Navigating the Storm: How Economic Uncertainty Shapes Audit Quality in BRICS Nations Amid CEO Power Dynamics

Abstract

This study investigates the association between economic uncertainty and audit quality in the BRICS nations, examining both input-based (e.g., audit fees, auditor tenure) and output-based (e.g., restatements, total accruals) measures of audit quality. Utilizing a dataset of 83,511 firm-year observations from 1995–2022, it reveals a significant negative impact of economic uncertainty on audit quality. Additionally, the research explores the moderating role of CEO power, employing principal component analysis to merge various indicators of CEO influence. Findings indicate that powerful CEOs can mitigate the adverse effects of economic uncertainty on audit quality, suggesting a U-shaped relationship between CEO power and audit quality. Methodologically robust, employing techniques like two-stage least squares (2SLS) and two-stage system generalized method of moments (system GMM) to address endogeneity, the study offers a comprehensive analysis of audit quality in the context of economic fluctuations and corporate governance, contributing significantly to the understanding of these dynamics in emerging economies, particularly in the diverse and influential BRICS nations. This study’s findings have significant implications for stakeholders and policymakers, providing insights that can inform policy decisions and enhance corporate governance frameworks.

1. Introduction

The dynamic and interrelated nature of the global economic environment requires a detailed comprehension of the forces that influence its condition and dynamics. Economic uncertainty is a crucial factor that significantly impacts many economic activities and corporate strategies (Al-Thaqeb and Algharabali 2019; Irwin et al. 2022; Li et al. 2023a). This paper aims to explore the relationship between economic uncertainty and its effects, with a specific focus on audit quality and corporate governance in the BRICS nations (Brazil, Russia, India, China, and South Africa).

Economic uncertainty refers to the inherent ambiguities and unpredictabilities found in economic situations, encompassing market shifts, policy changes, and geopolitical movements (Dokas et al. 2023). Studies such as those by Baker et al. (2016), Shabir et al. (2021), and Khoo and Cheung (2021) have underscored the catalytic role of uncertainty in driving business fluctuations, influencing market players’ attitudes, and necessitating adjustments in remuneration structures. The condition of uncertainty has extensive consequences, impacting investment choices, financial decision-making, market behavior, innovation, supply chain management, and risk management.

Despite the extensive impact of economic uncertainty, its effect on audit quality has received limited attention from researchers and academia, with findings being controversial. For instance, Zhang et al. (2018) found that both Big 4 and non-Big 4 auditors reduce their fees during times of economic uncertainty, with non-Big 4 auditors showing asymmetric pricing adjustments. On the contrary, Chen et al. (2019) observed a negative correlation between macroeconomic uncertainty and audit fees, particularly in firms using Big 4 and specialist auditors. Yun and Chun (2021) and Lee and Jeong (2023) suggest that economic uncertainty leads to increased audit hours and efforts, suggesting enhanced audit quality and more rigorous financial reporting during such periods. This research, therefore, aims to elucidate the impact of economic uncertainty on both input-based measures (e.g., audit fees, auditor tenure) and output-based measures (e.g., restatements, total accruals) of audit quality.

Furthermore, while previous studies have examined the variations in audit quality features during uncertain periods, none have specifically examined the influence of corporate governance factors, such as CEO power. CEOs play a crucial role in strategic decision-making, resource allocation, and shaping the future direction of their companies, especially during periods of economic uncertainty (Haynes and Hillman 2010; Graham et al. 2015; Faccio et al. 2016). Nevertheless, existing data indicate that CEOs exhibit distinct management styles in different nations, influenced by varying rules, stock market conditions, and investment strategies (Crossland and Hambrick 2011; Nielsen and Nielsen 2011). This study investigates how CEO power moderates the impact of economic uncertainty on audit quality, examining the extent to which powerful CEOs influence decision-making processes related to financial reporting and audits during uncertain times.

Overall, the primary objective of this study is to investigate how economic uncertainty affects audit quality in the BRICS nations. Specifically, this research seeks to (a) analyze the impact of economic uncertainty on both input-based and output-based measures of audit quality, (b) examine the moderating role of CEO power in the relationship between economic uncertainty and audit quality, and (c) provide a comprehensive analysis of audit quality in the context of economic fluctuations and corporate governance in emerging economies. Accordingly, the hypotheses guiding this research are H1—Economic uncertainty has an impact on audit quality and H2—CEO power moderates the impact of economic uncertainty on audit quality.

The main findings of this paper are outlined as follows. Based on an analysis of 83,511 firm-year observations over the period of 1995–2022 in BRICS nations, we have discovered that a rise in economic uncertainty has a substantial negative impact on both input-based measures (e.g., audit-fees-to-total-fees ratio and auditor tenure) and output-based measures (e.g., restatements and total accruals) of audit quality. Furthermore, we broadened our investigation to see whether the influence of CEO power has served as a moderating factor in reducing the negative effects of economic uncertainty on audit quality. To assess CEO power, we employ principal Component Analysis, a data reduction technique, to merge five distinct indicators of CEO power (e.g., CEO duality, CEO-founder, CEO ownership, CEO compensation slice, and CEO tenure). The findings demonstrate that influential CEOs play a vital role in reducing the negative impact of economic uncertainty on the quality of audits. In addition, we conduct several tests to ensure the reliability of our findings. We consider additional measures of audit quality, alternative indicators of economic uncertainty, and CEO power, and address potential endogeneity issues by employing two-stage least squares (2SLS) and two-stage system generalized method of moments (system GMM) estimate techniques. Our results remain consistent throughout these numerous analyses.

This study contributes to the existing literature in five important ways. First, our research is a comprehensive analysis of the impact of economic uncertainty on audit quality in BRICS nations, employing a large dataset of 83,511 firm-year observations from 1995 to 2022. The study finds a significant negative impact on both input-based (audit-fees-to-total-fees ratio, auditor tenure) and output-based measures (restatements, total accruals) of audit quality using the world uncertainty index that Ahir et al. (2022) developed to quantify economic uncertainty. This finding contrasts with previous studies by Yun and Chun (2021) and Lee and Jeong (2023), which suggest increased audit efforts during uncertain periods. This study also shows that higher audit risks lead to higher audit fees and hours, which is similar to what Zhang et al. (2018) found and shows how complicated the relationship is between audit fees, audit hours, and audit quality. Finally, the research underscores the exacerbation of information asymmetry during economic uncertainty, as noted by Tessema (2020) and Lee and Jeong (2023), challenging auditors to gather accurate information, thereby impacting the reliability of audits.

Second, our research provides an important academic contribution by examining the influence of CEO power as a moderating element in the effect of economic uncertainty on audit quality, specifically in BRICS nations. Utilizing a CEO power index that considers factors such as CEO duality, ownership, and tenure, the study supports previous research conducted by Jensen (1993) and Hermalin and Weisbach (1998) about the impact of CEO positions within an organizational framework. The statement recognizes the two-sided aspect of CEO power, as suggested by economic and organizational theories. CEO power can have both positive and negative effects on business performance and efficiency. Li et al. (2017) and Bristy et al. (2022) conducted research to support this idea. This study presents evidence of a U-shaped relationship between CEO power and audit quality. It suggests that CEO power can mitigate the adverse impact of economic uncertainty on audit quality. This finding aligns with the conclusions drawn by Ouyang et al. (2015) and Azizan (2019) regarding the influence of CEOs on financial reporting and auditing procedures. The study utilizes principal component analysis to evaluate CEO power, ensuring a thorough and comprehensive assessment of this intricate concept. Additionally, the study validates its conclusions through many analyses and tests. This study makes a substantial contribution to the comprehension of corporate governance and audit quality in developing economies. It emphasizes the intricate and influential position of CEO power in the midst of economic uncertainty.

Third, utilizing a substantial dataset and employing sophisticated statistical techniques such as 2SLS and system GMM to tackle potential endogeneity problems enhances the methodological robustness of the research. The conclusions of the study are academically credible since they are supported by consistent results from several tests and alternative metrics, which demonstrate their dependability and robustness.

Fourth, the inclusion of BRICS nations in our study offers a vital contribution to the global literature and has significant implications for the global economy. This is due to the unique economic and governance systems of these nations, their representation of emerging markets, and their substantial combined economic influence (Nayyar 2018; Larionova and Shelepov 2022). The BRICS nations provide a diverse perspective on the effects of economic uncertainty and CEO power on audit quality, enriching the predominantly developed economies’ global view. Their diverse regulatory, economic, and cultural contexts further enhance this perspective. Given their significant role in the global financial system, coupled with their large share of the world’s population and GDP, the corporate governance and financial reporting practices in BRICS countries have far-reaching impacts on global trade, investment flows, and economic stability (Vom Hau et al. 2012; Nölke et al. 2015). Additionally, insights gained from these nations can serve as valuable models for other developing economies, establishing benchmarks for improving audit quality and corporate governance standards. Therefore, examining BRICS nations is not only intellectually stimulating but also essential for understanding and navigating the complexities of the global economic landscape.

Fifth, the implications of this research are significant for policymakers, corporate stakeholders, and auditors. By highlighting the negative impact of economic uncertainty on audit quality and the mitigating role of CEO power, this study provides valuable insights that can inform policy decisions aimed at enhancing audit practices and corporate governance frameworks, particularly in emerging economies like the BRICS nations. Policymakers can utilize these findings to develop strategies that bolster audit quality during periods of economic uncertainty, ensuring more reliable financial reporting and greater market stability. Additionally, stakeholders and auditors can better understand the dynamics of CEO influence in mitigating adverse effects on audit quality, thereby enabling more effective oversight and improved corporate governance practices. This comprehensive analysis not only contributes to the academic literature but also offers practical guidance for managing audit quality amid economic fluctuations and varying levels of CEO power.

The structure of this paper is as follows. Section “Literature Review and Hypotheses Development” provides a concise overview of the relevant literature and formulates our hypothesis. Section “Data and Methodology” provides an explanation of the data and the empirical approach. Section “Results and Discussion” presents the estimation results of our baseline model. Section “Robustness Checks” presents a series of robustness checks. Section “Implications” presents the study’s practical and theoretical implications. The final section is concluded.

2. Literature Review and Hypotheses Development

2.1. The Role of BRICS in the International Economic System

BRICS is an acronym for Brazil, Russia, India, China, and South Africa. Jim O’Neill, an economist at Goldman Sachs, first used the term in 2001 as BRIC (excluding South Africa). He believed that by the year 2050, the four BRIC economies would emerge as dominant forces in the world economy. The inclusion of South Africa on the list occurred in 2010. The BRICS countries function as an intergovernmental organization with the objective of promoting economic cooperation among their member nations and enhancing their global economic and political influence. During its annual conference held in Johannesburg in August 2023, BRICS countries disclosed their intentions to pursue expansion, marking the first such endeavor since 2010. On 1 January 2024, the BRICS alliance is set to expand its membership by including six additional countries, namely Saudi Arabia, Argentina, Egypt, Ethiopia, Iran, and the United Arab Emirates. The proposed expansion will serve to solidify the group’s position as a viable alternative to the G7’s dominant global influence. This expansion will result in the BRICS countries collectively accounting for 26% of the global gross domestic product (GDP) and encompassing approximately half of the world’s population. The BRICS, in light of numerous states demonstrating their inclination to join the alliance, are evidently strategizing their stance in preparation for a multipolar global order wherein the dominance of the United States and other Western nations is not prevalent.

The BRICS countries have experienced significant growth in their proportion of the global GDP, which serves as an indicator of their increasing economic influence (Lowe 2016). The GDP of these countries has experienced a notable increase, rising from 7.83% in 1995 to 25.77% in 2022. This trend of growth underscores their substantial role in fostering global economic expansion. The forecast that China and India will ascend to the positions of the first and third largest economies, respectively, by 2050, while Russia and Brazil will also rank among the top six, highlights the changing power dynamics within the global economy, with a notable shift towards these growing countries (Cheng 2015).

BRICS nations, with over 40% of the global population, have a vast workforce and consumer base. The demographic advantage present in this context not only serves to foster economic growth within the home sphere but also generates substantial markets for international trade and investment (Jafrin et al. 2021). The substantial population indicates the possibility of a sizable workforce that may contribute to the international supply chain and stimulate worldwide demand for goods and services (Lowe 2016; Jafrin et al. 2021).

The BRICS nations exert a significant influence on the patterns of international trade. These countries possess considerable importance as both exporters and importers, and they play a crucial role in the transfer of environmental and resource consumption via trade (Lowe 2016; Tian et al. 2020). The heightened economic activity they engage in plays a role in the transformation of conventional economic power hubs and the modification of global trade patterns (Lowe 2016).

The BRICS countries add to the global economy’s diversity through their unique economic structures. The global economy benefits from the stability derived from the diversification of industries and markets, facilitated by the presence of Russia’s energy resources, India’s services sector, and China’s manufacturing base (Goldstein 2013; Li 2019). This diversity effectively distributes risks and opportunities across multiple sectors, contributing to the overall resilience of the global economic system (Goldstein 2013; Li 2019).

