“Shall Absolute Power Corrupt Absolutely?”: A Perspective From Financial Constraints and Earnings Quality Under Government Control

Abstract

This research investigates the efficacy of government control in the context of financial constraints and earnings quality, focusing on Vietnam’s listed companies. Employing a dynamic approach and utilizing the System Generalized Method of Moments (SGMM) to mitigate endogeneity concerns, the study examines annual data from non-financial Vietnamese firms listed on the Hanoi and Ho Chi Minh Stock Exchange (HNX & HOSE) spanning 2012 to 2022. Non-financial firms are the study’s main focus because their operational dynamics, capital structures, and strategic priorities markedly differ from their financial counterparts. Moreover, non-financial companies in Vietnam contend with multifaceted challenges arising from government control including potential bureaucratic hurdles, regulatory constraints, and strategic limitations, directly affecting their financial flexibility, and consequently their ability to ensure earnings quality. Earnings quality is assessed through the average values of four key characteristics—persistence, predictability, variability, and smoothness—while financial constraints are proxied by firm size, foundation years, financial leverage, and dividend pay-outs. Findings reveal a deleterious impact of financial constraints on earnings quality, with state ownership exhibiting varying effects at different levels. Remarkably, the optimal range for state ownership, specifically State-Owned Enterprises (SOEs) rates from 35% to 50%, is identified as pivotal in ameliorating the adverse consequences of financial constraints on earnings quality. The research acknowledges limitations in the exclusive focus on specific earnings characteristics and the potential multicollinearity issue arising from the interaction variable in the SGMM model. Practically, the study proposes policies emphasizing the need for investors to discern financial constraint signals, companies to mitigate financial constraints, and governments to play a pivotal role in alleviating financial constraints to enhance earnings quality. Socially, the research underscores the imperative nature of earnings quality in investment decisions, cautioning against investing in companies masking inefficiencies with manipulated profits, thereby posing risks to individual investors and the overall financial market. The originality of this study lies in its alternative representations of financial constraints, nuanced examination of state ownership at different levels, and its elucidation of the moderating role of state ownership in the relationship between financial constraints and earnings quality.

Keywords

earnings quality financial constraints state ownership SGMM

Introduction

Within the realm of corporate finance, the relationship between financial constraints and earnings quality has garnered significant attention as their interplay is supposed to be a pivotal determinant of firm-level performance (Boachie & Mensah, 2022; Farooq et al., 2023). Financial constraints, the limitations in accessing external capital (Hamilton et al., 2019), pose a formidable challenge for companies seeking to optimize their operational efficiency and ensure the reliability of their reported earnings. In the pursuit of sustained growth and competitiveness, companies are compelled to navigate their financial composition including debt levels, liquidity, and investment decisions to make informed financial decisions that may inherently influence the transparency and precision of financial reporting. Earnings quality serves as a barometer for a company’s capability to generate sustainable and transparent profits, which allows stakeholders to evaluate a company’s economic health and future prospects (Beyer et al., 2019). Therefore, the influence of financial constraints on earnings quality necessitates a rigorous empirical exploration to unravel the mechanisms through which these two variables interact, thereby shaping corporate decision-making.

Theoretically, the underlying mechanisms through which financial constraints exert an impact on earning quality can be constructed by Opportunism Theory (Rangan, 1998), Rational Expectation Theory (Shivakumar, 2000), and Signaling Theory (Gunny, 2010). The first theory posits that companies facing financial constraints tend to engage in opportunistic behaviors to enhance their reported profits. The second theory suggests that financial constraints prompt companies to adjust their reported profits to meet investor expectations. The third theory shows that financially constrained firms strive to maintain higher accruals to signal positive information about their outlook, making them more attractive to investors and easing their access to capital markets.

Empirically, the existing research body has recently shed light on the implications of financial constraints on discretionary accounting choices (Kurt, 2018), corporate cash holdings (Mansali et al., 2019), and financial flexibility (Islam et al., 2022). In Vietnam, recent studies also refer to diverse dimensions of corporate financial and reporting practices including the determinants of earnings quality (Hieu & Do Quyen, 2021; Hung & Van, 2020a; Le et al., 2021; Nguyen & Bui, 2019; Vo & Chu, 2019) and the effect of earnings quality on other factors (Dang & Pham, 2022; Hung et al., 2020; Hung & Van, 2020b; Van Khanh & Hung, 2020). As a result, the examination of the relationship between financial constraints and earnings quality is conspicuously absent from the literature, leaving the first critical gap to be filled in understanding how constraints on financing capacity may impact the reliability and quality of reported earnings.

Vietnam’s capital market has witnessed considerable growth and evolution since the establishment of the first stock market—Ho Chi Minh City Stock Exchange (HOSE) in 2000 and subsequently Hanoi Stock Exchange (HNX) in 2005. Over the years, both exchanges have matured into stock exchange centers, reflecting the dynamism of Vietnam’s economic landscape; nevertheless, such growth has not followed the same pattern, with Ho Chi Minh City emerging as a robust market boasting a market capitalization exceeding USD 170 billion in 2020, while Hanoi lags behind with a fraction below USD 10 billion (OECD, 2022). The disparity in market capitalization is attributed to Ho Chi Minh City’s reputation as a hub for large companies, whereas Hanoi tends to attract smaller enterprises. Despite the brisk expansion of Vietnam’s equity markets, the country’s overall market size remains relatively small compared to regional counterparts, constituting 104% of GDP in 2020 (OECD, 2022). Notably, Vietnam’s securities sector accounts for only 32% of total financial assets, highlighting its status as a predominantly bank-based economy (OECD, 2022). This is indicative of an underdeveloped capital market that has yet to effectively channel resources into the domestic private sector. In contrast to advanced economies where stock exchanges often become publicly listed entities, Vietnam’s stock exchanges remain state-owned institutions. Specifically, public sector ownership extends to 28% of the capital in Vietnam’s largest listed companies (OECD, 2022). Within this context, it becomes imperative to examine the financial dynamics faced by non-financial listed companies in Vietnam which are likely to encounter financial constraints.

