Does external assurance contribute to higher quality integrated reports?

J. Account. Public Policy xxx (xxxx) xxx

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Does external assurance contribute to higher quality integrated reports? Warren Maroun University of the Witwatersrand, School of Accountancy, 1 Jan Smuts Avenue, Johannesburg, South Africa

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Article history: Available online xxxx Keywords: Accountability Corporate social responsibility (CSR) assurance International Standards on Assurance Engagements Integrated reporting Sustainability

a b s t r a c t This paper contributes to the literature on internal benefits and positive capital market effects of integrated reporting by examining whether or not external assurance acts as a driver of high quality integrated reports. It answers calls for additional research on integrated reporting practice and is also timely, given the current discussions by standardsetters, regulators and other stakeholders on the need for integrated reports to be subject to some form of external verification. The research shows that an increase in the number of elements of an integrated report being subject to external assurance is associated with higher quality reporting. This is especially true when the assurance services are provided by one of the Big 4. The assurance engagements which contribute most significantly to report quality are those covering disclosures dealing broadly with social and environmental sustainability and compliance with the AccountAbility principles of materiality, inclusivity and responsiveness. In turn, if the assurance of only these aspects of an integrated report already improves report quality, there is a clear need for guidance on how the assurance process can be expanded to ensure that all material disclosures are subject to, at least, some form of verification. Ó 2019 Elsevier Inc. All rights reserved.

1. Introduction This paper deals with how external assurance of environmental, social and governance disclosures (ESG assurance) affects the quality of integrated reports. The research is motivated by the position taken by the International Integrated Reporting Council (IIRC, 2013) and the Global Reporting Initiative (GRI, 2016) that external assurance can be used to enhance the reliability of integrated and sustainability reporting and promote better quality disclosure (see also IIRC, 2014a; 2014b; 2015). The study is also timely, given the rapid increase in the use of different forms of sustainability or integrated reporting by the world’s largest companies over the last 10 years (Hughen et al., 2014; KPMG, 2017). This has gone hand-in-hand with greater attention paid to integrated reporting by capital market participants and regulators (Howitt, 2016) and an increase in the use of different types of assurance to add to the credibility of integrated and sustainability reports (Junior et al., 2014; KPMG, 2017). Against this backdrop, there is little research on the role played by external assurance in contributing to high quality reporting (De Villiers et al., 2014). Most of what has been done focuses on the demand for ESG assurance (Simnett et al., 2009; Farooq and De Villiers, 2017) including whether or not the extensiveness of (Cho et al., 2014) or the value assigned to non-financial disclosures (Wong and Millington, 2014) is a good predictor of the use of external assurance.

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Please cite this article as: W. Maroun, Does external assurance contribute to higher quality integrated reports?, J. Account. Public Policy, https://doi.org/10.1016/j.jaccpubpol.2019.06.002

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Testing the effect which ESG assurance has on the quality of integrated reporting makes an important contribution to the literature and practice. Firstly, the study adds to the limited body of empirical research on integrated reporting. It expands on Lee and Yeo (2016), Zhou et al. (2017) and Barth et al. (2017) who take the quality of an integrated report as an independent variable to examine the economic consequences of integrated reporting. These researchers have established a clear business case for preparing an integrated report but stop short of providing insights into the factors which contribute to more reliable and useful reporting. As a result, this paper answers the calls by De Villiers et al. (2017a) and Rinaldi et al. (2018) for research on the drivers of integrated reporting and, in particular, the role played by the accounting and corporate governance infrastructure (including the use of external assurance) in ensuring high quality reports. Secondly, more needs to be done to understand the functioning of monitoring mechanisms as part of the broader corporate reporting process (Simnett et al., 2009; Leuz and Wysocki, 2016). The need for and role played by financial statement audits has been extensively researched but the impact of assurance in other settings is not well understood (Cohen and Simnett, 2015; Hay, 2015; Simnett and Huggins, 2015). This is especially true, given the current debate on the possibility of having sustainability and integrated reports assured and the appropriateness of existing professional standards for addressing these emerging assurance needs (ibid). Michelon et al. (2015) is one of the few examples dealing with the effect of external assurance in a non-financial reporting context. They evaluate whether or not ESG assurance can promote higher quality sustainability reports measured according to a composite quality measure.1 This paper extends Michelon et al. (2015) who focus only on sustainability reporting from 2005 to 2007, by examining a broader period (from 2010 to 2016). This provides additional empirical evidence on the relationship between report quality and ESG assurance. The chosen period also coincides with the release of the IIRC’s framework on integrated reporting meaning that the study provides the first set of results on whether or not the relationship between report quality and the use of external assurance holds when integrated reporting starts to supersede sustainability reporting. Importantly, this is done by using a more refined measure of assurance practice. Michelon et al. (2015) only consider if sustainability reports have been subject to some form of external assurance. In this study, a three-stage approach is used. Assurance is gauged according to the total number of individual disclosures/elements which have been tested. The difference between the level of assurance being provided (either moderate or high) is then taken into account. As existing assurance standards do not cater for an integrated or a sustainability report to be the subject matter of an assurance engagement in its entirety (Maroun and Atkins, 2015), the study also considers how the choice to test specific parts of a report may be associated with better quality reporting. Finally, this study adds to the prior research on the importance of the choice of assurance provider. There is a large body of work dealing with differences in audit engagements performed by the Big N and other accounting firms (for summaries of this literature see: Francis, 2004; 2011). Less is known about the impact of the size of the firm and the use of specialist consultants on ESG assurance services. What research has been done has generated mixed results (for a summary of this work, see: Farooq and De Villiers, 2017; Maroun, 2018a) and has not considered the type of assurance provider as a possible driver of integrated report quality. The data for this study comes from South Africa. Focusing on a single jurisdiction may be seen as an inherent limitation but South Africa offers a unique environment for studying ESG assurance which adds to the paper’s contribution. It has played a leading role in the development of codes on corporate governance and different types of non-financial reporting, including integrated reporting (Solomon, 2013; De Villiers et al., 2014). As a result, South Africa offers a mature reporting environment in which variations in report quality and the use of ESG assurance are less likely to be affected by lack of experience with these practices. Similarly, because integrated reporting has, in substance, been mandated by the Johannesburg Stock Exchange (Atkins and Maroun, 2015), the effect of a voluntary decision to adopt integrated reporting (which has affected comparable studies in other jurisdictions) does not apply (Barth et al., 2017). Most importantly, a comprehensive scoring system for integrated reports in South Africa is available. The scores/ratings are issued by Ernst & Young (EY) and are based specifically on the quality of reports rather than an index of quantity of disclosures or a high level review of disclosure themes (EY, 2017). The ratings have also been corroborated (albeit indirectly) by at least two earlier studies (see Barth et al., 2017; Zhou et al., 2017). As a result, they offer a more reliable basis for gauging changes in report quality with variations in measures of ESG assurance than relying on a self-developed quality score. The remainder of this paper is structured as follows: Section 2 provides a brief background on integrated reporting and its determinants, followed by the derivation of the hypotheses. Section 3 discusses the method. Section 4 presents the findings. Section 5 discusses the results, concludes and presents areas for future research. 2. Background, theory and hypotheses 2.1. The nature of integrated reporting Per the IIRC (2013, p. 7), ‘an integrated report is a concise communication about how an organization’s strategy, governance, performance, and prospects, in the context of its external environment, lead to the creation of value in the short, medium and long term’. 1 This 4-part composite measure takes into account: (1) the extensiveness of reporting, (2) ‘density’ of the disclosures; (3) the mix of qualitative and quantitative information and (4) the extent to which the disclosures are either substantive/committed or symbolic/generic. See Section 3.2 for further details.

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The objective is to provide investors and other stakeholders with information on how an organisation manages multiple types of resources in order to generate value. These include: financial, manufactured, intellectual, human, social and relationship and natural capital (IIRC, 2013). Where an entity takes cognisance of the interconnection between its operations, the impact on these capitals and the legitimate interests of its stakeholders, integrated thinking can drive positive organisational behaviour. It should also prepare integrated reports which provide a more complete account of the value creation process than traditional financial statements and stand-alone environmental or social reports did (IIRC, 2014c; Atkins and Maroun, 2015; King, 2016). Public interest in integrated reporting has increased significantly following the release of the IIRC’s framework in 2013 (Howitt, 2016) and has been accompanied by a burgeoning body of research on the concept, purpose and limitations of integrated reporting.2 Most of this work focuses on how integrated reporting is being interpreted and operationalised with implications for organisations’ internal processes, how reports are being developed and how these are perceived by stakeholders (Rinaldi et al., 2018). The limitations of integrated reporting have also been discussed in detail. For example, McNally et al. (2017) examine the practical challenges of producing an integrated report and preparers’ concerns that the reports are not being used by investors or other key stakeholders. The result is that, while integrated reporting has the potential to promote more sustainable business practice, it tends to result in marginal gains rather than in dramatic changes in a company’s strategy or ethos (see also Brown and Dillard, 2014; Higgins et al., 2014). This may be due to an overemphasis on providers of financial capital to the detriment of the broader sustainability agenda (Flower, 2015); a lack of regulatory support (Dumay et al., 2017) or the difficulty of incorporating an integrated reporting logic in existing organisational cultures (Dumay and Dai, 2017; Gibassier et al., 2018). The possibility of integrated reporting being motivated by impression management should also be taken into account (Haji and Anifowose, 2016). Integrated reports are not always easy for stakeholders to read (du Toit et al., 2017). Recent surveys of reporting trends - mainly from South Africa – reveal that key details can be obfuscated by repetitive or generic disclosures. Positive information may be emphasised to the detriment of balanced reporting, and the absence of prospective information is limiting the usefulness of integrated reports (see, for example, Solomon and Maroun, 2012; PwC, 2015; Haji and Anifowose, 2016; Raemaekers et al., 2016; du Toit, 2017). This is not to say that integrated reporting is irrelevant. It is possible for preparers to interpret and apply the principles of integrated reporting in a way which reinforces the social, economic and environmental objectives of their organisation. This is true, notwithstanding the ambiguities in the IIRC’s framework and absence of a generally accepted basis for preparing an integrated report (Gibassier et al., 2018; Vesty et al., 2018). Consequently, while challenges will be encountered (McNally et al., 2017), integrated reporting can offer an opportunity for effectively engaging with and reporting to different stakeholders (Atkins and Maroun, 2015; Lai et al., 2018) and may contribute to a more integrated approach to business management (Al-Htaybat and von Alberti-Alhtaybat, 2018). Guthrie et al. (2017) and McNally and Maroun (2018), for example, draw on organisational change theory and logics of resistance to show how integrated reporting can alter mindsets. By accounting for multiple types of capital, integrated reporting can shed light on new fields of organisational activity and promote an internalisation of the relevance of socalled non-financial issues for an organisation’s long-term sustainability. Adams et al. (2016; 2017) reach a similar conclusion, finding that integrated reporting can strengthen an awareness of the importance ESG issues and lead to these being more clearly articulated. It is also possible for a sound understanding of integrated reporting - coupled with a holistic approach to managing social, economic and environmental dimensions of a firm - to be used to differentiate a firm from its competitors (Lodhia, 2015). As a result, while elements of impression management are present, it may be overly critical to conclude that this is the primary driver of integrated reporting (Lai et al., 2016). This is affirmed by a growing body of work pointing to the value-relevance of integrated reporting. Zhou et al. (2017) and Lee and Yeo (2016) find that high quality reporting has a positive capital market effect. Similarly, Barth et al. (2017) show that, in addition to affecting the accuracy of cash flow forecasts and firm value, well prepared integrated reports can be used as an indicator of the quality of internal management (see also Churet and Eccles, 2014). The findings are in line with the body of research on how integrated reporting and a multi-faceted approach to business management can develop hand-in-hand to yield improved social, environmental and economic performance (for example: Massa et al., 2015; Adams et al., 2016; Guthrie et al., 2017; Al-Htaybat and von Alberti-Alhtaybat, 2018; McNally and Maroun, 2018).

