Obscuring tax avoidance by alternative interpretations in applying segment disclosure under IFRS 8

We provide evidence on whether there is a problem with complex accounting standards allowing significant discretion by firm managers on the level and category of segment disclosure to obscure tax avoidance (Hanlon and Heitzman, 2010), captured by effective tax rates (ETRs). International Financial Reporting Standard (IFRS) 8, Operating Segments, is selected as an example of a complex accounting standard, which allows significant judgment and discretion by managers in applying that standard. A higher level of corporate tax avoidance is one potential adverse outcome of disclosure discretion in an IFRS for Australian listed firms. Indeed, identification of a significant link between tax avoidance and an IFRS provides regulators and standard setters with evidence that should be reflected in accounting standards that are amended or newly created.

IFRSs represent principles-based accounting standards with less precise implementation, guidance, and uniform interpretation than the US Financial Accounting Standards Board (FASB) standards (Jermakowicz and Gornik-Tomaszewski, 2006; Backof et al., 2016). More judgment is available to managers in fulfilling IFRS standards (Collins et al., 2012; Kadous and Mercer, 2016), and many standards have distinct levels of precision and complexity (Peytcheva et al., 2014). IFRS 8 represents an example of a complex standard allowing extensive judgment and discretion by managers in its application to financial reporting (Bugeja et al., 2015).

Discretion is allowed under IFRS 8 as managers are required to report segment information by applying the management approach (IASB, 2021b). Such an approach requires that the information reported is based on whether it is reported to the chief operating decision-maker (CODM) in the firm. Identifying the CODM decision-maker is open to interpretation, being defined as a function. This function could be the chief executive officer (CEO), the chief operating officer, a group of executive directors, or others. Further discretion is allowed by alternative interpretations of what is reported to the CODM. Preparers of financial information can withhold or report segment information to financial statement users by alternative interpretations of the identity and what is reported to the CODM in the firm without contravening the standard (IASB, 2021b).

The content of IFRS 8 also allows firms to adopt different disclosure categories. The standard distinguishes between mandatory and voluntary disclosures. Even mandatory disclosure items can be avoided under IFRS 8 if managers claim that these items are not reported to the CODM in the firm and, therefore, do not represent information related to a reportable segment (IASB, 2021b). These mandatory and voluntary disclosures can be further separated into hard and soft disclosures. Providing hard information, such as a measure of profit or loss for reporting segments, makes it easier for users of financial statements to determine whether low ETRs are likely to constitute tax avoidance. Conversely, soft information, such as explaining measurement differences between the reportable segments' profit or loss is subjective and possibly more challenging to verify, so ETRs and tax avoidance are less detectable (Bertomeu and Marinovic, 2016; Liberti and Petersen, 2019).

Disclosure items required or recommended by IFRS 8 are expected to help stakeholders identify firms avoiding tax (IFRS 8, para. 23 (c) (d) (e) (h)). More specifically, disaggregated segment disclosures in the notes to the financial statements provide analysts, investors, policymakers, regulators, and tax authorities with more detailed information for assessing a firm's after-tax financial performance compared to aggregated financial statements (Hope et al., 2013). The idea is that managers use the discretion allowed by IFRS 8 to selectively report different levels and categories of information to obscure firm behaviors (Leuz and Verrecchia, 2000; Lang et al., 2012), such as achieving lower ETRs (i.e., higher tax avoidance levels).

We construct a unique disclosure index of 32 items that cover the core principles of IFRS 8 (IASB, 2021a, b) to measure the level and disclosure category. We measure categories of disclosure of segment information as mandatory, voluntary, hard, and soft disclosures. We define mandatory disclosure items as segment information that must be reported by all firms under IFRS 8 that relate to a reportable segment. Voluntary disclosure items refer to those recommended by IFRS 8, so non-disclosure of these items does not breach the accounting standard. ^[1] Hard disclosure items refer to verifiable segment disclosure information, whereas unverifiable subjective descriptions and explanations are identified as soft disclosure items. ^[2].

Our study is motivated by three critical factors. First, understanding the association between corporate ETRs and the level and category of segment disclosure is important. Lower ETRs for some firms are a worldwide issue with economic and social implications that attract negative attention from policymakers, regulators, and the public (Hanlon and Heitzman, 2010; Lanis and Richardson, 2012a). Second, research on the association between ETRs and complex accounting standards, which allow significant discretion by preparers of financial statements, is lacking. The consequences of complex accounting standards enabling the obscuration of low ETRs have significant implications for regulators when creating and revising accounting standards. Third, lower ETR firms may vary in their relationship with categories of disclosures in terms of voluntary, mandatory, hard, or soft disclosures (Verrecchia, 1983, 1990, 2001; Bertomeu and Marinovic, 2016; Glaeser et al., 2020). Identifying inadequacies in real institutional settings with various disclosures is essential to understanding disclosure choices. Segment disclosure also provides a suitable setting for testing the link between different disclosure categories and firm reporting outcomes, as the standard includes provisions and recommendations for mandatory, voluntary, hard, and soft disclosures (IASB, 2021b).

