formal framework through which ESG information is reported, collected
and analyzed. This stands in contrast to the European Union which has
established specific directives on ESG. Although RepRisk combines
artificial intelligence with human analysis and uses an outside inside
firm approach to retrieve ESG-related data, it is generally incremental
Irene Fafaliou: Conceptualization, Writing - original draft, Review
& editing, Project administration. Maria Giaka: Conceptualization,
Writing - original draft, Review & editing, Methodology. Dimitris
Konstantios: Writing - original draft, Review & editing, Methodology,
Software. Michael Polemis: Writing - original draft, Review & editing,
Project administration, Formal analysis.
7.2. Managerial implications
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgments
The authors wish to thank Carlos M.P. Sousa (Associate Editor) and
four anonymous referees for their helpful and fruitful suggestions that
enhanced the paper both in substance and appearance. Any remaining
errors are solely the authors’ alone. The usual disclaimer applies.
Appendix
See the Table A1.Table A2..
173
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Table A1
Variable definitions.
Variable
Definition
Source
ESG reputational risks
(RRI)
Environmental reputational
risks (ERR)
Social reputational risks
(SRR)
Governance reputational
risks (GRR)
Low ESG reputational risks
RRI index that quantifies a company’s exposure to environmental, social, and governance
reputational risk
An index that quantifies a company’s exposure to environmental reputational risk
RepRisk Global Business Intelligence database
An index that quantifies a company’s exposure to social reputational risk
RepRisk Global Business Intelligence database
An index that quantifies a company’s exposure to governance reputational risk
RepRisk Global Business Intelligence database
A dummy that takes the value of one if firms ESG reputational risks are below the sample
median
A dummy that takes the value of one if firms ESG reputational risks are above the sample
median
A dummy that takes a value equal to one if RepRisk RRI index ranges between 0 and 24
Author’s calculations/ RepRisk Business Intelligence
database
Author’s calculations/ RepRisk Business Intelligence
database
Author’s calculations/ RepRisk Business Intelligence
database
Author’s calculations/ RepRisk Business Intelligence
database
Author’s calculations/ RepRisk Business Intelligence
database
Author’s calculations/ RepRisk Business Intelligence
database
Author’s calculations/ RepRisk Business Intelligence
database
High ESG reputational risks
Low ESG reputational risks
exposure
Medium ESG reputational
risks exposure
High ESG reputational risks
exposure
Very high ESG reputational
risks exposure
Extremely high ESG
reputational risks
exposure
Industry’s average ESG
reputational risk
WW Index
KZ Index
SA Index
Tobin’s Q
Control Variables:
Log (Sales)
Log (Sales growth)
Log (Firm Age)
Log (Advertising)
Log (ΤМ)
Log (Patents)
Log (Market Value)
R&D
R&D
SG&A
Religion
political beliefs
Firm Efficiency
A dummy that takes a value equal to one if RepRisk RRI index ranges between 25 and 49
A dummy that takes a value equal to one if RepRisk RRI index ranges between 50 and 59
A dummy that takes a value equal to one if RepRisk RRI index ranges between 60 and 74
A dummy that takes a value equal to one if RepRisk RRI index ranges between 75 and 100
RepRisk Global Business Intelligence database
ESG reputational risk average for each of the four-digit SIC code industries in our sample
RepRisk Business Intelligence database
Whited-Wu (2006) index = − 0.091CF − 0.062DD + 0.021LEV − 0.44LNTA +
0.102ISG − 0.035SG, where, CF: is operating cash flows scaled by the book value of total
assetsDD: is a dummy variable, which takes the value of 1 if a firm pays dividends and zero
otherwise.LEV: is the Leverage variableLNTA: is the FirmSize variableISG: is the firm’s industry
sales growth. The industry is defined as the 3-digit industry sic-codeSG: is sales growth between
t and t-1
Author’s calculations /Compustat
Kaplan and Zingales (1997) index: − 1.002CF + 3.139LEVR − 39.368DIV − 1.315CASH, were,
CF: is operating cash flows scaled by the book value of total assetsLEVR: is the Leverage
variableDIV: is cash dividends scaled by the book value of total assetsCASH: is the firm’s cash
and cash equivalents divided by the book value of total assets
Hadlock and Pierce (2010) index: = − 0.737SIZE + 0.043SIZE2 − 0.040AGE, where,SIZE: is the
logarithm of total assets
AGE: is the FirmAge variable
The TobinsQ variable is defined as the Market-to-book ratio, calculated as the market value of
assets((PRCC_F*CSHO) + AT – CEQ)) divided by the book value of assets (AT)
The natural logarithm of a firm’s sales
The natural logarithm of a firm’s sales growth
The natural logarithm of the number of years from a firm’s initial incorporation date.