In addition, Vietnam’s economic landscape is marked by a significant presence of state-owned enterprises (SOEs), which have traditionally played a dominant role in various sectors, tightly interwoven with government policies and directives. Despite commendable efforts by the government to reduce the prevalence of SOEs through equitization and restructuring, these entities continue to wield substantial influence, particularly through their preferential access to credit, land, and resources. The state’s supportive stance is evident, as SOEs are treated favorably in all aspects, receiving privileges that include access to capital, natural resources, land, and human resources.

The legal framework, embodied in the 2020 Law on Enterprises, classifies SOEs based on the percentage of state ownership, distinguishing between those with 100% state ownership and those with over 50% but less than 100% state ownership. This broader definition expands the scope of entities falling under state influence, totaling 2,109 SOEs held by the central government, providing employment for 1.1 million individuals as of 2019 (OECD, 2022). Additionally, there are around 1,100 SOEs at the subnational level. Even enterprises with less than 50% state ownership are treated similarly to private enterprises under Vietnamese law (OECD, 2022). Despite a noticeable decline in the number of SOEs and their share in various economic indicators over the past decade, the state-owned sector remains a substantial contributor to Vietnam’s national economy. SOEs constitute 22.8% of the country’s capital and account for around 30% of its GDP (OECD, 2022).

Vietnam’s context of pervasive state ownership characterized by preferential treatment and state support raises questions about the impact of state ownership on the earnings quality. In fact, the government’s role, which extends beyond ownership to include favorable lending conditions through state-owned banks, introduces a dynamic that may influence how SOEs manage their financial reporting. Empirical studies of Dinh et al. (2023) and Tessema et al. (2018) mentioned the impact of ownership structure and ownership concentration on earnings quality. Specifically, Tam et al. (2019) pioneered to reveal whether state ownership contributes to earnings quality in Vietnam. Contrary to the prevailing notion that private ownership tends to outperform state ownership concerning profitability and efficiency, Tam et al. (2019) indicate that private firms are inclined to engage in manipulating their reported earnings compared to SOEs in the Vietnamese financial market. Thus, such counterintuitive results in turn raise another question about the underlying dynamics of SOEs and their financial reporting practices. Although the seminal work of Tam et al. (2019) provides a valuable snapshot of earnings quality in Vietnam, it also highlights the need for further research to validate and contextualize these findings. As a result, the research attempts to expand the scope explored by Tam et al. (2019) by filling the gap on how the degree of government intervention influences earnings quality.

Besides, the research argues that financial constraints tend to manifest differently owing to the preferential treatments, particularly lower borrowing costs and easier access to capital and financial resources, from SOEs. A recent study conducted by Hoang et al. (2019) has shed light on the fact that the increasing proportion of shares held by the government weakens the positive impact of earnings quality on corporate social disclosure; however, a notable gap arises from the absence of exploration into how state ownership might moderate the impact of financial constraints on earnings quality among Vietnamese firms.

The research aims to address three pivotal research questions to deepen the understanding of earnings quality within the Vietnamese corporate landscape: (1) Is earnings quality associated with financial constraints? (2) Does earnings quality vary with different state ownership levels? (3) Does the degree of government intervention amplify or mitigate the impact of financial constraints on earnings quality? To answer these questions, the study collects data from Vietnamese corporate enterprises listed on HOSE and HNX from 2012 to 2022. All variables in the regression models are calculated from information in the financial statements (balance sheet, income statement, cash flow statement, and annual report) of listed firms. The financial statements are obtained from the data source of Thomson Reuters Eikon. Financial companies including commercial banks, investment trusts, and insurance agencies are eliminated because their products are different from those of non-financial ones.

The study employs a single index computed from four earnings characteristics (persistence, predictability, variability, and smoothness) to represent earnings quality. As for main independent variables, financial constraints are alternatively proxied by dividends, financial leverage, firm size, and the number of listing years; state ownership rates are divided into three levels (lower than 35%, from 35% to 50%, and higher than 50%) based on the intervention level of the government to firm operations. The research finds that financial constraints reduce earnings quality. Lower (0%–35%) and higher state ownership rate (50%–100%) are detrimental to earnings quality, while a proper state ownership rate (35%–50%) helps improve earnings quality. Surprisingly, the suitable state ownership rate (35%–50%) can mitigate the negative effect of financial constraints on earnings quality, while the other state ownership levels exacerbate the negative effect of financial constraints on earnings quality.