The BRICS nations play a pivotal role in global sustainability issues owing to the significant magnitude of their environmental influence (Tian et al. 2020). The methodology employed by the authors in addressing the challenges associated with urbanization and industrialization, as well as their tactics for integrating renewable energy sources and implementing sustainable practices, possess the capacity to establish benchmarks for other emerging countries (Tian et al. 2020).

The BRICS nations are key to global discussions on sustainability due to the scale of their environmental impact (Tian et al. 2020). Their approaches to tackling issues of urbanization and industrialization and their strategies for incorporating renewable energy and sustainable practices have the potential to set precedents for other developing nations (Tian et al. 2020).

2.2. Financial Reporting in BRICS Countries

The application of International Financial Reporting Standards (IFRS) and the legal and regulatory framework have a significant impact on the caliber and comparability of financial reporting among BRICS nations (Chen and Cheng 2007; Ali and Ahmed 2017). The adoption and impact of IFRS vary throughout BRICS nations, indicating that institutional elements, including enforcement procedures and regulatory frameworks, have a key role in shaping the consequences of IFRS adoption (Combs et al. 2013; Gu et al. 2019).

In Brazil, the adoption of IFRS, guided by local laws and governance codes, has resulted in higher-quality financial disclosures and reduced the cost of capital, as studies by Da Silva and Nardi (2017) and de Lima et al. (2018) demonstrate. Despite this, there’s a spectrum of impacts ranging from improved market relevance of financial data to inconsistent effects on forecasting and decision-making, as indicated by Eng et al. (2019) and Castro and Santana (2018). Similarly, in Russia, having to use IFRS has improved the quality of financial reporting, with researchers like Kim (2016) and Bagaeva (2008) noticing that it has made values more relevant and conservative. However, using IFRS along with Russian Accounting Standards creates its own challenges.

In contrast, India’s transition to IFRS has been fraught with obstacles, including training and infrastructure issues. According to research by Uzma (2016) and Krishnan (2018), factors like capital structure and auditor type have mixed effects on companies. China’s IFRS adoption since 2005 has improved disclosure and reduced earnings manipulation, aligning with its neoliberal economic trends (Lam et al. 2013; Elshandidy 2014), although subsequent reforms have shown diverse outcomes, as observed by Barhamzaid (2019). South Africa’s experience since mandating IFRS reflects enhanced earnings quality and governance correlation, notwithstanding concerns about compliance costs and institutional weaknesses. Nevertheless, IFRS has significantly improved financial information quality, particularly in the banking sector, as Ghio and Verona (2015) and Uzma and Nurunnabi (2021) have noted. These global experiences emphasize the transformative but complex influence of IFRS on financial reporting and governance.

2.3. Auditing in BRICS Countries

The audit systems in BRICS countries exhibit a wide range of economic underpinnings and regulatory landscapes, with each country adjusting its system to suit its specific environment while also gradually conforming to global standards. Brazil has experienced a significant economic transformation from a predominantly agrarian society to an industrialized economy. As part of this transition, Brazil has developed a strong accounting system that has been influenced by European and American practices. This system is regulated and supervised by key organizations such as the Commission of Security (CVM, Comissão de Valores Mobiliario) and the Central Bank (BCB, Banco Central do Brazil) (Provasi 2013; Kleinman et al. 2014). Despite some differences in areas like asset re-evaluation and financial leasing, the implementation of Brazilian GAAP demonstrates its convergence with international standards (Provasi 2013). In contrast, India has experienced significant economic growth since 2003, primarily due to the services sector. The Institute of Chartered Accountants of India (ICAI), as a regulatory body, demonstrates a notable predisposition towards international standards through the implementation of 35 accounting standards. However, it is important to note that the ICAI also incorporates distinct modifications in certain areas, such as financial statements and foreign currency treatments, as highlighted by Provasi (2013) and Kleinman et al. (2014). The Indian auditing system places significant emphasis on the International Auditing Practices Committee and the Auditing and Assurance Standards Board. This indicates a progressive and consistent integration of International Standards on Auditing (ISA) norms into the system (Provasi 2013).

Similarly, the audit systems of Russia and China exhibit differing characteristics due to their unique socio-economic transitions, and these systems are also subject to ongoing changes and developments. The transition of Russia from a Soviet-era economic system to a market-oriented one required the establishment of additional regulatory institutions, such as the Institute of Professional Accountants of Russia (IPAR). This was conducted in order to facilitate the alignment of Russian Accounting Standards (RAS) with IFRS, aiming for greater consistency and compatibility. Despite the advancements made in this area, notable disparities persist, including the failure to implement fair value and the presence of obstacles to achieving transparency and maintaining auditing independence (Provasi 2013; Kleinman et al. 2014). China, in the process of shifting from a centrally planned economy to a market-oriented one, has formulated the China Accounting Standards (CAS) and subsequently introduced the Accounting System for Business Enterprises (ASBE). These accounting frameworks have been designed to harmonize various procedures with international standards; however, many discrepancies exist, particularly in relation to asset valuation methods. The Chinese auditing system has undergone notable progress in recent years, namely in the integration of its standards with the International Standards on Auditing (ISA) (Provasi 2013; Kleinman et al. 2014). This development is particularly noteworthy, considering the system’s deep historical roots.

2.4. Audit Quality under Agency Theory

Audit quality under agency theory is a crucial concept as it addresses the mechanisms by which principals (such as shareholders) can ensure that agents (such as managers) act in the principals’ best interests. Agency theory, as proposed by Jensen and Meckling (1979), suggests that there is an inherent conflict of interest between principals and agents due to differing goals and the information asymmetry that exists between them. This conflict leads to agency costs, which include monitoring costs borne by the principal, bonding costs borne by the agent, and residual loss due to divergence of interests. External audits are a key mechanism to reduce these agency costs by providing independent verification of the financial information provided by agents, thereby reducing information asymmetry and opportunistic behavior by agents (Fossung et al. 2022; Rahman et al. 2023).

Furthermore, according to Jensen and Meckling (1979), the separation of ownership and control in a company creates potential conflicts of interest. Managers may pursue personal benefits rather than maximizing shareholder value. The demand for high-quality external audits is thus driven by the need to monitor and control managerial behavior, ensuring that managers act in the best interest of shareholders. Fossung et al. (2022) indicate that higher managerial ownership might reduce the demand for external audit quality as managers’ interests align more closely with those of shareholders.

Agency theory also addresses conflicts between shareholders and creditors (Jensen and Meckling 1979). Shareholders may prefer riskier projects since they benefit from the upside, while creditors prefer safer projects to ensure repayment (Adams 1994). High-quality audits can reduce this conflict by providing reliable financial information, thus reassuring creditors about the company’s financial health. This helps in lowering the cost of debt and improving the firm’s creditworthiness. However, the relationship between indebtedness and demand for audit quality has produced mixed empirical results (Adams 1994; Rahman et al. 2023).

2.5. Economic Uncertainty, Audit Quality, and CEO Power

The existing body of literature provides substantial evidence to support the notion that economic uncertainty plays a crucial role in influencing company dynamics and has a considerable impact on numerous aspects of economic activities and corporate strategies. In their studies, Baker et al. (2016) as well as Shabir et al. (2021) have emphasized the significant role of uncertainty as a key catalyst for business fluctuations and its detrimental impact on economic activity, hence hindering the speed of recovery. Additionally, Khoo and Cheung (2021) emphasizes that the increasing level of uncertainty not only impacts the attitudes of market players but also results in a need for greater remuneration, significantly influencing company plans. Numerous academic studies have extensively examined the varied consequences of uncertainty on several aspects of corporate operations, such as investment decisions (Al-Thaqeb and Algharabali 2019; Jackson and Orr 2019; Chu and Fang 2021; Kong et al. 2022), financial decision making (Lee et al. 2017; Tran 2019), market behavior and competition (Shabir et al. 2021; Desalegn et al. 2023), innovation and R&D (Saleem et al. 2018; Xu 2020; He et al. 2020; Cui et al. 2021; Tajaddini and Gholipour 2021), supply chain management (Charoenwong et al. 2023), consumer behavior and demand (Nguyen et al. 2020; Kuok et al. 2023), risk management and contingency planning (Hammoudeh and McAleer 2015; Chen et al. 2021), financial performance and reporting (Ozili 2021; Bermpei et al. 2022), mergers and acquisitions (Bonaime et al. 2018; Sha et al. 2020; Paudyal et al. 2021), sustainability and environmental impact (Ahmed et al. 2021; Xue et al. 2022), operational efficiency and cost management (Liu and Wang 2022), and insurance and risk transfer (Gupta et al. 2019), among other areas.

Information asymmetry is another consequence of economic uncertainty (Al-Thaqeb and Algharabali 2019). Research has indicated that the presence of political or policy uncertainty, commonly referred to as economic uncertainty, has an impact on both the extent and caliber of company disclosure (Lei and Luo 2023). According to Nagar et al. (2019), the presence of uncertainty leads to an expansion of bid-ask spreads and a reduction in the magnitude of stock market responses to earnings shocks, which suggests the existence of elevated levels of information asymmetry. According to Wellman (2017), political relationships have the potential to mitigate the adverse consequences of uncertainty and information asymmetry. The author contends that it is imperative for companies to allocate resources towards establishing and maintaining these ties in order to mitigate the level of uncertainty. This elucidates the significance and advantages of forging fresh contacts with politicians and lawmakers. In a similar vein, the study conducted by Farooq and Ahmed (2019) demonstrates that economic uncertainty exerts unequal impacts on dividend distributions within the United States. The research findings indicate that companies tend to distribute higher dividends in the context of a presidential election due to the presence of uncertainties surrounding monetary, fiscal, and national policies. The authors propose that the provision of larger dividends may serve as a potential mitigating factor in addressing the dangers entailed by uncertainty. According to Tang and Wan (2022), there exists a positive correlation between economic uncertainty and stock price informativeness, suggesting that economic uncertainty has the potential to impact the level of information asymmetry.

The presence of information asymmetry exerts an impact on the quality of audits. Several studies have indicated a negative relationship between higher levels of information asymmetry and audit quality, while a positive relationship has been observed between lower levels of information asymmetry and audit quality (Tessema 2020; Tessema and Abou-El-Sood 2023; Lee and Jeong 2023). In their study, Hakim and Omri (2010) see a negative correlation between the bid-ask spread, a market-based indicator of information asymmetry, and the utilization of audit industry specialists and large auditing firms. Conversely, they find a positive association between the bid-ask spread and the duration of the audit firm’s tenure. According to Clinch et al. (2012), the selection of auditors who are both from Big-n firms and possess audit industry specialization is linked to a reduction in information asymmetry. According to the findings of Ghanbari et al. (2018), a notable and statistically significant correlation exists between agency expenses and audit quality, indicating a negative association between these two variables. Furthermore, the findings indicate a substantial and positive correlation between the quality of audits and the presence of information asymmetry.

While previous studies on economic uncertainty have predominantly examined it from an economics or finance standpoint, Zhang et al. (2018) established a connection between economic uncertainty and auditors’ initial responses to it. Zhang et al. (2018) investigate the differences in audit pricing between Big 4 and non-Big 4 auditors in eight countries (Canada, France, Germany, India, Italy, Spain, the USA, and the UK). The findings reveal that both types of auditors reduce their fees when economic uncertainty increases, but non-Big 4 auditors adjust their pricing asymmetrically. Chen et al. (2019) utilize the volatility index provided by the Chicago Board Options Exchange as a proxy for measuring macroeconomic uncertainty. By examining a sample of U.S. firms spanning the years 2002 to 2014, their research demonstrates a negative correlation between macroeconomic uncertainty and audit fees. Furthermore, they find that the reduction in fees during periods of heightened uncertainty is particularly significant for firms that engage Big 4 and specialist auditors, as well as for firms with stronger financial positions. Yun and Chun (2021) suggest that there exists a positive correlation between Korean economic uncertainty and audit hours, suggesting that auditors tend to allocate more time to auditing when firms face elevated levels of economic uncertainty due to the increased risk of earnings manipulation. Moreover, the empirical findings indicate that auditors tend to increase their workload in terms of audit hours during fiscal years coinciding with presidential elections, particularly in the presence of elevated levels of economic uncertainty. Wang and Zhu (2022) conducted a study utilizing A-share-listed firms in China. Their findings indicate a positive relationship between economic uncertainty and abnormal audit fees. Moreover, they observe that this relationship is associated with a deterioration in the association between corporate risk-taking and abnormal audit fees. Further research shows that when economic uncertainty is high, auditors demand higher pay for audit risks and increase the amount of extra work they do. This makes the positive and negative abnormal audit fees stand out more. Lee and Jeong (2023) investigate the relationship between audit quality and the propensity for earnings management in the context of increased economic uncertainty in a manner akin to Yun and Chun’s (2021) study. Their investigation is based on a sample of Korean companies listed on the stock exchange. Hence, it is observed that heightened levels of economic uncertainty led to an augmentation in audit quality, as indicated by factors such as audit fees, audit hours, amended disclosure of financial reports, and discretionary accruals. Additionally, it has been observed that in times of heightened economic uncertainty, corporations exhibiting a greater likelihood of engaging in profit manipulation tend to provide financial information of superior quality while simultaneously incurring lower expenses related to audit costs.