The natural logarithm of a firm’s advertising expenses
The natural logarithm of a firm’s trademarks
The natural logarithm of a firm’s patents
The natural logarithm of a firm’s market value
The natural logarithm of a firm’s research and development expenses
The natural logarithm of a firm’s research and development expenses
The natural logarithm of a firm’s selling general and administration expenses
Religion ranking of the state in which the issuer’s headquarters are located. The ranking is
based on the ratio of the number of religious adherents in the issuer’s state to the total
population in that state in 2010.
Dummy variable equal to one if a firm’s headquarters are located in a Democratic state and
zero otherwise.
The measure of a firm’s efficiency within its industry, based on data envelopment analysis,
with values ranging from zero to one, as calculated by Demerjian et al. (2012).
Author’s calculations /Compustat
Author’s calculations /Compustat
Author’s calculations /Compustat
Compustat
Compustat
Orbis - Bureau van Dijk’s - database
Compustat
Orbis - Bureau van Dijk’s – database/ USPTO
Orbis - Bureau van Dijk’s – database /USPTO
Compustat
Compustat
Compustat
Compustat
Association of Religion Data Archive. Available: http
s://www.thearda.com/Archive/Files/Descriptions/
RCMSST10.asp
The list of blue states is available at https://en.wikipe
dia.org/wiki/Red_states_and_blue_states.
Constructed by the authors following the
methodology of Demerjian et al. (2012) and using
Compustat data.
Table A2
Pairwise correlations.
Variables
(1)
(1) Log (MarketValue)
1.000
(2) Log (Sales)
0.591***
(3) Log (Sales growth)
0.028***
(4) Log (Firm Age)
0.236***
(5) Log (Patents)
0.323***
(6) Log (Trademarks)
0.205***
(7) Log (Advertising)
0.318***
(8) Leverage
− 0.032***
*** p < 0.01, ** p < 0.05, * p < 0.1
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1.000
0.049***
0.264***
0.204***
0.174***
0.349***
− 0.036***
1.000
− 0.014*
0.004
0.003
0.010
0.000
1.000
0.156***
0.107***
0.123***
− 0.026***
1.000
0.415***
0.100***
− 0.032***
1.000
0.168***
− 0.035***
1.000
0.002
1.000
174
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
References
Dixit, A. K., & Pindyck, R. S. (1994). Investment Under Uncertainty. Princeton: Princeton
U. Press.
Donaldson, T., & Preston, L. E. (1995). The stakeholder theory of the corporation:
Concepts, evidence, and implications. Academy of Management Review, 20(1), 65–91.
Dowell, G., Hart, S., & Yeung, B. (2000). Do corporate global environmental standards
create or destroy market value? Management Science, 46(8), 1059–1074.
Drempetic, S., Klein, C., & Zwergel, B. (2019). The influence of firm size on the ESG
score: Corporate sustainability ratings under review. Journal of Business Ethics., 167.
https://doi.org/10.1007/s10551-019-04164-1
Dutordoir, M., Strong, N. C., & Sun, P. (2018). Corporate social responsibility and
seasoned equity offering. Journal of Corporate Finance, 50(C), 158–179.
Dyck, A., Lins, K. V., Roth, L., & Wagner, H. F. (2019). Do institutional investors drive
corporate social responsibility? International evidence. Journal of Financial
Economics.. https://doi.org/10.1016/j.jfineco.2018.08
Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability
on organizational process and performance. Management Science, 60(1), 2835–2857.
Edmans, A. (2011). Does the stock market fully value intangibles? Employee satisfaction
and equity prices. Journal of Financial Economics, 101(3), 621–640.
Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of
Management Review, 14(1), 57–74.
Erhemjamts, O., Li, Q., & Venkateswaran, A. (2013). Corporate social responsibility and
its impact on firms’ investment policy, organizational structure, and performance.
Journal of Business Ethics, 118, 395–412.
