sustainability assurance and sustainability disclosure
TRANSCRIPT
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Sustainability Assurance and Sustainability Disclosure Quality:
An Empirical Investigation of Environmentally
Sensitive Industries
Berto Usman
PhD Student at the Department of Economics and Management
University of Padova
Supervisors:
Prof. Federica Ricceri
Prof. Giovanna Michelon
Abstract
Our study examines the relationship between sustainability assurance and sustainability
disclosure quality in the context of European environmentally sensitive industry. The
investigation of sustainability assurance implication on sustainability disclosure quality has
attracted much attention and interest among accounting scholars. Nonetheless, prior studies
have resulted in inconclusive results. Utilizing the data from 226 public listed companies
operating in 24 European countries from 2014 to 2017, we test eight proxies of sustainability
assurance practice on sustainability disclosure quality. Our findings show that the presence of
sustainability assurance (SA), type of assurance provider (ACCOUNTANT and
CONSULTANT), the level of assurance (LIMITED) and assurance persistency (AS_PERS)
are positively and significantly associated with CSR disclosure quality, whilst the level of
assurance (REASONABLE and MIXED), and assurance persistency (AS_TENURED) do not
associate with CSR disclosure quality. Our findings remain consistent after being controlled
by using different sample groups, indicating that proper sustainability assurance engagement
enables firms to better provide their non-financial information disclosure to the public.
1. INTRODUCTION
Sustainability reporting is now becoming a standard business practice worldwide and
is firmly established as a global trend (GRI, 2014; Junior, Best, & Cotter, 2014; KPMG,
2017). As documented by KPMG in 2017, from 1997 to 2017 the growth in global
sustainability reporting is 93% for the G250 companies and 75% for the N100 companies in
49 surveyed countries. In Europe, European Union through its directive 2014/95/EU requires
large size companies (employees more than 500 people) and those who have a large public
interest to mandatorily disclose their non-financial and diversity information to the public
(European Commission, 2014; Schneider, Michelon, & Paananen, 2018). Non-financial
information is commonly disclosed through a stand-alone sustainability report or in a
dedicated section in annual report (Michelon, Pilonato, & Ricceri, 2015; Simnett,
Vanstraelen, & Chua, 2009; Briem & Wald, 2018; Simnett, 2012). This report is deemed as
one of the ways to increase the firms’ transparency and accountability, which is required to
make the disclosed information useful and credible to market and society (GRI, 2014;
Mercer, 2004; Junior et al., 2014). Regarding the reporting practice, sustainability reporting is
set as a process that can assist the organization in setting up their goals, particularly by
measuring performance and managing the change on long-term sustainable global economy.
More technically, this effort is mainly manifested through a platform which is aimed at
communicating and distributing the organization’s economic, environmental, social and
governance performance, reflecting the positive and negative impact (GRI, 2014; O’dwyer,
2011). As highlighted by Bagnoli & Watts, (2017), Deegan, Cooper, & Shelly, (2006) and ,
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Briem & Wald, (2018) sustainability reporting includes many forms of reports, such as (i) the
stand-alone reports; CSR reports, sustainability reports, integrated reports, triple bottom line
reports, environmental reports, ESG reports, citizenship reports, and (ii) the added
information in the financial report; Integrated financial report, consolidated annual report.
These reports are conceived to provide an account of the environmental, social, and economic
impacts of firms activity (GRI, 2014).
Given the widespread and massive growth of sustainability reports in the past
decades, this practice has turned into an important phenomenon in practice and considered as
a critical field in academia (Cheng, Ioannou, & Serafeim, 2014; Michelon et al, 2015; Junior
et al., 2014). In more specific case, firms may use sustainability report to distinguish their
performance compared with the other firms. By doing so, firms with good sustainability-
related engagement may provide a more credible commitment of information disclosure,
which is unique and cannot be easily replicated by the other competitors (Braam, Weerd,
Hauck, & Huijbregts, 2016). However, the previous studies pointed out that there are several
issues and critiques with respect to the unregulated sustainability disclosure (Merkl-Davies &
Brennan, 2007), lack of completeness and credibility of the reported information (Adams &
Evans, 2004), immateriality information (Khan, Serafeim, & Yoon, 2016), tool of reputation
risk management (Bebbington, Larrinaga, & Moneva, 2008), impression management (Cho,
Michelon, & Patten, 2012), greenwashing and CSR-Washing (Mahoney, Thorne, Cecil, &
LaGore, 2013; Pope & Wæraas, 2016), camouflaging (Michelon, Pilonato, Ricceri, &
Roberts, 2016), and symbolical use of CSR reports (Michelon et al., 2015; Rodrigue,
Magnan, & Cho, 2013). These problems, could be the initial signs which indicate that firms
are actually not properly engage in providing the factual and substantial source of
sustainable-related information to their investors and stakeholders, and simply completing a
formal box-ticking task in the reporting practice (Schneider et al., 2018). To deal with the
aforementioned matters, recent studies have suggested the companion of sustainability
assurance (SA) to overcome and reduce the problems due to the critique on sustainability
reporting (Adams & Evans, 2004; Mercer, 2004).
Although the companion of sustainability assurance on sustainability report is
expected to tackle and overcome the critiques on sustainability reporting, sustainability
assurance is also criticized due to the managerial capture problems (Manurung & Basuki,
2010; Owen, Swift, Humphrey, & Bowerman, 2000), lack of specific criteria and regulation
(Deegan et al., 2006; Junior, Best, & Cotter, 2014; O’Dwyer & Owen, 2005), lack of
stakeholder engagement (Adams & Evans, 2004), the lack of independence of the assurance
providers (O’Dwyer & Owen, 2005; Wong & Millington, 2014), and the trust on the
assurance providers (Wong & Millington, 2014). Hereby, the increasing number of criticisms
on the role of sustainability assurance on sustainability report provides interesting findings
and relevant setting of debates, whether the companion of sustainability assurance could be
the solution and truly demonstrate a significant role in enhancing the credibility, reliability,
validity (Kolk & Perego, 2010; Mercer, 2004; Adams & Evans, 2004) and quality of the
reported non-financial information (Michelon et al., 2015; Moroney, Windsor, & Aw, 2011).
Inspired by the burgeoning number of literature and interest in the sustainability
reporting studies among accounting scholars, our study emerges to investigate the implication
of sustainability assurance (SA) on the variation of sustainability disclosure quality. We have
systematically investigated that the previous studies in the arena of sustainability reporting
and SA draw on numerous results, but attempt to link the concept of SA by using specific
proxy of measurements with the sustainability disclosure quality is still limited. Also, most of
the effort in studying the assurance practice is more highlighted to the assurance statement
quality (Deegan et al., 2006; Manurung & Basuki, 2010; O’Dwyer & Owen, 2005) than the
role taken by assurance practice on the sustainability disclosure quality. Interestingly, most of
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the study on the sustainability disclosure quality is also relying on the quantity-based
measure, and have not dealt much with the quality of information (Beretta & Bozzolan,
2008). Among them, the study of Moroney et al., (2011) and Michelon et al., (2015) appeared
to offer different alternative measures in identifying the role of sustainability assurance
practice on the sustainability disclosure quality. Taken together, the study of Michelon et al.,
(2015) and Moroney et al., (2011) offered specific measures of disclosure indexes, and
considered the availability of assurance report. They further investigated whether the choice
of having SA on the sustainability report is associated with the higher sustainability
disclosure quality. On the one hand, Moroney et al., (2011) found a positive association
between SA and sustainability disclosure quality, whilst Michelon et al., (2015) on the other
hand found no association.
The main notion of our paper focuses on investigating whether the practice of SA
through several channels is (the presence of assurance report, the assurance providers, the
level of assurance, and assurance persistency) associated with the sustainability disclosure
quality. We have done an extensive literature review on the SA and sustainability studies and
found that only a few studies investigated the relationship between the type of assurance
provider, the level of assurance, assurance persistency and sustainability disclosure quality.
Prior studies focus more on addressing the issue or factors that drive the demand of SA
(Gillet-Monjarret, 2015; Cho, Michelon, Patten, & Roberts, 2014; O’Dwyer & Owen, 2007;
Ruhnke & Gabriel, 2013; Kolk & Perego, 2010; Mock, Strohm, & Swartz, 2007; Park &
Brorson, 2005; Briem & Wald, 2018), assurance as legitimacy tool (O’Dwyer, Owen, &
Unerman, 2011 p. 31; Michelon, Patten, & Romi, 2018), evolutionary trends of external
assurance on sustainability report (Cohen & Simnett, 2015; Briem & Wald, 2018; Park &
Brorson, 2005; Paolo Perego & Kolk, 2012; Simnett, Vanstraelen, & Chua, 2009), the role of
SA on the perceived quality, credibility and reliability of the sustainability report (Briem &
Wald, 2018; Coram, Monroe, & Woodliff, 2009; Park & Brorson, 2005; Pflugrath, Roebuck,
& Simnett, 2011; Simnett, Vanstraelen, & Chua, 2009; Braam et al., 2016), the type of
assurance providers (Mock, Strohm, & Swartz, 2007; Junior et al., 2014; Deegan et al., 2006;
2005; Wong & Millington, 2014; Perego, 2009), and the role of accountant in the assurance
market (Mock et al., 2007; Sierra, Zorio, & García-Benau, 2013; Huggins, Green, & Simnett,
2011; O’dwyer, 2011; Deegan et al., 2006). Therefore, with the spirit to expand the previous
work of Michelon et al., (2015) and Moroney et al., (2011), we introduce and propose the
variation of sustainability disclosure quality (measured by disclosure indexes, accounting
both for the quantity and quality; type of information, managerial orientation and materiality
of information) as the function of sustainability assurance, the type of assurance providers,
the levels of assurance, and assurance persistency. We conjecture that after the decision of
having SA on the sustainability report, the choice of assurance provider can influence the
degree of information provided by the firms (Gürtürk & Hahn, 2016). In particular, we
investigate whether the choice of having SA provided by accounting and consultancy firms is
positively associated with the sustainability disclosure quality.
In addition, because the implementation of assessment criteria depends on the level of
assurance engagement, we expect that the higher level of assurance is associated with the
higher CR disclosure quality. However, prior studies seem to be more concentrated to
investigate the general contents of assurance statement quality (Perego, 2009; Perego & Kolk,
2012; Manurung & Basuki, 2010) than studying the relationship between the level of
assurance and sustainability disclosure quality. Take, for instance, the evidence from the UK
as provided by O’Dwyer & Owen, (2005) reported that accounting firms tend to give limited
assurance (low level) to their clients, whilst environmental consultants provide reasonable
assurance (high level). These output is presented by conducting content analysis on the
recommended minimum content of assurance statements. Unfortunately, the previous studies
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have not dealt with the content of sustainability-related information as disclosed by the firms.
We conjecture that the study would have been more interesting if it had included the issue
concerning the level of assurance and further examine it on the sustainability disclosure
quality.
Taking into account the possible benefits of assurance engagement, we argue that
assurance on the sustainability report is useful for a wide range of users, and the persistence
of assurance appears as another significant aspect of the increased sustainability disclosure
quality. As documented by several studies, the presence of sustainability report allows the
increase in the number of financial analyst following, that leads to the lower cost of capital
(Dhaliwal, Li, Tsang, & Yang, 2011; Dhaliwal, Radhakrishnan, Tsang, & Yang, 2012;
Dhaliwal, Li, Tsang, & Yang, 2014). From the point of view of financial analyst, credible,
reliable, accurate, and timely available information is essentially required in minimizing the
probability of forecast errors (Hirst, Koonce, & Miller, 1999; Simpson, 2010). In line with
the study of Simpson (2010) and Axjonow et al., (2016) non-financial disclosure is expected
as the relevant information by the stakeholders, particularly professional stakeholders
(financial analyst, sophisticated investors etc). With the same spirit and notion, there would
therefore seem to be a definite need for a persistent companion of assurance statement
(assurance persistency) on the reported CR information may affect the sustainability
disclosure quality.
The potential contributions of our paper are threefold. First, we contribute to the
debate on the role of SA on the sustainability disclosure quality by providing thorough
evidence in the context of European sustainability report and its assurance practice. Second,
we expand the study of Moroney et al., (2011) and Michelon et al., (2015) by providing a
new alternative measure of disclosure indexes through the consideration of materiality aspect
from the perspective of capital market participants (i.e., financial analyst). Third, we adopt
the longitudinal study to address the change and behavior of sustainability and assurance
practice in the European context. To better explain the extent of SA on the sustainability
disclosure quality, this study is expected to provide thorough empirical evidence in the
European environmentally sensitive industry (ESI). By analyzing public listed companies
from 24 European countries with period observation spans from 2014 to 2017, it is expected
that this paper better captures the jurisdictional setting that is potentially can affect the
corporate reporting regulation.
Utilizing European dataset (226 public listed companies from 2014 to 2017), our
findings indicate that sustainability assurance is positively associated with sustainability
disclosure quality. This positive association is followed by the type of assurance providers,
level of assurance, and the assurance persistency. This evidence suggests that the issuers of
stand-alone sustainability report provide more disclosure and this is in line with the
increasing quality of the disclosed information. The presence of assurance ensuring that the
sustainability-related information is disclosed properly and indicating higher quality than
those companies that are not assured. We interpret that the presence of assurance is an
important practice to enhance the sustainability disclosure quality, that may lead to the
increasing value of the report for the report users. Also, we find that either the assurance
provider from the accounting or consultant professions show significant association with
sustainability disclosure quality. However, considering the level of assurance, only limited
level of assurance reflecting positive association with sustainability disclosure quality, while
the reasonable and mixed level of assurance engagement do not indicate any association with
sustainability disclosure quality. We also point out the importance of assurance persistency
for sustainability reporting. The empirical evidence shows that assurance persistency is
positively associated with sustainability disclosure quality. Recall back the obtained
empirical evidence, we believe that our study contributes to the debate on the role and
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implication of sustainability assurance on sustainability disclosure quality, providing an
extension to the prior studies conducted by Michelon et al., (2015) and Moroney et al.,
(2011).
The remainder of this paper is structured as follows: Section 2 highlights the literature
review by explaining the major theoretical foundations of the current trend of sustainability
assurance and CR reporting. Section 3 presents the method of data collection. Section 4
reports and discusses the findings, and Section 5 concludes with the implications of this
study.
2. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
In regards to the hypothesis development, we directly discuss the relationship of each
variable of interest and further provide a research model, which simplifies the overall idea in
our study. As can be seen in Figure 2.1, we employ a conceptual idea at the first layer (above
the dotted line), and the operationalization of the conceptual idea is available under the dotted
line.
[Insert Figure 2.1 around here]
Referring to Figure 2, It is obviously described that we test the relationship between
sustainability assurance and sustainability disclosure quality. It is also worth mentioning that
what we mean with sustainability disclosure quality is more on the environmentally-related
disclosure information. We follow the definition of environmental disclosure as defined by
Berthelot, Cormier, & Magnan, (2003 p. 22) in which environmental disclosure is defined as
”the set of information items that relate to a firm’s past, current, and future environmental
management activities and performance. This also comprises information about the past,
current, and future financial implication resulting from a firm’s environmental management
decision or action”.
2.1. Sustainability assurance and Sustainability disclosure quality
The extensive research in sustainability reporting has shown that firms need
legitimacy as the license to operate, and firms with poor environmental performance would
be expected to disclose more information regarding their environmental information
(Rodrigue et al., 2013; Cho & Patten, 2007). Aerts, Cormier, & Magnan, (2008), Cormier,
Magnan, & Van Velthoven, (2005), and Brown & Deegan, (1998) highlighted the importance
of implicit social contract between society and business entity as the major key factor for the
firms to obtain their legitimacy to operate. With the same spirit, Patten (1991) previously
documented that the type of industry can also affect and lead the sustainability disclosure to
minimize the critics and pressure from society. More recent findings regarding the relevance
of industry in sustainability reporting practice have been summarized by the study of Cho &
Patten, (2007), Moroney et al., (2011) and Axjonow et al., (2016). In their studies, they
assessed the effectiveness of voluntary environmental disclosure (Cho & Patten, 2007) and
assurance practice (Moroney et al., 2011) as the legitimacy tool and provided the evidence
that most of the firms operating in the environmentally sensitive industry (ESI) are more
likely to disclose more environmental-related information. Axjonow et al., (2016) also found
that the decision of disclosing non-financial information positively increase firms’ legitimacy
(reputation) from the point of view of professional stakeholders.
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However, all the studies reviewed so far suffered from the fact that not all
sustainability reports provide meaningful and substantial information. Several researchers
argue that the strategy of using sustainability reports as the tool to obtain legitimacy has not
been fully proper. Adams & Evans (2004), for example, pointed out that sustainability
reporting is facing a problem concerning the lack of completeness. Critics have also argued
that not only unstructured report and incomplete information provide less quality
sustainability information, but temporary sustainability activity engagement as the quick
response to counter the impact of negative news also leads to an effort of reputation risk
management (Bebbington et al., 2008). The more recent argument supporting the skepticism
of sustainability reporting also reported by the study of Michelon et al., (2015), which
suggests that sustainability reporting seemingly points to an effort to enhance perceived
accountability, in which sustainability report is not used to provide a higher quality of
disclosure information.
With respect to the problems as faced in the practice of sustainability reporting, a
reasonable approach to tackle the issue in sustainability reporting could be done by providing
sustainability assurance (Dando & Swift, 2003; Mercer, 2004). The findings from prior
studies suggest that assurance on sustainability report can have an effect on the quantity and
quality of the disclosed sustainability information. For instance, the study of Moroney et al.,
(2011). Using the sample from the Australian companies, Moroney et al., (2011) examined
the difference in quantity and quality of voluntary environmental disclosures. Their study
shows that the quantity of information is higher when the report is assured, and the quality of
voluntary environmentally disclosure is significantly higher for the assured companies. On
the other hand, in spite of these recent findings about the role of sustainability assurance, the
study of Michelon et al., (2015) provides information that assurance practice does not
associate with the sustainability disclosure quality. Utilizing the setting of the UK companies,
they report that assurance practice does not seem enough to avoid the criticism on the lack of
credibility of sustainability report. Taking the above discussion on board, we test the role of
sustainability assurance to the enhancement of sustainability disclosure quality. We predict
that sustainability assurance could be a channel through which firm can increase the quality
of their sustainability report.