Based on the previously mentioned scholarly articles, it is evident that economic uncertainty has a significant impact on the presence of information asymmetry. This is accomplished through various means, including the disruption of investment decisions, the promotion of market volatility, the distortion of risk assessments, the hindrance of information disclosure, and the influence of resource allocation. The presence of a high level of uncertainty in economic policies results in an imbalanced dissemination of information across participants in the market, hence giving rise to discrepancies in comprehending the consequences of policy adaptations (Wellman 2017; Nagar et al. 2019). The absence of clear and predictable information leads to diverse interpretations and evaluations among investors, businesses, and stakeholders, hence promoting information asymmetry (Wellman 2017; Nagar et al. 2019; Tang and Wan 2022; Lei and Luo 2023). However, it’s crucial to remember that information asymmetry can have a significant impact on audit quality. This is due to the fact that information asymmetry hinders the auditor’s capacity to obtain comprehensive and precise information, potentially resulting in reliance on biased or incomplete data provided by the client (Tessema 2020). The presence of information asymmetries has the potential to impede risk assessment, verification procedures, and the identification of any manipulation or misrepresentation of financial data. Consequently, this compromises the overall dependability of the audit (Tessema and Abou-El-Sood 2023; Lee and Jeong 2023). In this context, it is expected that economic uncertainty could potentially compromise the quality of audits by exacerbating information asymmetry in times of uncertainty.

On the contrary, the aforementioned articles suggest that economic uncertainty can have a beneficial impact on audit quality. This is due to the increased scrutiny and inspection that auditors have shown in response to potential dangers resulting from the presence of ambiguity. Regarding this matter, the existence of economic uncertainty frequently results in heightened vulnerabilities to earnings manipulation and financial irregularities within organizations (Nagar et al. 2019; Wang et al. 2023). In light of this awareness, auditors often intensify their audit endeavors during such periods (Yun and Chun 2021). Heninger’s (2001) earlier research has shown that auditors work to improve their examination and methodologies in order to spot any potential earnings manipulation that might occur as a result of ambiguous economic policies. Furthermore, the prevailing economic uncertainties have led auditors to increase their audit fees (Wang and Zhu 2022). The rise in fees can be viewed as a form of compensation that accounts for the increased litigation and potential damage to reputation that may arise in an uncertain economic climate. The provision of financial incentives serves as a driving force for auditors to allocate additional resources and focus on conducting comprehensive examinations of financial statements with the aim of identifying any possible abnormalities (Wang and Zhu 2022). Consequently, it is anticipated that the quality of audits will enhance as auditors endeavor to manage and alleviate the risks linked to economic uncertainty (Lee and Jeong 2023). Alharasis (2023) and Alharasis and Mustafa (2023) found that audit fees in Jordanian listed firms, as a measure of audit quality, increased during the COVID-19 period, reflecting the higher demand for thorough audits during times of increased financial uncertainty and risk. They suggested that the COVID-19 pandemic led to government-mandated measures such as social isolation, lockdowns, shutdowns, and quarantines which significantly affected various aspects of auditing, including fees, staff wages, procedures, and the ability to accurately estimate future assessments. Consequently, these circumstances potentially jeopardized the quality of auditing work. In the same vein, Alharasis et al. (2024) provided empirical evidence that COVID-19 had a substantial impact on audit quality. The positive correlation between the pandemic and audit quality indicated that auditors had to enhance their audit practices to meet the higher standards required during the crisis. This included addressing issues related to fraud risk, financial instability, and regulatory compliance. Moreover, previous studies have demonstrated that auditors actively take into account the possibility of lawsuits and reputation threats when evaluating audit risk. Consequently, they make appropriate modifications to their audit methods (Latham and Linville 1998; Brasel et al. 2016). Taking a proactive approach enhances the likelihood of detecting and resolving probable instances of earnings manipulation (Hillegeist 1999), thereby leading to a decrease in abnormal accruals (Caramanis and Lennox 2008). Hence, during periods of increased earnings management risk, auditors are motivated to intensify their endeavors in order to identify and mitigate the risks associated with potential earnings manipulation. According to Alshabibi et al. (2021), audit quality during economic uncertainty, such as the COVID-19 pandemic, is significantly influenced by audit committee characteristics like size, independence, meeting frequency, gender diversity, and overlapping memberships. These factors enhance oversight, improve financial reporting, and ensure better disclosure of information, reducing information asymmetry and maintaining investor confidence. In this context, it is anticipated that there will be an enhancement in the overall quality of audits, which will have positive implications for both the audited organizations and the stakeholders who depend on the financial statements for making informed decisions.

Based on prior argumentation, it is anticipated that there exists a reciprocal relationship between economic uncertainty and audit quality. In analyzing the effect of economic uncertainty on audit quality under the lens of agency theory, it becomes evident that economic uncertainty can have both positive and negative impacts on audit quality. Under the positive perspective, economic uncertainty often prompts auditors to intensify their scrutiny and diligence due to heightened risks of financial misreporting and manipulation. This increased vigilance can enhance audit quality as auditors are more thorough in their examination to mitigate these risks. Furthermore, higher audit fees during uncertain times, as a form of compensation for increased litigation risks and reputational damage, can incentivize auditors to allocate more resources and efforts towards ensuring comprehensive audits. Under the negative perspective, economic uncertainty can exacerbate information asymmetry between management and auditors. Agency theory posits that managers may exploit this asymmetry to manipulate financial reports to meet targets or mask poor performance, thus compromising audit quality. Thus, while economic uncertainty can lead to more rigorous audits, it also poses significant challenges that can undermine audit quality, reflecting a complex interplay of factors as delineated by agency theory. Consequently, our initial hypothesis is formulated as follows:

H1: 

Economic uncertainty has an impact on audit quality.

The managerial power hypothesis is a concept in economics positing that those executives at the top inside a firm possess considerable influence and authority, leading to imbalances in negotiating power (Gioia and Sims 1983; Van Essen et al. 2015). This theory places significant emphasis on the influential role that executives hold inside a corporation, attributing their impact on decision-making to factors such as their position, experience, and control over crucial resources (Finkelstein 1992; Eisenhardt and Zbaracki 1992). Extensive research has been conducted to investigate the significant influence of top management on business performance, namely through their strategic decision-making and entrepreneurial behavior within organizational contexts (Papadakis et al. 1998; Engelen et al. 2015). The principal individuals responsible for formulating the strategic direction of a corporation are top-level managers, specifically the Chief Executive Officer (CEO) and other executives in the C-suite. These individuals utilize their expertise and leadership abilities to establish objectives and make critical choices that guide the organization’s trajectory (Breene et al. 2007; Samimi et al. 2022). The significance of entrepreneurial behavior, which includes risk-taking and innovation, cannot be overstated in promoting growth and facilitating the ability of these enterprises to adapt to evolving market dynamics (Kuratko et al. 2004; Karneli 2023). The primary responsibility for strategic decision-making lies with the highest echelons of management, known as top management. However, the board of directors plays a crucial role in supporting this process by providing oversight and guidance (Kim et al. 2009). Nevertheless, the performance of a corporation is also dependent on various complicated aspects, including market conditions, competition, internal dynamics, and the impact of top management and entrepreneurial endeavors. The impact of the CEO on a company’s performance in the corporate sector is a subject of academic research. However, existing research has not reached a consensus regarding the relationship between CEO power and firm performance (Bristy et al. 2022; Chiu et al. 2022). The CEO assumes a crucial position within the upper echelons of management, exerting significant influence in steering the organization towards favorable prospects and molding its strategic direction (Chiu et al. 2022). Economic theory views CEO power as a potential issue of agency, while organizational theory posits that it can also contribute to the improvement of business performance and efficiency (Shahab et al. 2020; Shahab et al. 2022). The influence of CEO power is complex, as it has been found to have both positive and negative effects. Economic theory argues that excessive CEO authority may be detrimental to shareholder interests. On the other hand, organizational theory posits that CEO power can enhance decision-making and facilitate adaptation to market dynamics. Studies carried out by Li et al. (2017) and Bristy et al. (2022) support these findings.

Furthermore, the attributes of CEO power and dynamics exhibit a strong correlation with corporate governance, financial reporting, and auditing. A CEO with significant power has the ability to exercise a significant level of control over the processes related to financial reporting and disclosure, according to earlier research by Habib and Hossain (2013), Cormier et al. (2016), Lutfi et al. (2022), and Yami and Poletti-Hughes (2022). The management team may exert pressure or influence on the manipulation of financial information in order to achieve specific targets or expectations (Yami and Poletti-Hughes 2022), potentially compromising audit quality (Cohen et al. 2011; Bishop et al. 2017). The aforementioned influence has the potential to diminish the independence of the audit process. Additionally, the influence that CEOs have can potentially make the board of directors less effective at carrying out their oversight responsibilities (Tuggle et al. 2010; Haynes et al. 2019). The presence of an independent and attentive board is essential in guaranteeing the attainment of high standards in audit quality (Kleinman et al. 2014; Shan 2014). According to Carcello et al. (2011) and Ramanan (2014), the CEO may exert an excessive amount of control or influence, which could compromise the board’s ability to effectively monitor financial reporting and the audit process. In addition, it has been suggested that the power of CEOs may encompass the ability to exert influence over the selection, continuation, or dismissal of external auditors (Turley and Zaman 2004). The presence of a CEO with substantial influence in this process could potentially impact the independence and objectivity of auditors (Goodwin and Yeo 2001; Stewart and Subramaniam 2010). This, in turn, may affect the thoroughness of the audit procedures and the overall quality of the audit opinion.

According to Han et al. (2016), CEOs who possess significant power inside an organization are likely to exert a greater impact on decision-making procedures, particularly in times of uncertainty. This effect may extend to areas such as financial reporting and audit activities. This means that the presence of significant CEO power has the potential to facilitate increased influence over the audit process, thereby affecting the thoroughness and impartiality of audits. Moreover, it is plausible that CEOs with a significant degree of authority may have a greater propensity for engaging in risk-taking behaviors, hence influencing the perception and handling of economic policy uncertainty inside the organization (Hoskisson et al. 2017). This heightened inclination towards risk could potentially influence the examination and methodology employed in audits and financial reporting. Moreover, the power of the CEO frequently establishes the prevailing ethos within an organization (Liu and Nguyen 2020; Garrett et al. 2022). The views of a CEO with significant influence can permeate across an organization, impacting the level of emphasis attributed to audit quality, particularly in times of economic uncertainty. Hence, drawing from prior arguments, it is anticipated that the influence of CEO authority could have a substantial amplifying effect on the repercussions for audit quality in times of economic uncertainty. This assumption may center on the extent to which CEOs possessing significant authority have heightened influence over decision-making procedures pertaining to financial reporting and audits during periods of uncertainty. The research findings may indicate a positive association between CEO power and its influence on the independence of audits, which could have implications for the trustworthiness and precision of financial disclosures.

Agency theory, developed by Jensen and Meckling (1979), highlights the intrinsic conflicts of interest between principals (shareholders) and agents (managers), exacerbated by information asymmetry. In the context of economic uncertainty, these conflicts intensify as unpredictability heightens the risk of opportunistic behaviors by managers. The moderating effect of CEO power on the nexus between economic uncertainty and audit quality reflects this dynamic. Powerful CEOs can leverage their authority to ensure stringent financial reporting and higher audit quality, reducing agency costs and mitigating adverse effects. On the contrary, excessive CEO power might compromise audit independence and diminish board oversight, leading to actions misaligned with shareholders’ interests. It suggests that the impact of CEO power on audit quality during uncertain economic times hinges on governance mechanisms and the CEO’s ethical orientation. Strong corporate governance can harness CEO power positively, enhancing audit quality and mitigating risks, thus safeguarding shareholder interests. As a result, our second hypothesis is formulated as follows.

H2: 

CEO power moderates the impact of economic uncertainty on audit quality.

3. Data and Methodology

3.1. Sample Selection

This study utilizes data from publicly traded companies listed on stock markets in BRICS countries, covering the period from 1995 to 2022. The unavailability of consistent and reliable data for periods before 1995 and after 2022 justifies the selection of the timeframe from 1995 to 2022. Furthermore, we included only Brazil, Russia, India, China, and South Africa as members of the BRICS intergovernmental organization in our sample due to the unavailability of comprehensive data for the other BRICS nations. Therefore, out of the initial sample of 169,941 firm-year observations, we exclude 3326 observations pertaining to financial institutions and insurance, as well as 83,104 observations due to missing data that are used in the research.

Table 1. Sample construction.

Selection Criterion	Observations Lost	Remaining Observations
Firm-year observations during 1995–2022		169,941
Non missing firm-year observations	(83,104)	86,837
Nonfinancial institutions and insurances	(3326)	83,511

provides a full description of the process employed for selecting the sample, resulting in a final sample size of 83,511 firm-year observations that are utilized for subsequent study. In order to minimize the influence of outliers, we applied the winsorization technique to all variables, setting the threshold at the 1% level for both the upper and lower tails of their respective distributions.