2.2. Drivers of integrated reporting While the limitations and benefits of integrated reporting have been studied in detail, there is comparatively little on the factors which may be contributing to the quality of these reports. Building on earlier research in the environmental and sustainability reporting space, larger and more profitable companies have the resources and expertise to develop systems, processes and charts of accounts which are necessary for collecting, analysing and reporting on non-financial metrics (Melnyk et al., 2003; Hahn and Kühnen, 2013; Alrazi et al., 2015; Dienes et al., 2016). They are also more likely to have a significant social, environmental and economic impact and be subject to greater levels of stakeholder scrutiny. Consequently, the 2 The most recent development is two special issues on integrated reporting in Accounting, Auditing and Accountability Journal (2018) and Meditari Accountancy Research (2017).

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benefits of complementing financial statements with high quality sustainability or integrated reports will probably exceed the costs of doing so (De Villiers and Maroun, 2018). Since integrated reporting is usually voluntary, whether or not key members of management are committed to changing the way in which they engage with and report to stakeholders will also have an impact on how the principles of integrated reporting are interpreted and applied (Al-Htaybat and von Alberti-Alhtaybat, 2018; Lai et al., 2018). The extent to which integrated reporting can be used to reinforce an organisation’s culture or business philosophy is an additional consideration (Gibassier et al., 2018; Vesty et al., 2018). Where an organisation’s approach to governance includes a commitment to responsible economic, environmental and social performance, integrated reporting becomes a logical development in monitoring and communicating how the entity is being managed (King, 2016; Al-Htaybat and von Alberti-Alhtaybat, 2018; McNally and Maroun, 2018). Where this is the case, companies may be inclined to invest more time and effort in preparing a better quality report. The relevance of institutional forces should also not be overlooked. As integrated reporting grows in popularity (Howitt, 2016), companies will come under increasing pressure to demonstrate that they are making some attempt to prepare an integrated report which meets stakeholders’ expectations (De Villiers et al., 2017a; De Villiers and Maroun, 2018). There is already some evidence of this taking place in a broader sustainability reporting context where a combination of mimetic and normative pressures are resulting in the standardisation of ESG disclosures by companies with a high environmental impact and minimum levels of reporting (De Villiers and Alexander, 2014). Coercive pressures add to this when, even if not required by law, integrated reporting is championed by codes on corporate governance (e.g. IOD, 2016), listing requirements for local stock exchanges (e.g. JSE, 2016) and conventions on responsible investment practice (e.g. IOD, 2011). Finally, there is a strong business case for investing in high quality integrated reports. Focusing specifically on South Africa, Baboukardos and Rimmel (2016), Lee and Yeo (2016), Barth et al. (2017), Zhou et al. (2017) and Bernardi and Stark (2018) show that high quality integrated reports can enhance analysts’ understanding of ESG data, lower information asymmetry and reduce the cost of capital. What has not been considered is the impact which external assurance may be having on the quality of integrated reports (De Villiers et al., 2014; Rinaldi et al., 2018) 2.3. Assurance of the integrated report: theory and hypotheses One of the seminal papers on the topic explains how independent external audit contributes to a reduction of information asymmetry between managers and principals by enhancing the accuracy, completeness and reliability of financial statements (Watts and Zimmerman, 1983). In general, high quality audit is associated with lower levels of discretionary accruals (Francis and Krishnan, 1999); financial statements which more accurately track future profitability (Krishnan, 2003) and lower the costs of capital (Fernando et al., 2010). The same logic may hold when it comes to non-financial reporting. Simnett et al. (2009), for example, finds that the decision to have ESG disclosures tested by an independent expert demonstrates a commitment to high quality reporting and bolsters the credibility and reliability of voluntary nonfinancial disclosures (see also Park and Brorson, 2005; Farooq and De Villiers, 2017). In this way, ESG assurance can inform stakeholders’ understanding of a company’s non-financial performance and promote additional investment, especially when ESG disclosures deal with issues which are strategically relevant (consider Coram et al., 2009; Wong and Millington, 2014; Cheng et al., 2015). This is because external assurance can substitute for weaknesses in corporate governance systems and the legal mechanisms intended to protect investors (Choi and Wong, 2007; Simnett et al., 2009). Having sustainability reports tested by an independent expert against defined criteria can also drive compliance with reporting guidelines and promote more complete and accurate reporting on material ESG considerations (Adams and Evans, 2004; Moroney et al., 2012). As a result, Gray (2000, p. 247) concludes that ‘good quality attestation’ has become imperative for ensuring that sustainability reporting lives up to its potential to advance transparency and accountability for non-financial performance. In this context, as the benefits of high quality reporting increase (Barth et al., 2017; Zhou et al., 2017), companies should be more willing to invest in formal systems and processes for verifying the information being included in their integrated reports. This is because, according to the IIRC (2013), ‘the reliability of information is affected by its balance and freedom from material error. Reliability (which is often referred to as faithful representation) is enhanced by mechanisms such as robust internal control and reporting systems, stakeholder engagement, internal audit or similar functions, and independent, external assurance’ (p.21, emphasis added) The GRI (2016) takes a similar position stating that external assurance, while not mandatory, can be a useful tool for enhancing stakeholders’ confidence in the reliability of the information included in a sustainability report. Like the audit of financial statements, the verification of non-financial disclosures by an independent expert reduces the risk of undetected misstatements and the possibility of social and environmental accounting falling victim to management capture (Dando and Swift, 2003). Tests performed on data, systems and controls can be used to confirm the accuracy and completeness of individual disclosures found in an integrated or sustainability report. In addition to enhancing data quality, it is possible for external assurance to reveal weaknesses in reporting system and internal controls and provide recommendations for improving the integrity of the accounting infrastructure on which high quality reporting is dependent (GRI, 2013; Farooq

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and De Villiers, 2018). In turn, the attest function can provide those charged with governance with greater confidence in validity and reliability of the information being used internally and forming the basis for explaining to stakeholders how the organisation is managing risks and opportunities (GRI, 2013; IOD, 2016; Farooq and De Villiers, 2019a). Importantly, there are indications that ESG assurance services are focusing on sustainability and integrated reporting as a whole rather than just dealing with a limited number of disclosures (Maroun, 2017; Farooq and de Villiers, 2019b). For example, independent experts can be tasked with providing assurance over some aspects of stakeholder engagement, materiality determination and internal management processes. Most recently, external assurance engagements have started to be used in conjunction with other corporate governance mechanisms designed to ensure the reliability of integrated and sustainability reports. Examples include internal audit, system and report reviews by ESG specialists and monitoring by independent directors (for details see Junior et al., 2014; IOD, 2016; Maroun, 2017). The result is that, in addition to testing individual disclosures and internal controls, external assurance can promote more active stakeholder engagement (Morimoto et al., 2005), inform changes to sustainability policies (Gillet, 2012; GRI, 2013) and raise awareness of the importance of high quality sustainability or integrated reporting (Edgley et al., 2010). This is based on the position that the attest function is more than just a neutral verification process; it can ‘enlighten, inform, and enable criticism and substantive change’ (Power, 1997, p. 124), With these points in mind, the first hypothesis is stated is follows: H1. The quality of an organisation’s integrated report is better when ESG disclosures are independently assured. The International Auditing and Assurance Standards Board (IAASB) and AccountAbility are two of the most prominent standard-setters dealing with the assurance of different types of financial and non-financial reporting. Both provide clarity on the purpose of an assurance engagement and the practitioner’s responsibilities (AccountAbility, 2008a; IAASB, 2009a). They also emphasise the importance of the assurance provider being independent of the reporting entity so that stakeholders can place their full confidence in the assurance opinion (consider Dando and Swift, 2003). The IAASB issues International Standards on Assurance Engagements (ISAEs) which are applicable to historical information other than financial statements (see IAASB, 2009b; IAASB, 2013). Although not developed exclusively for sustainability or integrated reporting, the standards have provided a useful basis for providing assurance on select information included in these types of reports with the aim of enhancing the accuracy, completeness and reliability of disclosures (Ackers, 2009; Ackers and Eccles, 2015). Examples include assurance on greenhouse gas emissions, water usage, health and safety statistics and employee numbers (Jones and Solomon, 2010; Maroun, 2017). Unlike ISAEs, the guidance provided by AccountAbility (2008a)3 deals directly with how external assurance can be used to test the quality of reporting on sustainability performance. The AA1000 Assurance Standard (AA1000AS) was issued in 2003 and revised in 2008. The standard: ‘. . .provides a platform to align the non-financial aspects of sustainability with financial reporting and assurance. It provides a means for assurance providers to go beyond mere verification of data, to evaluate the way reporting organisations manage sustainability, and to reflect that management and resulting performance in its assurance statements’ (AccountAbility, 2008a, p. 6) In a Type-1 engagement, the practitioner evaluates how an organisation manages and reports on its sustainability performance. The aim is not to test the reliability of a company’s disclosures but to evaluate information, systems and process to reach a conclusion on the organisation’s adherence to the principles of inclusivity, materiality and responsiveness.4 A Type-2 engagement is the same as Type-1 but includes the use of test procedures to address the accuracy and reliability of information reported to stakeholders (AccountAbility, 2008a). The ISAEs make provision for an opinion on the subject matter of an engagement which give either a high (reasonable) or moderate (limited) level of assurance (see IAASB, 2009b; IAASB, 2013). The same applies to an engagement conducted according to AA1000AS. In limited assurance engagement, the practitioner relies primarily on inquiry and analytical procedures to reach a conclusion on the engagement subject matter (IAASB, 2015b). In contrast, reasonable assurance is based on an ‘extensive depth of evidence gathering, including corroborative evidence and sufficient sampling at lower levels in the organisation’ (AccountAbility, 2008a, p. 11). Like the audit of financial statements, it is not possible to provide stakeholders with absolute assurance on the subject matter of an ISAE (IAASB, 2009a) or AA1000AS (AccountAbility, 2008a) engagement. Nevertheless, there is, at least, some evidence that users place more reliance on information which has been assured (Simnett et al., 2009; Wong and Millington, 2014) and that the degree of reliance varies according to the level of assurance (Hasan et al., 2003).