The research is expected to make substantial theoretical and practical on three critical fronts. Firstly, by meticulously exploring the relationship between financial constraints and earnings quality among Vietnamese corporate enterprises, the research significantly advances the theoretical grasp of the diverse manifestations and mechanisms through which financial limitations affect reported earnings. Secondly, the study extends prior research by delving into the impact of different levels of state ownership on earnings quality, challenging conventional wisdom on how state intervention shapes financial reporting practices in the Vietnamese context. Finally, the paper offers a distinctive contribution by unveiling the moderation effect of different state ownership levels on the impact of financial constraints on earnings quality. The identification of a suitable state ownership rate that mitigates the negative effects of financial constraints introduces a novel layer of complexity, enriching the understanding of how state ownership dynamics interact with financial challenges to influence earnings quality. These contributions collectively offer valuable insights for policymakers, regulators, and investors navigating the Vietnamese corporate landscape.

The paper is structured into five sections. Section “Literature Review and Hypothesis Development” reviews theoretical and empirical studies on the relationship of financial constraints and state ownership with earnings quality. Section “Methodology” presents methodological approaches with earnings quality measurements, data collection, and estimation models. Section “Data analysis” shows statistical descriptions of all variables and explains the estimated results obtained from the regressions. The study concludes with the research summary and policy implications based on its findings in Section “Conclusion.”

Literature Review and Hypothesis Development

Financial Constraints and Earnings Quality

The relationship between financial constraints and earnings quality has been elucidated through the lenses of Opportunism Theory, Rational Expectation Theory, and Signaling Theory. Firstly, Opportunism Theory posits that businesses often employ accruals to artificially boost profits, manipulating stock prices and garnering higher valuation from investors (Rangan, 1998). However, the transient nature of such earnings becomes apparent as businesses adjust their discretionary accruals over time. Financially constrained companies, facing limitations in accessing external capital, resort to earnings management practices to artificially inflate stock prices (Lee & Chun, 2023). The research argues that this opportunistic behavior introduces a layer of complexity to the relationship between financial constraints and earnings quality, particularly in cases where investors may remain unaware of such actions or find it challenging to observe them.

Secondly, Rational Expectation Theory, on the other hand, posits that investors, anticipating earnings management, demand higher discounts (Shivakumar, 2000). Gonçalves and Coelho (2019) find that before M&A, companies attempt to increase their stock prices through raising accruals so that the target firm requires discounts from the acquiring firm. If the acquiring firm does not adjust to higher profits as expected, it will offer the target firm a large number of stocks and increase acquiring costs. Empirical evidence from Kurt (2018), focusing on seasoned equity offerings (SEOs), supports the theory by demonstrating that constrained firms engage in greater upward earnings management when selling equity. The study finds that aggressive earnings management by constrained issuers is associated with lower SEO announcement returns, aligning with the rational expectations hypothesis. This implies that financial constraints prompt companies to manipulate earnings as investors expect such behavior, shaping the dynamics of earnings quality in financially constrained environments.

Thirdly, Signaling Theory suggests that managers utilize reported profits as a communication tool with the market, conveying information about a company’s prospects (Gunny, 2010). George & Frank (2023) indicate that reporting higher positive accruals increases firm profitability, which worsens earnings quality. Besides, there exists an argument that financially constrained firms maintain higher accruals to publicize information related to their good prospect to investors. Owing to this, companies can access the stock market and obtain financing funds for investment more easily (Alrashidi et al., 2021). In short, all three theories support that financially constrained companies are more likely to use discretionary accruals to adjust cash flow, resulted in lower corporate earnings quality.

Empirical findings from Kurt (2018) and Mansali et al. (2019) further reinforce the relationship between financial constraints and earnings quality. Kurt (2018) supports the idea that constrained firms resort to aggressive earnings management, aligning with both Opportunism and Rational Expectation Theories. Mansali et al. (2019) reveal that poor accruals quality, driven by high information asymmetry, leads to higher cash holdings, particularly under financial constraints. These empirical insights substantiate the proposed hypothesis that financial constraints deteriorate earnings quality, offering an understanding of how financial limitations interact with managerial behaviors and reporting practices. Based on these above-mentioned theories and empirical studies, the paper proposes the first hypothesis “Financial constraints deteriorate earnings quality.”

State Ownership and Earnings Quality

Some studies examined the impact of state ownership on earnings quality. Karajeh (2020) found that earnings quality (the coefficient obtained from the regression model on profitability) accompanies ownership concentration because giving more stocks to managers can help balance interests between managers and shareholders. Barka and Hamza (2020) observed that the separation between the voting right and the control right leads to the entrenchment effect (controlling shareholders report earnings based on their interests) and the information effect (little information of firm operation uncovered to the public). These two effects are associated to earnings quality. Palas and Solomon (2022) developed the idea from Barka and Hamza (2020) by examining whether lower predicted earnings quality of companies with dual-class shares depend more on the signal of dividends but not the signal of earnings. Compared to companies with one-class shares, those with dual-class ones have lower earnings quality, and investors in these companies place a larger weigh on dividends than on earnings.

The privatization process is supposed to help companies reduce equity costs as well as heighten firm performance. Besides, it boosts the development of the capital market, enhance information transparency, and improve market liquidity (Khanchel & Bentaleb, 2022). Nevertheless, after privatization, the number of shares held by the government is still high in the ownership structure (Radić et al., 2021). State ownership has two characteristics: (1) enterprises receive favorable conditions and protection from the government (Vu & Pratoomsuwan, 2019); (2) the government controls firm operation and drives companies towards social and political goals instead of profit maximization (Aboud & Diab, 2022). Other issues are that managers do not pay attention to firm operation and managers are appointed based on their political relations but not true abilities (Ho et al., 2021). All these obstructions worsen firm performance (Nguyen & Vo, 2022).