3.2. Measurement of Variables

3.2.1. Audit Quality

Audit quality is a concept that is not easily measured or quantifiable. Consequently, scholars in the field have relied on several accounting measures as surrogate indicators for assessing audit quality (Knechel 2016). According to the findings of Rajgopal et al. (2021), the authors have categorized the 14 most commonly used proxies for measuring audit quality into two distinct groups: output-based proxies and input-based proxies. Output-based proxies encompass several indicators such as total accruals, restatements, small profits, and going concern opinions. Input-based and other proxies encompass a range of factors, such as big N auditors, the ratio of audit fees to total fees, client importance, auditor tenure, new client, client location, auditor-client location mismatch, city specialist auditors, and industry specialist auditors. In this context, we employ different indicators of audit quality by examining these two dimensions of audit quality, for which data are available from the EIKON DataStream database. In this study, we employ total accruals and restatements as output-based proxies to measure audit quality. Additionally, we utilize the audit-fees-to-total-fees ratio and auditor tenure as input-based proxies to assess audit quality.

Total accruals (TACC) are calculated as earnings before extraordinary items minus net cash flow from operations excluding extraordinary items and discontinued operations for a given firm year. Consistent with findings from the literature, total accruals are negatively associated with audit quality (Francis et al. 1999; DeFond and Zhang 2014).

Restatements (REST) is an indicator variable that equals 1 if the financial statements for the year are restated. Based on DeFond and Zhang (2014), Rajgopal et al. (2021), and John and Liu (2021), it is expected that auditors are more likely to violate auditing standards if their clients restate the financial statements.

The audit-fees-to-total-fees ratio (AFEE) is audit fees divided by the sum of audit fees and non-audit fees for a given firm year. Given the lack of consensus about the influence of the audit-fees-to-total-fees ratio on audit quality, we adopt the perspective that a high ratio positively impacts audit quality by ensuring auditor independence, focusing expertise on audit tasks, bolstering regulatory and public confidence, and reducing economic dependence on non-audit services (Reynolds et al. 2004; Hoitash et al. 2007; DeFond and Zhang 2014; Rajgopal et al. 2021; John and Liu 2021). This implies that when the ratio increases, the auditor’s level of independence also increases.

Auditor tenure (AUTE) is the number of years the current auditor has been serving the organization for a given firm year. Given the absence of unanimous agreement on the impact of auditor tenure on audit quality, we rely on the research conducted by Ghosh and Moon (2005), Chen et al. (2008), and Manry et al. (2008), which posits that extended auditor-client relationships may enhance auditor independence, thereby improving audit quality.

3.2.2. Economic Uncertainty

In this study, we follow Anser et al. (2021), Wang et al. (2021), Chu et al. (2023), Shabir et al. (2023), and Li et al. (2023b) in order to measure economic uncertainty (ECUN). Therefore, we use the World Uncertainty Index that Ahir et al. (2022) proposed as a stand-in for ECUN. The quantification of uncertainty is achieved by counting the frequencies of the term “uncertainty” within the nation reports published by the Economist Intelligence Unit (EIU). These reports encompass an analysis of the economic and political developments in each country, as well as estimates pertaining to future policies and economic trends (Bilgin et al. 2021; Shabir et al. 2023). ECUN is reported on a quarterly basis. We used the Demir and Danisman (2021) and Shabir et al. (2023) methodology, which involves averaging the four quarterly values for a specific country-year of ECUN created by Ahir et al. (2022), to establish an annual ECUN measure.

3.2.3. CEO Power

Shabir et al. (2023) assert that a consensus over the definition of CEO power has not been reached in earlier scholarly works. Prior research has extensively utilized the four sources of CEO power as outlined by Finkelstein (1992), which encompass structural power, ownership power, expert power, and prestige power. Nevertheless, this particular definition does not readily align with the concept of inherent and unambiguous CEO dominance, as noted by Luo (2015). Thus, the present study adopts the methodology employed by Jiraporn et al. (2012), Luo (2015), Chao et al. (2017), and Shabir et al. (2023) to develop a CEO power index (CEOP). This index incorporates five variables that are associated with CEO power: title, founder, ownership, compensation pay slice, and tenure.

To measure CEO title, we use the variable CEO duality (CEOD). CEOD is an indicator variable that equals 1 if the CEO serves as board chair for a given firm year. Prior research has indicated that when the CEO simultaneously holds the position of board chair, they tend to possess greater power and influence within the organizational structure. The presence of CEO-chair duality has been found to impede the dissemination of information to other members of the board, diminishing the board’s ability to exercise independent supervision over a manager (Jensen 1993).

CEO founder (CEOF) is an indicator variable that equals 1 if the CEO is one of the firm’s founders for a given firm year. Prior research has indicated that CEOs who are also founders of the company tend to possess greater influence in the process of making funding decisions. According to Fischer and Pollock (2004), organizations headed by founder-CEOs may experience lower agency costs due to the founder’s enhanced capacity to steer the organization, resulting in reduced internal conflicts.

CEO ownership (CEOO) is an indicator variable that equals 1 if the percentage of equity ownership held by the CEO is greater than or equal to 10% for a given firm year. Prior research has indicated that a greater level of CEO ownership inside a firm frequently exerts substantial sway over the decision-making processes related to finance. The augmented ownership structure serves to align the CEO’s incentives with the objectives of shareholders, thereby promoting an emphasis on the generation of long-term value (Pathan 2009; Chao et al. 2017).

The CEO compensation slice (CEOC) is the total compensation the CEO received divided by the combined total compensation of the top five executives (including the CEO) for a given firm year. The concept of CEOC has been recognized as a more straightforward method for assessing the level of CEO influence, as supported by previous studies (Bebchuk et al. 2011; Jiraporn et al. 2012; Chintrakarn et al. 2014).

CEO tenure (CEOT) is the total length of the CEO’s tenure for a given firm year. According to existing research, a CEO’s tenure lengthens with an increase in the level of authority they exercise (Zheng 2010; Altunbas et al. 2022).

In accordance with the studies conducted by Chao et al. (2017) and Shabir et al. (2023), the CEOP is developed through the utilization of Principal Component Analysis (PCA) to amalgamate the aforementioned five different variables into a single-dimensional index. An increase in CEOP is associated with an increase in CEO power.

3.2.4. Control Variables

In addition to economic policy uncertainty and CEO power, other factors that affect firms’ audit quality can be grouped by firm-specific and country-specific characteristics. Therefore, in this study, the firm-specific characteristics that we employ are firm size, cash flow from operations, leverage, profitability, firm loss, firm complexity, inherent risk, and quick ratio (Sarhan et al. 2019; Alzeban 2020; El-Dyasty and Elamer 2020). In addition, the country-specific characteristics that we employ are the national governance index, inflation, market capitalization, and national culture values (power distance index and individualism) (Beisland et al. 2015; Bik and Hooghiemstra 2017; Gunn et al. 2019; Sarhan et al. 2019).

A detailed explanation of the variables and their respective sources is presented in Appendix A.

3.3. Econometric Model

To analyze the effect of economic uncertainty on audit quality, we constructed our baseline models, which are estimated as follows:

AFEE_it = α₀ + β₁ECUN_jt + β₂X_it + β₃Ψ_jt + μ_j + ρ_t + ε_it

(1)

AUTE_it = α₀ + β₁ECUN_jt + β₂X_it + β₃Ψ_jt + μ_j + ρ_t + ε_it

(2)

REST_it = α₀ + β₁ECUN_jt + β₂X_it + β₃Ψ_jt + μ_j + ρ_t + ε_it

(3)

TACC_it = α₀ + β₁ECUN_jt + β₂X_it + β₃Ψ_jt + μ_j + ρ_t + ε_it

(4)

where subscripts i, j, and t denote firms, countries, and time, respectively. AFEE_it, AUTE_it, REST_it, and TACC_it are proxies for the audit quality as to the dependent variables. ECUN_jt is our main independent variable. The vector X_it contains firm-level control variables, and Ψ_jt is a vector of country-level variables. α₀ is the intercept, β₁, β₂, and β₃ are the estimated coefficients, μ_j and ρ_t are the country and time-specific effects, and ε_it is the error term. We estimate all the above equations with the fixed-effects model and standard error clustered at the firm level. We also use alternative econometric methodologies and proxies for dependent and independent variables and other sensitivity tests later to confirm the robustness of our results.

Additionally, we also observe the moderating role of CEO power on the nexus between economic uncertainty and audit quality. Therefore, we extend our baseline models (1 to 4) by including the interaction term of our moderator (CEOP_it) with ECUN_jt. Then, the above models are constructed as follows:

AFEE_it = α₀ + β₁ECUN_jt + β₂CEOP_it + β₃ECUN_jt*CEOP_it + β₄X_it + β₅Ψ_jt + μ_j + ρ_t + ε_it

(5)

AUTE_it = α₀ + β₁ECUN_jt + β₂CEOP_it + β₃ECUN_jt*CEOP_it + β₄X_it + β₅Ψ_jt + μ_j + ρ_t + ε_it

(6)

REST_it = α₀ + β₁ECUN_jt + β₂CEOP_it + β₃ECUN_jt*CEOP_it + β₄X_it + β₅Ψ_jt + μ_j + ρ_t + ε_it

(7)

TACC_it = α₀ + β₁ECUN_jt + β₂CEOP_it + β₃ECUN_jt*CEOP_it + β₄X_it + β₅Ψ_jt + μ_j + ρ_t + ε_it

(8)

where CEOP_it is a proxy for CEO power as the moderator variable, while ECUN_jt*CEOP_it is the cross-product of the moderator variable with our primary independent variable of economic uncertainty.

4. Results and Discussion

4.1. Theoretical Framework and Research Questions

This study is grounded in agency theory, which posits that conflicts of interest between principals (shareholders) and agents (CEOs) are exacerbated by information asymmetry. Economic uncertainty further intensifies these conflicts, making it challenging for auditors to gather complete and accurate information, thus potentially affecting audit quality. We examine how economic uncertainty impacts audit quality and whether CEO power moderates this relationship. Therefore, the research questions we explore are: (a) How does economic uncertainty impact audit quality in BRICS countries? and (b) Does CEO power moderate the relationship between economic uncertainty and audit quality in BRICS countries?

4.2. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
AFEE	83,511	0.399	0.589	0.000	2.276
AUTE	83,511	5.761	3.466	1.000	17.346
CAFL	83,511	8.179	0.780	6.201	10.416
CEOP	83,511	−0.004	0.974	−1.720	2.834
ECUN	83,511	0.176	0.133	0.000	0.636
FICO	83,511	0.735	0.572	0.022	3.614
FILO	83,511	0.123	0.329	0.000	1.000
FISI	83,511	9.411	0.694	7.798	11.474
INDI	83,511	42.447	3.384	24.000	46.000
INFL	83,511	3.239	2.984	−0.773	15.534
INRI	83,511	19.208	13.919	0.481	65.157
LEVE	83,511	0.487	0.241	0.056	1.389
MACA	83,511	1.732	0.164	1.244	2.100
NGIN	83,511	−0.450	0.187	−0.803	0.132
PODI	83,511	80.507	5.265	69.000	93.000
QUIC	83,511	1.524	1.965	0.093	13.760
REST	83,511	0.000	0.011	0.000	1.000
ROA	83,511	0.047	0.087	−0.351	0.301
TACC	83,511	9.311	0.712	7.645	11.405

Descriptive statistics of the variables employed in the present study. A detailed explanation of the variables and their respective sources is presented in Appendix A.

reports the descriptive statistics of the variables used in the regression. TACC exhibits a mean value of 9.311 and a standard deviation of 0.712. The observed values span a range from 7.645 to 11.405. This observation suggests that there is a very narrow range of values clustered closely around the average. REST displays a mean value that is very close to zero and a standard deviation of 0.011. The AFEE data have a mean of 0.399 and a standard deviation of 0.589, with values ranging from 0 to 2.276. The extensive range and slightly elevated standard deviation indicate significant diversity in this percentage among various firms. The mean duration of AUTE is 5.761 years, with a standard deviation of 3.466 years. The observed range spans from 1 to 17.346 years, suggesting considerable dispersion in auditor tenure among various firms. The variable ECUN exhibits a mean value of 0.176 and a standard deviation of 0.133. The observed values span a range from 0 to 0.636, indicating a considerable degree of fluctuation in economic uncertainty. CEOP has an average value of −0.004 and a standard deviation of 0.974. The observed range of CEOP, spanning from −1.720 to 2.834, signifies significant variations in the amounts of power held by CEOs among different firms.

4.3. Economic Uncertainty and Audit Quality

Table 3. The effect of economic uncertainty on audit quality.