Hypothesis 1: Sustainability assurance is positively associated with sustainability disclosure
quality.
2.2. Assurance provider and sustainability disclosure quality
Aside from the need to increase the quality of the reported sustainability-related
information, we argue that the choice of assurance provider can influence the degree of
information provided by the firms. In particular, we investigate whether the choice of having
sustainability assurance on sustainability report provided by accounting firms is positively
associated with the sustainability disclosure quality. In examining the different possible effect
of the assurance provider, we retrieve the assurance provider information for each sample
firm. We follow the study of Moroney et al., (2011) and Simnett et al., (2009) in
distinguishing the choice of assurance providers (accounting and consultancy firms).
Prior studies on the assurance practice document that firms can purchase assurance
service from a wide variety of providers (Bagnoli & Watts, 2017; Deegan et al., 2006). In
general, there are two main groups of assurance provider currently being classified in the
sustainability assurance research. One is the assurance provider from the accounting
profession (Big N, non-Big N), and second from environmental consultant or non-accounting
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profession (environmental & engineering consultant, environmental research organization,
social/ethical performance consultant) (Deegan et al., 2006; Moroney et al., 2011; Simnett et
al., 2009). Studies have compared the quality of assurance statement provided by the
accounting profession and environmental consultant is essentially different (Hodge,
Subramaniam, & Stewart, 2009). The finding of Deegan et al., (2006) and Gürtürk & Hahn,
(2016) suggest that most of non-professional accounting assurance provider engage in
different approaches (i.e., content, executed process, concrete implementation of the
standard) in compiling the assurance statement for the Triple Bottom Line (TBL) report. Such
approach, however, has failed to address the potential value of the assurance statement brings
to the TBL reporting process (Deegan et al., 2006).
With regard to the expertise, assurance providers from the accounting profession are
perceived by the users can provide better assessment (given the skills, competencies and
market recognition to perform financial audits) during the assurance process engagement
(Hodge, Subramaniam, & Stewart, 2009). As noted by Moroney et al., (2011), professional
accountants as the statutory auditors are trained and have a stringent education with a set of
skills which is strictly bound by the requirement of International Federation of Accountants
(IFAC) code of ethics (professionalism, independence, objectivity). Another recent argument
supporting the accountant skills in the assurance engagement is related to the study of
Huggins, Green, & Simnett, (2011). They highlighted that accountants might apply the model
used in the financial statement audits. This procedure requires the accountant to have a clear
comprehension of the entity and apprehension on the risk of material misstatement. However,
Huggins et al., (2011) also indicated that a major criticism emerges due to the subject matter
expertise. In this case, accountants presumably do not have the necessary subject matter and
sufficient knowledge to complete a particular task, while on the other hand, an environmental
consultant is deemed having specific skill-sets and extensive knowledge on the subject matter
(Huggins et al., 2011). Empirically, this circumstance is also supported by the findings of
Wong & Millington, (2014), which pointed out that stakeholders prefer the assurance service
from the consultant (specialist environmental assurors) rather than financial auditors.
Given the strengths and weaknesses as shown by the role and characteristics of
professional accounting firms and non-accounting firms in the assurance market, the previous
studies have provided a sufficient number of evidence regarding the effect of the type
assurance provider on the perceived credibility of sustainability report (Dando & Swift, 2003;
Hodge et al., 2009; Park & Brorson, 2005; Wong & Millington, 2014). However, concerning
the influence of the type of assurance provider on the sustainability disclosure quality is still
under-researched and deserved further investigation. To date, few studies particularly assess
the association between the type of assurance provider and the sustainability disclosure
quality. An international study conducted by Perego, (2009) provided the cause and
consequences of choosing different assurance provider of sustainability report. His study
showed that firms operating in the weak governance system are more likely to choose a Big4
accounting firm instead of an environmental consultant. However, by using logit regression
analysis and focused more on the assurance report quality, his study fails to consider the
different categories of CR disclosure quality. The recent work of Hummel, Schlick, & Fifka,
2017) also examined the relationship between assurance provider and the quality of the
assurance report. In this context, they revealed that there is a negative relationship between
the professional accounting provider and the breadth of assurance statement as the proxy of
assurance quality. Again, the works of Hummel et al., (2017) and Perego, (2009) stand on the
side of assurance provider, and do not provide any evidence concerning on the quality of
sustainability reports that were being assessed by the assurance provider. The study of
Moroney et al., (2011) seemed as one of the little studies which provided the evidence for the
type of assurance provider and its association with the quality of sustainability disclosure
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quality. Yet, their findings still indicated that there is no consensus about the particular
association of the type of assurance provider with the sustainability disclosure quality, in
which there is no different of voluntary environmental disclosure quality between those firms
have their reports assured by accountant and consultant. Given the above empirical
discussions and arguments, we hypothesize that accounting and consultant firms may have
the important role in the enhancement of sustainability disclosure quality. Formally, we
design hypothesis 2a and 2b as follows.
Hypothesis 2a: Accounting firm assurance provider is positively associated with the
sustainability disclosure quality
Hypothesis 2b: Consultant firm assurance provider is positively associated with the
sustainability disclosure quality
2.3. The level of assurance and sustainability disclosure quality
In this stage, we develop the argument to test the association between the level of
assurance and sustainability disclosure quality. The level of assurance practice describes the
coverage of assessment conducted by the assurance provider (Corporate Register, 2008). As
documented by prior studies in the assurance literature (Huggins et al., 2011; Manetti &
Becatti, 2009; Perego, 2009; Simnett, 2012) there are three assurance standards currently
being adopted by the assurance providers. One is the Global Reporting Initiative Guideline
(GRI), two is the AA1000 assurance standard (AA1000AS), and three is the International
standards on assurance engagement (ISAE3000). However, ISAE3000 and AA1000 are
dominantly adopted by the assurance providers during the assurance process, while GRI is
mostly adopted as the disclosure framework (Gillet-Monjarret, 2015; Manetti & Becatti,
2009)Some studies (Huggins et al., 2011; Simnett, 2012) distinguished two use of different
type of assurance standards based on the type of assurance providers. Perego, (2009) in his
study reported that the accounting professions are more likely to adopt the ISAE3000 in their
assurance process engagement, while environmental consultants incline to use the AA1000
standard. Huggins et al., (2011) further confirmed that ISAE3000 assurance standard is also
used by both accounting and non-accounting assurance providers. In more specific case of
assurance practice (e.g., assurance engagement on Greenhouse Gas statements) Huggins et
al., (2011) pointed out that there are several particular assurance standards that could be
utilized by assurance provider (e.g., ISAE 3410 and ISO 14064-3).
In the standard of sustainability assurance practice, AA1000 and ISAE3000 utilize
two types of assurance levels: the limited assurance vs. the reasonable assurance (some firms
may engage in the combination of these two levels; See the study of Gürtürk & Hahn, 2016).
According to the study of Hodge, Subramaniam, & Stewart, (2009) and Manetti & Becatti,
(2009) limited assurance refers to moderate or limited level of assurance, and the assurance
statement is worded in a more negative form. Meanwhile, reasonable assurance refers to
communication at a high level of assurance, which is not an absolute level of assurance due
to the limitation of the internal control system and the assurance process. In the level of
reasonable assurance, Hodge et al., (2009) also noted that the assurance statement is worded
in a more positive form. Moreover, Manetti & Becatti, (2009) argued that the different levels
of assurance are given based on the intrinsic characteristics of the subject matter and the
investigation implemented by the assurance provider. By having a specific level of assurance,
firms indirectly express the signal that specific standard (criteria of assessment) has been
used in assessing the sustainability disclosure quality to the stakeholder (Corporate Register,
2008; GRI, 2013; Hummel et al., 2017). It is also worth mentioning that the standard of
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engagement depends on the level of assurance as agreed by the firm (client) and the type of
assurance provider in the assurance process (Mock, Strohm, & Swartz, 2007).
Overall, because the implementation of assessment criteria depends on the on the
level of assurance engagement, we expect that the higher level of assurance associated with
the higher sustainability disclosure quality. Prior studies investigating the element of the level
of assurance and assurance statement quality has extensively exploited by the researchers in
this area (Perego, 2009; Perego & Kolk, 2012; O’Dwyer & Owen, 2005). The evidence from
the UK as provided by O’Dwyer & Owen, (2005) reported that accounting firms tend to give
low assurance level (limited level) to its client, whilst environmental consultants provide
higher level assurance (reasonable assurance). However, the previous studies have not dealt
with the content of sustainability-related information as disclosed by the firms. We argue that
the study would have been more interesting if it had included the issue concerning the level
of assurance and further examine it on the quality of the report. Thus, we formulate
hypothesis three as follows.
Hypothesis 3a: The limited level of assurance is positively associated with the sustainability
disclosure quality.
Hypothesis 3b: The reasonable level of assurance is positively associated with the
sustainability disclosure quality.
Hypothesis 3c: The mixed level of assurance is positively associated with the sustainability
disclosure quality.
2.4. Assurance persistency and sustainability disclosure quality
Although the evidence regarding the assurance persistency and its impact on the
sustainability disclosure quality is very scarce, there are several factors (i.e., from preparers’
and users’ point of views) that may trigger firms to and not to engage in persistent assurance
practice over time (Park & Brorson, 2005; Ruhnke & Gabriel, 2013; Wong & Millington,
2014). Park & Brorson, (2005) made an attempt to give sufficient consideration in explaining
the factors that may drive firms to and not engage in voluntary assurance practice from the
preparers’ perspective. Using the data from 28 Swedish firms, they pointed out that the first
factor that might relate to the motivation of assurance engagement is due to benchmarking
with other companies. Second, they conjectured that awards (e.g., European Sustainability
Reporting Awards; ESRA) on the environmentally or sustainability-related activity report
might increase the firm awareness on the importance of assurance engagement, and it sent a
positive signal to the users. This motive is in line with the findings of Ruhnke & Gabriel
(2013), in which voluntary assurance demand is driven by a self-selection mechanism. In this
context, firms with higher quality disclosure and more comprehensive sustainability
disclosure information are more likely to seek assurance. Third, during the process of
assurance engagement, there could be a big chance for the firm that they would have the
opportunity to deal with better internal reporting systems, which leads to the increased
credibility of the reported non-financial information (Park & Brorson, 2005). Additionally,
from the users’ perspective Wong & Millington, (2014) shows that the demand for assurance
report in the UK assurance market is predominantly triggered by the role of trust. As
previously reported by Dando & Swift, (2003) and Mercer (2004), the credibility gap could
be narrowed by the presence of third-independent party assurance.
Regardless of the driving factors in voluntary assurance engagement, Park & Brorson
(2005) also determined the underlying causes of firms reluctant to engage in assurance
practice. First, they mentioned that the early time horizon in sustainability reporting might
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lead the firms to postpone their decisions on assurance practice. Firms are aware that there is
no immediate significant benefit that could be perceived by the firms in the early stage of
business. Therefore, on average, the firms in Sweden would have their sustainability reports
assured by the independent assurance provider three years after they firstly engaged in
voluntary sustainability reporting. Second, the benefit of assurance engagement (enhanced
report’s credibility) does not seem to provide enough evidence to outweigh the cost as
incurred by the firms.
Taking into account the possible benefits of assurance engagement, we argue that
assurance on the sustainability information is useful for a wide range of users, and the
persistence of assurance report appears as another significant aspect of the increased
sustainability disclosure quality. Ruhnke & Gabriel, (2013) suggested two conditions for the
possibility of having persistence assurance engagement. By using the mechanism of agency
cost and signaling level reporting, they documented that there is variation in the signaling
level of voluntary assurance. They assumed that the higher the degree of conflict of interest
and information asymmetry among the stakeholders, the higher the potential benefits that can
be perceived as the results of assurance engagement. As previously described in the previous
pages, the presence of sustainability report allows the increase in the number of financial
analyst following, that leads to the lower cost of capital and increase access to finance (Cheng
et al., 2014; Dhaliwal et al., 2011, 2012). From the point of view of financial analyst,
credible, reliable and accurate non-financial disclosure information is essentially required in
minimizing the probability of forecast errors (Simpson, 2010). In line with the study of
Simpson (2010) and Axjonow et al., (2016), the non-financial disclosure is expected as
relevant information by the stakeholder, particularly professional stakeholders (financial
analyst, sophisticated investors etc). With the same motive and notion, there would therefore
seem to be a definite need for a persistent companion of assurance statement on the reported
sustainability information may affect the sustainability disclosure quality. Thus, we develop
hypothesis four as follows.
Hypothesis 4a: The assurance persistency is positively associated with sustainability
disclosure quality.
Hypothesis 4b: The assurance tenured is positively associated with sustainability disclosure
quality.
3. RESEARCH METHOD
3.1. Data and Sample
This study focuses on the empirical investigation of European firms operating in the
Environmentally Sensitive Industry (ESI). We use the European data since the European
directive 2014/95/EU requires every company operating in Europe to mandatorily disclose
their non-financial information, either in the integrated annual report or stand-alone report
(European Commission, 2014). The European Commission specifically entails firms with
large size (have more than 500 employees) and large public interest to publish non-financial
and diversity information (Schneider et al., 2018). More precisely, we begin the sample
construction with all public listed companies in the 4-year windows from 2014 to 2017. The
time horizon from 2014 to 2017 is also adopted since we would like to capture the CR reports
that adopt GRI G.4 framework after it was introduced in early 2013. Furthermore, we
eliminate all observations that cannot be attributed to the sample selection procedures as
follows.
11
[Insert Figure 3.1 around here]
[Insert Table 3.1 around here]
The data regarding firms’ financial information is collected from Thomson Reuters
EIKON database. As previously exhibited in the sample selection procedures, all firms
should be listed in the capital market of 29 European countries. The screening is further
conducted by ESI industries using Global Industry Classification Standard (i.e., Aerospace &
defense, Chemicals, Forestry & paper, pharmaceuticals, Metals, Mining, Oil & gas, and
Utilities (Cho, Michelon, Patten, & Roberts, 2014; Rodrigue et al., 2013; Michelon et al.,
2015) and squeezed out by considering the availability of sustainability reports collected from
Corporate Register-Global CR resources database (http://www.corporateregister.com/).
Researchers in the sustainability area consider this database as the most comprehensive
provider of non-financial reports worldwide (Casey & Grenier, 2015; Dhaliwal et al., 2012;
Simnett et al., 2009). If the particular data is not available in the Corporate Register database,
we further check the data on the GRI database (http://database.globalreporting.org/). In case
if the sustainability report is not available in both databases (Corporate Register and GRI), we
seek the report on the Internet and check it on the official website of the related firms. We
finally end up with 332 firms (25.15 percent of the total population of firms operating in the
ESI industries) which indicate complete sustainability reports and yearly financial data
observations from 2014 to 2017. However, it is worth reporting that out of 332 firms; we
discover that there are 106 firms that are not covered in the ASSET4 database, meaning that
these firms have no environmental performance information as the indicator of sustainability
performance information. Since we would like to control for the environmental performance,
we truncate our observations and finally have a unique database with complete environmental
performance score as released by ASSET4 database. We further use this unique database
(ASSET4 sample) in the OLS estimation in which we have 226 public listed companies (904
firm-year observations) as the sample group with environmental performance information. In
the next step, we continue to content analysis.
3.2. Content Analysis
Our study adopts content analysis to investigate the quantity and the quality of the
environmental information disclosed by the investigated firms. The previous study has
outlined that most of the literature in the field of sustainability reports, company narratives,
and its attribution are lack of meaning (Merkl-Davies & Brennan, 2007). This is due to the
concerns of prior literature which put attention more on the study of quantity of information
as disclosed by the firms. Consequently, as pointed out by Beretta & Bozzolan, (2008);
Michelon et al., (2015), the essence of content analysis needs to be ascertained, whether it
focuses more on the explicit quantity or the implicit quality of essence behind the reported
information.
First, we try to confirm whether the quantity of information does align with the
quality of information by applying a mechanistic approach. Second, we conjecture that the
disclosed information might be utilized by the preparers to engage in impression
management, camouflaging, or greenwashing (sustainability-washing) their business
activities (Cho et al., 2012; Michelon et al., 2015; Pope & Wæraas, 2016). Therefore, as
suggested by the study of Beretta & Bozzolan, (2008) and Michelon et al., (2015), we employ
content analysis approach that helps identify the quality of the disclosed information,
according to the type of information and the managerial orientation.
12
The disclosure framework is based on the GRI G.4 environmental dimensions,
comprising items related to materials, energy, water, biodiversity, emissions, effluents and
waste, products and services, transport, overall, supplier environmental assessment, and
compliance (GRI, 2014). Also, in order to empirically overcome the materiality issue for the
stakeholders, we deliberately identify the materiality aspect from the perspective of the
financial analyst (European Federation of Financial Analyst Societies (EFFAS)). As the
materiality concerns on a different type of information for different industry, we overcome
this matter by investigating the compatibility of financial analyst Key Performance Indicators
(KPIs) with the GRI framework. Figure 1 displays the compatibility of sustainability code
between GRI and analyst KPIs.
[Insert Figure 3.2 around here]
In the content analysis, we first identify the relative quantity index. The quantity
(RQT) is obtained from the standardized residual of an OLS regression model disclosure
(DISC), where disclosure (DISC) is the function of size (SIZE) and industry (IND) (Beattie,
McInnes, & Fearnley, 2004; Michelon et al., 2015). Controlling for size, when the firms
disclose more information than the other firms in the same industry, RQT will show greater
values and vice versa.
Second, we measure the density index (DEN) of the document. In this regard, dilution
of the CR information in the long document as a stand-alone report may serve as the
communication methods which is deemed relevant for users, albeit this report is hard to
understand, and there is a chance that the information is loaded by obfuscation and divert
attention (Cho, Roberts, & Patten, 2010; Merkl-Davies & Brennan, 2007; Michelon et al.,
2015). More precisely, density index (DEN) is defined as the ratio between the number of
sentences where sustainability information is provided over the total number of sentences
contained in the stand-alone report or sustainability section in the annual report (Michelon et
al., 2015). This index spans from 0 and 1, where the value close to 1 denotes that the report is
associated with less dilution of relevant information.
Third, we identify the type of information (TOI). This index measures whether the
sentences in the stand-alone or sustainability section contain the item in the GRI guideline.