In the context of tax planning, managers must weigh up the benefits of tax avoidance (e.g. increased after-tax earnings and cash flows) against the associated costs of tax avoidance (e.g. tax audit costs, fines, penalties, back taxes, and reputational costs) (Scholes et al., 2008; Chen et al., 2010). We speculate that managers set an aggressive level of tax avoidance through lower ETRs if the benefits of tax avoidance exceed the costs. Then, managers obscure their aggressive tax avoidance activities by applying lower levels of total, mandatory, voluntary, hard and soft segment disclosures in the annual report, according to the discretion available under IFRS 8. Such actions by managers are expected to reduce the likelihood of tax audit and reputational risk associated with tax avoidance, which could minimize the costs of tax avoidance (Scholes et al., 2008; Chen et al., 2010).

Based on a hand-collected sample of 6,038 firm-year observations over the 2016–2019 period, we provide compelling evidence showing that lower ETRs are significantly associated with a lower combined segment disclosure level score. Furthermore, our findings are also economically significant; a one standard deviation decline in ETRs is associated with a 27.42% decrease in the Combined Disclosure Score. Finally, we also provide evidence showing that lower ETRs are associated with lower mandatory, voluntary, hard, soft, mandatory-hard, mandatory-soft, voluntary-hard, and voluntary-soft segment disclosures.

This study provides several important contributions to the literature. First, it shows the outcomes of requiring principle based complex IFRSs that allow judgment and discretion in their implementation when a manager requires a significantly lower ETR. The objectives of IFRSs are to provide high-quality standards that are transparent, understandable, enforceable, and comparable. This is crucial for investors in the capital market. Another aim is that these standards should be written and formulated so that they can be rigorously applied. Allowing voluntary interpretations and judgment in the implementation of an IFRS by managers is unlikely to lead to universal rigor in its application (Pope and McLeay, 2011). We extend the literature by showing that the complex principles in applying IFRS 8 allow an interpretation by managers that is not consistent with the objectives of IFRSs. We provide unique evidence showing that ETRs vary with total, and a combination of mandatory, voluntary, hard, and soft disclosures. However, this conflicts with the aims of transparency, understandability, enforceability, and comparability when standards allow a manager discretion and judgment to obscure aggressive tax avoidance.

Our second contribution provides evidence of the interaction between a firm's ETRs and financial reporting (Hanlon and Heitzman, 2010). To date, there is limited evidence about the link between aggressive tax avoidance through lower ETRs and segment disclosures, especially under the IFRS 8 management approach. Hope et al. (2013) examine the link between tax avoidance and disclosure of geographic earnings for US multinational firms after the adoption of the US equivalent of IFRS 8 (i.e., SFAS 131). This study is the nearest reference point to our study, and we extend their study in at least two ways. Our first extension of Hope et al. (2013) is to develop a broad score for segment disclosure based on IFRS 8 requirements that go beyond a simple measure of geographical segments. Firms under IFRS 8 are not required to identify geographical segments, so it is essential to use a more complete measure of disclosure. Other studies (Akamah et al., 2018) ^[3] restrict their measures to the number of segments and line items and limit their tax avoidance measure to tax havens. However, tax havens as a measure of tax avoidance are inadequate, as it is difficult to identify tax havens, and tax avoidance can be achieved without tax havens. We expand this literature by exploring the link between ETRs and the overall segment disclosure level under IFRS 8. Our evidence should be helpful for investors and other stakeholders to understand that IFRS 8 allows extensive discretion for managers in preparing the annual report, which leads to a need for more transparency in reporting. We also offer evidence to regulators on one outcome of a complex accounting standard when reviewing accounting standards and converging IFRS and FASB standards. Our second extension of Hope et al. (2013) is to explore ETRs in a different institutional setting. Large differences exist between Australia and the US on the structure, rates, treatment of dividends, shareholder incentives, and corporate structures (Bond and Xing, 2015). Thus, it is essential to understand such differences between countries. Indeed, applying a US standard in another country might have dysfunctional effects despite the desire of international regulators to increase the comparability of accounting standards across countries. One possible outcome is that preparers are forced to apply similar accounting treatments to different events (Gordon and Gallery, 2012). Finally, other countries have diverse business, financial, accounting, auditing, regulatory, and tax cultures from those in the US (Zeff, 2007; Leung and Verriest, 2015; Tran-Nam and Le, 2022), so it is also important to consider taking these characteristics into account.