Variable	(1)	(2)	(3)	(4)
Panel A: Audit-fees-to-total-fees ratio (AFEE)
Constant	0.445 *** (131.666)	0.353 *** (11.711)	−0.155 ** (−3.222)	0.013 (0.249)
ECUN	−0.261 *** (−17.059)	−0.276 *** (−17.585)	−0.227 *** (−12.877)	−0.236 *** (−13.314)
CAFL		−0.056 *** (−12.668)		−0.043 *** (−9.876)
FICO		0.033 *** (8.597)		0.043 *** (10.846)
FILO		0.018 ** (2.297)		0.003 (0.405)
FISI		−0.068 *** (−13.736)		−0.063 *** (−12.569)
INRI		−0.001 *** (−7.449)		−0.001 *** (−8.834)
LEVE		0.246 *** (22.776)		0.240 *** (21.712)
QUIC		0.004 *** (3.486)		0.003 *** (2.895)
ROA		0.167 *** (5.000)		0.209 *** (6.263)
INDI			0.012 *** (13.020)	0.012 *** (13.077)
INFL			−0.000 (−1.140)	−0.001 (−1.552)
MACA			0.379 *** (27.213)	0.357 *** (25.529)
NGIN			−0.240 *** (−11.976)	−0.302 *** (−14.747)
PODI			−0.008 *** (−11.450)	−0.012 *** (−16.024)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.346	0.336	0.360	0.324
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	1.711	1.716	1.720	1.723
Panel B: Auditor tenure (AUTE)
Constant	6.226 *** (314.270)	6.018 *** (33.955)	1.550 *** (5.505)	2.094 *** (6.842)
ECUN	−2.637 *** (−29.392)	−2.643 *** (−28.638)	−1.386 *** (−13.470)	−1.380 *** (−13.269)
CAFL		−0.173 *** (−6.646)		−0.096 *** (−3.706)
FICO		0.066 ** (2.912)		0.054 ** (2.330)
FILO		0.240 *** (5.009)		0.118 ** (2.475)
FISI		−0.234 *** (−8.002)		−0.090 ** (−3.057)
INRI		−0.000 (−0.548)		−0.002 ** (−2.850)
LEVE		0.832 *** (13.072)		0.510 *** (7.866)
QUIC		0.029 *** (4.208)		0.013 * (1.967)
ROA		0.840 *** (4.275)		0.784 *** (4.011)
INDI			0.038 *** (6.854)	0.031 *** (5.598)
INFL			−0.113 *** (−23.741)	−0.106 *** (−22.044)
MACA			1.134 *** (13.960)	1.095 *** (13.363)
NGIN			−1.948 *** (−16.621)	−2.005 *** (−16.743)
PODI			0.004 (1.028)	0.005 (1.215)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.310	0.314	0.327	0.343
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	2.245	2.246	0.247	0.246
Panel C: Restatements (REST)
Constant	−0.000 (−1.028)	−0.003 *** (−6.365)	0.002 * (2.383)	−0.000 (−0.007)
ECUN	0.001 *** (3.903)	0.000 ** (2.843)	0.001 *** (3.730)	0.001 ** (3.070)
CAFL		0.000 (0.990)		0.000 (0.781)
FICO		−0.000 (−0.300)		−0.000 (−0.147)
FILO		−0.000 (−0.602)		−0.000 (−0.724)
FISI		0.000 *** (3.476)		0.000 *** (4.467)
INRI		−0.000 (−1.224)		−0.000 (−0.159)
LEVE		−0.000 (−0.695)		−0.000 * (−2.052)
QUIC		−0.000 (−0.760)		−0.000 (−0.931)
ROA		−0.000 (−0.322)		−0.000 (−0.239)
INDI			0.000 (0.900)	0.000 (0.401)
INFL			0.000 *** (5.069)	0.000 *** (5.638)
MACA			−0.000 * (−1.927)	−0.000 ** (−2.456)
NGIN			0.000 (1.638)	0.001 ** (2.537)
PODI			0.000 ** (2.276)	0.000 *** (4.123)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.317	0.380	0.364	0.314
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	1.834	1.835	1.834	1.836
Panel D: Total accruals (TACC)
Constant	9.156 *** (268.113)	−0.049 *** (−7.774)	5.494 *** (98.319)	−0.058 *** (−5.337)
ECUN	0.875 *** (47.881)	0.021 *** (6.395)	0.577 *** (28.256)	0.014 *** (3.989)
CAFL		0.017 *** (19.198)		0.015 *** (16.419)
FICO		−0.000 (−0.792)		−0.007 *** (−9.126)
FILO		0.001 (1.065)		−0.004 ** (−2.779)
FISI		0.982 *** (937.109)		0.979 *** (919.431)
INRI		0.001 *** (36.787)		0.001 *** (33.682)
LEVE		−0.021 *** (−9.543)		−0.020 *** (−8.903)
QUIC		−0.030 *** (−118.550)		−0.030 *** (−120.693)
ROA		−0.093 *** (−13.359)		−0.098 *** (−14.119)
INDI			−0.026 *** (−24.412)	−0.005 *** (−26.396)
INFL			0.011 *** (12.299)	0.000 ** (3.191)
MACA			−0.111 *** (−6.890)	−0.032 *** (−11.099)
NGIN			0.708 *** (30.447)	0.020 *** (4.835)
PODI			0.062 *** (71.609)	0.004 *** (26.669)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.967	0.970	0.984	0.970
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	0.220	0.216	0.209	0.596

This table shows the outcomes of our baseline regression models (1) to (4) regarding the impact of economic uncertainty on audit quality. The dependent variables under consideration in this study are the audit-fees-to-total-fees ratio (AFEE), auditor tenure (AUTE), restatements (REST), and total accruals (TACC). The independent variable under consideration in this study is economic uncertainty (ECUN). Panel A represents the result of ECUN on AFEE. Panel B represents the result of ECUN on AUTE. Panel C represents the result of ECUN on REST. Panel D represents the result of ECUN on TACC. The remaining control variables can be classified into two distinct categories: firm characteristics (cash flow from operations (CAFL), firm complexity (FICO), firm loss (FILO), firm size (FISI), inherent risk (INRI), leverage (LEVE), quick ratio (QUIC), and profitability (ROA)) and country conditions (individualism (INDI), inflation (INFL), market capitalization (MACA), national governance index (NGIN), and power distance index (PODI)). The fixed-effects estimator is employed for all estimations. The t-statistics are presented in brackets. The symbols ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively. A detailed explanation of the variables and their respective sources is presented in Appendix A.

reports the effects of economic uncertainty on audit quality. Panels A and B show the impact of economic uncertainty on input-based proxies of audit quality (audit-fees-to-total-fees ratio and auditor tenure), while Panels C and D present the results of economic uncertainty on output-based proxies of audit quality (restatements and total accruals). In addition, Column 1 displays the outcome of our baseline equations (1 to 4) without the inclusion of control variables. Still, we control the country and time effects to examine the influence of economic uncertainty on audit quality. In Column 2, we incorporate the firm-specific control variables, whereas in Column 3, we consider the country-specific control variables. Column 4 incorporates a combination of control factors that pertain to both the firm and the specific country. The Durbin-Watson test is used to assess the presence of autocorrelation among the variables under testing. The Durbin-Watson values in all panels are between 1.5 and 2.5, which provide evidence of no auto-correlation among the variables. Furthermore, the Wald test is employed to assess the statistical significance of individual coefficients within a model. The results from all panels, where the p-value is 0, indicate that the observed effect is extremely statistically significant. The Jarque-Bera test is also employed to assess the hypothesis that a given sample is drawn from a normally distributed population. Thus, the Jarque-Bera test conducted on all panels in Table 3 indicates that the data meet the criteria for normality.

The estimated coefficients of ECUN on TACC and REST exhibit a positive and statistically significant relationship, whereas the estimated coefficients of ECUN on AFEE and AUTE demonstrate a negative and statistically significant relationship. The findings indicate that heightened economic uncertainty has a detrimental impact on audit quality. These results show that an increase in economic uncertainty deteriorates audit quality, as indicated by increased total accruals and restatements and decreased audit-fees-to-total-fees ratio and auditor tenure, which is consistent with the previous literature by Zhang et al. (2018), Al-Thaqeb and Algharabali (2019), Tessema (2020), and Lee and Jeong (2023). According to research by Tessema (2020) and Lee and Jeong (2023), economic uncertainty exacerbates information asymmetry, making it challenging for auditors to gather complete and accurate information. This may result in auditors relying on biased or incomplete data provided by the client. This hampers the process of evaluating risks, conducting verification procedures, and detecting financial manipulations, thus undermining the trustworthiness of audits.

These relationships suggest that heightened economic uncertainty increases agency problems, as managers may exploit the complexity to manipulate earnings, and auditors face more challenges in providing high-quality audits. The results align with agency theory, which posits that information asymmetry and conflicting interests between managers and auditors worsen under uncertain conditions, leading to decreased audit quality. From an economic perspective, these findings indicate that increased economic uncertainty can degrade the quality of financial reporting, potentially affecting investor confidence and market stability. From an academic perspective, the results underscore the importance of understanding how macroeconomic factors like uncertainty influence audit practices and outcomes, providing a basis for further research into mitigating these effects. From a policy perspective, regulators may need to enforce stricter auditing standards and provide additional guidance during periods of high economic uncertainty to ensure the reliability of financial reporting and protect stakeholders’ interests.

Additionally, based on the rise in audit fees and audit hours, our findings imply that auditors need higher compensation to account for the increased audit risks during periods of economic uncertainty. This is because the heightened complexity and potential hazards associated with auditing in these time frames can also impact the auditors’ ability to perform comprehensive and unbiased audits. We also can see that auditors reduce their fees in response to increased economic uncertainty, which is consistent with research by Zhang et al. (2018). Given market pressures, it is possible to interpret this decrease in fees as a potential decline in the audit’s level of effort or quality. In summary, our results confirm H1.

Table 3 provides further evidence of a strong relationship between both types of control variables (firm-specific and country-specific) and audit quality, which is consistent with Sarhan et al. (2019), Alzeban (2020), and El-Dyasty and Elamer (2020). The initial set of control variables (firm-specific variables) demonstrates that companies with higher cash flow from operations, greater size, and higher inherent risk exhibit a propensity for higher-quality audits. This is likely attributed to a combination of increased resources, higher stakes, and the necessity for more stringent auditing in intricate and risk-prone settings. On the contrary, our results suggest that firm complexity, firm loss, leverage, quick ratio, and profitability are linked to lower audit quality, indicating that firms that are more complex, financially distressed, highly leveraged, less liquid, and less profitable may encounter difficulties in ensuring high-quality audits. These factors might complicate the audit procedure, perhaps resulting in diminished meticulousness and precision when evaluating a company’s financial well-being and adherence to regulations.

Regarding control variables, the results in Table 3 imply that in environments with a strong focus on individual achievements and in larger companies with higher market values, there might be factors that adversely affect the quality of financial audits, consistent with Bik and Hooghiemstra (2017) and Gunn et al. (2019). This could be due to a combination of increased complexity, potential conflicts of interest, and the influence of personal discretion over standardized practices. On the contrary, inflation, the national governance index, and the power distance index are positively associated with audit quality, consistent with Beisland et al. (2015) and Sarhan et al. (2019), indicating that under certain economic, cultural, and governance conditions, the standards, thoroughness, and effectiveness of audits are likely to be higher, ensuring more reliable and accurate financial reporting.

4.4. The Role of CEO Power

We are interested in investigating if the impact of economic uncertainty on audit quality differs among organizations depending on the level of CEO power and whether it neutralizes the effects of economic uncertainty on audit quality. For this purpose, we conduct different estimations of regressions (Equations (5)–(8)). In our analysis, we incorporate an interaction term between the CEO power index variable and economic uncertainty. The findings of this analysis are presented in

Table 4. The effect of economic uncertainty on audit quality: The role of CEO power.