As concerned by the study of Cho & Patten (2007), distinguishing the type of information is
necessary. The type of information measurement also focuses on differentiating the
qualitative, quantitative, monetary form of information. The purpose is to measure the
incidence of the recording unit, which is deemed more precise in terms of the type of
information, and presumably more significant.
Fourth, we investigate the managerial orientation (MAN). This index focuses more on
the investigation of time orientation of statements in sustainability disclosure. Michelon et al.,
(2015) considered that managerial orientation in the CSR disclosure can be classified into
two forms, namely boilerplate approach and committed approach. The index of managerial
orientation (MAN) is calculated by considering the number of sentences containing
sustainability information in the document analyzed. Since this index adopts the information
either from forward-looking and backward-looking information, this index also takes into
account whether the sustainability report contains the firm’s goals and objectives along with
its results and outcomes. Following the study of Michelon et al., (2015), the procedure of
sentence classification can be categorized by considering the time orientation and the
boilerplate versus committed approach information as follows.
13
Managerial orientation Forward-looking Backward-looking
Boilerplate approach Context - Expectation - Hypothesis Policies, initiatives, and strategies
Committed approach Objective and goals Results and outcome of actions
Lastly, we focus on the materiality aspects (MAT). Prior study has documented that
the materiality issue is essential to be considered in the disclosure analysis, particularly due to
its importance and informativeness for stakeholders (Khan et al., 2016). However, since
stakeholders comprise of many parties, each stakeholder has its own concern on the
materiality aspect for firms in different industries (Khan et al., 2016). We address this
problem by positioning the materiality aspect from the point of view of market participants
(i.e., financial analyst). In particular, we adopt the key performance indicators (KPIs) as
released by the financial analyst. All indicators of KPIs are available and matching with the
indicators in the GRI G4 environmental framework as displayed in Figure 2. If the required
information is available and provided in the sustainability report, it denotes that the
sustainability disclosure quality complies with the information inquired by the analyst.
Moreover, we are aware that each industry in environmentally sensitive industries may
demand a different type of material information. Therefore, we focus on investigating the
materiality of information by counting the aggregate material information based on the
industry category. We assume that the number of matching information between analysist
KPIs and the sustainability reports which adopt GRI framework reflects the material
information as needed by the capital market participants (KPIs). The example of coding is
available in Table 3.2.
[Insert Table 3.2 around here]
To empirically measure the non-financial information in the sustainability reports, we utilize
two types of computerized qualitative data analysis tools. We use Atlas.ti8 and R with
Quanteda package (Quantitative Analysis of Textual Data). Atlas.ti8 is used to extract
environmental-related sentences from the sustainability report, while R-Quanteda is
employed to identify the characteristics of information. It is worth mentioning that we use
sentence per sentence as the unit of analysis. We also further involved a machine-learning
process to train the computer in doing the content analysis approach. In this stage, we firstly
do manual content analysis on the reports of 73 companies (We extracted 8,861
environmental-related sentences from 292 CR reports published from 2014-2017) and further
used this manual content analysis outcome as the actual value to training the computer in
generating the predicted value of interpretive content analysis. Two research assistant
previously conducted the coding with manual content analysis. Based on the actual outcomes
of the manual content analysis, we proceed a group of word stems classification to train the
software in capturing and identifying the expected predicted value. The software eventually
releases a predicted value based on the probability identification and machine learning
process (naive based approach; See the study of Li, (2010) for further methodological
discussion) on the actual value of manual content analysis with the level of accuracy more
than 80 percent. We further use the software to identify and analyze the sentences for the
remaining sustainability reports (259 companies). The expected raw data for generating the
five indexes are formed in binomial data (0;1) and then proceeded into continuous data
(indexes) as follows.
[Insert Table 3.3 around here]
14
3.3. Regression Model
After conducting the content analysis, we obtained the raw data to generate five
disclosure indexes as the proxy of sustainability disclosure quality. The data collected for the
five sustainability disclosure indexes are utilized as the dependent variables. Recall back to
our hypotheses, our study investigates the association between the sustainability assurance-
related practices and sustainability disclosure quality. Therefore, we propose an OLS model
to estimate our four hypotheses. In hypothesis one, we test whether sustainability assurance
(SA) is positively associated with sustainability disclosure quality. Hypothesis 2a and 2b
conjectures that ACCOUNTING and CONSULTANT firms are positively associated with
sustainability disclosure quality. Hypothesis 3a, 3b, and 3c empirically test whether the levels
of assurance (LIMITED, REASONABLE. MIXED) are positively associated with the
sustainability disclosure quality. Hypothesis 4a and 4b, with the same spirit with the previous
hypotheses, examine if there is a positive association between assurance persistency
(AS_PERS and AS_TENURED) with sustainability disclosure quality. We posit the
following OLS regression model for sample i and time t to test our hypotheses in equation one
as follows.
𝑆𝐷𝑖𝑠𝑐𝑖,𝑡 = 𝛼 + 𝛽1𝑆𝐴𝑖,𝑡 + 𝛽2𝐴𝐶𝐶𝑂𝑈𝑁𝑇𝐴𝑁𝑇𝑖,𝑡 + 𝛽3𝐶𝑂𝑁𝑆𝑈𝐿𝑇𝐴𝑁𝑇𝑖,𝑡 + 𝛽4𝐿𝐼𝑀𝐼𝑇𝐸𝐷𝑖,𝑡
+ 𝛽5𝑅𝐸𝐴𝑆𝑂𝑁𝐴𝐵𝐿𝐸𝑖,𝑡 + 𝛽6𝑀𝐼𝑋𝐸𝐷𝑖,𝑡 + 𝛽7𝐴𝑆_𝑃𝐸𝑅𝑆𝑖,𝑡 + 𝛽8𝐴𝑆_𝑇𝐸𝑁𝑈𝑅𝐸𝐷𝑖,𝑡
+ 𝛾 ∑ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛾 ∑ 𝑌𝑒𝑎𝑟𝑖,𝑡 + 𝛾 ∑ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝜀
The dependent variables SDisc (sustainability disclosure) is proxied by five indexes:
relative quantity (RQT), density (DEN), type of information (TOI), managerial orientation
(MAN), and materiality (MAT). The first independent variable of interest is sustainability
assurance (SA). This variable is measured by employing dichotomous data, 1 if the firms
have assurance on the stand-alone report or the assurance on the dedicated part for the
sustainability information in the annual report, and 0 otherwise. ACCOUNTANT is
determined as 1 if the assurance is conducted by professional accounting firms, and 0 if the
assurance is prepared by non-accounting firms (certified bodies, environmental consultants).
CONSULTANT is marked as 1 if the assurance is conducted by consultancy firms, and 0
otherwise. Moreover, we use the level assurance as to investigate the association between the
level of assurance and sustainability disclosure quality. Since in the assurance practice there
are two levels of assurance (i.e., limited; reasonable), and to some extent firms also use the
combination between limited and reasonable assurance engagement (Gürtürk & Hahn, 2016),
we count this variable based on a dichotomous variable. Referring back to the model,
LIMITED denotes the limited level of assurance as indicated in the assurance statement of a
sustainability report. It is marked as 1 if the level of assurance is limited and 0 otherwise.
REASONABLE is the reasonable level of assurance, we mark it 1 if the firm engages in the
reasonable level of assurance and 0 otherwise. MIXED is the combination between a limited
and reasonable level of assurance, 1 if the firms use mixed level of assurance and 0
otherwise. The last variable of interest is assurance persistency. To measure the assurance
persistency, we use two variables (AS_PERS and AS_TENURED). AS_PERS is marked 1 if
in the previous year (t-1) companies have their CR report assured by the third-independent
party and in the current time (t0) the CR report is also assured by the assurance provider, 0
otherwise. AS_TENURED is the yearly period since the first time firms adopt assurance
service. The detail of operational definition of variable is available in the next subsection.
15
3.4. Variable Definition
To empirically test the association among the variables, it is important to define each
variable along with its measures and data source. Hereby is enclosed the information of
variable definition in Table 3.4.
[Insert Table 3.4 around here]
3.5. Consideration of the Potential Sample Bias
Since the focus of our study is investigating the association of sustainability assurance
with sustainability disclosure quality, we also consider the problem of sample selection bias.
As concerned by Tucker, (2010) many key corporate decisions made at the firm can be
classified as “choices”. Given that, we recognize the sample used in our study is non-
randomly assigned which raises the concerns about the validity of the obtained empirical
findings. In the procedure of sample selection, we focus on the companies that are
incorporated in the Environmentally Sensitive Industry (ESI), in which these companies
should have had sustainability information reported, assured, and being indexed by the
database provider (Corporate Register, GRI, ASSET4, and EIKON). In this regard, we
consider that our databases focus on the specific group of large public listed firms, which
might be induced a coverage bias in the collected sample. Therefore, there is an indication
that the sample selection driven either by the decision of only using the companies with
sustainability report and its assurance, or the companies with large size, and classified in the
ESI industries. To deal with this issue, we run the Heckman two-step procedures to make
sure that the self-selection sample bias does not threat the sampling procedure (Lennox,
Francis, & Wang, 2012). The first stage is conducted by employing a probit model as
follows.
𝑆𝑅/𝑁𝑜𝑛𝑆𝑅𝑖,𝑡 = 𝛼 + 𝛽1𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽2𝐸𝑁𝑉𝑆𝐶𝑅𝑖,𝑡 + 𝛽3𝐴𝐺𝐸𝑖,𝑡 + 𝛽4𝐿𝐸𝑉𝐸𝑅𝐴𝐺𝐸𝑖,𝑡 + 𝛽5𝑅𝑂𝐴𝑖,𝑡 +
𝛽6𝐴𝑔𝑒𝑖,𝑡 + 𝜀 Stage 1
After conducting the Heckman 2SLS selection procedure, we generate the Inverse
Mill Ratio that is used in correcting the potential self-selection sample bias. The value of the
inverse mill ratio is further inserted into the proposed empirical model in the second stage.
𝑆𝑅𝐷𝑖𝑠𝑐𝑖,𝑡 = 𝛼 + 𝛽1𝑆𝐴𝑖,𝑡 + 𝛽2𝐴𝐶𝐶𝑂𝑈𝑁𝑇𝐴𝑁𝑇𝑖,𝑡 + 𝛽3𝐶𝑂𝑁𝑆𝑈𝐿𝑇𝐴𝑁𝑇𝑖,𝑡 + 𝛽4𝐿𝐼𝑀𝐼𝑇𝐸𝐷𝑖,𝑡 +𝛽5𝑅𝐸𝐴𝑆𝑂𝑁𝐴𝐵𝐿𝐸𝑖,𝑡 + 𝛽6𝑀𝐼𝑋𝐸𝐷𝑖,𝑡 + 𝛽7𝐴𝑆_𝑃𝐸𝑅𝑆𝑖,𝑡 + 𝛽8𝐴𝑆_𝑇𝐸𝑁𝑈𝑅𝐸𝐷𝑖,𝑡 + 𝛾 ∑ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛾 ∑ 𝑌𝑒𝑎𝑟𝑖,𝑡
+ 𝛾 ∑ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝜀 Stage II
We are also aware that instead of the self-selection bias problem, we need to deal with
the other groups of sample which decided not to assure their sustainability reports to the
independent third-party. Therefore, we use the propensity score matching (PSM) to better
dealing with the causal effect and the variance among firms that have their report assured and
those who decide not to use the assurance service. We use all 332 companies (1,328 firm-
year observations) with the complete sustainability reports even though 106 (424 obs) of
them have no information about environmental performance. In this regards, we create a
treated group and a control group with a purpose of an observational study. As the number of
sample with sustainability assurance statement is 145 companies and the number of firms
without assurance is 187 companies, we decide to use the firms with assurance statement as
16
the treated group and the firms without sustainability assurance as for the control group. In
this case, we match the assurance adopters (treated group) with non-adopters (control group).
At the initial stage of the matching procedure, we report that the number of treated
group is 583 and the number of control group is 745. In total, we have 1328 firm-year
observations. By employing propensity score matching with nearest neighbor matching based
on the size of the companies in the nearest year and industry, the final propensity score
matching recommends creating eight blocks. In this regards, the number of blocks ensures
that the mean propensity score is not different for treated and control groups in each block.
The first block of pscore is started with 0.023 and the last blocks (eight) end with 8. In total,
we finally have 583 observations for treated group and 673 observations for the control group
(1,256 firm-year observations). However, since these two groups indicated that the obtained
final observations value is unbalanced with the nature of panel data analysis, we have to drop
eight (8) observations, and we finally have the strong balance panel data as 1,248
observations (See Table 4.9 for further details) that are used in the additional analysis.
4. RESULTS
4.1. Univariate Analysis
We start our empirical analysis by providing the final sample distribution. As
previously highlighted in the research method section, we start utilizing the data from public
listed companies operating in 24 European countries. We truncate our initial sample (332 or
1,328 firm-year observation) and eventually end up with 226 companies (904 firm-year
observations) operating in Environmentally Sensitive Industries (ESI). These unique datasets
have complete environmental performance information as covered by ASSET4 database.
[Insert Table 4.1 around here]
Table 4.1 presents the univariate analysis for sustainability reporting and
sustainability assurance practice. We classified our sample based on countries, year, and
industry classification. We utilize firms operating in the ESI industry as the final sample and
further group them into three panels. Panel A in Table 4.1 is the sample distribution by
countries. It can be seen that most of our sample is dominated by the sample from the UK as
30.97%, followed by Germany (10.62%), France (8.41%), Spain (7.52%), and Italy (6.64)
respectively. While Croatia, Estonia, Ireland, Luxemburg, Poland, Romania, and Slovakia
stand as the countries with the smallest number of firms operating in the ESI industry
(0.44%). Furthermore, we report the sample distribution by year in Panel B. In this regard, we
analyze the sustainability reporting practice and it assurance practice from 2014 to 2017.
Recall back to our sampling procedures, we use panel data (226 cross-section data) and a
four-year time series data. Although the number of our observations is equally distributed for
each year, interestingly we document that the trend of assurance practice on sustainability
report is steadily increasing over the years. In 2014, we record that 125 sustainability reports
were assured. This number is steadily increasing for the next consecutive years 2015 (132
reports), 2016 (136 reports), and 2017 (143 reports). This point suggests that sustainability
reporting and its assurance practice emerge as a major trend among the European companies
operating in ESI industries.
Interestingly, in Panel C (sample distribution by Global Industry Classification
group), we find that firms operating in Utilities (19.91%) and Pharmaceutical (19.03%)
industry are dominating the number of our sample distribution, followed by Mining
17
(16.37%), Oil & gas (14.6%), and Chemical (14.16%) respectively. Meanwhile, Metals
(3.1%), Forestry & paper (5.75%), and Aerospace & defense (7.08) industries contribute less
as compared with the other ESI industries.
In the next step, we provide the output of sentence extraction from either stand-alone
sustainability report or CR information integrated into the annual report. We extracted the
basic information that we need in generating our disclosure indexes. We use two types of
computerized qualitative data analysis tools (Atlast.ti8 and R-Quanteda) in extracting the data
for quantity, type of information, managerial orientation-related information, materiality, and
further analyze it from the sustainability reports. In this case, the data is analyzed by
implementing content analysis with mechanistic approaches. As the unit of analysis in our
study stands at the sentence level, the descriptive statistics of the disclosure component is
also provided in the sentence unit. Table 4.2 illustrates.
[Insert Table 4.2 around here]
Table 4.2 displays the outcome of content analysis. We divide this information into
three panels. Panel A, refers to the quantity of observation. We note that the total page of
either stand-alone sustainability report or sustainability information integrated into the annual
report stands for 146 pages on average. Meanwhile, the sustainability-related information is
recorded as 9.6 pages on average. The total sentence of sustainability-related information is
505 sentences on average, in which the total sentence relevant with GRI disclosure
framework (DISC) is documented as 171 sentences on average. In Panel B, we report the data
with respect to the type of information. Overall, our analysis on the type of information come
in conclusion that the type of information provided in the sustainability reports is dominated
by qualitative information (TOI1) as 112 sentences on average, followed by the sentence
contains quantitative information (56 sentences on average) and monetary information (3
sentences on average). The next information in Panel C presents the managerial orientation-
related information, in which we classified the disclosed sentences into expectation (52
sentences), program (56 sentences), objective (61 sentences) and results (41 sentences) on
average. Furthermore, the next Table highlights the sustainability disclosure indexes as
generated from information in Table 4.3, independent, and control variables.
[Insert Table 4.3 around here]
Table 4.3 shows the descriptive information on the final sustainability disclosure
information and the main independent variables of interest. We also consider inserting
several control variables. In this context, we aim at mitigating the likelihood of spurious
regression correlation due to the problem of omitted variables bias by controlling a number of
firm-specific information (financial information). More precisely, Panel 1 in Table 4.3
provides five measures of sustainability disclosure indexes which are useful as the dependent
variables. Moreover, in Panel 2, it is worth reporting that 41% of the total sample has
published stand-alone sustainability report instead of disclosing their non-financial
information in a dedicated page in the annual report (69%). Interestingly, 59% of the total
sample has engaged in assurance practice. Among the firms that have their sustainability
report assured, 46% of the firms used the assurance service provided by the accounting firms,
while the other 13% decided either to use the service from the consultant. In terms of the
level of assurance engagement, 54% firms engaged in limited level, 4% in reasonable, and
1% engaged with the combination of limited and reasonable. We also provided the
18
information with respect to assurance persistency during the observed period (2014-2017). In
Table 4.3, it is notified that 42% firms persistently assured their CR report, while 58% of the
remaining firms did not continuously engage with assurance practice. To further strengthen
the univariate analysis, we provided the information regarding the first time adoption of
assurance practice. According to the data, we report that most of the firms have engaged with
five-year adoption on average, in which the earliest adoption has been done for 16 years.
Given our focus on identifying the sustainability disclosure quality of the report, we
construct an index which is generated by weighting the five dimensions of sustainability
disclosure indexes (RQT, DEN, TOI, MAN, MAT). We label this indexes as the standardized
value of disclosure index QUALITY (QUALITYi,t = 1
5∗(RQTi,t + DENi,t+ TOIi,t+ MANi,t+MATi,t)). We used this
standardized index as the main dependent variable to empirical test the proposed hypotheses.
However, before proceeding into the hypotheses testing, we provide the visualization
regarding the average value of sustainability disclosure quality index (QUALITY) that is
categorized based on the availability of sustainability assurance, the assurance providers and
the level of assurance engagement as follows.