Our final contribution is to clearly identify the categories of segment information as a combination of mandatory, voluntary, hard, and soft disclosures and test their associations with ETRs. Such research has not been undertaken in prior studies, which allows us to add to the disclosure literature more generally. In fact, consensus does not exist about the relative utility of mandatory, voluntary, hard, and soft information and fluctuates depending on the disclosure (Bertomeu and Marinovic, 2016; Leuz and Wysocki, 2016).

The remainder of this paper proceeds as follows. Section 2 covers the regulatory background, while Section 3 discusses the discretion available to managers for segment reporting under IFRS 8 and develops our hypothesis. Section 4 describes the research design. Section 5 presents empirical results. Finally, Section 6 concludes the study.

In 2006, the International Accounting Standards Board (IASB) issued IFRS 8, Operating Segments, which came into effect for periods commencing on or after 1 January 2009 (IASB, 2021b). IFRS 8 states that operating segments are components of an entity that must meet the following conditions: (1) undertake business activities, yield revenues, and incur expenses, (2) whose operating results are regularly reviewed by the CODM, and (3) for which discrete financial information is available. The CODM is a function of the mechanism for deciding resource allocation and performance evaluation for a firm's operating segments.

Operating segments are identified as reportable segments when they meet several quantitative thresholds, such as (1) total revenue equal to or greater than 10% of combined revenue of all operating segments, (2) disclosed profit or loss (in absolute value) is equal to or greater than combined disclosed profit or loss (in absolute value) of all operating segments, and (3) assets are equal to or greater than 10% of combined assets of all operating segments. However, the standard also states that operating segments not meeting the above thresholds may be identified as reportable if managers believe the information under these segments is essential (AASB, 2015; IASB, 2021b).

Common factors that identify operating segments include products, services, and geographical locations. A statement concerning the factors/judgment used to identify a firm's reportable operating segments must be disclosed in segment reporting (AASB, 2015; IASB, 2021b). Firms that do not identify operating segments by products and services or geographic areas must report the relevant revenues for products and services or geographic areas in aggregated figures included in the annual report. Firms are also required to report total revenues from major customers without specifically identifying the customer or the amount of revenues for each operating segment.

IFRS 8 requires firms to provide segment information consistent with a firm's internal reports to the CODM (AASB, 2015; IASB, 2021b). The regulators intend this approach to provide more helpful information to investors and other stakeholders by allowing them to identify segment information from a manager's perspective (Nichols et al., 2013). However, variations in a firm's specific internal reporting structures give managers significant discretion in deciding which and how many items to disclose to the capital market and stakeholders (André et al., 2016; IASB, 2021b).

Managers can obscure favorable or unfavorable information about their firms' ETRs without breaching the standard. For example, a measure of income tax expense or income is disclosed for each reportable segment if the CODM regularly reviews the amount. Income tax expense or income is information that can be used by investors and other stakeholders to detect corporate tax avoidance (Balakrishnan et al., 2019). However, firms can withhold disclosing the amount of their income tax expense or income for each reportable segment if the CODM does not regularly review this information. Prior research exploring SFAS 131 finds that firms choose not to disclose their geographical earnings when they have significantly lower worldwide ETRs (Hope et al., 2013). Other studies with implications for our research find that firms with operations located in tax havens report their geographic segment information with higher aggregation levels (Akamah et al., 2018).

Managers can exclude segment information by claiming that the CODM does not regularly review the information and, therefore, does not relate to a reportable segment. This can be achieved to avoid attracting political attention for low ETR firms. Firms do not want stakeholders to suspect tax avoidance because of the loss of reputation for the firm associated with low ETRs and tax avoidance (Graham et al., 2014). Reduced reputation of firms can lead to negative consequences such as lower sales volume and negative stock returns (Chen et al., 2019). Additionally, firms conduct fewer tax avoidance activities if they have, for example, valuable brands and disclose more information to the capital market if social activists target them (Dyreng et al., 2017).

Various levels of segment disclosure information offer investors and other stakeholders differing levels of understanding of a firm's after-tax financial performance. Firms with low ETRs are expected to base some of their disclosure choices on managers ensuring their low ETRs are not conveyed clearly to the capital market. This leads to an expectation that firms report less detail and lower levels of segment information so that investors and other stakeholders cannot readily observe low ETRs. This occurs because the existence of lower ETRs is often perceived as aggressive tax planning, which is viewed as being immoral and unethical (Lanis and Richardson, 2012a). However, this perception may not be indisputable, as low ETR firms may increase their disclosures to deal with negative stakeholder perceptions by expanding the legitimacy of their image and identity (Lanis and Richardson, 2012b).