Variable	(1)	(2)	(3)	(4)
Panel A: Audit-fees-to-total-fees ratio (AFEE)
Constant	0.449 *** (131.850)	0.415 *** (13.596)	−0.028 (−0.586)	0.186 *** (3.515)
ECUN	−0.276 *** (−17.750)	−0.297 *** (−18.644)	−0.273 *** (−15.148)	−0.289 *** (−15.928)
CEOP	0.035 *** (8.922)	0.034 *** (8.574)	0.064 *** (15.201)	0.066 *** (15.723)
ECUN*CEOP	0.081 *** (4.906)	0.043 *** (2.602)	0.022 *** (1.335)	0.000 * (0.058)
CAFL		−0.058 *** (−13.233)		−0.045 *** (−10.356)
FICO		0.025 *** (6.574)		0.038 *** (9.672)
FILO		0.024 *** (3.017)		0.004 (0.583)
FISI		−0.065 *** (−13.146)		−0.062 *** (−12.392)
INRI		−0.001 *** (−7.545)		−0.001 *** (−9.908)
LEVE		0.253 *** (23.299)		0.262 *** (23.618)
QUIC		0.004 *** (3.712)		0.003 *** (3.086)
ROA		0.165 *** (4.950)		0.194 *** (5.831)
INDI			0.018 *** (18.730)	0.019 *** (19.203)
INFL			−0.006 *** (−7.376)	−0.004 *** (−5.113)
MACA			0.425 *** (30.091)	0.408 *** (28.762)
NGIN			−0.249 *** (−12.307)	−0.309 *** (−15.012)
PODI			−0.014 *** (−17.859)	−0.018 *** (−22.437)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.146	0.153	0.224	0.324
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	0.212	0.176	0.225	0.288
Panel B: Auditor tenure (AUTE)
Constant	6.216 *** (311.047)	6.000 *** (33.394)	2.114 *** (7.363)	2.686 *** (8.614)
ECUN	−2.466 *** (−26.999)	−2.522 *** (−26.925)	−1.259 *** (−11.945)	−1.273 *** (−11.954)
CEOP	0.127 *** (5.410)	0.129 *** (5.449)	0.242 *** (9.798)	0.245 *** (9.880)
ECUN*CEOP	0.948 *** (9.779)	0.807 *** (8.300)	0.910 *** (9.206)	0.850 *** (8.591)
CAFL		−0.168 *** (−6.438)		−0.096 *** (−3.729)
FICO		0.051 ** (2.227)		0.057 ** (2.451)
FILO		0.234 *** (4.875)		0.119 ** (2.497)
FISI		−0.227 *** (−7.738)		−0.085 *** (−2.863)
INRI		−0.000 (−0.905)		−0.002 *** (−2.749)
LEVE		0.772 *** (12.062)		0.503 *** (7.733)
QUIC		0.027 *** (3.881)		0.012 * (1.750)
ROA		0.814 *** (4.143)		0.736 *** (3.767)
INDI			0.043 *** (7.557)	0.038 *** (6.598)
INFL			−0.115 *** (−23.297)	−0.110 *** (−22.060)
MACA			1.097 *** (13.285)	1.073 *** (12.900)
NGIN			−2.125 *** (−17.910)	−2.165 *** (−17.896)
PODI			−0.005 (−1.184)	−0.004 (−0.998)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.317	0.349	0.339	0.354
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	0.144	0.145	0.148	0.148
Panel C: Restatements (REST)
Constant	−0.000 (−0.868)	−0.003 *** (−6.283)	0.001 * (1.951)	−0.000 (−0.442)
ECUN	0.001 *** (3.270)	0.000 ** (2.334)	0.001 *** (3.448	0.001 * (2.795)
CEOP	−0.000 *** (−1.552)	−0.000 ** (−2.259)	−0.000 * (−1.953)	−0.000 ** (−2.254)
ECUN*CEOP	−0.001 ** (−2.871)	−0.001 *** (−3.224)	−0.000 * (−1.693)	−0.000 * (−1.874)
CAFL		0.000 (0.923)		0.000 (0.789)
FICO		−0.000 (−0.089)		−0.000 (−0.113)
FILO		−0.000 (−0.634)		−0.000 (−0.718)
FISI		0.000 *** (3.579)		0.000 *** (4.510)
INRI		0.000 (1.089)		0.000 (0.187)
LEVE		−0.000 (−1.033)		−0.000 ** (−2.054)
QUIC		−0.000 (−0.879)		−0.000 (−0.977)
ROA		−0.000 (−0.270)		−0.000 (−0.183)
INDI			−0.000 (−0.629)	−0.000 (−1.057)
INFL			0.000 *** (5.025)	0.000 *** (5.651)
MACA			−0.001 * (−1.849)	−0.001 ** (−2.383)
NGIN			0.001 (1.342)	0.001 ** (2.218)
PODI			0.000 * (1.693)	0.000 *** (3.432)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.152	0.191	0.166	0.147
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	1.855	1.835	1.834	1.836
Panel D: Total accruals (TACC)
Constant	9.180 *** (277.588)	−0.025 *** (−3.982)	5.628 *** (98.985)	−0.058 *** (−5.231)
ECUN	0.770 *** (41.812)	0.007 ** (2.393)	0.534 *** (0.020)	0.009 ** (2.371)
CEOP	−0.150 *** (−31.596)	−0.007 *** (8.259)	−0.067 *** (−13.654)	−0.000 *** (0.396)
ECUN*CEOP	−0.222 *** (−11.372)	−0.020 *** (5.838)	−0.038 *** (−1.969)	−0.017 *** (−4.853)
CAFL		0.016 *** (17.987)		0.015 *** (16.286)
FICO		−0.004 *** (−4.958)		−0.007 *** (−9.448)
FILO		−0.000 (−0.363)		−0.004 ** (−2.826)
FISI		0.981 *** (938.312)		0.979 *** (919.585)
INRI		0.001 *** (37.514)		0.001 *** (33.953)
LEVE		−0.027 *** (−11.833)		−0.022 *** (−9.560)
QUIC		−0.030 *** (−119.709)		−0.030 *** (−120.859)
ROA		−0.094 *** (−13.480)		−0.098 *** (−14.108)
INDI			−0.020 *** (−18.377)	−0.005 *** (−24.061)
INFL			0.016 *** (17.145)	0.000 (1.334)
MACA			−0.155 *** (−9.488)	−0.028 *** (−9.608)
NGIN			0.696 *** (29.632)	0.023 *** (5.413)
PODI			0.056 *** (62.237)	0.004 *** (24.444)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.848	0.970	0.902	0.970
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	0.222	0.188	0.212	0.196

This table shows the outcomes of our regression models (5) to (8) regarding the moderating role of CEO power in the relationship between economic uncertainty and audit quality. The dependent variables under consideration in this study are the audit-fees-to-total-fees ratio (AFEE), auditor tenure (AUTE), restatements (REST), and total accruals (TACC). The independent variable under consideration in this study is economic uncertainty (ECUN). The moderating variable under consideration in this study is CEO power (CEOP). Panel A represents the result of CEOP on the ECUN-AFEE nexus. Panel B represents the result of CEOP on the ECUN-AUTE nexus. Panel C represents the result of CEOP on the ECUN-REST nexus. Panel D represents the result of CEOP on the ECUN-TACC nexus. The remaining control variables can be classified into two distinct categories: firm characteristics (cash flow from operations (CAFL), firm complexity (FICO), firm loss (FILO), firm size (FISI), inherent risk (INRI), leverage (LEVE), quick ratio (QUIC), and profitability (ROA)) and country conditions (individualism (INDI), inflation (INFL), market capitalization (MACA), national governance index (NGIN), and power distance index (PODI)). The fixed-effects estimator is employed for all estimations. The t-statistics are presented in brackets. The symbols ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively. A detailed explanation of the variables and their respective sources is presented in Appendix A.

. The coefficients of the interaction terms in these estimates demonstrate the conditional impact of CEO power on audit quality. In particular, Panels A and B demonstrate how CEO power affects the relationship between economic uncertainty and input-based measures of audit quality (such as the ratio of audit fees to total fees and auditor tenure). On the other hand, Panels C and D provide findings regarding how CEO power affects the relationship between economic uncertainty and output-based measures of audit quality (such as restatements and total accruals). Furthermore, Column 1 exhibits the results of our Equations (5)–(8) without the incorporation of control variables. However, we manipulate the variables of country and time in order to analyze the CEO’s power as a moderator in the relationship between economic uncertainty and audit quality. In Column 2, we include the firm-specific control variables, whereas in Column 3, we take into account the country-specific control variables. Column 4 encompasses a blend of control variables that relate to both the firm and the specific country. The Durbin-Watson test indicates the absence of any autocorrelation among the variables. The Wald test confirms that the observed effect is highly statistically significant. The Jarque-Bera test confirms that the data satisfy the conditions for normality.

In order to assess the CEO’s ability to exercise decision-making power in a more complete manner, we have created a CEO power index (CEOP). This index is constructed using Principal Component Analysis, which takes into account five variables linked to CEO power: CEO duality, CEO-founder status, CEO ownership, CEO compensation slice, and CEO tenure. The results presented in Panels A and B of Table 4 indicate that the coefficients of the CEO power index and its interaction terms with economic uncertainty are significantly positive. Conversely, the results are presented in Panels C and D of Table 4 indicate that the coefficients of the CEO power index and its interaction terms with economic uncertainty are significantly negative. This suggests that power enables the CEO to effectively implement strategies, leading to increased audit quality.

Regarding the impact of CEO power on audit quality, Panels A and B in Table 4 indicate a positive correlation between CEOP and AFEE as well as AUTE. In contrast, Panels C and D of Table 4 indicate a negative correlation between CEOP and REST as well as TACC, implying that while strong CEOs can reduce certain audit issues, they might manipulate financial information, affecting audit integrity. These findings are explained under the lens of agency theory, which examines conflicts between principals (shareholders) and agents (CEOs). From an economic perspective, the findings imply that powerful CEOs might increase audit costs (due to higher AFEE and AUTE) but also reduce financial misstatements (lower REST), which can affect company valuation and investor trust. From an academic perspective, this research expands the understanding of how CEO power influences audit quality, reinforcing theories by Ouyang et al. (2015) and Azizan (2019) about CEO dominance in financial reporting. From a policy perspective, the results suggest a need for regulations to limit CEO power over financial reporting and audits to maintain audit independence and integrity, ensuring accurate and reliable financial disclosures. Our findings concur with those of Ouyang et al. (2015) and Azizan (2019), which show that CEOs with significant power are more likely to have a substantial impact on financial reporting and audit processes. This influence can potentially impair the comprehensiveness and integrity of audits. This impact encompasses the organization’s guiding beliefs and places particular emphasis on audit quality. Furthermore, our research corroborates the conclusions reached by Bhandari et al. (2018) and Li (2019), indicating that a strong CEO has the ability to exercise significant power over financial reporting and disclosure processes, potentially impacting audit quality. This influence can have an impact on the management team, influencing the manipulation of financial information and compromising the independence of audits. Explained through the lens of agency theory, these findings highlight the conflicts between principals (shareholders) and agents (CEOs), illustrating how powerful CEOs can leverage their position to influence audit outcomes. Agency theory mechanisms suggest that while powerful CEOs may increase audit costs and demand more comprehensive audits, their ability to manipulate financial information underscores the need for stringent regulatory frameworks. In summary, our results confirm H2.

Regarding the impact of the interaction term ECUN*CEOP on the correlation between economic uncertainty and audit quality, Panels A and B of Table 4 indicate a positive relationship between ECUN*CEOP and AFEE—AUTE, while Panels C and D of Table 4 suggest a negative relationship between ECUN*CEOP and REST—TACC. This indicates that the power of the CEO has a major impact on the relationship between economic uncertainty and both input-based and output-based indicators of audit quality. In other terms, our findings indicate that CEO power intensifies the impact on audit quality at times of economic uncertainty, implying a positive correlation between CEO power and the meticulousness and independence of audits, which in turn influences the reliability and accuracy of financial disclosures. From the perspective of agency theory, our findings suggest that CEO power intensifies the impact on audit quality during times of economic uncertainty, implying a positive correlation between CEO power and the rigor and independence of audits, which in turn enhances the reliability and accuracy of financial disclosures. From an economic perspective, these findings underscore the importance of strong corporate governance structures to ensure high audit quality, particularly in uncertain economic periods, thereby boosting investor confidence and market stability. From a policy perspective, regulators might consider implementing stricter controls on CEO power to ensure unbiased and independent audits, thereby improving the transparency and accuracy of financial disclosures during economic uncertainty. In summary, our results confirm H2.

5. Robustness Checks

5.1. Alternative Audit Quality Measurement

We use going concern opinion (GOCO) as an alternative proxy for an input-based measure of audit quality. GOCO is an indicator variable that equals 1 if the auditor issues a going concern opinion for a given firm year. According to DeFond and Zhang (2014) and Rajgopal et al. (2021), going concern opinions signal low audit quality. In addition, we use audit industry specialists (AISP) as an alternative proxy for output-based measures of audit quality. AISP is an indicator variable that equals 1 if the auditor satisfies one of the two following definitions (Reichelt and Wang 2010; Rajgopal et al. 2021) for a given firm year: (1) an auditor is a city industry specialist if it has the largest annual market share in an industry, based on the two-digit SIC code, and if its annual market share is at least 10 percentage points greater than its closest competitor in a city audit market; or (2) if it has an annual market share greater than 50% in an industry, based on the two-digit SIC code in the city audit market. According to Rajgopal et al. (2021), firms with audit industry specialists signal high audit quality. The findings are presented in

Table 5. Alternative measure of audit quality.