[Insert Figure 4.1 around here]
Figure 4.1 displays the average value of QUALITY which is categorized based on the
presence of sustainability assurance (SA). As can be seen in the Figure, it is obvious that the
quality of sustainability disclosure for firms engaging with assurance is higher than firms that
have not engaged in assurance practice. In this case, the average value of sustainability
disclosure quality (QUALITY) for companies without assurance on their sustainability report
is -1.883 and firms with assurance on their sustainability report indicate QUALITY value as
8.887 on average. We also do a t-test and the output shows that there is a significant different
in terms of the sustainability disclosure quality (QUALITY) between firms that have their
sustainability reports assured and those without assurance. The result shows that the quality
of disclosure is higher for firms with assurance than firm without assurance (t value is -5.940;
p < 0.01). Furthermore, we also visualize the sustainability disclosure quality classified by the
assurance providers (accountant, consultant) as follows.
[Insert Figure 4.2 around here]
As seen in Figure 4.2, the sustainability disclosure quality is grouped based on the assurance
providers. In the left side, the information of disclosure quality (QUALITY) is provided by
comparing the disclosure quality conducted by accountant versus non accountant. On the
right side, the quality of disclosure is provided by comparing the quality as conducted by
consultant and non-consultant. As can be seen in the Figure, it is reflected that the disclosure
quality that is provided by accountant (8.496) is higher than non-accountant (1.038) with t
value -4.134 (p < 0.05). The similar propensity happens in the test of consultant (10.306) vs
non-consultant (3.648) with t value -2.460 (p < 0.05). Over all, when we compare the
disclosure quality between accountant and consultant, it can be seen that the disclosure
quality that is assured by the consultant is higher than the disclosure quality assured by
accountant. In more detail, we also classify the disclosure quality based on the level of
assurance engagement (limited, reasonable, mixed) that can be seen as follow.
[Insert Figure 4.3 around here]
19
Figure 4.3 presents the average value of sustainability disclosure quality which is categorized
based on the levels of assurance engagement. As previously explained, there are three levels
of assurance (limited, reasonable, mixed) that can be adopted by the firm when deciding to
assured their sustainability reports to the third party (accountant or consultant). To see the
average difference of disclosure quality according to the level of assurance, we create three
boxplots graph that are separated based on the limited assurance (upper left side), reasonable
(upper right side) and mixed (lower side). In the first upper left side Figure, we test the
disclosure quality by comparing between limited level vs non-limited levels. As can be seen,
the disclosure quality of firms that have their sustainability reports assured in limited level
(9.527) is higher than those firm with non-limited level (-1.470) with t value -6.157 (p <
0.05). Moreover, we do investigate the difference between disclosure quality of firms engage
in reasonable level of assurance vs non-reasonable level of assurance. The test indicates that
the average disclosure quality of firms with reasonable level of assurance (0.930) is not
statistically difference from non-reasonable assurance (4.664), where the t value stands at
0.835 (p > 0.05). In the lower side Figure, we provide the difference between the average
value of firms engaging with mixed level and non-mixed. The output shows that the average
disclosure quality of firm with mixed level of assurance (8.276) is higher than non-mixed
level assurance (4.477), but statistically insignificant where the t value stands at -0.3397 (p >
0.05). In the next step, we provide the correlation matrix regarding the correlation between
dependent and independent variables. Table 4.4 displays.
[Insert Table 4.4 around here]
Table 4.4 illustrates the correlation matrix among dependent and independent
variables. These variables are employed to investigate the implication of sustainability
assurance on sustainability disclosure quality. As informed in Table 4.4, we utilize several
proxies to measure sustainability assurance (SA) practice and sustainability disclosure
quality. We use relative quantity (RQT), density, (DEN), type of information (TOI),
managerial orientation (MAN), and materiality (MAT) as the proxies of sustainability
disclosure quality. Meanwhile, we use seven surrogate indicators to empirically measure the
sustainability assurance (SA) as the main independent variable of interest. These variables are
SA, ACCOUNTANT assurance provider, CONSULTANT assurance provider, LIMITED
level of assurance, REASONABLE level of assurance, MIXED level of assurance, assurance
persistency (AS_PERS), and assurance tenured (AS_TENURED). Variable SA as one of the
proxies of sustainability assurance has indicated a positive (0.185) and significant (p< 0.01)
correlation with RQT. However, variable SA is negatively correlated with the remaining
disclosure indexes (DEN, TOI, MAN, MAT).
The second proxy of sustainability assurance is variable ACCOUNTANT. Referring
to the correlation matrix output, it is reported that variable ACCOUNTANT is positively and
significantly (p< 0.01) correlated with RQT (0.120). While, the opposite signs appear on the
correlation of ACCOUNTANT and the remaining disclosure indexes. Moreover, variable
CONSULTANT shows a positive (0.093) and significant (p< 0.01) correlation with RQT.
The third proxy is the level of assurance (LIMITED, REASONABLE; MIXED). Variable
LIMITED indicates a positive (0.207) and significant (p< 0.01) correlation with RQT, whilst
negative correlation appeared in the correlation of LIMITED and the remaining disclosure
indexes. However, REASONABLE and MIXED reflects no correlation with the disclosure
indexes. In the fourth proxy, variable AS_PERS and AS_TENURED show positive (0.168;
0.158) and significant (p< 0.01) correlation with RQT, but indicate negative correlation with
the remaining disclosure indexes.
20
Additionally, we also employed several CR-related practices and firms’
characteristics variables as our control variables. As the representation of sustainability-
related practice, we use variable SR, ENVPERF, GRI, and CR_BOARD. While, as the
representation of firm characteristics with respect to financial information, we use SIZE
(natural logarithm of the total asset), AGE of the companies, LEVERAGE (the ratio of debt
to equity), and ROA (the ratio of return on asset).
4.2. Multivariate Analysis
In the next step, we further do the direct OLS test. We perform a direct test to obtain
the association of main independent variables with the dependent variables. In order to get a
clear and robust outcome regarding the association of sustainability assurance and
sustainability disclosure quality indexes, we run a panel corrected standard error model which
considers the role of control variables, year fixed-effect, and industry fixed-effect. The aim of
controlling the year and industry fixed-effect is as the attempt for addressing the endogeneity
problem (omitted variable bias). The multivariate panel data analysis outcomes regarding the
association between sustainability assurance practice (SA, ACCOUNTANT,
CONSULTANT, LIMITED, REASONABLE, MIXED, AS_PERS, AS_TENURED) and
sustainability disclosure quality indexes (RQT, DEN, TOI, MAN, MAT) are presented as
follows.
[Insert Table 4.5 around here]
We concurrently test the association by employing multivariate panel data analysis. In
more detail, we find that our first proxy of sustainability assurance (SA) is negatively
associated with managerial orientation (MAN) (β= 0.0123; p< 0.1). Meanwhile, variable SA
does not show any significant association with the remaining disclosure indexes (RQT, DEN,
TOI, MAT). Apart from the five sustainability disclosure indexes, we also generate a
standardized sustainability disclosure quality index (QUALITY) which is generated from the
five disclosure indexes. The result reflects that SA is not significantly associated with
QUALITY. We further test our second proxy and find that ACCOUNTANT is positively and
significantly associated with TOI (β= 0.0331; p< 0.1) and MAN (β= 0.0058; p< 0.1).
Variable CONSULTANT is excluded by STATA since it has dependency with variable
ACCOUNTANT. Therefore, the output of OLS test indicates that variable CONSULTANT
has to be omitted. The third proxy is the level of assurance (LIMITED, REASONABLE,
MIXED). None of the levels of assurance variable significantly associated with sustainability
disclosure indexes (RQT, DEN, TOI, MAN, MAT). In the test, variable MIXED is also
excluded since it has dependency with variable LIMITED and REASONABLE. The last
proxy is assurance persistency. To empirically test this, we use assurance persistency
(AS_PERS) and assurance tenured (AS_TENURED) and directly test them to sustainability
disclosure quality indexes. The result shows that only variable AS_PERS that shows
significant association with RQT (β= 24.21; p< 0.1) and TOI (β= -0.106; p< 0.01).
Interestingly, AS_PERS also indicates a positive and significant association with the
standardized disclosure quality index (QUALITY).
Besides the empirical evidence on the association between sustainability assurance
and sustainability disclosure quality, we also report the estimation results of the control
variable. In this stage, we control for the year and industry fixed effect. Recall back to the
multivariate panel data analysis output, we note that the environmental performance score as
reported by the ASSET4 research on firms’ environmental-impact (ENVSCR) is not
21
associated with the proposed sustainability disclosure indexes. sustainability reporting by
adopting GRI disclosure framework provides us with positive and significant association with
RQT. However, the opposite signs of association appear on the association of GRI-TOI (β= -
0.0867; p< 0.01) and GRI-MAT (β= -1.421; p< 0.1). The other control variables also suggest
various results as can be seen in Table 4.5.
In addition, to see the partial association between the proxies of sustainability
assurance (SA), we test the direct effect partially. We realize that sustainability assurance
practice is a choice-based decision. When the firms decide to release either a stand-alone or
sustainability information integrated into annual report, they will be faced by a decision
whether to assured the report to the external party. Moreover, when the firms decide to have
it assured by the external party, they need to choose whether the assurance will be provided
by accounting or consultant firms. In the next stage, firms and assurance provider have to
make a deal about the level of assurance that the firms want to engage in. After having these
all procedure, it is also firms’ decision whether to have persistent assurance engagement in
the next reporting period. Therefore, we argue that we cannot test the proposed hypothesis in
a single model due to the dependency among the proxies. To deal with this issue, we run a
partial test for each proxy to empirically test our hypothesis. To see the change on the
variation of dependent variable, we also used the standardized value of the overall
sustainability disclosure quality indexes (QUALITY) as the proxy of sustainability disclosure
quality instead of using all indexes. The partial effect on the association of the sustainability
assurance and the standardized CR disclosure quality is available as follow.
[Insert Table 4.6 around here]
We test our hypotheses by referring to the obtained empirical evidence in Table 4.6.
As seen in the Table, we used our proxies of sustainability assurance practice (SA,
ACCOUNTING, CONSULTANT, LIMITED, REASONABLE, MIXED, AS_PERS, and
AS_TENURED). We conjecture that our eight proxies of SA are positively associated with
the standardized sustainability disclosure index (QUALITY). In this context, we do the direct
partial test to examine whether our main independent variables of interest have shown any
significant association with the sustainability disclosure quality. The first test examines the
hypothesis 1. We conjecture that there is a positive association between sustainability
assurance (SA) and sustainability disclosure quality (QUALITY) (see column 1). The result
shows that SA is positively (β= 10.59) and significantly (p< 0.01) associated with
QUALITY, meaning that hypothesis one is supported. This finding provides support to the
study of Moroney et al., (2011) which indicates a positive role resulted from the presence of
assurance on the quality of the disclosed non-financial information. In this respect, the rough
test using dichotomous data of SA is predominantly providing evidence that the assurance
practice plays an essential role in helping the sustainability report preparer to better provide
their reporting mechanism and procedure.
In the next step, we examine the hypotheses 2a and 2b. In the hypothesis 2a, our
notion is, ACCOUNTING firms is positively associated with sustainability disclosure quality
(QUALITY). Recall back to the univariate analysis output, we do recognize that our samples
are mainly dominated by the firms that have decided to assure their sustainability reports (59
percent). In this case, we deliberately test whether having the sustainability report assured by
the professional ACCOUNTING firms would result in positive association with sustainability
disclosure QUALITY. The obtained result reports that assurance process provided by
ACCOUNTING firms shows positive (β= 9.711) and significant (p< 0.01) association with
sustainability disclosure quality (QUALITY) (see column 2), providing support for
22
hypothesis 2a. To strengthen the empirical test, we also test hypothesis 2b. In this context, we
test whether the assurance report provided by CONSULTANT is positively associated with
sustainability disclosure quality. The obtained output notes that CONSULTANT is positively
and significantly associated with sustainability disclosure QUALITY. This output provides
support for hypothesis 2b, which infers that there is a positive and significant association
between CONSULTANT and sustainability disclosure quality (QUALITY). Our finding in
this context provides a quite relevant result as compared with the findings of Moroney et al.,
(2011) who found that there was no difference in the sustainability disclosure quality among
those reports who were assured by the accounting firms and non-accounting firms.
To test hypothesis 3a, 3b, and 3c, we investigate the association as shown by the
variable level of assurance (LIMITED, REASONABLE, MIXED) and sustainability
disclosure quality (QUALITY). The empirical partial test indicates that there is positive (β=
11.23) and significant (p< 0.01) association (p> 0.05) between LIMITED level of assurance
and sustainability disclosure QUALITY, indicating support for hypothesis 3a. Meanwhile,
the test on hypotheses 3b and 3c show that REASONABLE and MIXED assurance do not
show any association with sustainability disclosure QUALITY. Meaning that hypothesis 3b
and 3c are not supported (see column 3). In the last hypotheses 4a and 4b, we conjecture that
there is a positive association between assurance persistency (AS_PERS, AS_TENURED)
and sustainability disclosure quality (QUALITY). As informed by Table 4.6, it can be seen
that variable AS_PERS is positively (β= 8.651) and significantly (p< 0.01) associated with
QUALITY, providing support for hypothesis 4a. However, further test using variable
AS_TENURED does not show any association with QUALITY. This denotes that hypothesis
4b is unsupported. Our finding of the hypothesis 4a provides support to the signaling effort as
done by the firms when dealing with the assurance practice. In this regards, the persistence of
assurance practice provides a positive signal to stakeholders, in which the quality of non-
financial information is presumably reliable. However, on the other hand, the first time
adoption of assurance engagement is not associated with the sustainability disclosure quality
(QUALITY).
We also conduct a simultaneous test by pooling up together the proxies of
sustainability assurance (SA, ACCOUNTANT, CONSULTANT, LIMITED,
REASONABLE, MIXED, AS_PERS, AS_TENURED) and further test them on the
standardized sustainability disclosure quality index (QUALITY). Interestingly, the obtained
output shows that only variable AS_PERS consistently indicates a positive association with
the sustainability disclosure quality (QUALITY), while the other three proxies of
sustainability assurance provide us with no significant association. In this circumstance, the
signs of the coefficient beta of each main independent variable remain the same (positive).
However, no statistical significance is found. We argue that the information whether the
sustainability report is assured or not assured is the fundamental information. However, the
persistence of assurance engagement is even more important to convince the users that the
sustainability report has provided reliable information in its reporting practice over time. The
other attributes, whether the assurance is provided by accounting firms or consulting firms,
the level of assurance is limited, reasonable or mixed, and the first time adoption of assurance
engagement is considered as the second important information by the public. In this regards,
we may predict that the decision in the assurance practice following a sequential decision (see
the study of Simnett et al., 2009 for further discussion), in which the first decision to disclose
non-financial information in the sustainability reporting itself is an endogenous decision
(choices). Moreover, the decision to assure the sustainability report to the third-independent
party is also endogenous (choices), particularly through the cost and benefit analysis. When
the firms have decided to engage in the assurance process to increase either the quality,
reliability or credibility of the report, firms through management discretion still have to deal
23
with several choices. The choices related to the option of assurance providers, the level of
assurance, and whether the sustainability reporting in the consecutive years are also expected
to be engaged in the assurance practice. Therefore, in our findings, we note that the
information whether the sustainability report have been assured or not is considered as the
most critical information. This is presumably necessary as the signal to the stakeholder that
firm has been dealing with proper sustainability reporting and the reported information is
reliable.
In order to get a comprehensive result in examining our hypotheses statement, we
further test the four proxies of sustainability assurance practice with the lag model (one-year
time lag). As previously written, we follow the study of Michelon et al., (2015), and
standardized the five disclosure indexes (RQT, DEN, TOI, MAT, and MAN) and further
create a single index which represents the overall component data of sustainability disclosure
quality (QUALITY). Overall, in the untabulated Table of partial test using lag variables, we
find that variable SA, ACCOUNTANT, CONSULTANT, LIMITED; REASONABLE;
MIXED, AS_PERS, AS_TENURED show positive and significant association with
QUALITY (see. Appendix 1). Only variable LEV_AS that is not associated with QUALITY.
We also did the concurrent test and pooled up the four main variables of interest together
with the control variables. Our result remains consistent with the previous output in Table
4.6. We report that in the simultaneous test, only variable SA is consistently showing a
positive association with the QUALITY.
Next, we organize additional tests to provide corroborating evidence. We further
advance the test to answer the proposed hypotheses by employing stepwise OLS regression
model (see. Appendix 2), particularly to identify the change of the magnitudes and the
coefficient values of the independent variables. The empirical OLS results remain consistent
with the previous findings of multivariate panel data analysis. Hereby, we report that variable
SA consistently shows positive and significant association with sustainability disclosure
quality. However, this positive association only holds until we gradually insert variable
LIMITED. When we insert the next variable, SA is no longer showing any significant
association with QUALITY. To the stage where we insert variable AS_PERS, this variable
shows consistent positive and significant association with QUALITY, even after being
controlled by adding control variables. The similar propensity appears when we do the
stepwise regression using the lag variable as the independent variables (see. Appendix 3). In
this phase, the previous information of sustainability assurance no longer shows positive
association with the QUALITY when we insert the next main independent and control
variables. However, interestingly, variable LIMITED level of assurance shows a positive and
significant association with QUALITY. This positive association also hold even after being
control by control variables. Indicating that the previous engagement of LIMITED level of
assurance positively increases the QUALITY of environmental-related information in the
next reporting year. Moreover, as reported by stepwise regression output using
contemporaneous variable, the stepwise regression using lag variable also indicates consistent
positive association.