The level of judgment and discretion permitted to managers and preparers increases since the standard distinguishes between mandatory and voluntary disclosure (IASB, 2021a, b). Reporting a profit or loss for each reportable segment is mandatory (IFRS 8, para. 23), whereas income tax expense or income for each reportable segment is voluntary (IFRS 8, para. 23(h)). Many mandatory and voluntary disclosure items are likely to help various stakeholders identify ETRs. Examples of these items are the voluntary disclosure of interest revenue (IFRS 8, para. 23(c)), interest expense (IFRS 8, para. 23(d)), and depreciation (IFRS 8, para. 23(e)) for reportable segments. Prior studies show that firms providing higher voluntary disclosures tend to rely less on mandatory disclosures, whereas firms with lower voluntary disclosures depend more on mandatory disclosures. This outcome suggests a substitution effect between voluntary and mandatory disclosures (Verrecchia, 1983, 1990, 2001; Bagnoli and Watts, 2007; Bertomeu and Marinovic, 2016; Glaeser et al., 2020). In combination, mandatory and voluntary disclosures are expected to be lower for firms with lower ETRs.

Furthermore, hard and soft disclosure theory (Bertomeu and Marinovic, 2016; Hollindale et al., 2022) can also explain an essential distinction about segment disclosure categories and low ETRs. IFRS 8 includes mandatory and voluntary information that is hard and soft in nature, illustrated in our disclosure score (see  Appendix 1). The combination of hard and soft information is a fundamental balance for many disclosures in annual reports. It is likely to have a different utility for users of annual reports in assessing the likelihood of tax avoidance (Bertomeu and Marinovic, 2016). Past research shows that users of annual reports potentially disregard soft disclosures as they are not seen as being credible. Specifically, users assume managers can distort soft information more easily than hard information. However, manager incentives to distort information are weaker when the market is likely to detect this distortion (Rogers and Stocken, 2005). Prior studies also show that some soft disclosures contain as much information as hard disclosures (Kirk and Vincent, 2014). Yet, bad news, such as low ETRs, ^[4] is conveyed when there is complete reliance on soft disclosures, and soft reporting lacks credibility even when the information appears unbiased (Bertomeu and Marinovic, 2016).

Hard and soft information likely assists investors and other stakeholders in detecting ETRs. Managers planning to distort segment information have incentives to provide less verifiable hard information. Low ETRs can also be concealed by soft disclosure items, such as excluding statements about judgments used to identify reportable segments (Bertomeu and Marinovic, 2016; Liberti and Petersen, 2019). Soft disclosure items can also provide helpful information and deliver more signals to the capital market than hard disclosures (Bertomeu and Marinovic, 2016; Liberti and Petersen, 2019). The choice between hard and soft disclosure of segment information represents yet another judgment made by managers.

Overall, IFRS 8 provides managers with a great deal of discretion in disclosing reportable segments, and the choice of mandatory, voluntary, hard, and soft disclosure combinations in the annual report (IASB, 2021b). From the perspective of tax planning, managers must weigh up the benefits of tax avoidance (e.g. increased after-tax earnings and cash flows) against its associated costs (e.g. tax audit costs, fines, penalties, back taxes, and reputational costs) (Scholes et al., 2008; Chen et al., 2010). If the benefits of tax avoidance exceed the costs, managers set an aggressive level of tax avoidance through lower ETRs. Then, they use the discretion available under IFRS 8 to obscure their aggressive tax avoidance activities by exploiting lower levels of total, mandatory, voluntary, hard and soft segment disclosures in the annual report. ^[5] Such actions by managers are expected to reduce the possibility of tax audit and reputational risk linked to tax avoidance, leading to a reduction in the costs of tax avoidance (Scholes et al., 2008; Chen et al., 2010).

Based on the above discussion, we expect that managers reduce the levels and categories of segment disclosures to conceal lower ETRs when the benefits of tax avoidance exceed the costs, leading to the following hypothesis:

Our sample selection begins with all firms listed on the Australian Securities Exchange (ASX) over the 2016–2019 period. The year 2016 was selected as a starting point since the latest Australian compiled version of Accounting Standard AASB 8 (the Australian equivalent of IFRS 8) was implemented on January 1, 2016. This version extended the standard's scope of application. The sample period was limited to four years since the dataset is extensive and requires manual collection. To measure the level of segment disclosure, annual reports must be downloaded, and 32 disclosure items hand-collected for each firm year (see  Appendix A). We gathered financial and corporate governance data from the MorningStar, Osiris, and Connect4 databases, respectively.