Espenlaub, S., Khurshed, A., & Mohamed, A. (2012). IPO Survival in a Reputational
Market. Journal of Business Finance & Accounting, 39(3–4), 427–463. https://doi.org/
10.1111/j.1468-5957.2012.02280.x
Fama, E. F., & French, K. R. (2004). The Capital Asset Pricing Model: Theory and
Evidence. Journal of Economic Perspectives, 18(3), 25–46.
Fatemi, A., Fooladi, I., & Tehranian, H. (2015). Valuation effects of corporate social
responsibility. Journal of Banking and Finance, 59(3), 182–192. https://doi.org/
10.1016/j.jbankfin.2015.04.028
Ferrell, A., Liang, H., & Renneboog, L. (2016). Socially responsible firms. Journal of
Financial Economics, 122(3), 585–606. https://doi.org/10.1016/j.jfineco.2015.12
Freeman, R. E. (1984). Strategic Management: A Stakeholder Perspective. Boston, MA, USA:
Pitman.
Friedman, M. (1998). The social responsibility of business is to increase its profits. In
L. B. Pincus (Ed.), Perspectives in Business Ethics (pp. 246–251). Singapore: McGrawHill.
Gerakos, J., Lang, M., & Maffett, M. (2013). Post-listing performance and private sector
regulation: The experience of London’s Alternative Investment Market. Journal of
Accounting and Economics, 56, 189–215.
Gillan, S. L., Koch, A., & Starks, L. T. (2021). Firms and social responsibility: A review of
ESG and CSR research in corporate finance. Journal of Corporate Finance, 66. https://
doi.org/10.1016/j.jcorpfin.2021.101889
Godfrey, P. C. (2005). The relationship between corporate philanthropy and shareholder
wealth: A risk management perspective. Academy of Management Review, 30(4),
777–798. https://doi.org/10.5465/AMR.2005.18378878
Godfrey, P. C., Merrill, C. B., & Hansen, J. M. (2009). The relationship between corporate
social responsibility and share- holder value: An empirical test of the risk
management hypothesis. Strategic Management Journal, 30(4), 425–445.
Gounopoulos, D., & Pham, H. (2018). Specialist CEOs and IPO survival. Journal of
Corporate Finance, 48, 217–243. https://doi.org/10.1016/j.jcorpfin.2017.10.012
Greening, D. W., & Turban, D. B. (2000). Corporate social performance as a competitive
advantage in attracting a quality workforce. Business and Society, 39(3), 254–280.
Gutierrez, R. G., Carter, S. L., & Drukker, D. M. (2001). On boundary-value likelihoodratio tests. Stata Technical Bulletin, 60, 15–18. (Reprinted in Stata Technical Bulletin
Reprints, 10, 269–273. College Station, TX: Stata Press).
Hadlock, C., & Pierce, J. (2010). New evidence on measuring financial constraints:
Moving beyond the KZ index. Review of Financial Studies, 23, 1909–1940.
Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting
method to produce balanced samples in observational studies. Political Analysis, 20
(1), 25–46.
Hammond, S. A., & Slocum, J. W. (1996). The impact of prior firm financial performance
on subsequent corporate reputation. Journal of Business Ethics, 15(2), 159–165.
Hart, O., & Zingales, L. (2017). Companies Should Maximize Shareholder Welfare Not
Market Value. Journal of Law, Finance, and Accounting, 2(2), 247–274.
Hartzmark, S. M., & Sussman, A. B. (2019). Do Investors Value Sustainability? A Natural
Experiment Examining Ranking and Fund Flows. The Journal of Finance, 74(6),
2789–2837.
Hasan, M. M., Lobo, G. J., & Qiu, B. (2021). Organizational Capital, Corporate Tax
Avoidance, and Firm Value. Journal of Corporate Finance, Forthcoming, Available at
SSRN. https://ssrn.com/abstract=3890520.
Heal, G. M., (2004). Corporate Social Responsibility - an Economic and Financial
Framework Available at SSRN: https://ssrn.com/abstract=642762 or https://doi.
org/10.2139/ssrn.642762.
Heal, G. M. (2008). When Principles Pay: Corporate Social Responsibility and the Bottom
Line. New York: Columbia University Press.
Hensler, D., Rutherford, R., & Springer, T. (1997). The survival of initial public offerings
in the aftermarket. Journal of Financial Research, 20, 93–110.