3 AccountAbility is a consulting and standard-setting firm which engages with businesses, governments and other stakeholder groups to advance sustainable business practices. It has developed the AA1000 which assist firms with managing and reporting on sustainability issues. AA1000AS deals with CSR assurance including, inter alia, how to test CSR system, processes and disclosures; the types of opinions which can be provided and the importance of independence for the credibility of assurance engagements. 4 Inclusivity – stakeholders are included in the process of developing a strategic response to sustainability and the organisation accepts that it is accountable to a broad group of stakeholders for its sustainability performance. Materiality – the reporting entity assesses the relevance and significance of each issue for its long-term sustainability. Responsiveness – the organisation responds reasonably to stakeholders’ concerns about sustainability performance.

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Following from this, if reasonable assurance engagements are based on more detailed and/or extensive test procedures than limited assurance engagements: H2. Integrated report quality will be better when a company requires a reasonable rather than a limited assurance engagement. The provision of reasonable rather than limited assurance on an integrated report may not automatically result in improved integrated reporting. Users may not understand the difference in the level of assurance being provided (Hodge et al., 2009). More critically, external assurance may be used only to create the impression that ESG disclosures are of a high quality (Ball et al., 2000; Gray, 2000; Michelon et al., 2015). This view is supported by a number of criticisms of the ESG assurance model. For example, there are concerns that assurance providers (normally trained in financial statement audits) may not have the expertise to test ESG disclosures. In addition, they may not be sufficiently independent of their clients (Gillet, 2012; Manetti and Toccafondi, 2012). Others argue that professional standards place too much emphasis on the accuracy of disclosures while ignoring completeness, relevance and the quality of information being reported (Dando and Swift, 2003; Adams and Evans, 2004). There are also concerns about a lack of detail on exactly how the subject matter of an ESG assurance engagement has been tested; a perceived reluctance to communicate the findings of test procedures and the limited number of recommendations on how ESG reporting and performance can be improved (O’Dwyer and Owen, 2005; Deegan et al., 2006a). In support of H1 and H2 is the steady increase in the extent to which companies are relying on different types of external assurance of the non-financial information being reported to stakeholders both internationally (KPMG, 2017) and in South Africa (Ackers and Eccles, 2015). These assurance engagements can be complex and costly and will, therefore, only be undertaken if they provided material benefits to stakeholders (Park and Brorson, 2005). When ESG information is used mainly for impression management, the inherent bias in the reporting erodes users’ confidence and negates the need for independent assurance (Coram et al., 2009; Wong and Millington, 2014). The prior research on the determinants of ESG assurance, however, points to more than just impression management. For example, Simnett et al. (2009), Kolk and Perego (2010) and Perego and Kolk (2012) find that companies are more likely to have ESG disclosures independently assured in response to a genuine expectation of higher quality reporting by stakeholders. Similarly, as the value-relevance of ESG disclosures increases, so does the demand for independent assurance (Wong and Millington, 2014; Cheng et al., 2015). As explained in Section 2.1, a growing body of research also shows that high quality integrated reporting is associated with more effective internal management, operating efficiency and real economic gains. In this environment, there is a clear business case for using external assurance to identify errors in integrated or sustainability reports; test underlying systems and processes for weaknesses and signal the credibility of the respective disclosures. As the benefits of having ESG assurance increase, the choice of assurance provider is expected to become more important. Research on the use of different assurance providers has, however, produced mixed results. Moroney et al. (2012) and Pflugrath et al. (2011), for example, find no indication that users perceive a difference between the assurance provided by accounting firms and by consultants. Others conclude that consultants are superior to audit firms because they are more specialised and less conservative in their approach to testing and reporting (O’Dwyer and Owen, 2007; O’Dwyer, 2011). This may be because consultants are more likely to be sustainability experts. Unlike their counterparts, they do not focus predominantly on financial performance and monetisation of ESG metrics. As a result, they may be more inclined to assure the entire sustainability report rather than select disclosures. Consultants also tend to provide recommendations for how to improve sustainability practices while accounting firms are less likely to do so (Deegan et al., 2006b; Mock et al., 2007; Farooq and de Villiers, 2019b). This is supported by an international review of ESG assurance practices which shows that the Big 4 provide lower levels of assurance than consultants(Mock et al., 2007, 2013) In contrast, similar to the financial statement audits, the large accounting firms may have the resources and expertise to provide higher quality ESG assurance than other practitioners do (Perego, 2009). They tend to follow more rigorous test methodologies; use multi-disciplinary teams of experts to test sustainability or integrated reports; and are more likely to test specific disclosures, reporting system and internal controls than consultants (Perego and Kolk, 2012; Maroun, 2018a), Due to their reputations, assurance provided by one of the Big 4 may also be more useful for signalling the quality of ESG disclosures than when a smaller accounting or ESG consulting firm is engaged (Kolk and Perego, 2010). In keeping with this view: H3. When an organisation assures elements of an integrated report, report quality will be better when the assurance provider is a Big 4 firm rather than another audit firm or consultant.

3. Method The study focuses on corporate reports5 dated from 2010 to 2016. This period is chosen as South Africa’s codes of corporate governance introduced a recommendation that companies prepare an integrated report effective from 2010 (see IOD, 2009). This was followed by the release of a discussion paper in 2011 which informed the form and content of some of the first 5 ‘Corporate reports’ is used because not all companies commence with the preparation of an integrated report at the same time. They differ in terms of whether or not their primary report to stakeholders is specifically identified as an integrated report or annual report. In addition, some prepare a stand-alone sustainability report in addition to their integrated or annual report.

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integrated reports (see IRCSA, 2011) and the IIRC’s framework on integrated reporting in 2013. As a result, the period from 2010 to 2016 captures some of the most recent (and material) developments in South African corporate reporting (King, 2016).6 The study focuses on the top 50 listed companies. This is a limitation but mitigates controlling for difference in factors which might affect the results such as access to financial capital, availability of resources to support detailed reporting and analyst coverage (see Hahn and Kühnen, 2013). The sample was also restricted by the fact that not all listed companies have had their report quality consistently evaluated, as discussed in Section 3.2. The final sample consisted of 42 firms providing 294 observations. Of these, approximately 50% made use of, at least, some form of ESG assurance over the period under review. The use of independent assurance is, therefore, higher than the 12% reported by Cho et al. (2014) and 31% found by Simnett et al. (2009). 3.1. Measuring integrated report quality Different approaches have been used to gauge report quality. For example, in a study on carbon reporting, Borghei et al. (2016) differentiate between symbolic and substantive disclosures, taking into account whether or not companies are providing details on specific plans being implemented to reduce greenhouse gas emissions. Cho and Patten (2007) and Al-Tuwaijri et al. (2004) follow a similar approach in their examinations of sustainability reporting. They distinguish between generic qualitative disclosures and quantitative measures which are more objective and easier for investors to use. Also dealing with sustainability reporting, Michelon et al. (2015) develop a composite quality measure based on the quantity of disclosure; whether or not disclosures are generic, and the mix between qualitative and quantitative information. These approaches have proved useful for dealing with sustainability or specific aspects of environmental reporting but were not specifically developed to evaluate integrated report quality. They have also not been calibrated by repeated application in different contexts. As a result, for the purpose of this research, quality is defined with reference to the EY Excellence in Integrated Reporting Awards. EY has been evaluating the quality of South African integrated and sustainability reports for the full period under review. Like Cho and Patten (2007), Michelon et al. (2015) and Borghei et al. (2016), the EY evaluation recognises that quality is not just a function of the quantity of information being disclosed. Companies are expected to provide a balanced account of how multiple types of capital are being managed which is context-specific and linked clearly to the organisation’s strategy, business model and key risks (EY, 2017; 2016). As such, EY focuses on the guiding principles in the IIRC’s framework including: strategic focus of the reports and future orientation, connectivity of information, stakeholder relationships, materiality, conciseness, reliability and completeness and consistency and comparability (see IIRC, 2013). The degree to which each of the content elements outlined by the IIRC7 has been addressed is also taken into account as is: ‘the extent to which the integrated report incorporates the < IR > Framework’s fundamental concepts, dealing with how value is created with reference to the six ‘‘capitals”, where relevant’ (Graham in EY, 2016, p. 25). EY does not publish individual score sheets, but it does make a ranking of companies’ integrated reports available. These range from 1 (progress to be made) to 5 (for the top 10 reports). The scores are widely used by practitioners and users as an indication of the quality of South African integrated reports (King, 2016) and have also been tested by the academic community. Most notably, Barth et al. (2017) confirmed that the EY scoring system is aligned with the IIRC’s framework on integrated reporting and tested the inter-coder reliability of adjudicator scores.8 While there have been some changes to the score sheets over time, the features of a high quality report were found to be consistent (ibid, p.49). In addition, EY’s results were largely in line with an independent examination of report quality by Zhou et al. (2017). This was based on the 2012 discussion paper issued by the IIRC and consisted of 31 quality indicators grouped under 8 theme headings including, for example, opportunities and risk, business model and future outlook. While not intended as a calibration of EY scores, Zhou et al. (2017) provide an additional indicator of the reliability of EY’s quality assessment. Finally, Michelon et al.’s (2015) composite quality measure was used to code 25 of the selected companies for 2014 to 2016.9 The quality scorers were compared with those reported by EY. The untabulated results show a moderate to strong relationship between the EY and composite quality scores (K = 0.60, p < 0.01; v2 = 11.058, p < 0.01). This reaffirms the conclusion by Barth et al. (2017) that the EY scores provide a consistent and reliable measure of integrated report quality. 3.2. The relationship between report quality and assurance The general proposition is stated as follows:

REPORT QUALITY ¼ f ðAssurance; type of assurance prov ider; controlv ariablesÞ

6

Reports from 2017 and 2018 were not available at the time results were generated. These are: organisational overview and external environment, governance, business model, risks and opportunities, strategy and resource allocation, performance, outlook, and the basis of presentation and preparation (see IIRC, 2013, p.16) 8 Barth et al (2017) were granted access to the detailed scores by EY for 2013 to 2016. 9 Michelon et al. (2015) focus on this period as it coincides with an increase in the demand for more disclosure on companies’ social and environmental impact. 7

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The proposition is tested in three separate stages by progressively refining the measure of assurance (treated as the independent variable) while holding the type of assurance provider and control variables constant. In each instance, a multiple regression model is used, similar to the approach followed by Simnett et al. (2009) and Cho et al. (2014). 3.2.1. Model 1

QUALITY it ¼ a1 þ b1 ASSURANCE1it þ b2 BIG4 þ bx CONTROL VARIABLESit þ eit The prior research usually assesses if CSR disclosures have been assured and measures assurance on a binary scale (see, for example, Simnett et al., 2009; Michelon et al., 2015). The assumption is that the decision to have an ESG disclosure independently verified is the primary consideration rather than the extent of the testing performed. ASSURANCE1 relaxes this assumption. It measures the total assurance provided by company i in year t as the sum of all subject matters identified and tested by external assurance providers. 3.2.2. Model 2 Users may place greater reliance on disclosures which have been subject to more rigorous test procedures (AccountAbility, 2008b; IAASB, 2009b). More extensive testing may also result in greater detection of errors (ibid) or more reliable information for managers to use for internal decision-making or reporting to stakeholders (Steinmeier and Stich, 2017). This is not taken into account in Model 1. As a result, Model 2 differentiates between subject matters of either reasonable (high) or limited (moderate) assurance engagements.