4.3. Discussion
In general, our result reports that there is a positive association between sustainability
assurance (SA) and sustainability disclosure quality, providing support for the study of
Moroney et al., (2011) and extension on the study of Michelon et al., (2015) and Simnett et
al., (2009). To empirically proof our hypotheses, we follow and further advance the study of
Michelon et al., (2015) and Moroney et al., (2011) by using the European dataset. We restrict
our sample to those companies operating in the environmentally sensitive industry (ESI)
24
taken from 226 public listed companies in 24 European countries from 2014 to 2017. Based
on the obtained empirical evidence, we point out that the association of sustainability
assurance (SA) on the different type of sustainability disclosure indexes (RQT, DEN, TOI,
MAN, MAT) is mixed. No significant association (p < 0.05) is found between SA and RQT,
DEN, TOI, MAN and MAT (See., Table 4.5). We further break down our measure of
sustainability assurance by employing seven indicators namely ACCOUNTANT,
CONSULTANT assurance provider, LIMITED; REASONABLE, and MIXED level of
assurance, and the assurance persistency (AS_PERS and AS_TENURED)). We partially test
these variables on the standardized sustainability disclosure quality (QUALITY; the weighted
index generated from RQT, DEN, TOI, MAN and MAT) and the results provide us with
answers that ACCOUNTANT, CONSULTANT assurance provider, LIMITED level of
assurance, and assurance persistency (AS_PERS) have shown positive and significant
association with sustainability disclosure quality (QUALITY), providing support for the
proposed hypotheses (See., Table 4.6).
Apart from our empirical evidence, we do notice that sustainability assurance practice
is the external verification process that is expected to address the problem of lack of
credibility of voluntary reporting document (Hodge et al., 2009; Martínez-Ferrero & García-
Sánchez, 2016; Mercer, 2004; Simnett et al., 2009; Adams & Evans, 2004). Also, in this
study, we conjecture that sustainability assurance can help to increase the quality of non-
financial disclosure (environmentally-related disclosure). The association of sustainability
assurance on sustainability disclosure quality has previously been reported by researchers.
Moroney et al., (2011) found that voluntary assurance positively affected the quality of
voluntary environmental disclosure, in which the presence of assurance on voluntary
environmental disclosure increase the number of hard disclosure information on CSR report
in Australia. While, using the setting of the UK based-sample, Michelon et al., (2015)
document that sustainability assurance does not associate with non-financial information
disclosure.
Recall back to our underlying theory, stakeholder theory points out that firms need to
legitimate their business and operational impact on the environment (Suchman, 1995).
Besides the motive to gain the legitimacy from society, the motive to maintain and increase
the legitimacy itself is deemed important for business sustainability (Milne & Patten, 2002).
Disclosing non-financial information is one of the ways to obtain the right to operate and
legitimate the business (Campbell, 2003). When the firm engages in sustainability reporting
per se is presumably insufficient by the stakeholder, thus the information regarding the value
of providing assurance from the third-independent party should be of interest to stakeholders.
The value of assurance engagement can lead the users to evaluate the disclosed non-financial
information better, in which the procedure of sustainability reporting has been under-
scrutinized and verified by assurance provider through the mechanism of market oversight.
Reconsidering our potential contributions, we note that our study contributes to the
debate on the role of SA on the sustainability disclosure quality. Prior study has offered
conflicting findings in respect of the specific role of SA on the variance of sustainability
disclosure quality. As reported by Moroney et al., (2011), their work used Australian setting
and provided empirical evidence that SA has shown a positive association with voluntary
disclosure quality. Meanwhile, in the context of the UK setting, Michelon et al., (2015) found
no association between SA and sustainability disclosure quality. Indicating an idea that there
was a problem of lack of completeness in the sustainability reporting in the UK, and the
assurance practice was presumably unable to provide any significant role in improving the
quality of the reported information. Therefore, by expanding the context of the study using
European Environmentally Sensitive Industry (ESI), we claim that our study can better
capture the essence of SA practice and its association with the sustainability disclosure
25
quality in the European setting of study. In this debate, we provide obvious evidence that
sustainability assurance (SA) is positively associated with sustainability disclosure quality in
the European ESI context.
Regarding the second contribution of our study, we provide a new disclosure index
which closely captures the aspect of materiality from the perspective of professional
stakeholder, i.e., the financial analyst as one of the market participants in the capital market.
We are aware that the problems of non-financial disclosure activity always related to the
problem of insufficient provision of relevant information, excessive provision of irrelevant
information, and inefficient communication of information. We assumed that the GRI
guideline framework as referred by the firms when dealing with reporting activities is too
broad, and it is considered to be appropriate for the global stakeholders. However, we see the
materiality aspect from a different point of view, in which we observe the materiality using
the perspective of a financial analyst. In Europe, European Federation of Financial Analyst
Society (EFFAS) has released the list of required information as needed by the financial
analyst. They need this information as the initial data when dealing with the earning forecast
procedure. Therefore, we adopt the financial analyst key performance indicators (KPIs) with
respect to the environmental framework and further compare it relative to the available
information as reported using the GRI framework. Our materiality index shows that the
presence of sustainability assurance (SA) does not associate with the materiality index (See.,
column 5 in Table 4.5). However, the empirical test using matched sample shows that
sustainability assurance conducted by accounting firms is positively associated with
materiality index (See., column5 Table 4.8). In this regards, the sustainability assurance
conducted by the accountant helps the firms to better deal with relevant information, that is
deemed more useful for the stakeholders in general and financial analyst in particular.
With respect to the implication of the study, our study has covered all the European
firms operating in the Environmentally Sensitive Industries, who are disclosing their CR-
relation information and have the reports assured by the third-independent party. The
obtained empirical findings will be most relevant for the financial planning, particularly for
the financial analyst in utilizing the benefits of publicly available non-financial information
and its relevance in the process of earning forecast activity. Unfortunately, prior studies
(Dhaliwal et al., 2011; 2012) that have tried to link the relevance of non-financial information
with the financial performance is mainly driven by the simple measure, in which the
availability of CR-related information is simply measured by using dichotomous data (0;1). It
would be more interesting to better capture the relevance of non-financial disclosure (i.e., the
quality of the reported information) if future research can develop more comprehensive
measures of non-financial information.
5. ROBUSTNESS TEST
5.1. Heckman two-stage regression
We do realize that the multivariate panel data analysis per se does not seem enough to
explain the hypotheses statement we propose. Reconsidering to our hypotheses, our goal is to
seek the empirical evidence that sustainability assurance is associated with CR disclosure
quality. Given the practice of providing an assurance statement on the disclosed non-financial
information depends on management discretion, the decision to publish non-financial
information is also endogenous. Therefore, we put concern on the endogeneity problem in
our estimation outcome. Since the sampling procedure in our final sample is also non-
randomly assigned, there could be possibilities that firms with and without environmental
information as covered by ASSET4 database might behave differently in their sustainability
reporting practice. Our notion is, ASSET4 put more attention on large-public interest firms
26
and regularly evaluate their environmental-impact activity. However, in fact, there are more,
and more companies recognize the importance and benefit of disclosing non-financial
information, but their sustainability-related performance is covered neither by ASSET4 nor
Bloomberg. This circumstance may lead to a self-selection sample bias. Thus, we organize
our additional analysis by starting to investigate the likelihood of firm engages in non-
financial information disclosure. We run a Heckman 2SLS procedure by firstly run a probit
regression to see the likelihood of firm disclosing non-financial information. In this stage, we
generate the inverse mills ratio (IMR) that could help to correct the problem of self-selection
bias (Lennox et al., 2012; Tucker, 2010). We further insert the IMR to the second stage and
found that variable sustainability assurance (SA) and IMR is positive but insignificantly
associated with sustainability disclosure quality (QUALITY).
[Insert Table 4.7 around here]
5.2. Propensity Score Matching
Subsequent to the Heckman procedures, we go through the next step by conducting
propensity score matching (PSM) test. It is discernible that there is a problem regarding the
observable heterogeneity between firms with and without sustainability assurance on their
sustainability report. As concerned by Angrist & Pischke, (2013) panel data analysis offers a
greater variation of variable and greater power of statistical estimation. The combination of
cross-sectional and time series enables the estimation process to have a greater variation that
cannot be overtaken by only doing either cross-sectional or times series regression per se.
Regardless of its benefit, doing analysis with pooled cross-sectional and time series sample
leads to a problem. Control group may not be appropriate due to the uncontrollable firms-
specific characteristics that may differ from one firm with other firms. In this case, we
consider that reporting and non-reporting firms might deal with different type of reporting
procedures prior to the assurance engagement. Therefore, considering the previous study that
highlighted the important issue of dealing with the endogeneity problem, we follow the study
of Dhaliwal et al., (2012) & Michelon et al., (2015).
In performing the matched-sample analysis, we match sustainability reports of firms
that have been assured and not assured by the third-independent party. We do the matching
for each firm-year from the same country and industry closest in size in the same year. At
first, we have 583 observations belong to the treated group and 745 observations in the
control group. After dealing with the procedures with the nearest neighbor matching method,
the number of control group reduced from 745 to 670, while the number of the treated group
remains the same.
[Insert Table 4.8 around here]
After examining the attributes of sustainability assurance on the sustainability
disclosure quality indexes, we further use the different sample to confirm the previous
outcome we get in testing our hypotheses (see Table 4.9). Three sets of sample are employed
to identify whether the different subset of sample provides us with different or consistent
results. At the first empirical test, we use the complete panel data set by controlling the
environmental performance information (ENVSCR) as released by the ASSET4 database.
The second group of sample is by utilizing the whole sustainability reports even though some
of the sustainability reports of particular European public listed companies are not covered by
the ASSET4 (we dropped the variable ENVSCR). The last sample group is obtained from the
27
propensity matching score based on the companies’ size. The details of empirical test on the
three subsamples are available as follows.
[Insert Table 4.9 around here]
Table 4.9 contains a summary of the overall empirical test. Our initial sample is
collected from 226 companies from 25 European countries (904 firm-year observations see
Table 4.6). to further prove that the obtained main output in Table 4.6 provides hold and
rebut results, we do the concurrent panel data analysis using the full sample and PSM sample.
The test using full sample (332 companies, 1328 observations) without controlling for
environmental performance also indicates a similar result, in which SA positively associated
with QUALITY (see column 1). ACCOUNTANT, CONSULTANT, LIMITED, and
AS_PERS also indicate a positive and significant association with QUALITY. Meanwhile,
REASONABLE; MIXED, and AS_TENURED do not show any association with QUALITY.
To further confirm the robustness of our results in the full sample, we also do propensity
score matching and report that the sign, coefficient, and significance of variable SA remains
consistent with the previous test using a different group of samples (see column 6). With this
respect, variable ACCOUNTANT, CONSULTANT, LIMITED, and AS_PERS also show
positive and significant association with QUALITY. Meanwhile, the concurrent test of these
two sample group also shows the same propensity, in which the simultaneous test by pooling
up together the main independent variables do not show any significant association with CR
disclosure QUALITY. In this regards, only variable AS_PERS that shows positive and
significant association in the concurrent test (see Table 5 and 10). These results suggest that
the presence persistent of sustainability assurance (SA) is considered a relevant practice and
important for firms when dealing with sustainability engagement. Whilst, whether the
assurance report provided either by ACCOUNTING or CONSULTANT firms, and any levels
of assurance engagement do not matter by the users.
6. CONCLUSION
Our results suggest that sustainability assurance is associated with sustainability
disclosure quality. After empirically test the proposed hypotheses with different type of
assurance proxies and sustainability disclosure indexes, our study provides robust empirical
evidence that the availability of sustainability assurance is positively associated with
sustainability disclosure quality. Our study also advances the previous studies which mostly
focus on considering the number of information rather than the quality of information
disclosed to the public. By employing a different type of sample groups, the output also
remains consistent, in which the presence of sustainability assurance is positively associated
with the sustainability disclosure quality.
We contribute to the call for empirical evidence on the relationship between
sustainability assurance and sustainability disclosure quality. Since the European directive
2014/95/EU has been enacted, non-financial information disclosure has become one of the
essential information and relevant to be released together with the financial information to
public (European Commission, 2014). With this motivation, we test whether firms that
voluntarily disclose their non-financial information through sustainability reporting and have
the report assured by the third-independent party also increase the quality of the reported
information. We find strong evidence that the presence of sustainability assurance on the
sustainability report is positively associated with the sustainability disclosure quality,
28
providing support for prior literature debating on the role of sustainability assurance on the
sustainability disclosure quality (Michelon et al., 2015; Moroney et al., 2011).
Several caveats apply to our empirical results and inference. The first limitation is due
to data limitation. We do recognize that not all firms in the European environmentally
sensitive industry (ESI) have sufficient data, particularly due to the missing observations. We
refer our main data source regarding the availability of environmental performance
information from the ASSET4 database. Even though there is an alternative of environmental
rating (i.e., Bloomberg), we still could not merge the data of Bloomberg and ASSET4 as
nature, and the method used by these two databases are different. We also note that even
though in general Bloomberg database cover more companies than ASSET4, yet in the
context of ESI industry firms, ASSET4 offers more coverage than Bloomberg. The second
limitation related to the number of missing observations. We do acknowledge apart from the
limited number of observations given by the database, sustainability reports not published in
English also limit our coverage. We report that there are 22 firms (88 firm-year obs.) that
have disclosed their non-financial information during the period of observations but not
written in English. We dropped these firms as their sustainability-report cannot further be
analyzed in the content analysis.
29
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Appendices
Figure 2.1 Research model
Sustainability Assurance Sustainability Disclosure Quality
1. SA (Sustainability assurance)
2. ACCOUNTANT (Professional
accountant)
3. CONSULTANT (Environmental
consultant or certified body)
4. LIMITED (Limited level of assurance)
5. REASONABLE (Reasonable level of
assurance)
6. MIXED (Combination of limited &
reasonable level)
7. AS_PERS (Assurance persistency)
8. AS_TENURED (Assurance tenured)
1. RQT (Relative quantity)
2. DEN (Density)
3. TOI (Type of information)
4. MAN (Managerial
orientation)
5. MAT (Materiality)
Legitimacy theory
Signaling theory
Control variables
36
Figure 3.1 Decision tree with observation period spans from 2014 to 2017.
Accountant
102 companies
(408 obs.) Assured
145 companies
(580 obs.)
SR provided in
English Consultant
332 companies
(1,328 obs.) 43 companies
(172 obs.)
Companies published SR at
least one time or more in the
observation period
Not Assured
354 companies
(1,416 obs.) 187 companies
748 (obs.)
Companies in ESI
Industry SR not provided in
English
1,320 companies
(5,280 obs.) 22 companies
(88 obs.)
Companies with No SR
Active public listed companies in 29
European countries (2014-2017) 966 companies
(3,864 obs.)
9,249 companies
(36,996 obs.)
Companies in
Non-ESI
7,929 companies
(31,716 obs.)
Notes: ESI (Environmentally Sensitive Industry), SR (Sustainability Report), Accountant (professional accounting companies; KPMG, PwC, Deloitte, Ernst &Young, Non-
big 4). Consultant (certified bodies or environmental consultants).
37
Figure 3.2. The sustainability code: Criteria and indicators to the declaration of
conformity in Environment aspect between GRI-indicators and EFFAS-indicators
Notes: GRI G.4 (Global Reporting Initiatives. Generation 4). In GRI G.4, we adopt the environmental dimensions
which represent the stakeholders’ concern on the environmentally related-activities. While, KPIs (Key Performance
Indicators) as released by The European Federation of Financial Analyst Societies (EFFAS) is used as the
representation of capital market participants. In this KPIs, we also adopt the environmental dimension which is
considered as the most material information by the financial analyst.
GRI
KPIs
GRI Environment
Framework
1. Materials
2. Energy 3. Water
4. Biodiversity
5. Emissions 6. Effluents and waste
7. Products and services
8. Transport 9. Overall
10. Environmental grievance
mechanism 11. Supplier environmental
assessment
12. Compliance
Source: Global Reporting Initiatives (GRI)
Analyst KPIs Dimensions in
ESI Industries
1. Raw material reserve 2. Energy efficiency
3. Water consumption
4. Remediation 5. Emission to air/water
6. GHG Emission
7. Waste 8. Radio-active waste
9. Eco design
10. Production shortfall
11. Leakage
12. Accidental oil & gas
13. Supply constraint 14. Environmental
compatibility
Source: The European Federation of
Financial Analyst Societies (EFFAS)
38
Table 3.1 Sample selection procedures
No Sampling procedures Number of
firms
Year-observations Percentage
(2014-2017) (%)
1 Active public listed companies in the capital market of 29
European stock exchanges from 2014 to 2017.
9,249 36,996 100
2 Firms not listed in the ESI industry. (7,929) (31,716) (86)
3 Firms with no sustainability report. (966) (3,864) (10)
4 Sustainability reports not provided in English. (22) (88) (1)
5 Eligible firms with consistent sustainability reports from 2014
to 2017 (Full sample)
332 1,328 4
6 Firms without any environmental performance scores from
ASSET4 database
106 424
7 Eligible firms with consistent sustainability reports and
environmental performance from ASSET4 database (ASSET4
sample)
226 904
Source: Own elaboration.
39
Table 3.2 Example of coding No Disclosure GRI item KPIs TOI1 TOI2 TOI3 EXP PROG OBJ RES
1 Overall, we spent approximately EUR 514 million on raw material in 2016. Materials 1 0 0 1 0 1 0 0
2 Expenditure on pulp and natural fibers was about EUR 213 million, on synthetic fibers about 142 million and
on chemicals about EUR 159 million.
Materials 1 0 0 1 0 1 0 0
3 Energy expenditure was about EUR 77 million. Energy 1 0 0 1 0 1 0 0
4 Overall, raw material and energy account for about 60% of our total cost base. Energy 0 0 1 0 0 1 0 0
5 By far the most important raw material for us is fiber with about 70% of the total. Materials 0 0 1 0 0 1 0 0
6 In 2016, 79% of the fibers we used as raw material came from renewable sources, compared with 82% in
2015.
Materials 0 1 0 0 0 0 0 1
7 The vast majority, or about 90% of the renewable fiber, is wood pulp from forests. Materials 0 1 0 0 0 1 0 0
8 Total energy consumption declined by 8.3%. Energy 0 0 1 0 0 0 0 1
9 Electrical efficiency improved by 2.5%. Energy 0 0 1 0 0 0 0 1
10 Process heat efficiency improved by 9.8%. Energy 0 0 1 0 0 0 0 1
11 Our approach is to manage and to reduce energy consumption while ensuring the competitiveness of our
business.
Energy 0 1 0 0 0 0 1 0
12 In 2016, our total energy consumption was 3,306 GWh, showing a decrease of 8.3% from the 3,605 GWh
consumed in 2015.
Energy 1 0 1 0 0 0 0 1
13 Electrical efficiency increased by 2.5% to 1.28 MWh per gross ton and process heat efficiency improved by
9.8% to 12.5 GJ per gross ton.