Hirsch, B. T. (1990). Market structure, union rent-seeking, and firm profitability.
Economics Letters, 32(1), 75–79. https://doi.org/10.1016/0165-1765(90)90052-3
Hoi, C. H., Qiang, W., & Hao, Z. (2013). Is Corporate Social Responsibility (CSR)
Associated with Tax Avoidance? Evidence from Irresponsible CSR Activities. The
Accounting Review., 88, 2025–2059. https://doi.org/10.2308/accr-50544
Ahlström, H., & Monciardini, D. (2021). The Regulatory Dynamics of Sustainable
Finance: Paradoxical Success and Limitations of EU Reforms. Journal of Buiness
Ethics. https://doi.org/10.1007/s10551-021-04763-x
Albuquerque, R., Durnev, A., & Koskinen, Y. (2019). Corporate social responsibility and
firm risk: Theory and empirical evidence. Management Science, 65(10), 4451–4949.
https://doi.org/10.1287/mnsc.2018.3043
Alhadab, M., Clacher, I., & Keasey, K. (2015). Real and accrual earnings management
and IPO failure risk. Accounting and Business Research, 45(1), 55–92.
Allison, P. D. (2000). Survival Analysis Using the SAS System: A Practical Guide. Cary, NC:
SAS Institute.
Almeida, H., & Campello, M. (2007). Financial constraints, asset tangibility, and
corporate investment. Review of Financial Studies, 20(5), 1429–1460.
Angelidis, J., & Ibrahim, N. (2004). An Exploratory Study of the Impact of Degree of
Religiousness Upon an Individual’s Corporate Social Responsiveness Orientation.
Journal of Business Ethics, 51(2), 119–128. https://doi.org/10.1023/b:
busi.0000033606
Ansari, S., Wijen, F., & Gray, B. (2013). Constructing a climate change logic: An
institutional perspective on the “tragedy of the commons”. Organization Science, 24
(4), 1014–1040.
Arifin, T., Hasan, I., & Kabir, R. (2020). Transactional and relational approaches to
political connections and the cost of debt. Journal of Corporate Finance, 65, Article
101768.
Ashraf, M., Michas, P. N., & Russomanno, D. (2020). The impact of audit committee
information technology expertise on the reliability and timeliness of Financial
Reporting. The Accounting Review.
Baker, H. K., Powell, G. E., & Veit, E. T. (2002). Revisiting managerial perspectives on
dividend policy. Journal of Economics and Finance, 26, 267–283.
Ballas, A., Naoum, V. C., & Vlismas, O. (2020). The Effect of Strategy on the Asymmetric
Cost Behavior of SG&A Expenses. European Accounting Review, 1–39. https://doi.org/
10.1080/09638180.2020.1813
Baron, D. P. (2001). Private Politics, Corporate Social Responsibility, and Integrated
Strategy. Journal of Economics Management Strategy, 10(1), 7–45. https://doi.org/
10.1111/j.1430-9134.2001.00007.x
Baumöhl, E., Iwasaki, I., & Kocenda, E. (2019). Institutions and determinants of firm
survival in European emerging markets. Journal of Corporate Finance., 58. https://
doi.org/10.1016/j.jcorpfin.2019.05.008
Becker-Olsen, K. L., Cudmore, B. A., & Hill, R. P. (2006). The impact of perceived
corporate social responsibility on consumer behavior. Journal of Business Research,
59, 46–53.
Benabou, R., & Tirole, J. (2010). Individual and Corporate Social Responsibility.
Economica, 77(305), 1–19. https://doi.org/10.1111/j.1468-0335.2009.00
Bentley, K. A., Omer, T. C., & Sharp, N. Y. (2013). Business strategy, financial reporting
irregularities, and audit effort. Contemporary Accounting Research, 30(2), 780–817.
Broadstock, D., Matousek, R., Meyer, M., & Tzeremes, N. (2020). Does corporate social
responsibility impact firms’ innovation capacity? The indirect link between
environmental & social governance implementation and innovation performance.
Journal of Business Research, 119, 99–110.
Brown, T. J., & Dacin, P. A. (1997). The company and the product: Corporate
associations and consumer product responses. Journal of Marketing, 61(1), 68–84.
https://doi.org/10.2307/1252190
Busch, T., Bauer, R., & Orlitzky, M. (2016). Sustainable development and financial
markets: Old paths and new avenues. Business & Society, 55(3), 303–329.