QUALITY it ¼ a1 þ b1 LIMITED ASSURANCEit þ b2 REASONABLE ASSURANCEit þ b3 BIG4 þ bx CONTROL VARIABLESit þ eit 3.2.3. Model 3 Integrated or annual report assurance is not mandatory in South Africa.10 It should also be reiterated that existing assurance frameworks do not provide a basis for expressing an assurance opinion on an integrated or a sustainability report in its entirety. As a result, there may be differences in the specific disclosures which companies (in consultation with their assurance providers) have selected for testing. With this in mind, Model 3 takes into account the types/categories of disclosures - labelled COMP(1) to COMP(N) - selected for assurance:

QUALITY it ¼ a1 þ b1 COMPð1Þit þ b2 COMPð2Þ þ    þ bn COMPðNÞit þ bx BIG4þbz CONTROL VARIABLESit þ eit Each disclosure category is based on the specific types of integrated report disclosures which are being independently assured by the selected sample of companies. As discussed in more detail in Section 4.2, the disclosure categories are determined using principle component analysis. 3.2.4. Choice of assurance provider Finally, while results are mixed, the general position is that an assurance opinion issued by one of the large accounting or auditing firms carries more weight than one provided by other accounting firms or ESG consultants (Pflugrath et al., 2011). There is also some evidence to suggest that the scope of ESG assurance, testing methodologies and the level of assurance provided varies between the accounting profession or consultants (see, for example, Perego, 2009; Green et al., 2017). As a result, the type of assurance provider may be a relevant consideration for the purpose of this research and is included in Model 1, 2 and 3. Since the South African assurance industry is dominated by the Big 4 and the use of ESG consultants is less common than in other jurisdictions (Ackers, 2009; Ackers and Eccles, 2015), the findings only distinguish between the Big 4 and other assurance providers. 3.3. Control variables Consistent with the prior research, this study controls for the fact that larger firms may be subject to greater stakeholder scrutiny, necessitating better quality reporting (SIZE). As explained in Section 2.1, larger companies are also more likely to have the financial resources, accounting infrastructure and expertise to prepare a high quality report than smaller organisations do. In addition, larger companies may have more complex business models and operations, with the result that they report more information to their stakeholders. As a result, a control for the quantity of disclosures (DENSITY) is introduced. The prior research shows that there is a direct correlation between social and environmental impact and the extent to which firms report to their stakeholders (Patten, 2002; Cho and Patten, 2007). This may be motivated by the need to manage legitimacy, given increased public attention concerning ESG performance (Deegan et al., 2002; Cho, 2009; De Villiers and Alexander, 2014). It is also possible that companies which have a material effect on society or the environment need to provide more detail on ESG metrics than do other organisations. As a result, a control variable is introduced for the sector in 10 Only the financial statements (which are often included in these reports) must be independently assured in terms of the country’s company law and the listing requirements of the Johannesburg Stock Exchange.

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Table 1 Variable definition and measurement. QUALITY ASSURANCE DENSITY

SIZE IMPACT

BIG4

Quality is measured according to the EY Excellence in Integrated Reporting (2013–2016) and EY Excellence in Corporate Reporting (2010– 2012) surveys (see Section 3.2) A measure of the amount of information being assured in integrated reports as per the three models in Section 3.1 The extensiveness of a company’s sustainability or integrated reports is measured as the ratio of the total number of pages in an integrated report to the number of sections in the reporta adapted from Michelon et al. (2015). This is designed to provide a sense of the amount of detail included in each part of the report, rather than as a measure of its total length. The variable controls for the fact that more complex groups will have more information to report to their stakeholders and, therefore, more detailed integrated or sustainability report. The size of each firm is measured according to its market capitalisation as at the end of a company’s reporting period in South African Rands. Environmentally sensitive industries include: extraction/mining, paper production, petro-chemicals and basic materials/industrials (Simnett et al., 2009; Cho et al., 2014). This is measured according to a dummy variable (IMPACT) which is 1 if the company is in an environmentally sensitive industry and 0 if this is not the case. The type of auditor is a dummy variable equal to 1 if the assurance provider is one of the Big 4 and 0 for other assurance providers.

a

The length of the report is total number of pages excluding the cover page. The number of sections is obtained from the table of contents to limit the degree of subjectivity involved when quantifying density.

which each sampled company is located (IMPACT). Table 1 summarises the dependent and control variable and explains how each is measured.

4. Results Table 2 summarises integrated report quality and assurance scores from 2010 to 2016. The seven-year average quality and assurance scores are 3.02 and 9.02 respectively. The results are presented in total and after excluding observations when no assurance was provided. The number of companies making use of some form of ESG assurance increases from 38% in 2010 to 54% by 2016. In each year, there is a statistically significant difference in QUALITY between the reported groups.

4.1. Model 1: Impact of assurance on integrated report quality At the univariate level, BIG4 shows a strong positive correlation with ASSURNACE (rs = 0.674) and QUALITY (rs = 0.541) suggesting that the Big 4 are providing more assurance than smaller audit firms and consultants and that this is associated with higher levels of report quality (see Table 3). In contrast, more detailed reporting (DENSITY) is only weakly correlated with ASSURANCE (rs = 0.164) and QUALITY (rs = 0.121). Similarly, IMPACT reports a weak correlation with DENSITY (rs = 0.214), ASSURANCE (rs = 0.241) and QUALITY (rs = 0.156). SIZE11 is also not associated better quality reporting (rs = 0.223). Finally, ASSURANCE (rs = 0.656) and BIG4 (rs = 0.541) report strong and positive correlations with QUALITY which are significant at the 1% level. These correlations are consistent with the view that external assurance has a positive effect on the quality of an integrated report, particularly when the assurance provider is a large auditing and accounting firm. Table 4 shows the coefficient estimates and associated t-statistics for Model 1 for all observations from 2010 to 2016 (n = 294) in Panel A. The model accounts for 40.8% of the total variance in QUALITY and 39.7% of the variance after adjusting for shrinkage. To control for the possibility that the binary decision either to rely or not to rely on external assurance was accounting for the majority of the variance in the model, the results are shown excluding cases where no external assurors were engaged (Panel B). For the remaining 158 observations, Model 1 reports a correlation coefficient of approximately 25%. The results in Panel A are consistent with the expectation that the use of external assurance is contributing to higher quality reporting (b = 0.4288, p < 0.01). Engaging one of the Big 4 also has a positive effect on QUALITY (b = 0.208, p < 0.01) although, as argued by Simnett et al. (2009), the relative standardised coefficients suggest that the decision to have disclosures in annual or integrated reports assured is making a greater contribution to QUALITY than is the choice of assurance provider. Contrary to the earlier research, SIZE (b = 0.048, p > 0.1) and IMPACT (b = 0.032, p > 0.01) are not good predictors of QUALITY.12 The amount of information which companies have to report (DENSITYb = 0.031, p > 0.1) is also not having a significant effect on report quality (see, for example, Simnett et al., 2009; Sierra et al., 2013; Cho et al., 2014). As with the conclusions on ASSURANCE and BIG4, these results are robust to exclusion of cases when no external assurors are engaged. This is probably because of the relative maturity of different types of non-financial reporting in South Africa and the fact that the country’s codes on corporate governance and listing requirements have been emphasising the importance of sustainability and integrated reporting since 2002. As a result, these types of reporting are not exclusive to the largest organisations, those with the greatest social or environmental impact or companies which have the most information to report to stakeholders. 11 12

Similar results are reported when the natural log of total sales or total assets is used as proxies for firm size, rather than market capitalisation. This finding holds when the natural log of total revenue or total assets is used as a proxy for firm size rather than market capitalisation.

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Table 2 Integrated report quality and assurance scores.

All cases

Firm years Assurance Quality

No assurance excluded

Firm years Assurance Quality

T-test/Mann-Whitney

Comparison of quality between groups

2010

2011

2012

2013

2014

2015

2016

Total

Total Mean SD Mean SD

42 6.79 10.31 3.07 1.16

42 7.31 11.10 3.07 1.17

42 8.90 12.33 2.93 1.24

42 10.69 13.80 2.93 1.24

42 10.88 13.80 3.05 1.21

42 9.14 11.79 3.00 1.27

42 9.43 11.34 3.10 1.27

294 9.02 12.09 3.02 1.20

Total Mean SD Mean SD

16 12.63 6.869 3.94 0.998

17 13.12 9.35 3.82 1.074

22 12.27 8.178 3.41 1.297

24 8.82 8.217 3.33 1.274

25 13.16 8.610 3.64 1.036

27 11.48 8.825 3.56 1.086

27 11.62 7.950 3.73 1.002

158 12.30 7.994 3.61 1.113

t-stat u-stat

5.103** 60.0**

4.477** 76.5**

2.897** 108.0**

3.285** 93.0**

4.818** 60.0**

4.508** 70.0**

5.640** 46.5**

11.248** 3824.5**

*Significant at 5% level. ** Significant at 1%.

Table 3 Correlations.

DENSITY SIZE IMPACT BIG4 ASSURANCE QUALITY

DENSITY

SIZE

IMPACT

BIG4

ASSURANCE

QUALITY

1 0.017 0.237* 0.135** 0.108 0.110

0.140* 1 0.270** 0.274** 0.066 0.074

0.214*** 0.195** 1 0.168** 0.262** 0.152**

0.157** 0.428** 0.168** 1 0.542** 0.525**

0.164** 0.277** 0.241** 0.674** 1 0.586**

0.121* 0.223** 0.156** 0.541** 0.656** 1

Spearman’s rho above the diagonal; Kendall’s tau-b below the diagonal. ** Significant at 1% level. * Significant at 5% level (2-tailed test).