The sample reconciliation is reported in Table 1, Panel A. Our initial sample is 7,391 firm-year observations over the 2016–2019 period. However, 758 firm-year observations were excluded as they involved firms in the financial industry sector. We also excluded 102 firm-year observations for firms in the utility sector. We excluded financial and utility firms because of their different accounting and tax rules (Hanlon et al., 2005). Financial firms have different accounting and tax rules due to the unique nature of their operations, which involve managing complex financial instruments like loans, securities, and derivatives. These firms must adhere to specialized accounting standards, such as fair value accounting (IASB, 2021b) ^[6] to reflect the risks associated with their assets and liabilities. Also, financial firms are subject to stringent capital adequacy requirements and regulatory frameworks, such as Basel III, which affect their financial reporting and tax treatment (Gup, 2011). Their revenue recognition practices also differ, as they earn income through interest, fees, and trading activities, rather than the sale of goods or services (Calmès and Liu, 2009). These factors result in financial firms being subject to accounting and tax rules adapted to their specific business models, making them different from other industries. Utility firms are also subject to distinct accounting and tax rules due to their regulated nature, capital-intensive infrastructure, and government oversight (Hogan, 2002). Unlike firms in other sectors, Australian utilities are regulated by bodies such as the Australian Energy Regulator, which oversees the pricing of services to ensure that they align with the cost of providing those services while allowing utilities a reasonable return on capital invested (Australian Energy Regulator, 2025). For example, utilities can use a cost-plus pricing model, where rates are set based on the cost of service plus an approved return on investment. This regulation impacts their revenue recognition and profit margins, which differ from those of industries that operate in competitive markets. These factors require utilities to follow accounting practices that reflect long term capital expenditures, regulatory constraints, and environmental goals, distinguishing them from other sectors in financial reporting. Finally, we dropped 351 firm-year observations with missing values for total assets and 142 firm-year observations with missing values for debt and equity. Our final sample consists of 6,038 firm-year observations. ^[7].

Our dependent variables are segment disclosures. The core objective of IFRS 8 is to enhance the disclosure of information that helps financial statement users understand the category and consequences of an entity's business activities and economic environment. IFRS 8, paragraph 21, states that the information recommended in paragraphs 22–30 should be disclosed to achieve this core principle and objectives. IFRS 8 also notes that disclosure information in paragraphs 32–34 should be reported when excluded by the reportable segment. We construct a disclosure index including 32 items (see  Appendix 1) selected from the information listed in paragraphs 22–30 ^[8] and 32–34 to test whether the core principle of IFRS 8 has been achieved. We download all annual reports of Australian listed firms over the 2016–2019 period and manually check whether firms have correctly disclosed information in the disclosure index. We allocate 1 if the required/recommended information has been disclosed, and 0 otherwise. ^[9] The combined disclosure score described below captures the segment disclosure level in our study.

The Combined Disclosure Score comprises four mutually exclusive categories of segment disclosure information: (1) Mandatory-Hard Disclosure Score (8 items), (2) Mandatory-Soft Disclosure Score (2 items), (3) Voluntary-Hard Disclosure Score (16 items), and (4) Voluntary-Soft Disclosure Score (6 items) (see  Appendix B). These four categories are classified based on whether the information is mandatory, voluntary, hard, or soft. All segment disclosure items are equally weighted and scored as binary values for each item, so the firm's highest possible combined Disclosure Score is 32. Figure 1 provides a graphical representation of the segment disclosure categories in our study.

Following prior studies by Porcano (1986) and Hope et al. (2013), we use two measures to capture ETRs (i.e., tax avoidance): (1) accounting ETR adjusted for deferred tax expense (DTE_ETR); and (2) accounting ETR (ACC_ETR). Specifically, DTE_ETR is measured as income tax expense less deferred tax expense scaled by pre-tax accounting profit. It is truncated to the 0–1 range to remove potential outliers. A lower DTE_ETR reflects a higher level of tax avoidance (Hope et al., 2013). It captures tax ETRs that relate to permanent and temporary book-tax differences. We use DTE_ETR as the measure of our main tests since the potential alternative of using income tax expense has the disadvantage of consisting of current and deferred tax expenses that can result from large deferred tax adjustments (Hanlon, 2005).^10- 14

Our second tax avoidance proxy, ACC_ETR, is a simple measure of ETR, and the results for this measure are shown in additional analyses. It is calculated by income tax expense scaled by pre-tax accounting profit (Chen et al., 2010). It is truncated to the 0–1 range to remove potential outliers (e.g. tax refunds and additional tax payments due to tax audits). A lower ACC_ETR denotes the potential for a higher level of tax avoidance. The disadvantage of this measure is that it reflects ETRs resulting from permanent book-tax differences. This occurs since the numerator, income tax expense, can be influenced by income tax accruals that does not account for the present-value of deferred taxes (Chen et al., 2010; Balakrishnan et al., 2019).