Higher state ownership rates indicate ownership concentration of the government and increase the management control over companies (Boubakri & Saffar, 2019). According to Predation Theory, ownership concentration makes it possible for large shareholders to take control of assets over small shareholders (Zhou et al., 2023). Therefore, companies with stronger political relationship tend to perform less effectively (Tihanyi et al., 2019). In case state-owned enterprises (SOEs) show bad performance, the problem of earnings management can originate from the fact that (1) the government forces managers to adjust earnings to hide their property expropriation, which worsens earnings quality in companies with political relationship (Akcigit et al., 2023) and (2) earnings manipulation aims to increase financing access; however, SOEs have no incentives to practice earnings management because they have favorable conditions in loans and financing costs. Therefore, earnings quality accurately reflects firm operation (Saleh et al., 2020).

On the other hand, Agency Theory says that the devolution (management and ownership) encourages managers to manipulate earnings (Jiraporn et al., 2008). The theory explains the reason why managers of private companies have the motivation to expropriate corporate assets. Therefore, their earnings quality is often lower than SOEs. Furthermore, the market demand for information transparency of SOEs is higher, and earnings quality also becomes better. Interest Alignment Theory also supports that ownership concentration can mitigate agency conflicts (Chen et al., 2022). Recently, Tam et al. (2019) is a pioneer in examining the specific impact of state ownership on earnings quality in the Vietnamese financial market. The study compares state-owned and private-owned enterprises, indicating that private firms are more likely to manipulate their reported earnings numbers than state-owned firms. This empirical evidence suggests that state ownership, in this context, may be associated with a higher level of earnings quality, providing a perspective on the impact of ownership structures on financial reporting practices. Nevertheless, the research argues that higher state ownership rates result in ownership concentration which can either increase earnings quality (an increase in agency costs and asymmetric information) or decrease earnings quality (the government makes firm performance worse and helps SOEs hide their earnings management). From these arguments, the research proposes the second hypothesis “Different levels of state ownership differently affect earnings quality.”

Financial Constraints and Earnings Quality Under Government Intervention

Besides, the research suggests the influence of state ownership may extend beyond mere ownership structures, playing a crucial role in moderating the impact of financial constraints on earnings quality, especially in the context of emerging markets. Companies with low levels of state ownership may appear less subject to government control, but they face challenges in accessing the capital market, highlighting the paradox between political relationships and efficient performance (Bui et al., 2022). In contrast, entities with strong political ties and government relationships, often characterized by higher state ownership, benefit from preferential treatment, including improved access to external finance and reduced financial constraints (Gao et al., 2023).

Empirical evidence of Hoang et al. (2019) has taken into consideration the moderation role of state ownership. In particular, the research explores the moderating effects of state ownership towards the relationship between earnings quality (EQ) and corporate social disclosure (CSD). The findings claim that as the proportion of shares held by the government in firms increases, the relationship between EQ and CSD weakens. This suggests that state ownership, a manifestation of strong political ties, significantly moderates the link between EQ and CSD. Finally, the study argues that state ownership, with its association to favorable financial conditions, legal protection, and government support, may emerge as a key factor moderating the effect of financial constraints on earnings quality. Therefore, the research proposes the third hypothesis “State ownership can moderate the effect of financial constraints on earnings quality.”

Methodology

Data Collection

The research collects data from listed companies on Ho Chi Minh (HOSE) and Hanoi Stock Exchange (HNX) from 2012 to 2022. This longitudinal dataset captures the dynamic financial landscape, allowing for an analysis of the interplay between financial constraints, earnings quality, and state ownership. The nature of the dataset is annual, aligning with the frequency of financial reporting by the listed firms.

The research’s concentration on non-financial listed companies in Vietnam is strategic and rooted in several characteristics that make these companies particularly suitable for the research objectives. The decision to exclude financial companies, such as commercial banks, investment trusts, and insurance agencies, is driven by the following considerations. Firstly, non-financial companies typically engage in a diverse range of industries, manufacturing, or service-oriented activities. Excluding financial firms ensures a more homogeneous sample in terms of the nature of their products and operations. This homogeneity allows for a more focused analysis, as the impact of financial constraints on earnings quality can be examined within a more comparable context. Secondly, financial institutions including banks and investment trusts offer financial products and services that differ significantly from those of non-financial companies. The unique financial structures and reporting requirements of these entities may introduce additional complexities and variables that could potentially confound the analysis. Thirdly, non-financial companies often play a pivotal role in driving economic growth, employment, and innovation. As such, their financial health and reporting practices are of substantial interest to policymakers, investors, and the public. Studying the impact of financial constraints on earnings quality in this segment contributes valuable insights into the broader economic landscape of Vietnam. Fourthly, non-financial companies typically adhere to a consistent set of financial reporting practices, making it easier to draw meaningful comparisons, and conclusions from the dataset. This consistency facilitates the establishment of robust relationships between financial constraints and earnings quality, without the potential noise introduced by variations in reporting standards among diverse financial institutions. Finally, findings derived from non-financial companies are more likely to be generalizable to a broader spectrum of businesses operating in different industries. This generalizability strengthens the external validity of the research and enhances its applicability to various sectors.