Table 4 Regression of assurance on QUALITY. Panel A: All cases considered (n = 294) Independent variables DENSITY SIZE IMPACT BIG4 ASSURANCE

Panel B: Excluding no assurances cases (n = 158)

Stand coeff. 0.031 0.048 0.032 0.308** 0.428**

t-stat 0.663 0.965 0.627 5.416 7.749

Stand coeff. 0.000 0.057 0.048 0.270** 0.399**

t-stat 0.004 0.723 0.562 3.714 5.319

R2 0.284 0.408

Adj R2 0.274 0.397

R2 0.111 0.250

Adj R2 0.088 0.226

Model summariesa,b Stage 1 Stage 2

The table reports the results of a 2-stage hierarchical model using the enter method. DENSITY, SIZE, IMPACT and BIG4 are added in Stage 1. ASSURANCE is added in Stage 2. Significance at the 1% and 5% level are denoted by ** and * respectively. The model is robust to fixed year effects. Case-wise diagnostic confirmed that there were only 2 observations with residual errors more than 3 standard deviations from the norm which – based on their Cook’s (average < 0.1) and Mahalanobis (<10) distance – are not having a significant impact on results. The untabulated VIF scores give no indication of multicollinearity problems. a While not directly comparable, the R2 for stage 2 is consistent with that reported by Cho et al. (2014). To confirm the predictive power of the regression equation, an ANOVA (untabulated) is used to test the null hypothesis that the independent and control variables make no contribution to the predicted outcome. The F-statistics for each stage of the regression models (in total and when excluding cases with no assurance) are significant at the 1% level and the null hypothesis is rejected. b A logit model is used to corroborate the findings and control for assumptions about the continuity of the dependent variable. As the findings are qualitatively similar to those shown above, the results are un-tabulated.

4.2. Model 2: Effect of limited and reasonable assurance on integrated report quality Findings for Model 1 are confirmed by separating the assurance score into the frequency of limited and reasonable assurance engagements (Table 5). BIG4 remains significant and the standardised coefficient is largely unaltered (b = 0.296, Please cite this article as: W. Maroun, Does external assurance contribute to higher quality integrated reports?, J. Account. Public Policy, https://doi.org/10.1016/j.jaccpubpol.2019.06.002

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W. Maroun / J. Account. Public Policy xxx (xxxx) xxx Table 5 Regression of assurance on QUALITY. Panel A: All cases considered (n = 294) Independent variables DENSITY SIZE IMPACT BIG4 LIMITED ASSURANCE REASONABLE ASSURANCE

Panel B: Excluding no assurances cases (n = 158)

Stand coeff. 0.022 0.053 0.033 0.296** 0.272** 0.286**

t-stat 0.465 1.048 0.652 5.111 5.044 5.734

Stand coeff. 0.009 0.060 0.050 0.268** 0.257** 0.341**

t-stat 0.114 0.765 0.587 3.675** 3.502 4.556

R2 0.284 0.410

Adj R2 0.274 0.398

R2 0.111 0.253

Adj R2 0.088 0.223

Model summariesa Stage 1 Stage 2

The table reports the results of a 2-stage hierarchical model using the enter method. DENSITY, SIZE, IMPACT and BIG4 are added in Stage 1. ASSURANCE is added in Stage 2. Significance at the 1% and 5% level are denoted by ** and * respectively. The model is robust to fixed year effects. Case-wise diagnostic confirmed that there were only 2 observations with residual errors more than 3 standard deviations from the norm which – based on their Cook’s (average < 0.1) and Mahalanobis (<10) distance – are not having a significant impact on results. The un-tabulated VIF scores give no indication of multicollinearity problems. a To confirm the predictive power of the regression equation, an ANOVA (un-tabulated) is used to test the null hypothesis that the independent and control variables make no contribution to the predicted outcome. The F-statistics for each stage of the regression models (in total and when excluding cases with no assurance) are significant at the 1% level and the null hypothesis is rejected.

p < 0.01). The combined use of reasonable (b = 0.272, p < 0.01) and limited assurance (b = 0.286, p < 0.01) accounts for almost twice the variance in QUALITY explained in total by Model 2 (Panel A) than that attributed to the choice of assurance provider. As shown by Panel B, this finding holds when excluding firm years when no external assurance has been used. The relative contribution of limited and reasonable assurance to the total explanatory power of Model 2 is approximately equal in both instances. As was the case for Model 1, DENSITY, SIZE and IMPACT are not significant at either the 1% or 5% level. This is the case when considering all observations and when excluding cases where companies do not engage an external assurance provider. 4.3. Model 3: Examining the effect of the type of assurance To provide additional insights, the types of disclosures being assured were evaluated rather than focusing on assurance in total or the distinction between limited and reasonable assurance engagements in aggregate. A review of each integrated and sustainability report revealed 21 types of disclosures listed as subject matters for ESG assurance engagements such as water usage, pollution statistics, total material recycled, staff turnover and total community spend. To limit subjectivity, these were grouped only when differences were due to variations in wording or the engagements clearly related to the same types of disclosures. For example, ‘CO2 emissions’ and ‘carbon emissions’ were treated as the same category or type of subject matter. Similarly, engagements on the different levels of carbon reporting in line with the Carbon Disclosure Projects were seen as part of the process of assuring total carbon emissions. Principal component analysis was conducted on the 21 subject matter types with orthogonal rotation (varimax method) to aggregate subject matters. To balance ease of interpretation with exploratory power, only components with an eigenvalue>1 are retained. The result is 5 components which account for 74.4% of total variance in assurance scores.13 The Kaiser-Meyer-Olkin measure suggests that the sample size is adequate (KMO = 0.722)14 and, based on the result of Bartlett’s test of sphericity, the null hypothesis that the variables included in the analysis are uncorrelated is rejected (v2 [1 7 1] = 3976.34, p < 0.01). Table 6 shows component loadings after rotation.15 The clustering of subject matters (as well as their relative loadings) is used to label the components. Component 1 comprises the assurance of disclosures dealing with social and environmental sustainability in general. This includes items such as water consumption, energy usage, health and safety statistics and compliance with internal sustainability policies or reporting guidelines. Component 2 is made up primarily of assurance engagements dealing with the AccountAbility (2008a) principles of materiality, inclusivity and responsiveness. It also includes the assurance of financial information 13 Using an eigenvalue cut-off greater than 1 is consistent with the approach generally followed in the social science literature. The decision to retain 5 factors was confirmed by an un-tabulated scree plot which showed points of inflexion at 4 (61% of the cumulative exploratory power) and 5 factors (74% of cumulative exploratory power). 14 During the analysis process, 2 subject matters reported correlations less than 0.5 on the diagonal of the anti-image correlation matrix and were excluded. This resulted in an increase in the KMO statistic from 0.674 to 0.722 in total. All remaining items reported KMO values in excess of the generally accepted limit of 0.5. The determinant of correlation matrix (before rotation) is above the recommended threshold. 15 A correlation matrix based on the final model (untabulated) reports only 28% of residuals with absolute values greater than 0.05. Loadings of less than 0.4 have been excluded.

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W. Maroun / J. Account. Public Policy xxx (xxxx) xxx

Table 6 Rotated Component Matrix.a

a

Subject matters of assurance engagements

Component

Workplace injuries/fatalities Health and safety Water consumption Energy consumption and emissions Compliance with sustainability policies Other community investment Responsiveness to stakeholder concerns Inclusivity Materiality Financial ratios/ statistics Waste and recycling Plausibility Completeness and accuracy of data Pollution statistics and rehabilitation Employee relations, worker rights Number of environmental incidents Employee statistics Training and education statistics Anti-corruption

0.882

1: General sustainability disclosures

2: AccountAbility principle and financial statistics

3: Data, systems and processes

4: Environmental impact and employee relations

5: Employee development

0.818 0.815 0.696 0.537

0.522

0.428 0.855

0.427

0.819 0.803 0.783

0.414

0.721 0.949 0.927 0.891 0.880 0.590

0.813 0.797 0.439

Rotation converged in 6 iterations.

(other than information included in the financial statements).16 Component 3 deals with assurance of the data and systems supporting the preparation of annual, integrated and sustainability reports including the determination of materiality and stakeholder engagement process. Direct environmental impact (including pollution statistics and the number of environmental incidents) is covered by Component 4. This component also includes the assurance of employee relations measures such as the number of disputes recorded, the results of employment satisfaction surveys and reviews of employee rights. Finally, Component 5 includes subject matters relating to employee training and development, such as the number of training programmes in place, the total cost of employee training and statistics on anti-corruption measures, such as whistle-blowing, by employees. Component correlations are reported in Table 7 below. 4.3.1. Univariate analysis As before, the size of a company (rs = 0.223, p < 0.01) and the amount of information included in its corporate report (rs = 0.121, p < 0.01) appear to be only weak predictors of QUALITY. The same is true for an organisation’s social and environmental impact (rs = 0.156, p < 0.01). Component 1 (rs = 0.507, p < 0.01) and the type of assurance provider (rs = 0.541, p < 0.01) report the strongest correlation with QUALITY suggesting that higher quality reporting is associated with having social and environmental disclosures assured by one of the Big 4. The correlation between Component 1 and BIG4 (rs = 0.580, p < 0.01) also suggests that the Big 4 are more likely to be engaged to provide assurance on the subject matters comprising Component 1. The correlations between Component 2 (rs = 0.183, p < 0.01), Component 5 (rs = 0.146, p < 0.01) and QUALITY are statistically significant at the 1% level but are relatively low. Component 3 and 4 report weak negative correlations with QUALITY but these are not significant at either the 5% or 10% level.17 Unlike Component 1, Components 2–5 report weak correlations with BIG4 (rs < 0.4). They are also not associated strongly with SIZE (rs < 0.4) or DENSITY (rs < 0.4). 16 Assurance of waste and recycling are included in this component probably because companies which are providing assurance on the AA1000AS principles (inclusivity, materiality and responsiveness) are also having these environmental disclosures assured. 17 These results hold for both the Spearman’s rho and Kendall’s tau-b.

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W. Maroun / J. Account. Public Policy xxx (xxxx) xxx Table 7 Correlations.

DENSITY SIZE IMPACT BIG4 COMP 1 COMP 2 COMP 3 COMP 4 COMP 5 QUALITY

DENSITY

SIZE

IMPACT

BIG4

COMP 1

COMP 2

COMP 3

COMP 4

COMP 5

QUALITY

1.00 0.099* 0.175** 0.129** 0.108** 0.046 0.068 0.002 0.164** 0.087*

0.140* 1.00 0.160** 0.350** 0.143** 0.026 0.060 0.093* 0.123** 0.162**

0.214** 0.195** 1.00 0.168** 0.188** 0.202** 0.107* 0.012 0.146** 0.141**

0.157** 0.428** 0.168** 1.00 0.509** 0.063 0.003 0.225** 0.342** 0.488**

0.152** 0.202** 0.215** 0.580** 1.00 0.029 0.046 0.256** 0.189** 0.404**

0.072 0.045 0.230** 0.071 0.002 1.00 0.370** 0.036 0.041 0.152**

0.103 0.078 0.122* 0.003 0.042 0.370** 1.00 0.145** 0.022 0.067

0.003 0.134* 0.014 0.256** 0.321** 0.065 0.153** 1.00 0.305** 0.044

0.231** 0.181** 0.167** 0.390** 0.192** 0.020 0.001 0.341** 1.00 0.114*

0.121* 0.223** 0.156** 0.541** 0.507** 0.183** 0.087 0.054 0.146* 1.00

Spearman’s rho above the diagonal; Kendall’s tau-b below the diagonal. ** Significant at 1% level. * Significant at 5% level (2-tailed test).