Our regression model includes the following control variables identified in prior segment disclosure studies. First, we include firm size (SIZE), measured by the natural logarithm of total assets, because larger firms often disclose more information because of higher market visibility (Chithambo et al., 2022). We include leverage (LEV), measured by total liabilities to total assets, to capture the agency costs of debt of firms. Agency costs are higher for firms with more debt, and these firms are expected to increase disclosure to reduce agency costs (Kent and Zunker, 2013; Sierra-García et al., 2019). The literature also suggests that increased and more credible disclosures lower the cost of external financing and improve a firm's capacity to engage in potentially profitable projects (Khurana et al., 2006; Bertomeu and Marinovic, 2016; Hollindale et al., 2022). We also include return on assets (ROA), measured by the ratio of net profit after tax to total assets, to control firms' profitability. ROA is a measure of profitability and is shown to be related to firms' disclosure levels. Increased profitability may encourage managers to disclose more information to stakeholders to increase investor confidence and, in turn, increase managers' compensation. Another explanation is that more profitable firms disclose more information in their annual report to signal their superior performance to the market (Ahmed and Courtis, 1999). GROWTH is also included and measured as the annual growth rate of operating revenue and is used to control for firm growth opportunities.

We also use the Herfindahl-Herschel Index (HHI), property, plant, and equipment (PPE), and research and development expenditure (R&D) to capture proprietary costs of disclosing more segment information. The Herfindahl Index is calculated by summing the squares of the revenue of all firms in each of the Global Industry Classification (GICS) sub-industry sectors scaled by the total revenue of all firms in that industry (Hollindale et al., 2022). A higher Herfindahl index score implies lower competition in the sub-industry sector. Property, plant, and equipment (PPE) is measured by property, plant, and equipment scaled by total assets. Research and development (R&D) is calculated by the ratio of research and development expenditure to total assets (Ellis et al., 2012). Higher PPE and R&D indicate higher barriers to entry and lower potential proprietary costs. Prior research also shows that managers are incentivized to provide higher disclosure levels before external financing calls (Lang and Lundholm, 2000; Ettredge et al., 2006; Wang et al., 2011). So, we include new debt (DEBT) and new equity (EQUITY) as measures of a firm's financing calls. DEBT is measured by the natural logarithm of proceeds from borrowing in the following year. EQUITY is measured by the natural logarithm of proceeds of the issue of equity in the following year. ^[10].

Furthermore, prior studies find that firms with stronger corporate governance structures are more likely to increase their segment disclosures (Leung and Horwitz, 2004; Kobbi-Fakhfakh et al., 2020). Prior studies also show that various corporate governance attributes may function as substitutes or complements to each other (Aguilera et al., 2008; Kent and Zunker, 2013; Elms and Kent, 2024). Corporate governance quality is measured by a combined corporate governance score, which is a better estimate than a single indicator (García Lara et al., 2007). Following prior Australian studies by O'Sullivan et al. (2008) and Kent and Zunker (2013), we construct a corporate governance score (CORGOV) to capture eight specific governance characteristics, including board size, board independence and CEO duality, audit, remuneration, nomination committees, women's presence on the board, and whether firms use a Big 4 auditor (see  Appendix 4) ^[11].

All continuous variables are converted to dummy variables for CORGOV. In line with prior studies (O'Sullivan et al., 2008; Kent and Zunker, 2013), we chose the average size of the board of directors (five directors) as the distinguishing point for board size. The size of the board is equal to 1 if the number of directors on the board is greater than five, and 0 otherwise. Board independence is equal to 1 if the ratio of non-executive directors on the board is larger than 0.5, and 0 otherwise. The duality of chair and CEO is equal to 1 if the two roles are separate, and 0 otherwise. Moreover, the audit, remuneration, and nomination committees are specified by dummy variables equal to 1 if a firm has the respective committee, and 0 otherwise. Gender diversity is a dummy variable equal to 1 if at least one female is on the board, and 0 otherwise. Finally, firms are allocated 1 if they use a Big 4 auditor, and 0 otherwise.

We estimate the following regression model to test our hypothesis:

where DISC_SCORE = disclosure score (Combined Disclosure Score and four individual categories), DTE_ETR = ETR, Industry fixed effects = industry dummy variables based on the two-digit GICS codes, Year fixed effects = year dummy variables, and e = error term. All the variables are defined in  Appendix 3. Finally, we winsorize all continuous variables at the 1st and 99th percentiles to reduce the potential impact of outliers on our empirical results.