The data is sourced from financial statements including the balance sheet, income statement, cash flow statement, and annual reports of the selected listed firms. The use of Thomson Reuters Eikon as the primary data source ensures a robust and reliable dataset, given its reputation for providing comprehensive and accurate financial information on global companies. Data collection involves meticulous extraction and compilation of relevant financial metrics from the a forementioned statements, allowing for the calculation of variables essential for the regression models. This process is executed using statistical software, with the specific software chosen being STATA. The selection of this particular software is motivated by its widespread use in empirical research and its robust capabilities for data analysis.

The chosen methodology prioritizes an annual dataset due to the nature of financial reporting cycles, enabling a holistic examination of the variables of interest over the specified timeframe. The focus on non-financial Vietnamese companies ensures a more direct exploration of the research questions, aligning with the study’s objectives and contributing to the generalizability of the findings to the broader corporate landscape. While the primary analysis focuses on standard regression models, the research incorporates robustness analyses to enhance the reliability and validity of the results. Sensitivity analyses and alternative model specifications are employed to ensure the robustness of the findings and validate the consistency of the relationships observed (Christensen & Connault, 2023; Pang et al., 2020). This rigorous approach enhances the credibility of the research outcomes and strengthens the contributions to the academic discourse on financial constraints, earnings quality, and state ownership in the Vietnamese corporate context.

Model Specification

\begin{matrix} E Q_{it} = α_{0} + α_{1} E Q_{it - 1} + α_{2} F C_{it} + α_{3} SO E_{it} + α_{4} SO E_{it} \times F C_{it} \\ + α_{5} LI Q_{it} + α_{6} PRO F_{it} + α_{7} RE V_{it} + α_{8} Audi t_{it} + ε_{it} \end{matrix}

(1)

In Equation 1, $E Q_{it}$ and $E Q_{it - 1}$ are respectively earnings quality of firm i at time t. As mentioned above, earnings quality representatives include persistence, predictability, variability, and smoothness. To compute a common measurement, the research applies the ranking method to change all characteristics into ratios and calculate the mean value of the four (Tahat et al., 2022); $F C_{it}$ refers to financial constraints represented by firm size (Karlsson, 2021), foundation years (Patel & Guedes, 2022), financial leverage (Poursoleiman, 2020), and dividend pay-outs (Liang et al., 2023); $SO E_{it}$ is a dummy variable of state ownership of firm i at time t; $LI Q_{it}$ is liquidity of firm i at time t (Ramos & Righi, 2020); $PRO F_{it}$ is profitability of firm i at time t (Cakici et al., 2021); $RE V_{it}$ is sales growth of firm i at time t (Salsabila et al., 2023); $Audi t_{it}$ , a dummy variable, is denoted 1 for companies audited by Big4 and otherwise (Awuye, 2022); $ε_{it}$ is the error term (see Table 1 for operational definitions and Table A1 for variable measurements).

Table 1.

Operational Definitions for All Variables in the Model.

Variables	Operational definition
Dependent Variable: earnings quality
Persistence	The degree to which a firm’s reported earnings exhibit consistency and continuity over time. It is computed as the ratio of the cumulative sum of positive earnings changes to the cumulative sum of absolute earnings changes over a specified period.
Predictability	The ability to forecast future earnings based on historical earnings data. This measure evaluates the reliability of past earnings as a predictor of the firm’s future financial performance.
Variability	The extent of fluctuations or volatility in a firm’s reported earnings. It is operationalized by measuring the dispersion or variance of earnings values over a specified period.
Smoothness	The evenness and regularity of a firm’s earnings pattern. It is operationalized by evaluating the steadiness and absence of abrupt changes in reported earnings.
Explanatory variables
Financial constraints
Firm size	The size of a firm, typically measured by its total assets. Larger firms may have more resources and flexibility, potentially indicating lower financial constraints.
Foundation years	The number of years since a firm’s establishment. It reflects the firm’s age, and younger firms may face different financial constraints than more established ones.
Financial leverage	The extent to which a firm utilizes debt in its capital structure. It is operationalized by assessing the ratio of debt to equity, reflecting the firm’s reliance on borrowed funds.
Dividend payouts	The proportion of earnings distributed to shareholders as dividends. It is operationalized by calculating the ratio of dividends to earnings, indicating the firm’s dividend distribution policy.
State ownership	The extent of ownership or control exerted by the state or government in a firm.
Control variables
Liquidity	The ratio of a firm’s current assets to its current liabilities, reflecting its ability to meet short-term obligations.
Profitability	The return on assets (ROA) or net income divided by total assets, reflecting a firm’s ability to generate profits from its assets.
Revenue	The total income generated by a firm through its primary business activities, often referred to as total sales or turnover.
Audit	Categorical variable denoted 1 if a firm’s financial statements are audited by one of the Big4 auditing firms and 0 otherwise. Big4 audit firms typically include Deloitte, PwC, EY, and KPMG.

Source. Synthesized by the author.

Table 2.

Summary Statistics.

Variable	Obs.	Mean	Std. Dev.	Min	Max
Dependent variable
Earnings quality	3,968	0.4903	0.1787	0.0352	0.9731
Financial constraints
Firm size—FC1	4,409	5.9774	1.5304	−0.9999	12.209
Foundation years—FC2	4,217	21.968	13.007	4.0000	55.000
Financial leverage—FC3	4,190	0.5179	0.2266	0.0163	0.9486
Dividend payouts—FC4	4,300	0.0700	0.0994	0.0000	0.4350
Control variables
SOE rates	3,909	0.1883	0.2407	0.0000	0.9672
Liquidity	4,418	1.1192	0.8767	0.2182	5.9745
Profitability	4,413	0.1333	0.1169	−0.2255	0.4581
Revenue	4,382	0.0615	0.2930	−0.9279	0.9900
Audit	4,249	0.2015	0.4012	0.0000	1.0000

Source. Calculated by the author.