Energy 1 0 1 0 0 0 0 1
14 For the future, the energy roadmap will be developed further to better visualize the trends and baseline of our
manufacturing process.
Energy 0 1 0 0 1 0 0 0
Note:
TOI1 : 1 if the sentence is categorized as qualitative information.
TOI2 : 1 if the sentence is categorized as quantitative information.
TOI3 : 1 if the sentence is categorized as monetary information.
EXP : 1 if the sentence is categorized as context, expectation, or hypothesis information.
PROG : 1 if the sentence is categorized as policies, initiatives and strategies information.
OBJ : 1 if the sentence is categorized as objective and goals information.
RES : 1 if the sentence is categorized as results and outcome of actions information.
The environmental-related sentence was extracted from the environmental performance section of annual report Ahlstrom-Munksjo 2016 (Finland) Page 56-68.
40
Table 3.3 Disclosure quality indexes
Variable Definition Measure
GRI G.4
Environmental
Framework
Environmental-
related KPIs of
Financial Analyst
DISC Disclosure
quantity DISCi,t,k = ∑ CRi,t,k
n
j=1
1. Materials
2. Energy
3. Water 4. Biodiversity
5. Emissions
6. Effluents and waste
7. Products and
services 8. Transport
9. Overall
10. Environmental grievance
mechanism
11. Supplier
environment
al assessment
12. Compliance
1. Raw material
reserve
2. Energy efficiency 3. Water consumption
4. Remediation
5. Emission to air/water
6. GHG Emission
7. Waste 8. Radioactive waste
9. Eco-design
10. Production shortfall 11. Leakage
12. Accidental oil &
gas
13. Supply constraint 14. Environmental
compatibility
RQT Relative
Quantity RQTi,t = DISCi,t − DISCi,t
DEN Density
DENi,t = 1
ki,t ∑(CRi,t
ki,t
j=1
)
TOI Type of
Information TOIi,t = ∑ (w ∗ CRi,j,t
ni,t
j=1 )
QNTi,k,t
MAN Managerial Orientation MANi,t =
∑ (PROGi,j,t+RESi,j,tni,tj=1
)
DISCi,t)
MAT Materiality of
Information MATi,t =1
ni,t ∑
(DISC_KPIi,j,t)
(KPIf)
ni,t
j=1
QUALITY Standardized
sustainability
disclosure
index
QUALITYit = 1
5 ∗ (RQTi,t + DENi,t + TOIi,t + MANi,t + MATi,t)
Source: Michelon et al., (2015) with some additional new CR disclosure indexes.
41
Table 3.4 Variable definition
No Variable Measure Source
1 Sustainability
disclosure
Indexes
(RQT)
Relative quantity
RQTi,t = DISCi,t − DISCi,t
RQTi,t is the relative quantity index for company i in year t,
DISCi,t is the observed level of disclosure for company i in
year t and DISCi,t is the estimated disclosure level for
company i in year t.
Corporate
Register/
GRI
database
2 (DEN)
Density
DENi,t =
1
ki,t ∑(SRi,t
ki,t
j=1
)
DEN i is the density index for company i in year t, ki,t is the
number of sentences in the document analyzed for company i
in year t and SRi,t = 1 if the sentence j in the document
analyzed for company i in year t contains environmental-
related information and SRi,t = 0 otherwise.
Corporate
Register/
GRI
database
3 (TOI)
Type of
information
TOIi,t = ∑ (w ∗ SRi,j,t
ni,t
j=1 )
QNTi,k,t
TOI measures, for each item of environmental disclosure
reported in Figure 3.2. We count the information based on the
type of data. We scale the data as 0 if the companies do not
provide any information of the environmental dimension, 1 if
the company report the qualitative information, 2 if the
companies present the quantitative information, and 3 if the
companies release the monetary information with respect to
the environmental dimensions. The incidence of recording
units containing environmental information in quantitative,
qualitative, and or monetary terms over the total of the
recording quantitative units containing environmental
information.
Corporate
Register/
GRI
database
4 (MAN)
Managerial
orientation
MANi,t = ∑ (PROGi,j,t+RESi,j,t
ni,t
j=1 )
DISCi,t
MANi is the managerial orientation index for company i in
year t, nit is the number of sentences containing
environmental-related information in the document analyzed
for company i in year t, PROGijt = 1 if sentence j in the
document analyzed for company i in year t contains
environmental-related information on program, initiatives,
and strategies, otherwise PROGijt equals to 0. RESijt = 1 if the
sentence j in the document analyzed for company i in year t
contains environmental-related information on results and
outcomes and RESijt = 0 otherwise. DISCi,t = the total of GRI
environmental-related disclosures.
Corporate
Register/
GRI
database
5 (MAT)
Materiality
MATi,t =
1
ni,t ∑
(DISC_KPIi,j,t)
(KPI)f
ni,t
j=1
MATi is the materiality index for company i in year t, n is the
number of sentences containing environmental-related
information in the document j analyzed for company i in year t
based on KPIs. KPIf is the number of relevant environmental
key performance indicators as required by the financial
analyst in industry f.
Corporate
Register/
GRI
database
42
No Variable Measure Source
6 Standardized
sustainability
disclosure
index
(QUALITY)
The standardized
sustainability
disclosure quality
index
QUALITYi,t
= 1
5 ∗ (RQTi,t + DENi,t + TOIi,t + MANi,t + MATi,t)
Following the study of Michelon et al., (2015), we generate a
standardized CR disclosure quality index by weighting the
standardized value of each five disclosure indexes; RQT,
DEN, TOI, MAN, and MAT.
Corporate
Register/
GRI
database
7 SR 1 if the firm published a stand-alone sustainability report, and
0 if the CR information is published in a dedicated page of
the annual report.
Corporate
Register/
GRI
database
8 (SA)
Sustainability
assurance
1 if the firm has assurance report, and 0 otherwise.
EIKON /
Corporate
Register/
GRI
database
9 Assurance
provider
Accountant
1 if the assurance service is provided by the accounting firms,
0 otherwise.
Corporate
Register/
GRI
database
10 Consultant 1 if the assurance service is provided by the consultant firm, 0
otherwise.
11 Level of
assurance
Nolevel 1 if the firm has no assurance report, 0 otherwise
12 Limited 1 if the assurance statement indicates that the company
engages with limited assurance, 0 otherwise
13 Reasonable 1 if the assurance statement indicates that the company
engages with reasonable assurance, 0 otherwise
14 Mixed 1 if the assurance statement indicates that the company
engages with the combination of limited and reasonable
assurance, 0 otherwise
15 Assurance
persistency
(AS_PERS)
Assurance
Persistency
1 if in the prior year (t-1) and the current year (t0) company
have its CR report assured by the assurance provider.
(AS_TENURED)
Assurance
Tenured
The yearly time since the first time firm adopted the
assurance practice.
ASSET4/
Corprate
Register
16 Control
variable
(ENVSCR)
Environmental
score
Environmental pillar score from ASSET4 database. ASSET4
17 (GRI)
Global Reporting
Initiatives
1 if the analyzed document contains a statement declaring
GRI adoption, 0 otherwise.
ASSET4
18 (CR_BOARD)
CSR
sustainability
committee
1 if the company has a CR committee, and 0 otherwise. ASSET4
19 (SIZE)
Size
Natural logarithm of the total asset. EIKON
20 (AGE)
Companies age
The age of the company is counted since the first time it starts
to operate.
EIKON
21 (LEVERAGE)
Leverage
Total debt to total equity. EIKON
43
No Variable Measure Source
22 (ROA)
Return on Asset
Fiscal year-end net income divided by year-end total assets. EIKON
Source: own elaboration.
44
Table 4.1 Sample distribution
Panel A. Sample distribution by country (firm-year observations)
No Country Firms Obs. % Stand-alone SR SA SA Provider Level of assurance GRI
No Yes No Yes Accountant Consultant No level Limited Reasonable Mixed No Yes
1 Austria 3 12 1.33 5 7 3 9 8 1 3 9 0 0 0 12
2 Belgium 4 16 1.77 10 6 4 12 8 4 4 9 3 0 4 12
3 Croatia 1 4 0.44 4 0 2 2 2 0 2 2 0 0 0 4
4 Czech Republic 2 8 0.88 6 2 7 1 1 0 7 1 0 0 6 2
5 Denmark 11 44 4.87 17 27 28 16 16 0 28 11 5 0 31 13
6 Estonia 1 4 0.44 0 4 0 4 0 4 0 4 0 0 4 0
7 Finland 9 36 3.98 9 27 12 24 24 0 12 24 0 0 6 30
8 France 19 76 8.41 49 27 7 69 63 6 7 61 6 2 32 44
9 Germany 24 96 10.62 63 33 41 55 49 6 41 50 5 0 29 67
10 Greece 8 32 3.54 9 23 13 19 5 14 13 19 0 0 0 32
11 Hungary 3 12 1.33 12 0 8 4 4 0 8 4 0 0 8 4
12 Ireland 1 4 0.44 4 0 4 0 0 0 4 0 0 0 4 0
13 Italy 15 60 6.64 16 44 11 49 46 3 11 49 0 0 11 49
14 Luxemburg 1 4 0.44 0 4 4 0 0 0 4 0 0 0 3 1
15 Netherland 11 44 4.87 29 15 24 20 16 4 24 16 4 0 19 25
16 Poland 1 4 0.44 0 4 0 4 4 0 0 4 0 0 0 4
17 Portugal 7 28 3.1 23 5 15 13 13 0 15 9 4 0 9 19
18 Romania 1 4 0.44 4 0 4 0 0 0 4 0 0 0 3 1
19 Slovakia 1 4 0.44 4 0 3 1 0 1 3 1 0 0 4 0
20 Slovenia 2 8 0.88 6 2 8 0 0 0 8 0 0 0 8 0
21 Spain 17 68 7.52 31 37 32 36 33 3 32 36 0 0 17 51
22 Sweden 9 36 3.98 8 28 8 28 26 2 8 28 0 0 0 36
23 Swiss 5 20 2.21 11 9 8 12 12 0 8 12 0 0 5 15
24 UK 70 280 30.97 213 67 122 158 90 68 122 142 12 4 141 139
Total 226 904 100% 533 371 368 536 420 116 368 491 39 6 344 560
Panel B. Sample distribution by year (firm-year observations)
Year Obs. % Stand-alone SR SA SA Provider Level of assurance GRI
No Yes No Yes Accountant Consultant No level Limited Reasonable Mixed No Yes
2014 226 25 141 85 101 125 99 26 101 114 9 2 97 129
2015 226 25 134 92 94 132 102 30 94 121 9 2 86 140
2016 226 25 127 99 90 136 105 31 90 124 11 1 83 143
2017 226 25 131 95 83 143 114 29 83 132 10 1 78 148
Total 904 100% 533 371 368 536 420 116 368 491 39 6 344 560
45
Panel C. Sample distribution by Industry Group (firm-year observations)
ESI Industry Firms Obs. % Stand-alone SR SA SA Provider Level of assurance GRI
No Yes No Yes Accountant Consultant No level Limited Reasonable Mixed No Yes
Aerospace & Defense 16 64 7.08 46 18 16 48 31 17 16 48 0 0 32 32
Chemicals 32 128 14.16 83 45 55 73 68 5 55 57 16 0 52 76
Forestry & paper 13 52 5.75 32 20 29 23 19 4 29 23 0 0 14 38
Pharmaceuticals 43 172 19.03 118 54 106 66 56 10 106 65 1 0 97 75
Metals 7 28 3.1 11 17 16 12 12 0 16 12 0 0 9 19
Mining 37 148 16.37 77 71 50 98 65 33 50 87 7 4 37 111
Oil & gas 33 132 14.6 73 59 44 88 68 20 44 83 5 0 38 94
Utilities 45 180 19.91 93 87 52 128 101 27 52 116 10 2 65 115
Total 226 904 100% 533 371 368 536 420 116 368 491 39 6 344 560
Notes: The sample period in our study spans from the fiscal year-end of 2014 to 2017, covers a total of 904 firm-year observations (226 firms) from 24 European countries.
We dropped the sample from the five other countries (Bulgaria, Cyprus, Latvia, Lithuania, and Malta) due to incomplete number of data observations.
46
Table 4.2 Descriptive statistics of sustainability disclosure components
Variables Obs mean sd p25th p50 th p75 th min max
Panel: A Quantity
Total Page 904 146.68 86.75 80 139.5 200 2 508
CR_Page 904 9.68 12.5 3 6 12 1 162
Total_Sentence 904 505.76 533.02 149 336 705 3 4,548
Total DISC 904 171.67 178.3 48 115 247 1 1,516
Panel: B Type of Information
TOI1 904 112.9 106.58 36 80 161 0 739
TOI2 904 56.55 78.95 8 33 78 0 773
TOI3 904 3.63 6.57 0 2 4 0 70
Panel: C Managerial Orientation
EXP 904 52.88 59.20 16 38 68 0 627
PROG 904 56.17 80.28 7 28 78 0 777
OBJ 904 61.25 75.76 9 33 91 0 649
RES 904 41.21 52.00 9 25 55 0 496
Notes: Panel A of this Table contains the information needed to generate relative quantity (RQT) and
density (DEN) indexes. Panel B provides the material information for generating the type of information
(TOI) index, while information in panel C will be used in generating the managerial orientation (MAN)
index. All information provided in Table 4.2 refers to the number of sentences as the unit of analysis.
47
Table 4.3 Descriptive statistics of sustainability disclosure indexes and independent
variables
Variables Obs mean sd p25th p50th p75th min max
Panel 1: Disclosure Indexes
DISC 904 171.67 178.30 47.50 115.00 246.50 1.00 1,516
RQT 904 0.00 153.82 -81.16 -14.78 52.39 -344.72 1,220
DEN 904 3.46 4.14 3.00 3.00 3.00 0.03 75.43
TOI 904 0.11 0.35 0.00 0.01 0.06 0.00 5.03
MAN 904 0.01 0.04 0.00 0.00 0.00 0.00 0.67
MAT 904 1.93 3.93 0.27 0.60 1.55 0.00 28
Panel 2: Independent and Control Variables
SR 904 0.41 0.49 0 0 1 0 1
SA 904 0.59 0.49 0 1 1 0 1
ACCOUNTANT 904 0.46 0.50 0 0 1 0 1
CONSULTANT 904 0.13 0.32 0 0 0 0 1
LIMITED 904 0.54 0.50 0 1 1 0 1
REASONABLE 904 0.04 0.20 0 0 0 0 1
MIXED 904 0.01 0.08 0 0 0 0 1
AS_PERS 904 0.42 0.49 0 0 1 0 1
AS_TENURED 904 5.11 5.48 0 3 10 0 16
ENVSCR 904 63.36 20.11 51.40 66.18 78.76 2.33 97.81
GRI 904 0.62 0.49 0 1 1 0 1
CR_BOARD 904 0.68 0.47 0 1 1 0 1
SIZE 904 9.70 0.91 9.14 9.70 10.31 6.54 12.67
AGE 904 22.97 19.29 10 18 27 3 140
LEVERAGE 904 0.34 9.52 0.22 0.56 1.13 -251.31 48.79
ROA 904 2.70 9.89 0.91 3.39 6.14 -79.75 68.71
Notes: Panel 1 shows the final data of CR disclosure quality indexes consisting of relative quantity (RQT),
density (DEN), type of information (TOI), managerial orientation (MAN) and materiality (MAT). Panel 2
presents the information of main independent variables and the control variables.
48
Figure 4.1 The average of sustainability disclosure quality based on the presence of
sustainability assurance (SA)
Figure 4.2 The average of sustainability disclosure quality based on the assurance
providers (accountant, consultant)
Figure 4.3 The average of sustainability disclosure quality based on the the levels of
assurance (limited, reasonable, mixed)
-50
51
0-.5 0 .5 1 1.5 -.5 0 .5 1 1.5
0 1
(mean) quality hiquality/lowquality
sa
Graphs by sa
05
10
15
-.5 0 .5 1 1.5 -.5 0 .5 1 1.5
0 1
(mean) quality hiquality/lowquality
accountant
Graphs by accountant
05
10
15
-.5 0 .5 1 1.5 -.5 0 .5 1 1.5
0 1
(mean) quality hiquality/lowquality
consultant
Graphs by consultant
-50
51
01
5
-.5 0 .5 1 1.5 -.5 0 .5 1 1.5
0 1
(mean) quality hiquality/lowquality
limited
Graphs by limited
-50
51
0
-.5 0 .5 1 1.5 -.5 0 .5 1 1.5
0 1
(mean) quality hiquality/lowquality
reasonable
Graphs by reasonable
-20
02
04
0
-.5 0 .5 1 1.5 -.5 0 .5 1 1.5
0 1
(mean) quality hiquality/lowquality
mixed
Graphs by mixed
49
Table 4.4 Correlation matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 RQT 1 2 DEN -0.128*** 1 3 TOI -0.100** 0.021 1 4 MAN -0.194*** 0.195*** 0.032 1 5 MAT -0.360*** 0.097** 0.093** 0.338*** 1 6 SR 0.137*** -0.038 -0.124*** 0 -0.070* 1 7 SA 0.185*** -0.086** -0.196*** -0.124*** -0.174*** 0.298*** 1 8 ACCOUNTANT 0.120*** -0.067* -0.145*** -0.067* -0.114*** 0.269*** 0.772*** 1 9 CONSULTANT 0.093** -0.026 -0.071* -0.081* -0.086** 0.036 0.318*** -0.357*** 1 10 LIMITED 0.207*** -0.067* -0.169*** -0.111*** -0.178*** 0.318*** 0.903*** 0.690*** 0.299*** 1 11 REASONABLE -0.057 -0.041 -0.051 -0.027 0.016 -0.055 0.176*** 0.141*** 0.049 -0.232*** 1 12 MIXED -0.007 -0.009 -0.019 -0.004 0 -0.013 0.068* 0.088** -0.031 -0.089** -0.017 1 13 AS_PERS 0.168*** -0.056 -0.161*** -0.082* -0.131*** 0.199*** 0.710*** 0.552*** 0.220*** 0.647*** 0.115*** 0.04 1 14 AS_TENURED 0.158*** -0.051 -0.178*** -0.107** -0.143*** 0.219*** 0.697*** 0.580*** 0.160*** 0.604*** 0.192*** 0.036 0.616*** 1 15 ENVSCR 0.138*** -0.035 -0.071* -0.089** -0.096** 0.220*** 0.443*** 0.361*** 0.111*** 0.427*** 0.024 0.001 0.385*** 0.523*** 1 16 GRI 0.195*** -0.044 -0.227*** -0.109** -0.220*** 0.399*** 0.459*** 0.410*** 0.062 0.406*** 0.099** 0.036 0.335*** 0.422*** 0.302*** 1 17 CR_BOARD 0.108** -0.070* -0.204*** -0.090** -0.162*** 0.286*** 0.518*** 0.390*** 0.179*** 0.466*** 0.088** 0.056 0.399*** 0.495*** 0.268*** 0.484*** 1 18 SIZE 0 -0.088** -0.175*** -0.095** -0.099** 0.232*** 0.515*** 0.494*** 0.02 0.455*** 0.119*** 0.026 0.388*** 0.606*** 0.367*** 0.435*** 0.468*** 1 19 AGE -0.043 -0.04 -0.053 -0.051 0.070* 0.048 0.099** 0.112*** -0.021 0.099** 0.023 -0.062 0.070* 0.226*** 0.094** 0.059 -0.001 0.140*** 1 20 LEVERAGE 0.026 0.017 0.002 0.001 0 -0.039 -0.039 0.029 -0.101** -0.043 0.008 0.004 -0.06 -0.051 -0.042 -0.032 0.012 -0.018 -0.031 1 21 ROA -0.047 0.047 -0.038 -0.001 0.039 0.055 0.093** 0.109** -0.025 0.065* 0.055 0.026 0.054 0.072* -0.001 0.01 0.071* 0.148*** 0.085* -0.015 1
Notes: *** Significant at the 0.01 level. ** Significant at the 0.05 level. * Significant at the 0.10 level.