Carpentier, C., & Suret, J. M. (2011). The survival and success of Canadian penny stock
IPOs. Small Business Economics, 36, 101–121. https://doi.org/10.1007/s11187-0099190-x
Carroll, A. B., Lipartito, K. J., Post, J. E., & Werhane, P. H. (2012). Corporate
Responsibility: The American Experience. Cambridge: Cambridge University Press.
Chemmanur, T. J., Signori, A., & Vismara, S., (2019). The Exit Choices of European
Private Firms: A Dynamic Empirical https://ssrn.com/abstract=2530987 or https://
doi.org/10.2139/ssrn.2530987.
Cheng, B., Ioannou, I., & Serafeim, G. (2013). Corporate social responsibility and access
to finance. Strategic Management Journal, 35(1), 1–23. https://doi.org/10.1002/
smj.2131
Cho, C. H., Roberts, R. W., & Patten, D. M. (2010). The language of US corporate
environmental disclosure. Accounting, Organizations and Society, 35, 431–443.
Chung, Y., Na, H. S., & Smith, R. (2013). How Important is Capital Structure Policy to
Firm Survival? Journal of Corporate Finance., 22. https://doi.org/10.2139/
ssrn.1952199
Clark, G., & Viehs, M. (2014). The Implications of Corporate Social Responsibility for
Investors: An Overview and Evaluation of the Existing CSR Literature. SSRN
Electronic Journal. https://doi.org/10.2139/ssrn.2481877
Cornell, B., & Shapiro, A. C. (1987). Corporate stakeholders and corporate finance.
Financial Management, 16(5), 14.
Cox, D. R. (1972). Regression Models and Life Tables. Journal of the Royal Statistical
Society, B34, 187–220.
Delmas, M. A., & Burbano, V. C. (2011). The Drivers of Greenwashing. California
Management Review., 54(1), 64–87. https://doi.org/10.1525/cmr.2011.54.1.64
Demerjian, P., Lev, B., & McVay, S. (2012). Quantifying managerial ability: A new
measure and validity tests. Management Science, 58(7), 1229–1248.
Deng, X., Kang, J.-K., & Sin Low, B. (2013). Corporate social responsibility and
stakeholder value maximization: Evidence from mergers. Journal of Financial
Economics, 110(1), 87–109.
Derwall, J., Guenster, N., Bauer, R., & Koedijk, K. (2005). The eco-efficiency premium
puzzle. Financial Analysts Journal, 61(2), 51–63.
175
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Montiel, I., Gallo, P. J., & Antolin-Lopez, R. (2020). What on earth should managers learn
about corporate sustainability? A threshold concept approach. Journal of Business
Ethics, 162(4), 857–880.
Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when
firms have information that investors do not have. Journal of Financial Economics, 13
(2), 187–221. https://doi.org/10.1016/0304-405x(84)90023
Oikonomou, I., Brooks, C., & Pavelin, S. (2012). The impact of corporate social
performance on financial risk and utility: A longitudinal analysis. Financial
Management, 41(2), 483–515.
Ortiz-Villajos, J. M., & Sotoca, S. (2018). Innovation and business survival: A long-term
approach. Research Policy, 47(8), 1418–1436. https://doi.org/10.1016/j.
respol.2018.04.019
Patel, P. C., & Pearce, J. A. (2018). The survival consequences of intellectual property for
retail ventures. Journal of Retailing and Consumer Services, 43, 77–84. https://doi.
org/10.1016/j.jretconser.2018.03.005
Patel, P. C., Pearce, J., & Oghazi, P. (2021). Not so myopic: Investors lowering short-term
growth expectations under high industry ESG-sales-related dynamism and
predictability. Journal of Business Research. https://doi.org/10.1016/j.
jbusres.2020.11.013
Porter, M. E. (1991). Towards a dynamic theory of strategy. Strategic Management
Journal, 12(Special Issue: Fundamental Research Issues in Strategy and Economics),
95–117.
Porter, M., & Kramer, M. (2011). The Big Idea: Creating Shared Value. How to Reinvent
Capitalism—and Unleash a Wave of Innovation and Growth. Harvard Business
Review, 89, 62–77.