The proposed model is crucial as it delves into the nuanced relationship between financial constraints and earnings quality, considering the moderating effect of state ownership. It provides a comprehensive understanding of how state ownership levels impact the influence of financial constraints on earnings quality. This is essential for policymakers, investors, and corporate managers seeking insights into the intricacies of financial decision-making and reporting practices.

Additionally, the model incorporates a comprehensive measure of earnings quality, considering multiple dimensions (persistence, predictability, variability, smoothness), providing a more nuanced evaluation than previous studies that often focused on isolated aspects. The model uniquely explores how state ownership acts as a moderator in the relationship between financial constraints and earnings quality. This addresses a significant gap in the literature, which has primarily overlooked the role of state ownership in shaping financial reporting practices. While prior studies of Tam et al. (2019) and Vo and Chu (2019) have separately examined the impact of foreign and state ownership on earnings quality, they lacked a comprehensive earnings quality measure and did not explicitly explore the moderating role of state ownership in the context of financial constraints. Hence, the proposed model bridges this gap by integrating a holistic earnings quality assessment and explicitly addressing the moderating effect of state ownership on the financial constraints-earnings quality relationship.

- The marginal effect of financial constraints

\frac{\partial E Q_{it}}{\partial F C_{it}} = α_{2} + α_{4} SO E_{it}

(2)

Equation (2) shows the influence of financial constraints on earnings quality at specific levels of state ownership. $α_{2}$ captures the effect of financial constraints in companies that have zero state ownership rates. $α_{4}$ represents the moderation impact of state ownership rates towards the relationship between financial constraints and earnings quality. The research evaluates the regulation role at three levels of state ownership (lower than 35%, from 35% to 50%, and higher than 50%).

Estimation Techniques

The research adopts a dynamic model in the main regression, strategically incorporating the lag of earnings quality as an independent variable. This inclusion is underpinned by the notion that superior earnings quality in the past can wield a positive influence on current earnings quality (Abd Alhadi et al., 2020; Boachie & Mensah, 2022). The dynamic approach is imperative to capture the temporal dynamics inherent in the relationship under investigation.

Given the dynamic nature of the model, traditional estimators for the static approach such as Pooled Ordinary Least Square (POLS), Fixed Effects (FE), and Random Effects (RE) are susceptible to bias (Saddam & Jaafar, 2021). The endogenous problem introduced by lagged earnings quality necessitates a sophisticated solution. To address this, the research employs the System Generalized Methods of Moments (SGMM; Roodman, 2009), a technique adept at handling endogeneity concerns in dynamic models (see Table 3). However, it is paramount to note that the application of SGMM hinges on meeting specific requirements including the Hansen test, Arellano–Bond test, and ensuring an adequate number of instruments.¹

Table 3.

Main regression.