50
Table 4.5 Multivariate analysis using sustainability disclosure indexes
This Table reports the result of multivariate estimation using panel data analysis of equation 1. In this test, we use the
sample group as collected from ASSET4 database to control for the environmental score. The dependent variables are
the disclosure indexes generated by considering the quantity (RQT, DEN) and quality (TOI, MAN, MAT) of the non-
financial disclosure. In addition, a standardized disclosure index of the overall five indexes (QUALITY) is created. All
variables are defined in Table 3.3. Column from one to six reports the concurrent test output using the four proxies of
sustainability assurance (SA, ACCOUNTANT, CONSULTANT, LIMITED, REASONABLE, MIXED, AS_PERS, and
AS_TENURED) on the sustainability disclosure quality. All the specifications are estimated using OLS regression and
include year and industry fixed-effect. Robust standard errors clustered at the year and firm-levels and presented below
the coefficient estimates.
(1) (2) (3) (4) (5) (6)
VARIABLES RQT DEN TOI MAN MAT QUALITY
SR 18.96 -0.121 -0.0131 0.00565 0.225 3.815 (18.75) (0.386) (0.0205) (0.00602) (0.482) (3.237)
SA 9.322 -1.026 -0.0196 -0.0123* 0.135 8.921 (35.32) (0.638) (0.0498) (0.00731) (1.193) (12.61)
ACCOUNTANT -19.10 0.0353 0.0331* 0.00580* 0.352 -3.588 (28.62) (0.305) (0.0199) (0.00295) (0.389) (5.087)
o.CONSULTANT - - - - - -
LIMITED 30.70 0.314 0.0110 -0.00138 -1.028 -0.597 (24.98) (0.441) (0.0423) (0.00598) (1.116) (12.00)
REASONABLE -37.24 -0.175 0.00543 0.00115 -0.0125 -9.967 (43.59) (0.467) (0.0418) (0.00604) (1.961) (13.87)
o.MIXED - - - - - -
AS_PERS 24.21* 0.233 -0.106*** 0.00778 -0.0268 5.318** (13.28) (0.352) (0.0366) (0.00513) (0.376) (2.416)
AS_TENURED 3.822 0.0351 0.00119 2.90e-05 -0.0542 0.476 (2.524) (0.0360) (0.00323) (0.000317) (0.0793) (0.416)
ENVSCR 0.300 0.00598 0.000791 -6.83e-05 0.00266 0.00985 (0.415) (0.0104) (0.000756) (6.49e-05) (0.0125) (0.0676)
GRI 56.65*** 0.0134 -0.0867*** -0.00490 -1.421* 7.152** (19.58) (0.298) (0.0332) (0.00390) (0.845) (3.038)
CR_BOARD -7.580 -0.157 -0.0619** -0.000865 -0.197 -2.968 (18.68) (0.327) (0.0296) (0.00358) (0.750) (2.991)
SIZE -34.50*** -0.423* -0.0157 -0.00174 0.106 -3.594 (12.45) (0.240) (0.0165) (0.00153) (0.417) (2.292)
AGE -0.646 -0.00596 -0.000962 -5.59e-05 0.0213 -0.109 (0.396) (0.00450) (0.000614) (4.62e-05) (0.0291) (0.0726)
LEVERAGE 0.818*** 0.00341 4.54e-05 -1.35e-05 -0.00445 0.153*** (0.246) (0.00578) (0.000275) (3.36e-05) (0.00497) (0.0399)
ROA -0.645 0.0234*** -0.000968 -2.83e-06 0.0135 -0.0277 (0.412) (0.00900) (0.00195) (0.000183) (0.0233) (0.0619)
Year FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
Constant 270.9** 7.324*** 0.379** 0.0372** 0.869 29.86 (110.6) (2.238) (0.160) (0.0157) (3.851) (19.92)
Observations 904 904 904 904 904 904
R-squared 0.109 0.075 0.107 0.054 0.084 0.088
Notes: Robust standard errors in parentheses. *** Significant at the 0.01 level, ** Significant at the 0.05 level, and *
Significant at the 0.10 level respectively using a two-tail test.
51
Table 4.6 Partial test of SA proxies on sustainability disclosure quality (QUALITY)
This Table reports the result of estimation using equation 1. We do the partial test to examine our hypothesis. The
proxies of sustainability assurance practices (SA, ACCOUNTANT, CONSULTANT, LIMITED, REASONABLE,
MIXED, AS_PERS, and AS_TENURED) are partially examined on the standardized value of CR disclosure quality
(QUALITY). All variables are defined in Table 3.3. In the partial test, it is documented that several surrogate indicators
of sustainability assurance (SA, ACCOUNTANT, CONSULTANT, LIMITED, AS_PERS) display a positive and
significant association with the standardized value of CR disclosure quality (QUALITY). In the pooled test, only
variable AS_PERS remains consistent by showing a positive and significant association. All the specifications are
estimated using OLS regression and include year and industry fixed-effect. Robust standard errors clustered at the year
and firm-levels and presented below the coefficient estimates.
Asset4 Sample
VARIABLES (1) (2) (3) (4) (5) QUALITY QUALITY QUALITY QUALITY QUALITY
SR 3.740 3.827 3.257 4.545 3.815 (3.239) (3.258) (3.191) (3.239) (3.237)
SA 10.59***
8.921 (2.856)
(12.61)
ACCOUNTANT
9.711***
-3.588 (3.241)
(5.087)
CONSULTANT
13.01***
- (4.475)
LIMITED
11.23***
-0.597 (3.005)
(12.00)
REASONABLE
2.579
-9.967 (6.559)
(13.87)
MIXED
11.30
- (12.48)
AS_PERS
8.651*** 5.318** (2.780) (2.416)
AS_TENURED
0.507 0.476 (0.397) (0.416)
ENVSCR 0.0517 0.0518 0.0467 0.0235 0.00985 (0.0679) (0.0680) (0.0686) (0.0662) (0.0676)
GRI 6.633** 6.795** 6.832** 7.241** 7.152** (2.998) (3.017) (3.017) (3.031) (3.038)
CR_BOARD -1.691 -1.903 -1.706 -2.197 -2.968 (3.131) (3.156) (3.094) (3.025) (2.991)
SIZE -3.258 -3.008 -3.127 -3.811* -3.594 (2.118) (2.152) (2.113) (2.277) (2.292)
AGE -0.0907 -0.0897 -0.0920 -0.109 -0.109 (0.0740) (0.0741) (0.0742) (0.0723) (0.0726)
LEVERAGE 0.128*** 0.139*** 0.129*** 0.140*** 0.153*** (0.0318) (0.0364) (0.0318) (0.0343) (0.0399)
ROA -0.0427 -0.0425 -0.0399 -0.0219 -0.0277 (0.0622) (0.0627) (0.0620) (0.0611) (0.0619)
Year FE Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes
Constant 22.33 19.68 20.96 34.04* 29.86 (17.23) (17.64) (17.20) (19.74) (19.92)
Observations 904 904 904 904 904
R-squared 0.076 0.077 0.080 0.080 0.088
Notes: Robust standard errors in parentheses. All control variable are included from the previous test of
multivariate panel data regression. *** Significant at the 0.01 level, ** Significant at the 0.05 level, and *
Significant at the 0.10 level respectively using a two-tail test.
52
Table. 4.7 Heckman two-stage regression
This Table presents the Heckman two-stage regression procedure. In the first stage, we examine the likelihood to
disclose sustainability reports and further generate the Inverse Mill Ratio to be included in the second stage of
regression. All the specifications are estimated using OLS regression and include year and industry fixed-effect. Robust
standard errors clustered at the year and firm-levels and presented below the coefficient estimates.
VARIABLES
(1)
Stage I
likelihood to disclose
Sustainability report
(2)
Stage II
Sustainability
disclosure quality
SA
8.866 (12.67)
ACCOUNTANT
-3.570 (5.089)
o.CONSULTANT
-
LIMITED
-0.550 (12.07)
REASONABLE
-9.960 (13.93)
o.MIXED
-
AS_PERS
5.318** (2.417)
AS_TENURED
0.478 (0.416)
IMR
2.245 (1.946)
GRI
7.184** (3.039)
CR_BOARD
-2.966 (2.992)
ENVSCR 0.0100** 0.0232 (0.00413) (0.0678)
SIZE 0.236** -3.281 (0.103) (2.277)
AGE 0.00281 -0.105 (0.00401) (0.0716)
LEVERAGE -0.00497 0.147*** (0.00630) (0.0389)
ROA 0.00630 -0.0202 (0.00705) (0.0621)
Year FE Yes Yes
Industry FE Yes Yes
Constant -3.786*** 26.69 (0.988) (19.68)
Observations 904 904
R-squared 0.088
Notes: *** Significant at the 0.01 level, ** Significant at the 0.05 level, and * Significant at the 0.10 level
respectively using a two-tail test.
53
Table 4.8 Multivariate regression using propensity score matched sample
This Table reports the result of multivariate estimation using panel data analysis of equation 1. The dependent variables
are the disclosure indexes generated by considering the quantity (RQT, DEN) and quality (TOI, MAN, MAT) of non-
financial disclosure. In this empirical test, we increase the number of observation by taking into account the European
public listed companies that disclosed their sustainability report but not covered by the ASSET4 database. Utilizing this
full sample, we further conduct propensity score matching (PSM) by firm size and run OLS regression. All the
specifications are estimated using OLS regression and include year and industry fixed-effect. Robust standard errors
clustered at the year and firm-levels and presented below the coefficient estimates.
(1) (2) (3) (4) (5) (6)
VARIABLES RQT DEN TOI MAN MAT QUALITY
SR 16.28 -0.0874 0.00196 0.00360 0.293 2.884 (16.81) (0.313) (0.0315) (0.00524) (0.487) (2.915)
SA 13.39 -0.911* -0.0684 -0.0211*** -0.868 8.904 (36.04) (0.520) (0.0465) (0.00791) (1.231) (12.91)
ACCOUNTANT -12.19 -0.00633 0.0623*** 0.00661** 0.754* -2.061 (26.33) (0.273) (0.0227) (0.00309) (0.399) (4.651)
o.CONSULTANT - - - - - -
LIMITED 30.23 0.314 -0.00204 -0.000413 -0.658 -0.464 (28.07) (0.348) (0.0312) (0.00590) (1.141) (12.40)
REASONABLE -27.61 0.00486 -0.00735 0.00334 0.437 -7.221 (48.97) (0.416) (0.0442) (0.00670) (2.102) (14.54)
o.MIXED - - - - - -
AS_PERS 16.42 0.144 -0.0988** 0.00715 -0.0406 3.196* (10.67) (0.242) (0.0462) (0.00480) (0.363) (1.929)
AS_TENURED 4.727** 0.0488* 0.00351 0.000380 -0.00752 0.573 (2.289) (0.0285) (0.00321) (0.000358) (0.0665) (0.391)
GRI 48.54*** 0.305 -0.112*** -0.00470 -1.431* 6.058** (17.93) (0.252) (0.0372) (0.00391) (0.789) (2.778)
CR_BOARD -5.006 -0.244 -0.0121 -0.00126 0.114 -1.709 (13.86) (0.254) (0.0530) (0.00373) (0.578) (2.178)
SIZE -34.37*** -0.364** -0.0851*** -0.00336 -0.371 -3.609** (8.947) (0.184) (0.0255) (0.00267) (0.329) (1.641)
AGE -0.415 -0.00962** 0.000306 -0.000121* 0.0150 -0.0759 (0.342) (0.00455) (0.00104) (6.61e-05) (0.0241) (0.0601)
LEVERAGE 0.832*** 0.00448 -0.000173 -2.50e-05 -0.00679 0.152*** (0.240) (0.00587) (0.000333) (4.05e-05) (0.00499) (0.0391)
ROA -0.189 0.0193*** 0.00128 9.10e-05 0.00173 -0.0132 (0.212) (0.00562) (0.00137) (0.000118) (0.0140) (0.0318)
Year FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
Constant 281.7*** 7.268*** 1.039*** 0.0575** 5.715* 30.06** (79.07) (1.750) (0.223) (0.0229) (2.931) (14.46)
Observations 1,248 1,248 1,248 1,248 1,248 1,248
R-squared 0.114 0.058 0.091 0.056 0.092 0.096
Notes: *** Significant at the 0.01 level, ** Significant at the 0.05 level, and * Significant at the 0.10 level
respectively using a two-tail test.
54
Table 4.9 Analysis with different sample groups
This Table reports the result of multivariate estimation using panel data analysis of equation 1. The dependent variable
is the standardized value of five sustainability disclosure quality indexes (QUALITY). In this empirical test, we
partition the sample into two groups. The first group (column 1-5) is the full sample samples. The second group is the
matched sample generated from the propensity score matching (PSM) procedure (column 6-10). All the specifications
are estimated using OLS regression and include year and industry fixed-effect. Robust standard errors clustered at the
year and firm-levels and presented below the coefficient estimates. Full sample (N: 1,328)
PSM sample (N: 1,248)
VARIABLES (1) (2) (3) (4) (5)
(6) (7) (8) (9) (10) Quality Quality Quality Quality Quality
Quality Quality Quality Quality Quality
SR 2.612 2.632 2.260 3.249 2.540
2.777 2.804 2.546 3.491 2.884 (2.879) (2.887) (2.839) (2.881) (2.861)
(2.916) (2.926) (2.886) (2.922) (2.915)
SA 11.06***
8.705
11.45***
8.904 (2.173)
(13.05)
(2.205)
(12.91)
ACCOUNTANT
10.64***
-2.056
11.03***
-2.061 (2.490)
(4.635)
(2.541)
(4.651)
CONSULTANT
12.28***
-
12.64***
- (3.983)
(3.993)
LIMITED
11.50***
-0.406
11.78***
-0.464 (2.286)
(12.58)
(2.304)
(12.40)
REASONABLE
4.857
-7.832
6.194
-7.221 (5.970)
(14.22)
(6.609)
(14.54)
MIXED
11.54
-
11.95
- (13.95)
(13.69)
AS_PERS
7.063*** 2.932
7.567*** 3.196* (2.176) (1.867)
(2.227) (1.929)
AS_TENURED
0.658* 0.569
0.660* 0.573 (0.355) (0.380)
(0.364) (0.391)
GRI 6.290** 6.344** 6.425** 6.770** 6.324**
6.151** 6.192** 6.183** 6.640** 6.058** (2.686) (2.694) (2.700) (2.699) (2.700)
(2.759) (2.765) (2.773) (2.779) (2.778)
CR_BOARD 0.0784 0.0127 0.109 -0.575 -1.007
-0.611 -0.673 -0.549 -1.270 -1.709 (2.269) (2.276) (2.258) (2.142) (2.125)
(2.322) (2.331) (2.319) (2.188) (2.178)
SIZE -3.247** -3.188** -3.207** -3.644** -3.790***
-2.965* -2.890* -2.914* -3.496** -3.609** (1.348) (1.350) (1.346) (1.451) (1.444)
(1.510) (1.517) (1.509) (1.646) (1.641)
AGE -0.0542 -0.0532 -0.0546 -0.0747 -0.0732
-0.0578 -0.0568 -0.0575 -0.0780 -0.0759 (0.0605) (0.0603) (0.0607) (0.0591) (0.0591)
(0.0617) (0.0615) (0.0617) (0.0602) (0.0601)
LEVERAGE 0.129*** 0.134*** 0.130*** 0.143*** 0.150***
0.131*** 0.137*** 0.132*** 0.146*** 0.152*** (0.0302) (0.0340) (0.0302) (0.0343) (0.0384)
(0.0310) (0.0350) (0.0309) (0.0346) (0.0391)
ROA -0.0162 -0.0163 -0.0150 -0.00350 -0.00235
-0.0271 -0.0266 -0.0260 -0.0140 -0.0132 (0.0262) (0.0260) (0.0263) (0.0270) (0.0268)
(0.0314) (0.0314) (0.0314) (0.0320) (0.0318)
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Industry Fe Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 24.68** 24.02** 24.05** 32.28** 31.10**
22.53* 21.71* 21.82* 31.66** 30.06**
(11.38) (11.40) (11.37) (12.64) (12.52)
(12.93) (13.01) (12.94) (14.55) (14.46)
Observations 1,328 1,328 1,328 1,328 1,328
1,248 1,248 1,248 1,248 1,248 R-squared 0.083 0.084 0.086 0.085 0.093
0.087 0.087 0.088 0.088 0.096
Notes: *** Significant at the 0.01 level, ** Significant at the 0.05 level, and * Significant at the 0.10 level
respectively using a two-tail test.