Variable	Dependent Variable: Going Concern Opinion (GOCO)	Dependent Variable: Audit Industry Specialist (AISP)
Constant	−0.095 *** (−12.295)	−0.019 (−0.531)
ECUN	0.024 *** (9.487)	−0.214 *** (−17.499)
CAFL	0.000 (0.584)	−0.001 (−0.427)
FICO	−0.003 *** (−6.373)	−0.005 ** (−2.043)
FILO	0.013 *** (11.495)	−0.016 ** (−2.943)
FISI	−0.005 *** (−7.248)	0.018 *** (5.377)
INRI	0.000 *** (2.921)	−0.000 *** (−3.711)
LEVE	0.022 *** (13.878)	−0.032 *** (−4.238)
QUIC	0.000 ** (2.477)	0.000 (0.806)
ROA	−0.031 *** (−6.350)	−0.224 *** (−9.775)
INDI	−0.001 *** (−7.803)	−0.012 *** (−18.662)
INFL	−0.000 ** (−2.937)	−0.020 *** (−36.108)
MACA	−0.017 *** (−8.250)	0.127 *** (13.186)
NGIN	0.012 *** (4.126)	−0.297 *** (−21.129)
PODI	0.002 *** (23.019)	0.009 *** (17.025)
Obs.	83,511	83,511
Country FE	YES	YES
Year FE	YES	YES
Adjusted R-squared	0.271	0.494
Wald test (p-value)	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000
Durbin-Watson test	0.247	0.104

This table shows the outcomes of our baseline regression models using alternative proxies of audit quality as dependent variables as robustness. The alternative dependent variables under consideration in this study are going concern opinion (GOCO) and audit industry specialist (AISP). The independent variable under consideration in this study is economic uncertainty (ECUN). The remaining control variables can be classified into two distinct categories: firm characteristics (cash flow from operations (CAFL), firm complexity (FICO), firm loss (FILO), firm size (FISI), inherent risk (INRI), leverage (LEVE), quick ratio (QUIC), and profitability (ROA)) and country conditions (individualism (INDI), inflation (INFL), market capitalization (MACA), national governance index (NGIN), and power distance index (PODI)). The fixed-effects estimator is employed for all estimations. The t-statistics are presented in brackets. The symbols ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively. A detailed explanation of the variables and their respective sources is presented in Appendix A.

. The coefficients of ECUN exhibit a notable positive correlation with GOCO and a notable negative correlation with AISP, which aligns with the results shown in Table 3. These data suggest that an increase in economic uncertainty leads to a decrease in audit quality.

5.2. Alternative Economic Uncertainty Measurement

We adopt two additional approaches for ECUN that Demir and Danisman (2021) and Shabir et al. (2023) suggested in order to strengthen and improve the comparability of our results. These methods expand on the initial ECUN measure that Ahir et al. (2022) created. AlterECUN is measured as the weighted average of the quarterly country-specific ECUN with weights 1 and 2 for the first and last 6 months of a given country-year. AlterECUN1 is measured as the weighted average of the quarterly country-specific ECUN with weights 1 and 4 for each subsequent quarter of a given country-year. The estimated results based on these economic uncertainty metrics are presented in

Table 6. Alternative measure of economic uncertainty.

Variable	Dependent Variable: Audit-Fees-to-Total-Fees Ratio (AFEE)		Dependent Variable: Auditor Tenure (AUTE)		Dependent Variable: Restatements (REST)		Dependent Variable: Total Accruals (TACC)
Constant	0.024 (0.476)	0.053 (1.035)	2.187 *** (7.172)	2.329 *** (7.698)	−0.000 (−0.113)	−0.000 (−0.126)	−0.059 *** (−5.452)	−0.059 *** (−5.528)
AlterECUN	−0.218 *** (−13.292)		−1.241 *** (−12.888)		0.001 *** (2.822)		0.013 *** (3.861)
AlterECUN1		−0.201 *** (−13.102)		−1.176 *** (−13.080)		0.001 *** (3.362)		0.014 *** (4.506)
CAFL	−0.043 *** (−9.822)	−0.043 *** (−9.820)	−0.094 *** (−3.654)	−0.094 *** (−3.650)	0.000 (0.770)	0.000 (0.767)	0.015 *** (16.403)	0.015 *** (16.400)
FICO	0.043 *** (10.839)	0.042 *** (10.772)	0.054 * (2.340)	0.055 ** (2.404)	−0.000 (−0.144)	−0.000 (−0.131)	−0.007 *** (−9.123)	−0.007 *** (−9.106)
FILO	0.003 (0.469)	0.004 (0.490)	0.122 ** (2.557)	0.122 ** (2.558)	−0.000 (−0.697)	−0.000 (−0.722)	−0.004 *** (−2.754)	−0.004 *** (−2.784)
FISI	−0.063 *** (−12.494)	−0.063 *** (−12.405)	−0.087 *** (−2.954)	−0.085 *** (−2.888)	0.000 *** (4.503)	0.000 *** (4.489)	0.979 *** (919.843)	0.979 *** (920.224)
INRI	−0.001 *** (−8.857)	−0.001 *** (−8.801)	−0.002 *** (−2.835)	−0.002 *** (−2.882)	0.000 (0.158)	0.000 (0.160)	0.001 *** (33.676)	0.001 *** (33.680)
LEVE	0.240 *** (21.724)	0.239 *** (21.611)	0.511 *** (7.878)	0.503 *** (7.767)	−0.000 ** (−2.049)	−0.000 ** (−2.078)	−0.020 *** (−8.899)	−0.020 *** (−8.938)
QUIC	0.003 ** (2.919)	0.003 *** (2.910)	0.014 * (2.014)	0.014 ** (1.980)	−0.000 (−0.910)	−0.000 (−0.950)	−0.030 *** (−120.690)	−0.030 *** (−120.730)
ROA	0.210 *** (6.290)	0.210 *** (6.310)	0.788 *** (4.031)	0.793 *** (4.059)	−0.000 (−0.241)	−0.000 (−0.257)	−0.098 *** (−14.113)	−0.098 *** (−14.092)
INDI	0.012 *** (12.781)	0.012 *** (12.650)	0.029 *** (5.285)	0.029 *** (5.164)	−0.000 (−1.480)	−0.000 (−1.498)	−0.005 *** (−26.325)	−0.005 *** (−26.306)
INFL	−0.001 (−1.500)	−0.001 (−1.326)	−0.107 *** (−22.180)	−0.107 *** (−22.328)	0.000 *** (5.700)	0.000 *** (5.644)	0.000 *** (3.233)	0.001 *** (3.164)
MACA	0.362 *** (25.802)	0.359 *** (25.596)	1.120 *** (13.626)	1.103 *** (13.445)	−0.000 *** (−2.498)	−0.001 ** (−2.515)	−0.032 *** (−11.153)	−0.032 *** (−11.184)
NGIN	−0.321 *** (−16.101)	−0.318 *** (−15.821)	−2.134 *** (−18.247)	−2.098 *** (−17.845)	0.0001 ** (2.297)	0.001 ** (2.504)	0.022 *** (5.286)	0.020 *** (4.985)
PODI	−0.013 *** (−16.351)	−0.013 *** (−16.583)	−0.004 (−0.893)	−0.003 (−0.740)	0.000 *** (4.039)	0.000 *** (4.070)	0.004 *** (26.847)	0.004 *** (26.871)
Obs.	83,511	83,511	83,511	83,511	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
Adjusted R-squared	0.248	0.247	0.342	0.342	0.142	0.146	0.970	0.971
Wald test (p-value)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Durbin-Watson test	0.241	0.223	0.147	0.146	1.836	1.836	0.196	0.196

This table shows the outcomes of our baseline regression models using alternative proxies of economic uncertainty as independent variables as robustness. The dependent variables under consideration in this study are the audit-fees-to-total-fees ratio (AFEE), auditor tenure (AUTE), restatements (REST), and total accruals (TACC). The alternative independent variables under consideration in this study are alternative economic uncertainty (AlterECUN) and alternative economic uncertainty (AlterECUN1). The remaining control variables can be classified into two distinct categories: firm characteristics (cash flow from operations (CAFL), firm complexity (FICO), firm loss (FILO), firm size (FISI), inherent risk (INRI), leverage (LEVE), quick ratio (QUIC), and profitability (ROA)) and country conditions (individualism (INDI), inflation (INFL), market capitalization (MACA), national governance index (NGIN), and power distance index (PODI)). The fixed-effects estimator is employed for all estimations. The t-statistics are presented in brackets. The symbols ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively. A detailed explanation of the variables and their respective sources is presented in Appendix A.

. The results align with the initial findings shown in Table 3, indicating that an increase in economic uncertainty has a negative impact on the quality of audits.

5.3. Alternative CEO Power Measurement

The utilization of the standard PCA on dummy variables for the purpose of assessing the CEOP, as outlined in Section 3.2.3, has the potential to introduce estimation bias (Kolenikov and Angeles 2004). To tackle this matter, a discrete PCA is suggested. Incorporating both continuous and indicator variables into a discrete PCA can successfully address the issue of bias in the correlation matrix, according to research by Chao et al. (2017). In this section, we use a strict method to obtain the first part of discrete PCA and then develop an alternative way to measure CEO power, which we call AlterCEOP. An increase in AlterCEOP is associated with an increase in CEO power.

Table 7. Alternative measure of CEO power.

Variable	Dependent Variable: Audit-Fees-to-Total-Fees Ratio (AFEE)	Dependent Variable: Auditor Tenure (AUTE)	Dependent Variable: Restatements (REST)	Dependent Variable: Total Accruals (TACC)
Constant	−0.002 (−0.052)	1.893 *** (6.224)	−0.000 (−0.015)	−0.057 *** (−5.207)
ECUN	−0.236 *** (−13.290)	−1.369 *** (−13.242)	0.001 *** (3.067)	0.014 *** (3.984)
AlterCEOP	0.043 *** (11.947)	0.465 *** (22.024)	−0.000 *** (1.310)	−0.006 ** (−8.694)
ECUN*AlterCEOP	0.055 *** (−3.350)	0.380 *** (3.934)	−0.000 *** (−0.921)	−0.003 *** (4.394)
CAFL	−0.043 *** (−9.777)	−0.090 *** (−3.516)	0.000 (0.793)	0.015 *** (16.347)
FICO	0.045 *** (11.383)	0.030 (1.307)	−0.000 (−0.106)	−0.007 *** (−9.450)
FILO	0.004 (0.492)	0.126 ** (2.651)	−0.000 (−0.733)	−0.004 *** (−2.719)
FISI	−0.067 *** (−13.264)	−0.132 *** (−4.466)	0.000 *** (4.507)	0.978 *** (918.737)
INRI	−0.001 *** (−8.473)	−0.003 *** (−3.611)	0.000 (0.186)	0.001 *** (33.914)
LEVE	0.238 *** (21.519)	0.480 *** (7.450)	−0.000 ** (−2.040)	−0.020 *** (−9.024)
QUIC	0.003 *** (3.078)	0.016 ** (2.347)	−0.000 (−0.941)	−0.030 *** (−120.658)
ROA	0.219 *** (6.572)	0.885 *** (4.554)	−0.000 (−0.196)	−0.097 *** (−13.863)
INDI	0.013 *** (14.290)	0.044 *** (8.002)	−0.000 (−1.506)	−0.005 *** (−27.129)
INFL	−0.001 (−1.159)	−0.110 *** (−22.992)	0.000 *** (5.614)	0.001 *** (3.404)
MACA	0.352 *** (25.172)	1.040 *** (12.766)	−0.001 ** (−2.502)	−0.031 *** (−10.801)
NGIN	−0.309 *** (−15.138)	−2.090 *** (−17.561)	0.001 ** (2.570)	0.021 *** (5.084)
PODI	−0.013 *** (−17.028)	−0.003 (−0.744)	0.000 *** (4.202)	0.004 *** (27.281)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.279	0.465	0.143	0.970
Wald test (p-value)	0.000	0.000	0.000	0.000
Jarque-Bera test (p-value)	0.000	0.000	0.000	0.000
Durbin-Watson test	0.234	0.247	1836	0.196

This table shows the outcomes of our baseline regression models using an alternative proxy of CEO power as a moderator variable for robustness. The dependent variables under consideration in this study are the audit-fees-to-total-fees ratio (AFEE), auditor tenure (AUTE), restatements (REST), and total accruals (TACC). The independent variable under consideration in this study is economic uncertainty (ECUN). The alternative moderate variable under consideration in this study is alternative CEO power (AlterCEOP). The remaining control variables can be classified into two distinct categories: firm characteristics (cash flow from operations (CAFL), firm complexity (FICO), firm loss (FILO), firm size (FISI), inherent risk (INRI), leverage (LEVE), quick ratio (QUIC), and profitability (ROA)) and country conditions (individualism (INDI), inflation (INFL), market capitalization (MACA), national governance index (NGIN), and power distance index (PODI)). The fixed-effects estimator is employed for all estimations. The t-statistics are presented in brackets. The symbols ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively. A detailed explanation of the variables and their respective sources is presented in Appendix A.

presents the findings of the alternative CEO power. The statistical significance of the coefficients on the alternative CEO power index persists. These results once again validate the findings in Table 4, indicating a U-shaped correlation between the CEO power index and audit quality. In addition, in line with the observations presented in Table 4, the outcomes displayed in Table 7 indicate that the power of the CEO limits the detrimental effect of economic uncertainty on the quality of audits.