Earnings quality_it	Firm size—FC1			Foundation years—FC2			Financial leverage—FC3			Dividend payouts—FC4
	Low	Medium	High	Low	Medium	High	Low	Medium	High	Low	Medium	High
	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate
$E Q_{i, t - 1}$	0.579***	0.594***	0.448**	0.749***	0.692***	0.575***	0.644***	0.670***	0.683***	0.374***	0.616***	0.647***
	(0.141)	(0.165)	(0.192)	(0.103)	(0.111)	(0.131)	(0.0282)	(0.0122)	(0.0126)	(0.0338)	(0.110)	(0.110)
Liquidity_it	0.00691*	0.00554*	0.00637*	0.00710*	0.00617*	0.00625	0.00569***	0.00381***	0.00270***	0.00715***	0.00505	0.00474
	(0.00383)	(0.00311)	(0.00379)	(0.00418)	(0.00381)	(0.00401)	(0.00139)	(0.00100)	(0.00103)	(0.00189)	(0.00336)	(0.00333)
Profitability_it	5.08e-06	−2.85e-05	−7.59e-05*	−3.87e-06	−1.88e-06	−9.55e-06	−0.00024***	−8.9e-05***	−7.9e-5***	−9.5e-6**	−1.94e-06	−9.48e-07
	(5.88e-06)	(4.00e-05)	(3.98e-05)	(5.96e-06)	(2.65e-05)	(2.85e-05)	(4.86e-05)	(1.98e-05)	(1.95e-05)	(3.77e-06)	(3.23e-05)	(2.94e-05)
Revenue_it	−2.72e-06	7.52e-06	1.39e-05*	2.97e-06**	2.94e-06	5.17e-06	2.93e-05***	1.51e-05***	1.42e-5***	3.63e-6***	1.74e-06	1.82e-06
	(1.98e-06)	(7.08e-06)	(7.86e-6)	(1.16e-06)	(5.93e-06)	(6.19e-06)	(5.61e-06)	(2.78e-06)	(2.77e-06)	(1.10e-06)	(6.72e-06)	(6.23e-06)
Audit_it	0.146***	0.0240	0.149***	0.180***	0.101**	0.230***	0.191***	0.161***	0.175***	0.286***	0.157***	0.179***
	(0.0485)	(0.0546)	(0.0404)	(0.0522)	(0.0433)	(0.0615)	(0.0104)	(0.00489)	(0.00510)	(0.0192)	(0.0465)	(0.0481)
$F C_{it}$	−0.0131*	−0.0273***	−0.0208**	0.00119	−0.0019***	8.13e-05	1.15e-05	−2.67e-6***	−1.6e-6***	−0.00134**	−0.0026***	−0.0017***
	(0.00778)	(0.00818)	(0.00977)	(0.00117)	(0.000439)	(0.000888)	(7.12e-06)	(5.55e-07)	(5.48e-07)	(0.000652)	(0.000339)	(0.000319)
$SO E_{it}$	−0.214*	0.914*	−0.231	−0.125*	0.379***	−0.246*	−0.0145	0.0728***	−0.0419**	−0.00348	0.137***	−0.0836***
	(0.131)	(0.474)	(0.223)	(0.0746)	(0.102)	(0.139)	(0.0145)	(0.0194)	(0.0163)	(0.0227)	(0.0344)	(0.0320)
$F C_{it} \times SO E_{it}$	−0.0323*	0.140*	−0.0290	−0.00413*	0.0107***	−0.00621*	−1.53e-05**	3.18e-05***	−2.82e-5**	−0.0025***	0.00269***	−0.0022***
	(0.0199)	(0.0736)	(0.0330)	(0.00223)	(0.00291)	(0.00387)	(7.06e-06)	(4.98e-06)	(1.43e-05)	(0.000751)	(0.000659)	(0.000816)
No. of observations	3,345	3,345	3,345	3,345	3,345	3,345	3,345	3,345	3,345	3,345	3,345	3,345
No. of instruments	12	12	11	12	12	11	33	58	58	89	12	12
No. of groups	615	615	615	615	615	615	615	615	615	615	615	615
F-statistics	3201.5	2765	2394.1	5718.8	4365.9	3723	8618.4	6804.2	8314.5	3513.4	3743.3	5231.3
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AB test—AR(1)	−4.904	−4.289	−3.691	−5.748	−5.399	−5.248	−9.063	−9.973	−10.02	−8.89	−5.415	−5.494
p-value	9.37E-07	1.79E-05	0.000223	9.01E-09	6.7E-08	1.54E-07	1.27E-19	1.99E-23	1.23E-23	6.11E-19	6.13E-08	3.92E-08
AB test—AR(2)	1.295	1.194	1.043	1.441	1.36	1.709	−0.0408	1.368	1.494	1.378	1.371	1.486
p-value	0.195	0.232	0.297	0.15	0.174	0.0875	0.967	0.171	0.135	0.168	0.17	0.137
Hansen test	3.412	4.39	0.554	3.619	4.577	0.245	24.87	49.42	46.51	97.48	1.5	1.962
p-value	0.491	0.356	0.907	0.46	0.334	0.97	0.47	0.497	0.614	0.103	0.827	0.743

Source. Calculated by the author.

Note. Standard errors in parentheses.

p < 0.1. **p < 0.05. ***p < 0.01.

In the pursuit of robustness, the research explores the static approach, providing a comparative lens to evaluate the efficacy of estimation techniques. The application of POLS, FE, and RE in static models enables a comprehensive assessment of the relationship without the temporal lag dynamics. Importantly, the Hausman test serves as a pivotal tool in selecting the most suitable static model. The findings from the test affirm that the Fixed Effects model is the best-fitted for the static approach, ensuring a robust examination of the specified relationships (see Table A2).

Data Analysis

Statistical Summary

Table 2 provides a comprehensive overview of the descriptive statistics for the variables integrated into the regression model. The dependent variable, Earnings Quality, is characterized by four distinct measurements—persistence, predictability, variability, and smoothness. The amalgamation of these measurements yields a mean Earnings Quality value of 0.49, ranging from 0.035 to 0.973. This metric serves as a pivotal benchmark for assessing the financial health and reporting integrity of the observed firms.

Financial Constraints, a critical facet of the study, manifests through four proxies—Firm size (FC1), Foundation years (FC2), Financial leverage (FC3), and Dividend payouts (FC4). Higher values for financial leverage suggest more pronounced financial constraints. To ensure consistency among proxies, financial leverage is inverted, aligning a higher value with minor financial constraints. The descriptive statistics highlight the diversity within the financial landscape, with firm size showcasing the widest range of observations and financial leverage representing the extremities of the constraint spectrum. SOE rates exhibit a mean value of 0.188, underscoring the efficacy of the privatization process in limiting the presence of SOEs. However, a noteworthy observation reveals that some companies still maintain substantial SOE rates, reaching up to 96.72%. This variability underscores the nuanced nature of ownership structures within Vietnamese listed companies.

Delving into additional control variables, Liquidity, Profitability, Revenue, and the Audit dummy variable, the statistics paint a vivid picture of financial robustness, operational efficiency, revenue generation, and the prevalence of Big4 audit engagements. Each variable’s distribution is encapsulated in their respective means, standard deviations, and range of observations. Crucially, the detailed descriptive statistics affirm the absence of abnormalities within the data sample, instilling confidence in the robustness, and reliability of the dataset. This meticulous exploration sets the stage for a nuanced and granular analysis, unraveling the intricacies of financial constraints, earnings quality, and their interplay within the Vietnamese business landscape.

Estimated Results and Discussions

With the assumption that earnings quality will depend on its past value, the research considers the dynamic model with the estimator “System Generalized Methods of Moments” (SGMM). Table 3 analyses the relationship between financial constraints and earnings quality. Financial constraints are represented by firm size, foundation years, financial leverage, and dividend pay-outs. Furthermore, state ownership is divided into three levels (lower than 35%, 35%—50%, and higher than 50%)².