55
Appendix 1
Partial test using the independent lag variables on the sustainability disclosure QUALITY VARIABLES (1) (2) (3) (4) (5)
SR i,t-1 5.369 5.527 5.076 6.291* 5.720 (3.740) (3.774) (3.769) (3.716) (3.824)
SA i,t-1 11.83***
-6.896 (3.209)
(9.894)
ACCOUNTANT i,t-1
9.873**
-7.284 (3.802)
(7.550)
CONSULTANTi,t-1
17.05***
(6.333)
LIMITED i,t-1
12.29***
18.24** (3.355)
(7.113)
REASONABLE i,t-1
8.753
13.98 (9.605)
(13.53)
MIXED i,t-1
-7.519
(6.551)
AS_PERS i,t-1
10.68*** 7.558*** (2.811) (2.716)
AS_TENURED i,t-1
0.486 0.391 (0.393) (0.418)
ENVSCR i,t-1 0.0351 0.0372 0.0337 0.0136 0.00522 (0.0741) (0.0745) (0.0748) (0.0713) (0.0734)
GRI i,t-1 4.976 5.261 5.032 5.814 5.568 (3.571) (3.603) (3.660) (3.651) (3.663)
CR_BOARD i,t-1 -2.903 -3.376 -2.865 -3.169 -4.234 (3.523) (3.585) (3.509) (3.410) (3.464)
SIZE i,t-1 -2.602 -2.068 -2.540 -3.109 -2.631 (2.231) (2.238) (2.224) (2.408) (2.377)
AGE i,t-1 -0.0871 -0.0841 -0.0928 -0.109 -0.107 (0.0741) (0.0742) (0.0747) (0.0743) (0.0743)
LEVERAGE i,t-1 0.198* 0.232* 0.201* 0.217* 0.265** (0.111) (0.121) (0.108) (0.114) (0.127)
ROA i,t-1 -0.0303 -0.0279 -0.0174 0.00909 0.00415 (0.0703) (0.0711) (0.0698) (0.0705) (0.0719)
2016.year 0.719 0.649 0.725 -5.043*** -3.450* (0.961) (0.973) (0.958) (1.859) (1.850)
2017.year 2.272 2.194 2.208 -3.851* -2.218 (1.696) (1.683) (1.635) (2.310) (1.997)
2.ind 5.115 6.338 5.622 3.781 6.145 (4.689) (4.803) (4.983) (4.698) (5.161)
3.ind -1.878 -1.204 -1.866 -3.397 -1.916 (5.714) (5.534) (5.751) (5.801) (5.689)
4.ind 2.202 3.565 2.332 0.231 3.006 (4.182) (4.196) (4.200) (4.297) (4.265)
5.ind -0.989 -0.855 -0.263 -1.593 -0.197 (4.105) (4.115) (4.096) (4.139) (4.187)
6.ind 0.698 1.065 0.842 -0.548 0.566 (4.595) (4.598) (4.650) (4.551) (4.702)
7.ind 5.421 6.207 5.539 4.153 6.032 (3.796) (3.940) (3.858) (3.675) (4.008)
8.ind 7.159 7.504 7.541 5.751 7.046 (7.107) (7.173) (7.191) (7.033) (7.175)
Constant 15.29 9.658 14.66 26.87 19.85 (18.21) (18.59) (18.05) (20.70) (20.24)
Observations 672 672 672 672 672
R-squared 0.080 0.086 0.084 0.083 0.097
56
Appendix 2
Stepwise regression using contemporaneous independent variables on sustainability disclosure QUALITY VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)
SR 7.442** 4.643 4.794 4.794 4.367 4.370 4.370 4.548 4.521 4.466 3.141 3.361 3.420 3.671 3.787 3.815 (3.210) (3.141) (3.180) (3.180) (3.140) (3.134) (3.134) (3.150) (3.153) (3.159) (3.203) (3.196) (3.206) (3.240) (3.236) (3.237)
SA
9.847*** 12.67*** 12.67*** 5.932 15.33 15.33 11.29 10.83 10.89 9.735 10.70 9.894 8.329 8.730 8.921 (2.545) (4.608) (4.608) (7.229) (12.42) (12.42) (12.52) (12.11) (11.99) (13.20) (13.23) (13.20) (12.63) (12.65) (12.61)
ACCOUNTANT
-3.579 -3.579 -3.459 -3.636 -3.636 -3.768 -3.866 -3.879 -4.386 -4.572 -3.195 -3.087 -3.588 -3.588 (4.847) (4.847) (4.934) (4.946) (4.946) (4.961) (5.017) (5.018) (5.039) (5.027) (5.076) (5.078) (5.081) (5.087)
o.CONSULTANT
- - - - - - - - - - - - -
LIMITED
7.362 -1.888 -1.888 -1.929 -1.947 -2.072 -1.900 -2.106 -1.683 -0.414 -0.470 -0.597 (5.865) (11.49) (11.49) (11.68) (11.41) (11.31) (12.57) (12.64) (12.58) (12.05) (12.06) (12.00)
REASONABLE
-10.70 -10.70 -10.54 -10.77 -10.79 -11.07 -11.35 -10.60 -9.723 -9.868 -9.967 (13.46) (13.46) (13.68) (13.51) (13.38) (14.39) (14.44) (14.37) (13.92) (13.90) (13.87)
o.MIXED
- - - - - - - - - -
AS_PERS
5.996** 5.591** 5.569** 5.895** 5.994** 5.635** 5.163** 5.332** 5.318** (2.463) (2.449) (2.450) (2.469) (2.476) (2.418) (2.414) (2.415) (2.416)
AS_TENURED
0.113 0.0861 0.00836 0.0863 0.324 0.463 0.480 0.476 (0.352) (0.362) (0.366) (0.355) (0.394) (0.415) (0.416) (0.416)
ENVSCR
0.0166 0.0111 0.00523 0.0138 0.0107 0.0104 0.00985 (0.0663) (0.0661) (0.0669) (0.0677) (0.0673) (0.0674) (0.0676)
GRI
5.073* 5.926** 7.007** 7.120** 7.212** 7.152** (2.666) (2.931) (3.047) (2.993) (2.995) (3.038)
CR_BOARD
-3.351 -2.088 -2.728 -2.995 -2.968 (2.922) (3.046) (2.990) (2.990) (2.991)
SIZE
-3.866* -3.689 -3.659 -3.594 (2.237) (2.236) (2.228) (2.292)
AGE
-0.111 -0.109 -0.109 (0.0729) (0.0728) (0.0726)
LEVERAGE
0.154*** 0.153*** (0.0400) (0.0399)
ROA
-0.0277 (0.0619)
2015.year -1.263 -1.482 -1.526 -1.526 -1.534 -1.537 -1.537 -4.704*** -4.532*** -4.544*** -4.831*** -4.956*** -5.016*** -4.828*** -4.880*** -4.886*** (1.017) (1.032) (1.037) (1.037) (1.041) (1.043) (1.043) (1.666) (1.604) (1.604) (1.596) (1.623) (1.628) (1.629) (1.627) (1.626)
2016.year 0.0492 -0.257 -0.308 -0.308 -0.283 -0.232 -0.232 -3.465* -3.344* -3.347* -3.587** -3.653** -3.799** -3.671** -3.705** -3.689** (1.209) (1.232) (1.234) (1.234) (1.237) (1.207) (1.207) (1.835) (1.781) (1.781) (1.766) (1.772) (1.790) (1.796) (1.795) (1.797)
2017.year 1.858 1.197 1.203 1.203 1.164 1.211 1.211 -1.913 -1.848 -1.836 -2.110 -2.244 -2.545 -2.500 -2.389 -2.358 (1.599) (1.590) (1.597) (1.597) (1.611) (1.627) (1.627) (2.039) (2.048) (2.052) (2.064) (2.060) (2.121) (2.143) (2.148) (2.157)
2.ind 4.721 6.687 7.351* 7.351* 8.408* 8.625* 8.625* 8.797* 8.700* 8.771* 8.297* 7.747 6.451 5.637 5.725 5.867 (4.248) (4.414) (4.447) (4.447) (4.596) (4.626) (4.626) (4.651) (4.701) (4.655) (4.667) (4.693) (4.789) (4.702) (4.722) (4.807)
3.ind 0.577 3.896 4.323 4.323 4.573 4.599 4.599 4.836 4.718 4.793 3.339 2.892 2.222 0.00750 0.0763 0.183 (4.393) (4.949) (4.857) (4.857) (4.924) (4.923) (4.923) (4.980) (4.980) (5.010) (5.004) (5.078) (5.203) (5.515) (5.535) (5.556)
4.ind 0.194 4.271 4.929 4.929 5.276 5.314 5.314 5.382 5.340 5.322 4.426 4.468 2.766 0.555 0.687 0.808 (3.458) (3.699) (3.735) (3.735) (3.800) (3.800) (3.800) (3.833) (3.812) (3.834) (3.976) (3.870) (3.890) (4.062) (4.092) (4.129)
5.ind -0.168 1.253 1.309 1.309 2.001 1.824 1.824 1.991 2.025 1.985 0.860 1.083 0.0513 -2.151 -1.908 -1.958 (2.902) (3.183) (3.132) (3.132) (3.169) (3.203) (3.203) (3.231) (3.264) (3.279) (3.309) (3.311) (3.304) (3.770) (3.775) (3.780)
6.ind 0.994 2.278 2.599 2.599 2.997 3.069 3.069 3.162 2.996 3.000 2.202 1.792 1.930 -0.485 -0.444 -0.529 (3.512) (3.728) (3.703) (3.703) (3.786) (3.796) (3.796) (3.800) (3.853) (3.849) (3.820) (3.775) (3.734) (4.277) (4.300) (4.332)
7.ind 2.878 6.576** 7.036** 7.036** 7.341** 7.377** 7.377** 7.658** 7.641** 7.625** 7.422** 7.200** 6.905** 4.951 5.307 5.353
57
(2.532) (3.092) (3.163) (3.163) (3.225) (3.229) (3.229) (3.280) (3.282) (3.293) (3.321) (3.337) (3.273) (3.621) (3.609) (3.608)
8.ind 6.468 7.417 7.771 7.771 8.363 8.359 8.359 8.471 8.333 8.408 8.086 7.817 8.101 5.758 5.686 5.667 (6.686) (6.718) (6.761) (6.761) (6.892) (6.888) (6.888) (6.898) (6.895) (6.954) (6.892) (6.863) (6.829) (6.790) (6.796) (6.806)
Constant -1.373 -7.674** -8.077** -8.077** -8.479** -8.532** -8.532** -6.447** -6.520** -7.385* -7.756* -6.226 28.26 30.97 30.42 29.86 (2.238) (3.144) (3.219) (3.219) (3.315) (3.320) (3.320) (3.123) (3.172) (4.437) (4.495) (4.773) (19.24) (19.53) (19.42) (19.92)
Observations 904 904 904 904 904 904 904 904 904 904 904 904 904 904 904 904
R-squared 0.028 0.055 0.057 0.057 0.060 0.061 0.061 0.064 0.064 0.064 0.070 0.072 0.080 0.085 0.088 0.088
58
Appendix 2
Stepwise regression using lag independent variables on sustainability disclosure QUALITY VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)
SR i,t-1 9.182*** 6.006* 6.296* 6.296* 5.992* 5.998* 5.998* 6.186* 6.187* 6.144* 5.167 5.375 5.401 5.669 5.722 5.720 (3.502) (3.463) (3.550) (3.550) (3.528) (3.547) (3.547) (3.559) (3.562) (3.574) (3.778) (3.761) (3.779) (3.825) (3.821) (3.824)
SA i,t-1
10.33*** 16.23** 16.23** 10.42 -0.535 -0.535 -4.668 -5.042 -5.022 -5.662 -4.540 -5.314 -7.172 -6.853 -6.896 (2.846) (6.570) (6.570) (9.069) (10.34) (10.34) (9.971) (10.17) (10.25) (9.440) (9.455) (9.570) (9.935) (9.943) (9.894)
ACCOUNTANT i,t-1
-7.535 -7.535 -7.388 -7.166 -7.166 -7.520 -7.603 -7.615 -7.882 -8.100 -7.122 -6.973 -7.283 -7.284 (7.235) (7.235) (7.280) (7.302) (7.302) (7.350) (7.443) (7.463) (7.500) (7.503) (7.502) (7.466) (7.543) (7.550)
o.CONSULTANT i,t-1
- - - - - - - - - - - - -
LIMITED i,t-1
6.283 17.04** 17.04** 16.89** 16.90** 16.85** 16.80*** 16.54*** 16.86** 18.18** 18.21** 18.24** (7.025) (7.139) (7.139) (7.028) (7.218) (7.299) (6.450) (6.363) (6.525) (7.201) (7.139) (7.113)
REASONABLE i,t-1
12.89 12.89 13.05 12.83 12.82 12.73 12.46 13.02 13.91 13.96 13.98 (13.57) (13.57) (13.48) (13.55) (13.63) (13.01) (12.97) (13.10) (13.61) (13.54) (13.53)
o.MIXED i,t-1
- - - - - - - - - -
AS_PERS i,t-1
7.223** 7.045*** 7.025*** 7.322*** 7.534*** 7.391*** 7.294*** 7.554*** 7.558*** (2.819) (2.699) (2.678) (2.650) (2.660) (2.684) (2.686) (2.719) (2.716)
AS_TENURED i,t-1
0.0745 0.0553 -0.00857 0.0760 0.252 0.383 0.390 0.391 (0.370) (0.374) (0.376) (0.360) (0.398) (0.414) (0.415) (0.418)
ENVSCR i,t-1
0.0115 0.00651 0.00264 0.00854 0.00570 0.00516 0.00522 (0.0716) (0.0718) (0.0726) (0.0735) (0.0731) (0.0732) (0.0734)
GRI i,t-1
3.427 4.576 5.311 5.427 5.558 5.568 (3.268) (3.600) (3.681) (3.619) (3.622) (3.663)
CR_BOARD i,t-1
-4.073 -3.065 -3.692 -4.230 -4.234 (3.219) (3.443) (3.435) (3.461) (3.464)
SIZE i,t-1
-2.750 -2.585 -2.622 -2.631 (2.319) (2.329) (2.320) (2.377)
AGE i,t-1
-0.105 -0.107 -0.107 (0.0742) (0.0745) (0.0743)
LEVERAGE i,t-1
0.264** 0.265** (0.126) (0.127)
ROA i,t-1
0.00415 (0.0719)
2016.year 1.238 1.014 0.922 0.922 0.914 0.918 0.918 -2.916 -2.851 -2.857 -3.081* -3.288* -3.390* -3.396* -3.450* -3.450* (0.921) (0.928) (0.939) (0.939) (0.930) (0.932) (0.932) (1.868) (1.832) (1.839) (1.813) (1.813) (1.829) (1.835) (1.849) (1.850)
2017.year 2.708 2.399 2.293 2.293 2.313 2.249 2.249 -1.673 -1.641 -1.640 -1.826 -1.977 -2.140 -2.210 -2.214 -2.218 (1.689) (1.677) (1.657) (1.657) (1.648) (1.606) (1.606) (1.957) (1.957) (1.957) (1.957) (1.944) (1.984) (1.988) (1.993) (1.997)
2.ind 4.729 6.758 8.019* 8.019* 8.968* 8.635* 8.635* 8.814* 8.771* 8.822* 8.527* 7.806 6.944 6.178 6.169 6.145 (4.415) (4.627) (4.723) (4.723) (4.956) (5.091) (5.091) (5.103) (5.135) (5.079) (5.070) (5.042) (5.102) (5.012) (5.042) (5.161)
3.ind -2.172 0.787 1.598 1.598 1.764 1.732 1.732 2.015 1.944 2.013 1.232 0.471 0.303 -1.821 -1.900 -1.916 (4.609) (5.153) (4.938) (4.938) (4.998) (5.008) (5.008) (5.049) (5.029) (5.072) (5.094) (5.181) (5.320) (5.652) (5.673) (5.689)
4.ind 1.184 5.612 6.868* 6.868* 7.135* 7.085* 7.085* 7.166* 7.137* 7.120* 6.429 6.416* 5.100 2.983 3.025 3.006 (3.898) (3.723) (3.844) (3.844) (3.878) (3.888) (3.888) (3.914) (3.892) (3.905) (3.990) (3.882) (3.881) (4.178) (4.233) (4.265)
5.ind 0.127 1.888 1.843 1.843 2.423 2.613 2.613 2.710 2.733 2.703 1.954 2.220 1.571 -0.516 -0.205 -0.197 (3.306) (3.634) (3.600) (3.600) (3.665) (3.666) (3.666) (3.688) (3.718) (3.728) (3.745) (3.779) (3.741) (4.193) (4.192) (4.187)
6.ind 1.379 2.992 3.522 3.522 3.818 3.732 3.732 3.833 3.720 3.720 3.243 2.687 2.877 0.558 0.551 0.566 (3.754) (4.031) (4.018) (4.018) (4.099) (4.098) (4.098) (4.094) (4.127) (4.130) (4.101) (4.062) (4.029) (4.639) (4.673) (4.702)
7.ind 3.020 7.185** 8.006** 8.006** 8.279** 8.227** 8.227** 8.453** 8.431** 8.418** 8.332** 8.066** 7.896** 6.016 6.047 6.032 (2.684) (3.358) (3.515) (3.515) (3.581) (3.587) (3.587) (3.628) (3.614) (3.624) (3.644) (3.660) (3.584) (3.949) (4.010) (4.008)
59
8.ind 7.190 8.628 9.129 9.129 9.511 9.589 9.589 9.658 9.541 9.583 9.433 9.141 9.450 7.230 7.046 7.046 (7.018) (7.116) (7.186) (7.186) (7.252) (7.281) (7.281) (7.298) (7.277) (7.320) (7.281) (7.239) (7.237) (7.173) (7.170) (7.175)
Constant -3.377 -10.09*** -10.76*** -10.76*** -11.11*** -11.05*** -11.05*** -8.739** -8.756** -9.351* -9.504* -7.796 16.71 19.44 19.77 19.85 (2.386) (3.443) (3.602) (3.602) (3.694) (3.698) (3.698) (3.560) (3.582) (4.878) (4.910) (5.161) (19.64) (19.83) (19.77) (20.24)
Observations 672 672 672 672 672 672 672 672 672 672 672 672 672 672 672 672
R-squared 0.038 0.066 0.073 0.073 0.075 0.077 0.077 0.081 0.081 0.081 0.083 0.086 0.091 0.095 0.097 0.097