Porter, M. E., & van der Linde, C. (1995). Toward a new conception of the
environment–competitiveness relationship. Journal of Economic Perspectives, 9(4),
97–118. https://doi.org/10.1257/jep.9.4.97
Qiu, M. Y., & Yin, H. (2019). Analysis of enterprises’ ESG performance and financing
costs under the background of ecological civilization construction. Journal of
Quantitative and Technical Economics., 3, 108–123.
Rajan, R. G., & Zingales, L. (1998). Financial Dependence and Growth. American
Economic Review, 88(3), 559–586.
Rubin, A. (2008). Political views and corporate decision making: The case of corporate
social responsibility. Financial Review, 43, 337–360.
Schauer, C., Elsas, R., & Breitkopf, N. (2019). A new measure of financial constraints
applicable to private and public firms. Journal of Banking & Finance. https://doi.org/
10.1016/j.jbankfin.2019.01
Seele, P., & Gatti, L. (2015). Greenwashing Revisited. In Search of a Typology and
Accusation-Based Definition Incorporating Legitimacy Strategies. Business Strategy
and the Environment., 26, 239–252. https://doi.org/10.1002/bse.1912
Servaes, H., & Tamayo, A. (2013). The impact of corporate social responsibility on firm
value: The role of customer awareness. Management Science, 59(5), 1045–1061.
Shapiro, S. P. (2005). Agency theory Annual Review of Sociology, 31, 263–284.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under
conditions of risk. The Journal of Finance, 19, 425–442.
Shleifer, A., & Vishny, R. (1989). Management Entrenchment: The Case of ManagerSpecific Investments. Journal of Financial Economics, 25, 123–139.
Shleifer, A., & Vishny, R. (1997). A survey of corporate governance. Journal of Finance,
52(2), 737–783.
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model.
Journal of Business, 74(1), 101–124.
Siano, A., Vollero, A., Conte, F., & Amalibe, S. (2017). “More than words”: Expanding the
taxonomy of greenwashing after the Volkswagen scandal. Journal of Business
Research, 71, 27–37.
Starks, L., Venkat, P., & Zhu, Q., (2017). Corporate ESG profiles and investor horizons.
Available at SSRN: https://ssrn.com/abstract=3049943 or https://doi.org/10.2139/
ssrn.3049943.
Statman, M., & Glushkov, D. (2009). The wages of social responsibility. Financial Analysts
Journal, 65(4), 774–800.
Tang, H. (2022). The Effect of ESG Performance on Corporate Innovation in China: The
Mediating Role of Financial Constraints and Agency Cost. Sustainability, 14, 3769.
https://doi.org/10.3390/su14073769
Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of Money,
Credit and Banking, 1(February), 15–29.
Tobin, J., & Brainard, W. C. (1968). Pitfalls in financial model building. American
Economic Review, 58, 99–122.
Tran, K. A., & Tsionas, E. (2010). Local GMM Estimation of Semiparametric Panel Data
with Smooth Coefficient Models. Econometric Reviews, 29(1), 39–61.
Van de Ven, W. P. M. M., & Van Praag, B. M. S. (1981). The demand for deductibles in
private health insurance. Journal of Econometrics, 17(2), 229–252. https://doi.org/
10.1016/0304-4076(81)90028Vanhamme, J., & Grobben, B. (2009). ‘‘Too Good to be True!’’. The effectiveness of CSR
history in countering negative publicity. Journal of Business Ethics, 85(2), 273–283.
Wagner, M. (2010). The role of corporate sustainability performance for economic
performance: A firm-level analysis of moderation effects. Ecological Economics, 69(7),
1553–1560. https://doi.org/10.1016/j.ecolecon.2010.02.017
Wang, Z. (2007). Technology innovation market turbulence. Review of Economic
Dynamics, 10(1), 78–105.
Wang, H., Choi, J., & Li, J. (2008). Too little or too much? Untangling the relationship
between corporate philanthropy and firm financial performance. Organization.
Science, 19(1), 143–159. https://doi.org/10.1287/orsc.1070.0271
Hong, H., & Kacperczyk, M. (2009). The price of sin: The effects of social norms on
markets. Journal of Financial Economics, 93(1), 15–36. https://doi.org/10.1016/j.
jfineco.2008.09
Hung, M., Shi, J., & Wang, Y. (2018). Mandatory CSR Disclosure and Information
Asymmetry: Evidence from a Quasi-Natural Experiment in China. Journal of
Accounting and Economics, 65(1), 169–190.