4.3.2. Multivariate analysis The analysis in Sections 4.1 and 4.2 showed that the use of assurance and type of assurance provider were significant determinants of QUALITY. The specified control variables made only weak contributions to the exploratory power of Model 1 and 2. To provide a better sense of the relative weighting of the variables under review, a 3-stage hierarchical regression is used. Control variables are added in Stage 1 followed by BIG4 (in Stage 2) and COMP1-5 in Stage 3. The final standardised coefficients and model summary statics for each stage of the regression are presented in Table 8. As in Sections 4.1 and 4.2, the final coefficients for determinants and control variables are presented using all observations (Panel A; n = 294) and after removing cases where no external assurors were engaged (Panel B, n = 158). In the first stage of the analysis, DENSITY, SIZE and IMPACT are included in the regression and account for approximately 4.2% of the total variance in the model (3.2% after controlling for shrinkage). The untabulated coefficient matrix shows that only IMPACT (b = 0.168, p < 0.01) is significant. When BIG4 is added in Stage 2, the untabulated results show that IMPACT is no longer significant at either the 5% or 10% level. DENSITY and SIZE remain statistically insignificant. BIG4 (b = 0.532, p < 0.01) is statistically significant at the 1% level

Table 8 Regression of assurance on QUALITY. Panel A: All cases considered (n = 294) Stage 3 variables DENSITY SIZE IMPACT BIG4 COMP 1 COMP 2 COMP 3 COMP 4 COMP 5

Panel B: Excluding no assurances cases (n = 158)

Stand coeff. 0.028 0.059 0.004 0.355 0.293 0.305 0.011 0.081 0.060

tstat 0.591 0.968 0.078 6.141** 5.440** 6.765** 0.182 1.791 1.234

Stand coeff. 0.009 0.047 0.026 0.328 0.303 0.399 0.033 0.110 0.044

tstat 0.126 0.470 0.301 4.515** 3.776** 5.577** 0.349 1.569 0.569

R2 0.042 0.284 0.437

Adj R2 0.032 0.274 0.419

R2 0.019 0.111 0.312

Adj R2 0.000 0.088 0.271

Model summariesa Stage 1 Stage 2 Stage 3

The table reports the results of a 2-stage hierarchical model using the enter method. Component scores have been calculated using the Anderson-Rubin method to ensure that these are uncorrelated. Significance at the 1% and 5% level are denoted by ** and * respectively. The model is robust to fixed year effects. VIF scores for each variable is under 2 and none of the tolerance statistics (un-tabulated) is below 0.2. As a result, multi-collinearity is not having a material effect on the model’s predictive power. In addition, the untabulated collinearity diagnostics show that predictors do not load predominantly on the same dimensions. A Kolmogorov-Smirnov and Levene’s test confirmed that residual errors were approximately normally distributed and the absence of material heteroscedasticity. Nevertheless, results for all cases were bootstrap for robustness based on 1000 iterations and a bias-corrected and accelerated method (un-tabulated). The model summary has been prepared after running a case-wise diagnostic to test for significant outliers. This revealed only 2 observations with standardised residual errors greater than 3 standard deviations from the normal. In addition, each of these had a Cook’s distance well below 1 (average < 0.1) and a Mahalanobis distance less than10. As a result, it was concluded that there are no outliers which have an undue effect on the regression model a To confirm the predictive power of regression equation, an ANOVA (untabulated) is used to test the null hypothesis that the independent and control variables make no contribution to the predicted outcome. The F-statistics for each stage of the regression models (in total and when excluding cases with no assurance) are significant at the 1% level and the null hypothesis is rejected.

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and the standardised beta coefficient suggests that it accounts for approximately half of explained variance. In total, DR2 = 0.242 (F[1] = 97.862, p < 0.01) reaffirming the conclusion that the type of assurance provider has a significant effect on QUALITY, as was the case with Model 1 and Model 2 (see Section 4.1). This finding holds when excluding cases where no external assurance is provided (DR2 = 0.088, F[1] = 15.796, p < 0.01) and is confirmed when adding the 5 assurance components in Stage 3. BIG4 remains statically significant although its standardised coefficient decreases (b = 0.355, p < 0.01). The inclusion of the 5 assurance components increases the model’s explanatory power by approximately 15% (DR2 = 0.153, F[5] = 15.459, p < 0.01). When adjusting for cases where no external assurance is being relied on, BIG4 (b = 0.328, p < 0.01) and the change in the model’s exploratory power is significant (DR2 = 0.202, F[1] = 8.679, p < 0.01). This suggests that, overall, the decision to have ESG information assured, coupled with the choice of assurance provider, has a positive effect on QUALITY in support of the conclusions reached in Section 4.1 and Section 4.2.18 More specifically, when considering all cases, the assurance of general environmental and social disclosures (COMP 1b = 0.293, p < 0.01) and the AccountAbility principles, including select financial statistics (COMP 2b = 0.305, p < 0.01) account for almost 60% of the variance in QUALITY explained by the model. An increase in the assurance of subject matters comprising COMP 3 appears to result in a decrease in QUALITY but the standardised coefficient is small and the effect is insignificant at even the 10% level (b = -0.011. p > 0.1). The sign of COMP 4 (b = 0.081. p > 0.05) and COMP 5 (b = 0.060. p > 0.1) is in line with the general proposition that assurance may be useful for improving report quality but the contribution made by each variable to the model’s total exploratory power is insignificant. As a result, companies which focus their assurance on data, systems and processes (COMP 3); environmental impact and employee relations (COMP 4) and employee development (COMP 5) do not necessarily produce higher quality reports. These findings hold when excluding observations with no external assurance with only COMP 1 and COMP 2 reporting coefficients which are statistically significant at the 1% or 5% level.

4.4. Sensitivity tests 4.4.1. Releases of the IIRC’s framework To control for the possibility that the quality of reporting is being affected to a greater extent by the adoption of the IIRC’s framework on integrated reporting than is the use of external assurance, the analysis is re-performed considering the period 2010 to 2012 separately from 2013 to 2016 (see Table 9). For brevity only the results based on Model 3 are reported. The findings are consistent with those presented in Table 8. Specifically, BIG4, COMP 1 and COMP 2 are not sensitive to the introduction of an integrated reporting framework and remain statistically significant. For robustness, the regression was repeated by using ASSURANCE instead of the five assurance components (as in Section 4.1). The untabulated results show that the total level of assurance is significant for the period 2010–2012 (b = 0.561, p < 0.01) and 2013–2016 (b = 0.382, p < 0.01). Similarly, LIMITED ASSURANCE and REASONABLE ASSURANCE are significant at the 1% level both before and after the adoption of the IIRC’s framework (un-tabulated).

4.4.2. Financial performance Preparing a high quality sustainability or integrated report can require significant investment in accounting, management and reporting systems (Alrazi et al., 2015; De Villiers and Maroun, 2018), something which only the most profitable organisations are able to afford. From a different perspective, Lee and Yeo (2016) find a positive association between the quality of South African integrated reports and financial performance. Better quality reporting may also be associated with a lower cost of capital (Zhou et al., 2017) and improved liquidity (Barth et al., 2017). To confirm the effect which external assurance is having on integrated report quality, controls are introduced for firm performance, leverage and solvency. Following a similar approach to Simnett et al. (2009) and Cahan et al. (2016), financial performance is measured by return on assets (ROA). Leverage is measured by total debt scaled to total equity (LEV). Solvency is measured by total debt scaled to total assets (SOLV) (see also De Villiers et al., 2017b). Results are summarised in Table 10 When controlling for the effect of financial performance, ASSURANCE (b = 0.456, p < 0.01) and BIG4 (b = 0.271, p < 0.01) continue to have a positive effect on QUALITY confirming the initial findings for Model 1 (Section 4.1). Similarly, in Model 2, LIMITED ASSURANCE (b = 0.230, p < 0.01), REASONABLE ASSURANCE (b = 0.342, p < 0.01) and BIG4 (b = 0.271, p < 0.01) remain statistically significant. COMP 1 (b = 0.348, p < 0.01) and COMP 2 (b = 0.253, p < 0.01) are significant and continue to contribute over 50% of Model 30 s exploratory power. BIG 4 (b = 0.314, p < 0.01) is significant at the 1% level. To provide further evidence in support of the positive effect which assurance has on the quality of integrated reports, changes in financial performance are taken into account. The analysis in Table 10 is repeated using year-on-year changes in DEN, SIZE, ROA, LEV and SOLV19 as regresors. The findings hold. They are also robust to the effect of replacing ROA with Tobin’s-q as a measure of financial performance and using the natural log of revenue or total assets as proxies for firm size rather than market capitalisation. Finally, the period from 2010 to 2012 is evaluated separately from 2013 to 2016. ASSURANCE 18 19

These findings hold when using an enter method and a stepwise (forward) method to control for suppressor effects.The sensitivity tests are un-tabulated. There were only a limited number of cases where assurance providers changed and no changes in IMPACT.

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W. Maroun / J. Account. Public Policy xxx (xxxx) xxx Table 9 Controlling for the release of the IIRC’s framework. Pre IIRC (2010–2012) (n = 126) Variables DENSITY SIZE IMPACT BIG4 COMP 1 COMP 2 COMP 3 COMP 4 COMP 5

Post IIRC (2013-2016) (n = 168)

Stand coeff. 0.085 0.142 0.052 0.219 0.397 0.339 0.146 0.088 0.084

t-stat 1.161 1.573 0.718 1.972* 4.147** 4.738** 1.607 1.255 1.078

Stand coeff. 0.039 0.152 0.010 0.455 0.219 0.333 0.046 0.067 0.099

t-stat 0.624 1.615 0.143 6.336** 3.253** 5.474** 0.543 1.108 1.545

R2 0.147 0.286 0.510

Adj R2 0.126 0.263 0.473

R2 0.025 0.305 0.452

Adj R2 0.007 0.288 0.421

Model summaries Stage 1 Stage 2 Stage 3

The table reports the results of the hierarchical model using the enter method. Component scores have been calculated using the Anderson-Rubin method to ensure that these are uncorrelated. Significance at the 1% and 5% level are denoted by ** and * respectively.

Table 10 Sensitivity test: financial performance.a Model 1

Model 2

Model 3

Independent variables

Stand coeff.

t-stat

Stand coeff.

t-stat

Stand coeff.

t-stat

DENSITY SIZE IMPACT BIG4 ROA LEV SOLV ASSURANCE LIMITED ASSURANCE REASONABLE ASSURANCE COMP 1 COMP 2 COMP 3 COMP 4 COMP 5

0.051 0.054 0.054 0.271 0.117 0.090 0.227 0.456 – – – – – – –

1.041 1.101 0.999 4.931** 2.420* 1.789 4.125** 8.497** – – – – – – –

0.051 0.054 0.054 0.271 0.117 0.090 0.228 – 0.230 0.342 – – – – –

1.027 1.095 1.000 4.877** 2.403* 1.789 3.995** – 4.304** 6.987** – – – – –

0.047 0.027 0.060 0.314 0.089 0.092 0.206 – – – 0.348 0.253 0.061 0.091 0.054

0.943 0.444 1.112 5.524** 1.814 1.820 3.389** – – – 6.469** 5.415** 1.045 2.078* 1.130

R2 0.330 0.466

Adj R2 0.330 0.451

R2 0.331 0.476

Adj R2 0.313 0.449

R2 0.330 0.481

Adj R2 0.313 0.458

Model summaries Stage 1 Stage 2

The table reports the results of a 2-stage hierarchical model using the enter method. DENSITY, SIZE, IMPACT, BIG4, ROA, LEV and SOLV are added in Stage 1. The respective assurance scores are added in Stage 2. Significance at the 1% and 5% level are denoted by ** and * respectively. The un-tabulated VIF scores give no indication of significant multi-collinearity problems. The model summary has been prepared after running a case-wise diagnostic to test for significant outliers. This revealed only 4 observations with standardised residual errors greater than 3 standard deviations from the normal. a These findings hold when using an enter method and a stepwise (forward) method to control for suppressor effects. These results are untabulated.

