sustainability assurance and sustainability disclosure

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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 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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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).


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.


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.


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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35

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


748 (obs.)

Companies in ESI

Industry SR not provided in

English

1,320 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


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


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


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


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


accountant

Graphs by accountant

05

10

15

-.5 0 .5 1 1.5 -.5 0 .5 1 1.5

0 1


consultant

Graphs by consultant

-50

51

01

5

-.5 0 .5 1 1.5 -.5 0 .5 1 1.5

0 1


limited

Graphs by limited

-50

51

0

-.5 0 .5 1 1.5 -.5 0 .5 1 1.5

0 1


reasonable

Graphs by reasonable

-20

02

04

0

-.5 0 .5 1 1.5 -.5 0 .5 1 1.5

0 1


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



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



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

sustainability assurance and sustainability disclosure

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