Table 2 reports the descriptive statistics for our main variables. DTE_ETR has a mean (median) of 0.088 (0.000), which is low, given that Australia's statutory corporate tax rate is 30%. The Combined Disclosure Score has a mean (median) of 6.803 (7.000). Thus, segment disclosure levels appear low in Australia, as our study's maximum score is 32. However, the possible score of 32 includes voluntary items, so this is not seen as a lack of compliance with the standard by firms. The four categories of segment disclosure information range from a mean (median) of 0.132 (0.000) for the Voluntary-Soft Disclosure Score to a mean (median) of 3.007 (2.000) for the Voluntary-Hard Disclosure Score. Large variations exist in the mean and median values of the four separate disclosure categories. ^[12] Finally, the descriptive statistics for the control variables (SIZE, LEV, ROA, GROWTH, HHI, PPE, R&D, DEBT, EQUITY, and CORGOV) generally align with those of prior studies (Lanis and Richardson, 2011; Taylor and Richardson, 2014) and are also shown in Table 2.

The Pearson correlation results are presented in Table 3. All disclosure Scores correlate positively and significantly with DTE_ETR (p < 0.01). The highest correlation for the explanatory variables is between SIZE and CORGOV (r = 0.681; p < 0.01), which is moderate (Hair et al., 2006). ^[13] We compute variance inflation factors (VIFs) when estimating our regression models to test for signs of multicollinearity among the explanatory variables. No VIFs exceed the threshold of five, so multicollinearity is unlikely to threaten the reliability of our results (Hair et al., 2006).

Table 4 reports the OLS regression results for the association between ETRs and segment disclosure levels. The t-statistics are based on clustered robust standard errors at the firm level (Petersen, 2009). Significance tests are one-tailed for the directional hypothesis and two-tailed otherwise. Finally, industry and year fixed effect coefficients are not tabulated for brevity.

Table 4 shows that the coefficient on DTE_ETR is positively and significantly associated with the Combined Score (p < 0.01). This finding also suggests that lower levels of ETRs are associated with lower levels of disclosure, supporting H1. Our finding is economically important, indicated by a one standard deviation decrease in ETRs, which is associated with a 27.42% reduction in the Combined Disclosure Score. Also, some of the control variable coefficients (SIZE, LEV, and GROWTH) are significantly correlated with DTE_ETR (p < 0.01) and are consistent with the results of prior studies. Finally, we find that DTE_ETR is positively and significantly associated with the eight individual disclosure categories. In particular, the DTE_ETR coefficients are positively and significantly associated with the Mandatory, Voluntary, Hard, Soft, Mandatory-Hard, Mandatory-Soft, Voluntary-Hard, and Voluntary-Soft Disclosure scores (p < 0.10 or lower). These results suggest that firms that have lower ETRs provide less segment information for all levels and categories of disclosure, consistent with H1.

It is possible that firms may not have randomly selected the level of ETRs. Prior research suggests that size, leverage, and corporate governance influence a firm's tax rates (Spooner, 1986; Chithambo et al., 2022). Accordingly, we perform a robustness check using the two-stage least squares (2SLS) regression analysis approach to ensure unbiased disclosure coefficients in our analyses (Chen and Liao, 2015).

We use the deferred tax ETR in the previous year (Lag DTE_ETR) as the instrumental variable (IV) in our model. It meets the requirements of being a valid instrument that is highly correlated with the endogenous variable but has a low correlation with the regression error term (Larcker and Rusticus, 2010). The first-stage regression model is as follows:

The results of the 2SLS regression are shown in Table 5. Column (1) of Table 5 presents the first-stage regression results based on Model (2). The results indicate that the IV (Lag DTE_ETR) has high correlations with the endogenous variable (DTE_ETR) (p < 0.01). This finding shows that the correlation between the IV and endogenous variables is satisfied for all models. Columns (2) to (10) present the results of the second-stage regressions. They support the main findings that DTE_ETR has a significant and positive association with all levels and categories of segment disclosure scores (p < 0.05 or lower).

Recall that we follow prior studies (Rego, 2003; Chen et al., 2010; Balakrishnan et al., 2019; Jemiolo and Farnsel, 2023) and use the ACC_ETR as an additional measure of ETRs that reflect ETRs resulting from permanent book-tax differences.

The regression results for ACC_ETR are presented in Table 6. The coefficient on ACC_ETR is positively and significantly associated with the Combined Score (p < 0.05). This suggests that higher (lower) ETRs are associated with higher (lower) disclosure, which aligns with the main findings shown in Table 4. Finally, we also observe that ACC_ETR is positively and significantly associated with the eight separate disclosure categories, i.e., Mandatory, Voluntary, Hard, Soft, Mandatory-Hard, Mandatory-Soft, Voluntary-Hard, and Voluntary-Soft Disclosure scores (p < 0.10 or lower). These results also align with our main regression results in Table 4.

To mitigate any effects arising from winsorizing, we trimmed outliers and re-estimated the main regression model using the reduced sample. We find a positive and significant association between DTE_ETR and Combined Score (p < 0.01). Similarly, we also observe a positive and significant association between DTE_ETR and the eight individual disclosure categories, i.e., Mandatory, Voluntary, Hard, Soft, Mandatory-Hard, Mandatory-Soft, Voluntary-Hard, and Voluntary-Soft Disclosure scores (p < 0.10 or lower). The regression results in Table 7 are again consistent with our main findings in Table 4.