(Model 1), LIMITED ASSURANCE and REASONABLE ASSURANCE (Model 2) and COMP 1 and COMP 2 (Model 3) remain significant at the 1% level when controlling for financial performance and the effect of the release of the IIRC’s framework. 4.4.3. Decision to engage an external assurance provider There is a possible self-selection bias in that only companies which are already preparing high quality integrated reports engage an external assurance provider. To control for the effect of management’s choice to have integrated report disclosures assured, a two-stage Heckman model is used (see De Villiers et al., 2017b). First, a probit selection model is used to predict the presence or absence of external assurance. The model is derived from Simnett et al. (2009) and Cho et al. (2014) and includes the following independent variables: IMPACT, SIZE, LEV and ROA. An additional variable, listing status (LISTING), is also taken into account. This is because South Africa’s codes on corporate governance refer to the benefits of external assurance for enhancing the accuracy, completeness and reliability of integrated and sustainability reporting (IOD, 2009; 2016). Read with the listing requirements of the Johannesburg Stock Exchange (JSE), companies are expected either to comply with these codes or to explain why they have not done so (De Villiers et al., Please cite this article as: W. Maroun, Does external assurance contribute to higher quality integrated reports?, J. Account. Public Policy, https://doi.org/10.1016/j.jaccpubpol.2019.06.002

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Table 11 Heckman two-stage tests.a Model 1

Model 2

Model 3

Independent variables

Coefficient

t-stat

Coefficient

t-stat

Coefficient

t-stat

DENSITY SIZE IMPACT BIG4 ASSURANCE LIMITED ASSURANCE REASONABLE ASSURANCE COMP 1 COMP 2 COMP 3 COMP 4 COMP 5 Likelihood Ratio v2

0.000 0.000 0.138 0.627 0.037 – – – – – – – 371.44**

0.010 0.730 0.540 3.460** 5.180** – – – – – – –

0.000 0.000 0.146 0.621 – 0.044 0.069 – – – – – 371.17**

0.120 0.780 0.570 3.440** – 3.530** 4.470** – – –

0.000 0.000 0.094 0.784 – – – 0.298 0.333 0.026 0.089 0.037 364.59**

0.130 0.440 0.390 4.490** – – – 3.810** 5.760** 0.350 1.630 0.580

–

Probit (stage 1): ASSURANCE½BINARYit ¼ a1 þ b1 LnSALESit þ b2 LEV it þ b3 IMPACT it 3 þ b4 LISTINGit þ eit . *Significant at 5% level. ** Significance at 1% level. a Results generated using the full maximum likelihood method. Note that the coefficients are in the unstandardized form for the Heckman two-stage selection procedure. The un-tabulated Mills ratios are insignificant suggesting that unobserved firm factors which influence the decision to engage an assurance provider are not having a material effect on the model.

2014; JSE, 2016). Even if not mandated, the effect is significant pressure on listed companies to follow recommended governance practices, including the use of external assurance over parts of an integrated report.20 This is not, however, the case for companies which only have a secondary listing in South Africa and are, therefore, not subject to the JSE’s listing requirements. The first-stage probit regression is used to calculate an inverse Mill’s ratio which is included in the second stage estimation regression to control for self-selection bias. Unstandardised coefficients from the second stage are presented in Table 11. After controlling for endogeneity problems associated with selection bias, ASSURANCE and BIG4 remain significant at the 1% level (Model 1). Similarly, for Model 2, LIMITED ASSURANCE, REASONABLE ASSURANCE and BIG 4 continue to make significant contributions to QUALITY at the 1% level. The results for Model 3 are also robust to self-selection effects (COMP1b = 0.298, p < 0.01; COMP2 b = 0.333, p < 0.01; BIG4 b = 0.784, p < 0.01).21 To corroborate these findings, the Heckman two-stage test is re-performed for each model but including the additional controls for financial performance which were significant in Section 4.3.2 (ROA and SOLV) in the estimation stage. The results are consistent with those presented in Table 11. Next, a two-stage least squares test is performed treating ASSURANCE as an endogenous variable and using LISTING as an instrumental variable.22 In the interest of brevity, the test is only performed for Model 1. The Durban (v2 = 1.992, p > 0.05) and Wu-Hausman (H = 1.037, p > 0.05) test statistics suggest that, in total, endogeneity is not having a significant effect on Model 1. The un-tabulated results show that ASSURANCE remains statistically significant at the 1% level. Similarly, BIG 4 is significant for both the OLS and 2SLS regressions at the 1% level. These findings are confirmed by re-performing the OLS regression of ASSURANCE on QUALITY separately for firms with a primary listing on the JSE and other exchanges. The un-tabulated coefficients for both groups are consistent and significant at the 1% level. The decision to engage an external assurance provider may reflect firm characteristics other than listing status or the binary decision either to engage or not to engage an ESG assurance provider. As a result, the OLS regressions for Models 1, 2 and 3 were repeated after controlling for firm-specific effects by randomly assigning observations to 5 equal groups (adapted from Ntim, 2013). The untabulated results for each model show that the respective assurance scores and BIG4 remain significant at the 1% level. This was the case when the models were run in the original form with fixed firm effects and when considering the additional control variables for financial performance (per Section 4.3.2) together with fixed firm effects.23 As a final test for possible endogeneity problems, a one-year lagged measure of QUALITY is introduced as an additional independent variable (see Klein, 1998). In Model 1, ASSURANCE (b = 0.441, p < 0.01) remains statistically significant and its standardised coefficient is comparable to that reported in Section 4.1. Similarly, LIMITED ASSURANCE (b = 0.210, p < 0.01)

20 Following the same logic, De Villiers et al. (2014) conclude that, even though integrated reporting is not required by statute, the listing requirements effectively result in integrated reporting being seen as mandatory by South African listed companies. 21 To confirm these findings, an alternate approach is used for Model 1. An estimation model for assurance is developed with DEN, SIZE, IMPACT, LEV, ROA and BIG4 as drivers. The inverse Mills from the same probit model is used to control for self-selection bias. The Heckman-adjusted assurance values from the estimation model are used in Model 1 (rather than the un-adjusted ASSURANCE scores) and the results are compared with those presented in Section 4.1. ASSURANCE (after adjustment) and BIG4 remain significant at the 1% level. Control variables remain insignificant at the 10% level. 22 LISTING is likely to be exogenous because, for the companies under review, the decision to list primarily on the JSE or a different exchange was made well before the release of the IIRC’s framework in 2013. Nevertheless, a Heckman sample selection model and 2SLS regression is used, rather than only the former, because of the inherent limitations of using instrumental variables in accounting-based research. For details, see Lar**cker and Rusticus (2010) 23 The same result is achieved when a dummy variable is introduced for each firm in the sample under review.

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and REASONABLE ASSURANCE (b = 0.339, p < 0.01) continue to make significant contributions to Model 20 s exploratory power and are in proportion to the results presented in Section 4.2. Model 3 is also robust to the effect of the lagged quality measure on COMP 1 (b = 0.348, p < 0.01) and COMP2 (b = 0.258, p < 0.01). 4.4.4. Decision to prepare a separate sustainability report In South Africa, the integrated report should be the primary report issued to stakeholders (see Rensburg and Botha, 2014; Atkins and Maroun, 2015; IOD, 2016) but companies are not precluded from publishing a separate or complementary sustainability report. In many studies, the decision to prepare a separate or stand-alone sustainability report is seen as a proxy for the quality of ESG disclosures (Dhaliwal et al., 2012; 2014). Others, however, question whether this practice may be more symbolic than indicative of a genuine commitment to higher quality reporting and sustainable business management (Michelon et al., 2015). To control for the possibility that the decision to prepare a separate sustainability report is having an effect on integrated report quality, Models 1, 2 and 3 are repeated considering companies which publish only an integrated report separately from those which publish an integrated report complemented by a sustainability report. Results are presented in Table 12. Table 12 shows that the ASSURANCE, LIMITED ASSURANCE,24 REASONABLE ASSURANCE and BIG4 are robust to the decision to complement an integrated report with a separate sustainability report. When considering individual assurance categories, COMP 1 and COMP 2 remain significant at the 1% level. COMP 3 is also significant but only for companies which prepare just an integrated report. Overall, the results suggest that the decision to complement an integrated report with a separate sustainability report is not having a material moderating effect on the impact of external assurance on the quality of an integrated report. 4.4.5. Social and environmental performance External assurance can be used as a mechanism to signal the quality of environmental or social disclosures (Simnett et al., 2009). If this is the case, only companies with good social or environmental performance would engage external assurors to report on their non-financial disclosures. From a different perspective, EY (2016; 2017) points out that high quality integrated or sustainability reports require more than just detailed disclosures. There should be a clear indication that material ESG issues are being actively managed by an organisation and incorporated in its strategy, risk assessment and business model development (see also Churet and Eccles, 2014; Barth et al., 2017; De Villiers and Maroun, 2018). This requires established accounting and management control systems for collecting, analysing and reporting on non-financial disclosures which may be supported by the use of external assurance as a means of enhancing the reliability of controls and data being included in integrated reports (consider IAASB, 2015a; IIRC, 2015; Maroun, 2017). It is possible that only companies which already have systems in place to support good social and environmental performance are making use of external assurance, with the result that the assurance measures discussed in Section 4.1 and Section 4.2 become proxies for social and environmental performance. In response, three additional control variables are introduced: CDP, BEE and SRI (see Table 13). The first assesses environmental performance in terms of the systems and processes in place to manage greenhouse gas emissions. The scores assigned by the Carbon Disclosure Project take into account the quantity of CO2 emissions and changes in total emissions. They also provide ‘an indication of a company’s awareness of climate change issues, management methods and progress towards action taken on climate change as reported in the response [to the CDP]’ (CDP, 2017). Performance is rated from A to D where A represents the highest level of performance and is converted to an ordinal environmental performance score. The Carbon Disclosure Project does not consider a company’s social performance. This is done using a company’s black economic empowerment score (BEE). BEE is a direct response to the exclusion of large sections of the population from the formal economy under Apartheid and forms part of a national policy on social development (for details see De Villiers and van Staden, 2006). A company’s BEE score is based on: (1) the extent to which individuals disadvantaged under Apartheid hold ownership interests in an organisation; (2) the inclusion of these individuals on management bodies; (3) a company’s investment in developing skills and creating employment opportunities; (4) the extent to which a company procures goods and services from other companies with a high BEE score and (5) the total contribution to broad socio-economic development in South Africa (Republic of South Africa, 2013; DTI, 2015). The BEE score is measured on a scale from 0 to 100 where 100 represents the highest level of social performance. The third performance measure (SRI) takes into account whether or not a company is included in the FTSE/JSE Responsible Investment Index Series. Unlike CDP or BEE, which, arguably, provide a narrow definition of environmental or social performance, respectively, the index ratings are based on a multi-dimensional review of ESG measures including, for example, climate change, biodiversity impact, human rights, health and safety standards, anti-corruption and corporate governance systems (FTSERussell, 2015). For brevity, the sensitivity test results are only presented for Model 1. To avoid the risk of multi-collinearity affecting the results, each performance measure is considered separately. CDP, BEE and SRI are also converted to percentage scores which are used to calculate an unweighted composite performance measure (COMP). 24 While the coefficient for LIMITED ASSURANCE is lower for companies which only prepare an integrated report, the variable remains significant at the 10% level.