Ioannou, I., & Serafeim, G. (2014). The impact of corporate social responsibility on
investment recommendations: Analysts’ perceptions and shifting institutional logics.
Strategic Management Journal, 36(7), 1053–1081. https://doi.org/10.1002/smj.2268
Jain, B. A., & Kini, O. (2000). Does the Presence of Venture Capitalists Improve the
Survival Profile of IPO Firms. Journal of Business Finance Accounting, 27(9&10),
1139–1183. https://doi.org/10.1111/1468-5957.00350
Jain, B. A., & Martin, C. L., Jr. (2005). The association between audit quality and postIPO performance: A survival analysis approach. Review of Accounting and Finance, 4
(4), 50–75.
Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers.
American Economic Review, 6(2), 323–339.
Jensen, M. C. (1994). Self-interest, altruism, incentives, and agency theory. Journal of
Applied Corporate Finance, 7(2), 40–45.
Jensen, M. C. (2001). Value maximization, stakeholder theory, and the corporate
objective function. Journal of Applied Corporate Finance, 14, 8–12.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behaviour,
agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.
Jo, H., & Na, H. (2012). Does CSR reduce firm risk? Evidence from controversial industry
sectors. Journal of Business Ethics, 110, 441–456.
Kacperczyk, A. (2009). With greater power comes greater responsibility? Takeover
protection and corporate attention to stakeholders. Strategic Management Journal, 30
(3), 261–285.
Kaplan, S. N., & Zingales, L. (1997). Do Investment-Cash Flow Sensitivities Provide
Useful Measures of Financial Constraints. Quarterly Journal of Economics, 112,
159–216.
Kasioumi, M., & Stengos, T. (2020). The Environmental Kuznets Curve with Recycling: A
Partially Linear Semiparametric Approach. Journal of Risk and Financial Management,
13(11), 1–26.
Kay, J. (2012). The Kay review of UK equity markets and long-term decision making.
Final Report, 9.
Kempf, A., & Osthoff, P. (2007). The effect of socially responsible investing on portfolio
performance. European Financial Management, 13(5), 908–922.
Keynes, J. M. (1935). The General Theory of Employment Interest and Money. New York:
Harbinger.
Kim, E. H., & Lyon, T. (2015). Greenwash vs. Brownwash: Exaggeration and Undue
Modesty in Corporate Sustainability Disclosure. Organization Science., 26, 705–723.
https://doi.org/10.1287/orsc.2014.0949
Kim, Y., Li, H., & Li, S. (2014). Corporate social responsibility and stock price crash risk.
Journal of Banking and Finance, 43, 1–13.
Klein, J. P., & Moeschberger, M. L. (2003). Survival Analysis: Techniques for Censored and
Truncated Data. Springer Science & Business Media.
Landier, A., & Nair, V. B. (2009). Investing for Change: Profit from Responsible Investment.
Oxford: Oxford University Press.
Langfield-Smith, K. (2007). A review of quantitative research in management control
systems and strategy. In C. S. Chapman, A. G. Hopwood, & M. D. Shields (Eds.),
Handbook of management accounting research (Vol. 2, pp. 753–783). Elsevier.
LeClere, M. J. (2000). The occurrence and timing of events: Survival analysis applied to a
study of financial distress. Journal of Accounting Literature, 19, 158–189.
Lee, G., & Masulis, R. W. (2009). Seasoned equity offerings: Quality of accounting
information and expected flotation costs. Journal of Financial Economics, 92(3),
443–469.
Lipton, M. (1979). Takeover bids in the target’s boardroom. The Business Lawyer,
101–134.
Lokshin, M. (2006). Difference-based semiparametric estimation of partial linear
regression models. The Stata Journal, 6(3), 377–383.
Lu, H., Oh, W. Y., Kleffner, A., & Chang, Y. K. (2021). How do investors value corporate
social responsibility? Market valuation and the firm specific contexts. Journal of
Business Research, 125, 14–25.
Luo, X., & Bhattacharya, C. B. (2009). The debate over doing good: Corporate social
performance, strategic marketing levers, and firm-idiosyncratic risk. Journal of
Marketing, 73, 198–213.