5.4. Endogeneity

When analyzing the impact of economic uncertainty on audit quality, several endogeneity problems can arise, complicating the interpretation of results. Reverse causality is a key concern, as it’s possible that poor audit quality might contribute to economic uncertainty rather than being a consequence of it. Measurement errors in quantifying economic uncertainty and audit quality can introduce biases, while omitted variables might influence both factors, leading to spurious correlations. Simultaneity problems, in which economic uncertainty and audit quality are both influenced by other factors, can also skew results. Lastly, selection bias in the sample may skew results if these firms inherently have better audit practices. We currently employ alternate economic approaches to investigate these issues in order to determine whether our main findings would be influenced by different assumptions in the process of generating data. Hence, to address potential endogeneity, we use the methodologies proposed by Carmona et al. (2015), Kyriakou and Dimitras (2018), Guizani and Abdalkrim (2021), Ocak (2022), and Shabir et al. (2023), specifically the two-stage least squares (2SLS) and two-stage System Generalized Method of Moments (System GMM).

First, we re-estimate our regressions by employing a 2SLS regression with an instrumental variables approach. Following Carmona et al. (2015) and Ocak (2022), we employ the lagged first-order difference of ECUN as an independent variable.

Table 8. Endogeneity test using two-stage least squares (2SLS).

Variable	Dependent Variable: Audit-Fees-to-Total-Fees Ratio (AFEE)	Dependent Variable: Auditor Tenure (AUTE)	Dependent Variable: Restatements (REST)	Dependent Variable: Total Accruals (TACC)
Constant	−0.149 ** (−2.333)	−0.441 (−1.177)	−0.001 (−0.442)	−0.028 ** (−2.142)
ECUN	−0.471 *** (−8.432)	−3.764 *** (−11.499)	0.001 * (1.719)	0.057 *** (4.941)
CAFL	−0.043 *** (−9.868)	−0.096 *** (−3.701)	0.000 (0.785)	0.015 *** (16.416)
FICO	0.043 *** (10.945)	0.049 ** (2.123)	−0.000 (−0.169)	−0.007 *** (−9.219)
FILO	0.002 (0.327)	0.056 (1.169)	−0.000 (−0.843)	−0.005 *** (−3.377)
FISI	−0.068 *** (−13.209)	−0.141 *** (−4.625)	0.000 *** (4.192)	0.978 *** (896.245)
INRI	−0.010 *** (−9.150)	−0.002 ** (−2.217)	0.000 (0.220)	0.001 *** (33.241)
LEVE	0.240 *** (21.670)	0.508 *** (7.809)	−0.000 ** (−2.056)	−0.020 *** (−8.913)
QUIC	0.002 * (1.991)	0.003 (0.452)	−0.000 (−1.063)	−0.030 *** (−119.102)
ROA	0.216 *** (6.464)	0.857 *** (4.368)	−0.000 (−0.276)	−0.097 *** (−13.905)
INDI	0.013 *** (13.586)	0.038 *** (6.689)	−0.000 (−1.267)	−0.005 *** (−26.654)
INFL	−0.003 *** (−3.567)	−0.085 *** (−15.133)	0.000 *** (4.484)	0.000 (0.817)
MACA	0.377 *** (25.636)	1.298 *** (15.030)	−0.000 ** (−2.577)	−0.036 *** (−11.743)
NGIN	−0.205 *** (−6.863)	−1.023 *** (−5.830)	0.001 ** (2.307)	0.003 (0.483)
PODI	−0.011 *** (−12.810)	−0.021 *** (−4.236)	0.000 *** (4.084)	0.004 *** (22.740)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.228	0.282	0.137	0.970
Durbin-Watson test (p-value)	0.029	0.061	0.036	0.059
Wu-Hausman test (p-value)	0.000	0.000	0.000	0.000

This table shows the outcomes of our baseline regression models using the two-stage least squares (2SLS) as robustness. The dependent variables under consideration in this study are the audit-fees-to-total-fees ratio (AFEE), auditor tenure (AUTE), restatements (REST), and total accruals (TACC). The independent variable under consideration in this study is economic uncertainty (ECUN). The remaining control variables can be classified into two distinct categories: firm characteristics (cash flow from operations (CAFL), firm complexity (FICO), firm loss (FILO), firm size (FISI), inherent risk (INRI), leverage (LEVE), quick ratio (QUIC), and profitability (ROA)) and country conditions (individualism (INDI), inflation (INFL), market capitalization (MACA), national governance index (NGIN), and power distance index (PODI)). The fixed-effects estimator is employed for all estimations. The t-statistics are presented in brackets. The symbols ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively. A detailed explanation of the variables and their respective sources is presented in Appendix A.

reports the second-stage estimation results. We find that the coefficients of ECUN are statistically significant and still show the same signs as our previous estimates, which are presented in Table 3.

Second, we apply the System GMM proposed by Blundell and Bond (1998), which is particularly well suited to overcome the inconsistency caused by endogeneity. Kyriakou and Dimitras (2018) and Guizani and Abdalkrim (2021) have used this approach in order to evaluate different aspects of audit quality and eliminate the endogeneity bias. It checks for important types of endogeneity, like unobserved heterogeneity, simultaneity, and dynamic endogeneity. These are very important in complex econometric models where results can be skewed by many unobserved factors and relationships happening at the same time (Pham et al. 2021; Shabir et al. 2023). Furthermore, System GMM uses internal instruments that are based on a company’s past data to specifically target endogeneity problems that come up from unobserved simultaneity and heterogeneity (Eugster 2020; Shabir et al. 2023). Additionally, System GMM improves conventional estimation techniques by incorporating extra assumptions and moment conditions, as seen in its application to examining audit characteristics. This results in a more thorough and robust analysis, as seen in the use of the Hansen J-statistic for testing overidentifying restrictions (Meah et al. 2021; Shabir et al. 2023).

Table 9. Endogeneity test using two-stage System Generalized Method of Moments (System GMM).

Variable	Dependent Variable: Audit-Fees-to-Total-Fees Ratio (AFEE)	Dependent Variable: Auditor Tenure (AUTE)	Dependent Variable: Restatements (REST)	Dependent Variable: Total Accruals (TACC)
Constant	0.365 *** (7.139)	5.943 *** (31.965)	0.000 (0.746)	6.654 *** (105.267)
ECUN	−0.002 *** (−1.382)	−0.039 ** (−2.164)	0.001 *** (4.083)	0.016 *** (1.569)
CAFL	−0.000 (−1.110)	−0.004 (−0.911)	0.000 * (1.842)	0.113 *** (37.754)
FICO	0.000 (0.164)	0.011 (1.436)	−0.000 (−0.255)	−0.018 *** (−4.456)
FILO	0.000 (1.216)	0.009 (1.215)	−0.000 (−0.009)	−0.004 (−1.053)
FISI	−0.001 ** (−2.162)	−0.014 * (−1.742)	0.000 *** (3.016)	0.145 *** (32.992)
INRI	−0.000 (−1.014)	−0.000 (−0.224)	0.000 (0.226)	0.001 *** (11.129)
LEVE	0.000 (0.199)	0.025 (1.366)	−0.000 * (−1.657)	−0.101 *** (−9.786)
QUIC	0.000 (0.444)	0.000 (0.142)	−0.000 (−0.868)	−0.008 *** (−8.903)
ROA	0.001 (0.494)	0.031 (0.886)	−0.000 (−0.269)	−0.310 *** (−15.159)
INDI	0.001 *** (4.494)	0.001 (0.456)	−0.000 (−0.363)	−0.010 *** (−7.703)
INFL	−0.000 (−1.466)	−0.001 * (−1.792)	0.000 (0.333)	0.007 *** (13.857)
MACA	0.000 (0.084)	0.015 (1.120)	−0.000 (−1.149)	−0.043 *** (−5.335)
NGIN	−0.001 (−0.664)	−0.153 *** (−4.899)	0.001 *** (2.910)	0.075 *** (4.399)
PODI	−0.000 *** (−4.758)	−0.000 (−0.015)	0.000 ** (2.335)	0.010 *** (9.483)
Obs.	83,511	83,511	83,511	83,511
Country FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Adjusted R-squared	0.991	0.966	0.776	0.778
AR(2) test (p-value)	0.150	0.196	0.182	0.135
Hansen J-statistic test (p-value)	0.418	0.316	0.384	0.388

This table shows the outcomes of our baseline regression models using the two-stage System Generalized Method of Moments (System GMM) as robustness. The dependent variables under consideration in this study are the audit-fees-to-total-fees ratio (AFEE), auditor tenure (AUTE), restatements (REST), and total accruals (TACC). The independent variable under consideration in this study is economic uncertainty (ECUN). The remaining control variables can be classified into two distinct categories: firm characteristics (cash flow from operations (CAFL), firm complexity (FICO), firm loss (FILO), firm size (FISI), inherent risk (INRI), leverage (LEVE), quick ratio (QUIC), and profitability (ROA)) and country conditions (individualism (INDI), inflation (INFL), market capitalization (MACA), national governance index (NGIN), and power distance index (PODI)). The fixed-effects estimator is employed for all estimations. The t-statistics are presented in brackets. The symbols ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively. A detailed explanation of the variables and their respective sources is presented in Appendix A.

reports our results using the System GMM approach. The coefficient of the lagged dependent variable remains significant, which verifies the persistence of lower audit quality from one year to the next. This finding is similar to the previous outcome, which is reported in Table 3, and shows that ECUN decreases audit quality. Additionally, Table 9 shows that the AR(2) and Hansen J statistics are not significant, indicating that there is no second-order autocorrelation among errors and that the overidentifying restrictions are valid.

6. Implications

The research outlined in this manuscript holds significant practical implications for stakeholders, including firms, policymakers, regulatory authorities, standard setters, and investors, especially those involved in BRICS countries. For multinational firms and investors, understanding the evolving role of BRICS nations in the global economy is crucial for making informed strategic and investment decisions, given the substantial impact these countries have on global GDP. The varied implementation and influence of IFRS among BRICS states highlight the need for tailored financial reporting and compliance strategies to align with each country’s regulatory framework. Additionally, the findings on audit quality in relation to different national auditing standards emphasize the importance of effectively managing these variations. Observations on the correlation between economic uncertainty, CEO power, and audit quality underscore the necessity of strong corporate governance practices to maintain the integrity and reliability of financial reporting, particularly during periods of economic uncertainty. Policymakers can utilize these insights to develop strategies that enhance audit quality and ensure more reliable financial reporting and market stability.

From an academic perspective, this study makes significant contributions to multiple fields of research. An in-depth examination of the BRICS nations’ contribution to the global economy provides a fresh outlook for economic studies, emphasizing the shift towards these emerging economies and their impact on global economic patterns. This is particularly relevant for research focused on international economics and market dynamics. The implementation of IFRS in BRICS nations presents valuable opportunities for studying international accounting practices and their effects on financial transparency and comparability. Furthermore, the diversity in auditing standards and practices across different nations offers a unique opportunity for comparative research on auditing and regulatory compliance. Exploring the impact of economic uncertainty on company operations opens new avenues for research in business strategy and risk management. The relationship between CEO power and audit quality, especially in the context of economic uncertainty, offers new perspectives on corporate governance and the dynamics of executive power in influencing financial reporting and auditing. Overall, this research enhances the understanding of global economic systems, corporate governance, and financial reporting standards, providing valuable insights for both practitioners and academics.

7. Conclusions

This study examines whether economic uncertainty relatively influences audit quality by using a sample of 83,511 firm-year observations from BRICS countries for 1995–2022. We use the World Uncertainty Index that Ahir et al. (2022) developed to quantify economic uncertainty. Our research indicates that increased economic uncertainty has a substantial negative impact on audit quality. This relationship is evident in both input-based measures (e.g., audit-fees-to-total-fees ratio and auditor tenure) and output-based measures (e.g., restatements and total accruals). In this regard, the study indicates that increased economic uncertainty results in a decline in audit quality, as evidenced by an increase in total accruals and restatements and a fall in the audit-fees-to-total-fees ratio and auditor tenure. The decline is ascribed to the intensified information asymmetry during times of economic uncertainty, making it challenging for auditors to gather complete and accurate information.

Moreover, we expanded our study to consider whether CEO power has played a moderating role in mitigating the adverse impact of economic uncertainty on audit quality. We have used the data reduction technique principal component analysis (PCA) to combine five different proxies for CEO power (e.g., CEO duality, CEO-founder, CEO ownership, CEO compensation slice, and CEO tenure) into a one-dimensional index for CEO power. We find that CEO power moderates the effects of economic uncertainty on both input-based and output-based measures of audit quality. This moderation effect of CEO power suggests that the level of CEO power within an organization can influence how economic uncertainty impacts audit quality, potentially neutralizing some of its negative effects.

To conclude, while this study offers a comprehensive analysis of economic uncertainty’s impact on audit quality in BRICS nations from 1995 to 2022, it has limitations including a narrow geographical and temporal scope, data availability issues, and potential measurement inadequacies of key variables such as audit quality and CEO power. Future research should broaden the scope to include more emerging economies and extended timelines, and focus on the specific effects of major crises like the 2008 Global Financial Crisis and the 2020 COVID-19 pandemic. Additionally, exploring the role of cultural, regulatory, and institutional contexts, examining the long-term impacts of sustained economic uncertainty, and conducting comparative studies on varied auditing standards across nations could provide valuable insights. Employing advanced methodological approaches like machine learning and big data analytics may also enhance the robustness of future findings.