All segment disclosure items are equally weighted and scored as binary values for each item in our disclosure index (see above). However, the association between ETRs and disclosure level and categories may have varied across different items. Hence, we construct a weighted disclosure score. Six items including: (1) profit or loss for each reportable segment, (2) revenue from geographical areas, (3) inter-segment revenues for each reportable segment, (4) income tax expense or income for each reportable segment, (5) an explanation of the measurement differences between the reportable segments' profit or losses and the entity's profit or loss before income tax expense, and (6) any changes from prior periods in the measurement methods used to determine reported segment profit or loss (see  Appendix 5) have been given double the weight of the other items in the index.

We identify the above items because they are more crucial for identifying ETRs. Reporting profit or loss for each reportable segment is a disclosure item directly related to a firm's tax payment. Reporting revenue from geographical areas may reflect a firm's ETR because different countries have different tax rates. It could have been a signal of low ETRs if a firm earned revenue from tax havens with limited information disclosure. Inter-segment transactions are risky, allowing firms to manage their profit or loss and influence ETRs strategically. Finally, any measurement changes used to calculate profit or loss can be helpful information for detecting a firm's tax rates and tax avoidance.

The regression results presented in Table 8 show that DTE_ETR is significant and positively associated with all levels and categories of segment disclosure, after applying the weighted disclosure index (p < 0.10 or lower), consistent with the main results in Table 4.

Finally, data reported by the ATO show that around one-third of large firms pay no tax on their taxable income because of current and past tax losses (Khadem, 2023). To further consider the robustness of our main results, we re-estimate our regression model excluding all firms that incurred a loss from our sample.

The regression results in Table 9 show that the coefficient on DTE_ETR is significantly and positively associated with the combined segment disclosure score (p < 0.01). We further find that DTE_ETR is also positive and significantly associated with the seven of the eight individual disclosure categories of Mandatory, Voluntary, Hard, Soft, Mandatory-Hard, Mandatory-Soft, and Voluntary-Hard scores (p < 0.01), which is consistent with the results reported in Table 4. However, we find no significant association between the Voluntary-Soft disclosure category and DTE_ETR. This finding could reflect the fact that profit-making firms have less motivation to provide voluntary statements or clarify loss-making activities.

This study examines the impact of ETRs on the levels and categories of segment disclosure under IFRS 8 in Australia. We find that lower ETRs are significantly associated with a lower combined segment disclosure score. Additional analysis demonstrates that lower ETRs are also associated with lower mandatory, voluntary, hard, soft, mandatory-hard, mandatory-soft, voluntary-hard, and voluntary-soft disclosure category scores. Finally, our main results are robust to sample selection bias and several other robustness tests.

Our findings have important implications for standard setters because managers use discretion in applying a complex accounting standard (i.e. IFRS 8), requiring judgment to undertake opportunistic reporting behavior. This differs from the faithful representation of underlying economic objectives required under the international conceptual frameworks. In short, this generates concerns over whether international accounting standards should continue to be principles-based rather than rule-based.

We identify limitations to our study. First, the validity of our results relies on our ETR measures. We apply widely used measures from the literature to deal with this limitation. However, precise ETR measures are difficult to obtain (Hanlon and Heitzman, 2010). Second, some firms may have randomly selected the level and categories of their segment disclosures, so our inferences between ETRs and segment disclosure might need revising. We use 2SLS analysis to help ensure that the disclosure coefficients are unbiased, but we cannot rule out the influence of random selection. Third, we test only one hypothesis because of limitations related to unknown variables, which restrict the overall depth of our research. Finally, our results apply to IFRS in one country and may not be generalizable to other IFRS countries and those adopting FASB accounting standards.

Future research can be conducted in other countries that adopt IFRS or FASB accounting standards, as countries have different regulatory backgrounds. Societal attitudes toward low tax avoidance also vary between cultures, which can alter the disclosure of the level and categories of segment information. Also, we use ETRS as a proxy for tax avoidance. The difficulties in identifying ETRs from public accounting records can be reduced using private tax return information from tax administrators if made publicly available (Hanlon and Heitzman, 2010). This should allow a more precise measure of tax avoidance and lead to better inferences involving a manager's choice of the level and category of segment disclosures. Finally, future research can explore additional hypotheses by incorporating measures for currently unobserved variables that permit a more thorough examination of the underlying associations.

The authors are grateful for useful comments from Reza Monem, participants at JCAE Mid-Year Symposium, University of the Sunshine Coast, Sippy Downs Campus, Australia, 2023; Jilin University, Changchun, China 2025.