Malik, M. (2014). Value-Enhancing Capabilities of CSR: A Brief Review of Contemporary
Literature. Journal of Business Ethics., 127. https://doi.org/10.1007/s10551-0142051-9
Margolis, J., Elfenbein, H., & Walsh, J. (2009). Does it Pay to Be Good...And Does it
Matter? A Meta-Analysis of the Relationship between Corporate Social and Financial
Performance. SSRN Electronic Journal.. https://doi.org/10.2139/ssrn.1866371
Margolis, J. D., & Walsh, J. P. (2003). Misery Loves Companies: Rethinking Social
Initiatives by Business. Administrative Science Quarterly, 48(2), 268–305. https://doi.
org/10.2307/3556659
Marwick, A., Hasan, M. M., & Luo, T. (2020). Organization capital and corporate cash
holdings. International Review of Financial Analysis, 68, 1–17.
Masulis, R., & Reza, S. (2013). Agency Problems of Corporate Philanthropy. Review of
Financial Studies., 28. https://doi.org/10.2139/ssrn.2234221
McWilliams, A., & Siegel, D. (2001). Corporate social responsibility: A theory of the firm
perspective. Academy of Management Review, 26(1), 117–127.
Miles, R. E., Snow, C. C., Meyer, A. D., & Coleman, H. T. (1978). Organizational strategy,
structure, and process. The Academy of Management Review, 3(3), 546–562.
176
I. Fafaliou et al.
Journal of Business Research 149 (2022) 161–177
Whited, T. M., & Wu, G. (2006). Financial constraints risk. Review of Financial Studies, 19
(2), 531–559.
Yan, S., Ferraro, F., & Almandoz, J. (2019). The rise of socially responsible investment
funds: The paradoxical role of the financial logic. Administrative Science Quarterly, 64
(2), 466–501.
Yang, Q. G., & Pickford, M. (2016). Modeling the duration of merger reviews in New
Zealand. Journal of Competition Law & Economics, 12(1), 69–97.
Economy, and Marketing. Moreover, she has teaching experience in the areas of Business
Sustainability, International Business Activity, Industrial Policy, Marketing Strategies and
Entrepreneurship.
Dr. Dimitris Konstantios is a Post-Doc researcher at the Economics Department of the
University of Piraeus, Greece. His research lies in studying the relationship between
innovation and finance. Moreover, he is interested in the areas of Sustainability and
Corporate Finance. Dimitris holds a Ph.D in Economics (‘Essays on Innovation and
Finance’, 2018) from the University of Piraeus, a M.Sc.in Economic and Business Strategy
from the University of Piraeus (2014), and a B.Sc. in Mathematics from the University of
Patras (2010). Dimitris has been a tutor of various macroeconomics and finance courses at
the graduate and undergraduate level at the Economics Department of the University of
Piraeus.
Dr Irene Fafaliou is an Emeritus Professor in Enterprise and Small Business Support Pol­
icies at the Economics Department of the University of Piraeus. Much of her research is
focused on industrial organization and competitiveness, entrepreneurship, technology
transfer, corporate social responsibility & business ethics, and Innovation in Small &
Medium-Sized Enterprises. Her work has been published in several leading referred
journals such as International Advances in Economic Research, International Journal of Eco­
nomics and Business Research, International Journal of Entrepreneurship and Innovation
Management, International Journal of Social Economics, Marine Policy Journal, Production
Planning and Control Journal, International Journal of Value-based Management, International
Journal of Economic Research, Global Business & Economic Anthology.
Michael Polemis is an Associate Professor at the University of Piraeus and a Member of the
Hellenic Competition Commission. His main areas of research include industrial organi­
zation, competition policy, regulatory economics, energy & environmental economics.
Michael has published more than 90 papers in journals such as the European Journal of
Operational Research, Economics Letters, International Business Review, Regional Science, The
Energy Journal, Energy Economics, Energy Policy, International Journal of the Economics of
Business, Managerial and Decision Economics, Bulletin of Economic Research, Journal of Pro­
ductivity Analysis. Based on IDEAS, he is ranked at the Top 5% of economists in Greece. He
has more than 2,200 citations as appeared in Google Scholar.
Maria Giaka holds a Ph.D. from the University of Piraeus. She holds an M.Sc. in Economics
and Business Strategy from the University of Piraeus. She also holds a bachelor’s degree in
economics from the National and Kapodistrian University of Athens. Her research interests
are in the areas of Economics, International Business, Innovation, Sustainability, Ethical
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