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Table 12 Sensitivity test: separate sustainability report. Model 1

Independent variables DENSITY SIZE IMPACT BIG4 ROA LEV SOLV ASSURANCE LIMITED ASSURANCE REASONABLE ASSURANCE COMP 1 COMP 2 COMP 3 COMP 4 COMP 5 R2

Model 2

Model 3

Integrated report only (N = 150)

Comp reports (N = 144)

Integrated report only (N = 150)

Comp reports (N = 144)

Integrated report only (N = 150)

Comp reports (N = 144)

Stand coeff.

Stand coeff

Stand coeff

Stand coeff

Stand coeff

Stand coeff

0.117 0.075 0.086 0.120* 0.032 0.109 0.274* 0.469** –

0.050 0.040 0.233** 0.306** 0.222** 0.158 0.158* 0.372** –

0.129 0.082 0.085 0.163* 0.036 0.112 0.274 – 0.161^

0.061 0.044 0.217 0.298** 0.209** 0.072 0.132 – 0.257**

0.151 0.043 0.068 0.115 0.094 0.068 0.400* – –

0.011 0.038* 0.192 0.330** 0.182** 0.053 0.102 – –

–

–

0.375**

0.265**

–

–

– – – – – 0.365

– – – – – 0.459

– – – – – 0.413

– – – – – 0.489

0.293** 0.401** 0.328** 0.198 0.126 0.424

0.325** 0.237** 0.014 0.153 0.007 0.489

The table reports the final stage of the 2-stage hierarchical model using the enter method for Models 1, 2 and 3. Significance at the 1% and 5% level are denoted by ** and * respectively. Significance at the 10% level denoted by ^.

Table 13 Sensitivity tests: social and environmental performance. Variables

Samplea DENSITY SIZE IMPACT BIG4 PERFORMANCE ASSURANCE

Standardised coefficients Performance = CDP

Performance = BEE

Performance = SRI

Performance = COMP

N = 247 0.023 0.047 0.052 0.309** 0.061 0.371**

N = 179 0.204 0.052 0.169 0.311* 0.193 0.369**

N = 294 0.040 0.061 0.024 0.250** 0.306** 0.353**

N = 179 0.206 0.052 0.166 0.308** 0.200* 0.369**

The sensitivity tests are run using the same approach as for Model 1. PERFORMANCE is added in Stage 1 of the regression models using the enter method. Significance at the 1% and 5% level are denoted by ** and * respectively. a Published scores were not available for all of the firm years under review. The sample size for each sensitivity test is indicated.

When considered individually, CDP (b = 0.061, p > 0.05) and BEE (b = 0.193, p > 0.05) are not making statistically significant contributions to QUALITY. BIG4 and ASSURANCE remain statistically significant at the 1% level as per Model 1 (see Section 4.1). This finding holds when performance is measured more broadly in terms of the FTSE/JSE Responsible Investment Index. SRI (b = 0.306, p > 0.05) accounts for approximately 30% of the explained variance in QUALITY. Compared to Model 1 in Section 4.1, the standardised coefficient for BIG4 reduces from 0.308 to 0.250 but remains significant (p < 0.01). ASSURANCE also reports a lower coefficient (b = 0.353 versus 0.428) but continues to account for the majority of the explained variance in QUALITY and is still significant at the 1% level. Similar results are reported when a composite measure is used to gauge performance (ASSURANCEb = 0.369, p < 0.01).25 The findings are robust to including the financial performance controls in addition to the composite measure of environmental and sustainability performance. The results also hold when a Heckman two-stage test is run using the same probit and estimation regressions as discussed in Section 4.4.3 but with the composite environmental and sustainability performance measure included in the latter. 5. Discussion and conclusion This research provides an account of the role of external assurance as a driver of integrated report quality. The data from this study comes from among the largest listed companies on the Johannesburg Stock Exchange (JSE) where integrated 25

Similar results (un-tabulated) are reported when controlling for social and environmental performance for Model 2 and Model 3.

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reporting has, in essence, become mandatory. Report quality is gauged according to the extent to which companies apply the guiding and content principles of the IIRC’s framework as assessed by EY. In support of H1, an increase in the number of ESG disclosures/elements being externally assured is associated with higher quality reporting. This is the case when assurance is evaluated in total and when considering the number of subject matters tested to provide reasonable and limited assurance. More specifically, H2 considers if integrated report quality will be higher when a company requires a reasonable, rather than a limited assurance engagement. While the former may allow users to place more reliance on the respective disclosures (AccountAbility, 2008a; IAASB, 2009b), the provision of moderate and high assurance make significant and approximately equal contributions to total report quality, contrary to H2. More relevant are the specific parts/elements of an integrated report which have been selected for testing. Higher quality reporting is also associated with assurance of ESG disclosures which are recommended by the GRI and codes of best practice and the principles of materiality, inclusivity and responsiveness (see AccountAbility, 2008b). The assurance of other subject matters does not appear to have a material effect on integrated report quality. The type of assurance provider is also important. The results offer evidence in support of H3: using the Big 4 as an assurance provider is associated with better quality integrated reporting. This is in line with the prior research on the relationship between use of the Big 4 and financial reporting quality (Francis, 2004) and initial findings from other jurisdictions that the Big 4 may provide assurance over a broader range of ESG issues using more developed test methodologies (Perego and Kolk, 2012; Mock et al., 2013). The findings on the relevance of external assurance (H1; H2) and choice of assurance provider (H3) hold both before (2010–2012) and after (2013–2016) the introduction of the IIRC’s framework on integrated reporting. In other words, the fact that companies are provided with additional guidance on how to prepare an integrated report post 2013 is not reducing the effect of external assurance on report quality. The results are unaffected by firm size, the extensiveness of reporting, industry membership, financial performance and ESG performance. (cf Simnett et al., 2009). They are also robust to fixedyear and firm effects. This is in keeping with the fact that the use of ESG assurance is widespread in South Africa, with 62% of companies relying on, at least, some external assurance for their 2016 integrated reports. Consequently, it cannot be said that only companies which have more to report (due to their size or environmental impact) or which have good news for investors (as indicated by ESG or financial performance) are engaging an external assurance provider. The possibility that external assurance is used as a signalling mechanism cannot be excluded (Simnett et al., 2009) but there is also evidence that external assurance is a real driver of integrated report quality. The research findings have a number of important implications. Firstly, the results support the positon of the IIRC (2013), the GRI (2016) and codes on corporate governance (IOD, 2009; 2016) to recommend the use of external assurance as a method for enhancing the credibility and reliability of integrated and sustainability reports. If external assurance can be used to improve report quality, the possibility of mandatory testing for a minimum number of disclosures should also be considered, especially as reporting practices start to mature and common features of integrated reports begin to emerge. This goes hand-in-hand with the need for more detailed guidance on exactly how an integrated or sustainability report could be the subject matter of an assurance engagement. The findings show that not all assurance services are contributing significantly to the quality of integrated reports. These include, inter alia, the assurance of companies’ reporting systems, details on environmental impact and investment in human capital. It is possible that these engagements do not deal in sufficient detail with the accuracy, completeness and reliability of the respective subject matters either to reassure stakeholders about the quality of the reports or to provide useful insights for managers to improve their reports. This should be contrasted with the assurance given on more specific sustainability indicators, as well as a company’s adherence to the principles of materiality, inclusivity and responsiveness under the AccountAbility framework. These assurance engagements may be more useful for both stakeholders and preparers to understand how a company is reporting on key issues, especially when the focus is on reporting principles, rather than only on specific disclosures. In addition to the choice of subject matter, the findings suggest that the level of assurance is not affecting report quality. This may be due to the fact that the conclusions for reasonable and limited assurance engagements are often included in the same report to stakeholders and, from the perspective of non-experts, may be interpreted in a similar way (see Hodge et al., 2009). The results also suggest that the focus of the engagement, the test procedures performed and the recommendations communicated to management may be more relevant than the wording of the opinion provided to stakeholders. Finally, additional guidance should be provided on making an integrated or a sustainability report the subject matter of an assurance engagement in its entirety. The assurance of only some parts of these reports is already contributing positively to their quality. It is conceivable that a broader approach to assurance which covers more of the information being reported, the controls used to safeguard the reliability of data and the methods used to compile a report will be of even greater value to the users of an integrated report (see also Simnett and Huggins, 2015; Maroun, 2018b). As in any study of this nature, this research is not without limitations. Additional research will be required. Most notably, the chosen sample is limited to the largest listed companies in a single jurisdiction. Future researchers may be able to extend on the relevance of external assurance for the quality of integrated reports by drawing on a larger number of integrated reports from multiple countries. The definition and measurement of assurance is also limited. Companies are making increasing use of different systems and processes for ensuring the quality of their corporate reporting (Junior et al., 2014; Maroun, 2018b). This forms part of a combined approach to assurance which includes the concurrent use of conventional external assurance, internal audit and different types of internal monitoring and review. How combined assurance functions in both a financial and broader corporate reporting setting needs to be studied in more detail. Finally, like most quantitative Please cite this article as: W. Maroun, Does external assurance contribute to higher quality integrated reports?, J. Account. 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research, the results are inherently reductionist. While this study shows that external assurance contributes to integrated report quality, exactly how and why this is the case cannot be determined. This opens opportunities for more exploratory work. For example, what is the role of external assurance engagements in revealing weaknesses in companies’ reports and identifying areas for improvement (AccountAbility, 2008a; IAASB, 2009b)? Similar to the audit of financial statements, does the fact that ESG disclosures are being tested by an independent expert make preparers more aware of the need for accurate, reliable and complete reporting? From a more critical perspective, to what extent does ESG assurance change a company’s attitude to and methods for preparing an integrated report? It may be the case that more competent reporters simply use external assurance as a quality signal, while poor quality reporting is not assured and remains largely unchanged. 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