The analysis commences by validating the assumption that past earnings quality exerts a positive influence on the present state, substantiating the argument that historical financial robustness begets sustained quality in contemporary reporting. This aligns with extant research by Attia and Mehafdi (2023) and Dang et al. (2021) . The control variables, including liquidity, profitability, revenue, and the audit dummy variable, collectively demonstrate a positive impact on earnings quality. The rationale behind this positive relationship is expounded through various lenses. High solvency acts as a deterrent to opportunistic earnings management, fostering higher earnings quality—an observation supported by Lestari and Khafid (2021) and Park et al. (2021) . Sales growth emerges as a potent indicator of sustained firm performance, contributing positively to earnings quality—a perspective substantiated by Alipour et al. (2019) and Sundkvist and Stenheim (2023). In contrast, elevated profits, while seemingly positive, trigger caution as they may signify aggressive earnings management to attract investors, resulting in diminished earnings quality—an observation corroborated by Hakim and Naelufar (2020) and Kalbuana et al. (2022). The presence of Big4 auditors emerges as a pivotal factor, with the positive coefficient underscoring that firms audited by Big4 entities exhibit higher earnings quality. This aligns with the expectation that these auditors adhere rigorously to accounting and auditing standards, mitigating the likelihood of earnings manipulation. This finding resonates with Rajeevan and Ajward (2020) and Viana et al. (2022).

With regard to main variables, financial constraints are represented by four alternative measurements including firm size, foundation years, financial leverage, and dividend pay-outs. The research uses the opposite value of foundation years and dividend pay-outs to be consistent with the others, meaning that higher values imply more serious financial constraints. Financial constraints reduce a company’s access to financing funds in the capital market, and it has to practice earnings management to draw attention from investors. As a result, the research has addressed the first question by indicating that financial constraints may lead to a lower earnings quality. Such empirical results robustly support the first hypothesis “Financial constraints deteriorate earnings qualit.” In particular, Opportunism Theory’s prediction of accruals manipulation by financially constrained firms is validated, showcasing how companies resort to earnings management practices, influencing investor perceptions. Rational Expectation Theory finds support as well, emphasizing that financial constraints prompt companies to engage in aggressive earnings management during key financial events, ultimately shaping the trajectory of earnings quality. Signaling Theory’s role in communication via reported profits aligns with the findings, indicating that financially constrained firms strategically use higher accruals to convey positive prospects. Previous findings from Kurt (2018) and Mansali et al. (2019) serve as touchpoints for the discussion. Kurt (2018) supports the idea that constrained firms resort to aggressive earnings management, while Mansali et al. (2019) shed light on the relationship between poor accruals quality and higher cash holdings, particularly under financial constraints. In contrast to prior studies, the research concentrates on how financial constraints shape earnings quality; nevertheless, the results consistently point towards a negative association between financial constraints and earnings quality, showing the convergence of the study with previous empirical studies.

Furthermore, different state ownership rates are equivalent to the government’s intervention level. Companies characterized by low state ownership rates, presumed to be private entities, emerge as intriguing subjects. The absence of strong government ties might free them from direct state intervention, but it comes at the cost of limited financial support. In such a scenario, the research uncovers a compelling narrative—these firms resort to earnings manipulation as a strategic tool to showcase financial robustness, a phenomenon corroborated by empirical studies (Akcigit et al., 2023; Saleh et al., 2020). This lends credence to the argument that, in the absence of state backing, private firms might succumb to manipulative reporting practices to attract financing.

The sweet spot emerges for companies with moderate state ownership rates, ranging from 35% to 50%. Here, the government’s participatory role in business management strikes a balance. The findings assert that this moderate state ownership level has a positive impact on earnings quality. Drawing from Khanchel and Bentaleb (2022), this can be attributed to enhanced capital access and protective measures facilitated by state involvement. The results resonate with Interest Alignment Theory, which posits that ownership concentration can serve to mitigate agency conflicts (Chen et al., 2022).

Intriguingly, as state ownership rates soar beyond the 50%, denoting companies predominantly controlled by the government, the narrative shifts. SOEs, despite benefiting from governmental control and support, showcase a proclivity for earnings management. The research unveils a divergence from the conventional wisdom that associates high state ownership with improved earnings quality (Tam et al., 2019). The findings echo concerns raised by Attia et al. (2023) and Foroghi et al. (2022), suggesting that SOEs may manipulate earnings to align with governmental objectives, thereby compromising the fidelity of their financial reports. In short, the juxtaposition of findings with the second hypothesis—“Different levels of state ownership differently affect earnings quality”—asserts that different levels of state ownership uniquely affect earnings quality, highlighting the diverse impact of state ownership at various concentration levels. This divergence challenges previous assumptions that higher state ownership invariably correlates with enhanced earnings quality.

In case companies suffer financial constraints, the findings underscore the multifaceted nature of state ownership, transcending mere ownership structures to exert a pronounced influence on how companies navigate financial challenges and articulate their earnings quality. The study acknowledges the paradox faced by companies with low state ownership, seemingly less subject to government control but grappling with impediments in accessing the capital market—a phenomenon elucidated by Bui et al. (2022). Specifically, companies with state ownership rates lower than 35% exhibit an exacerbation of the negative relationship between financial constraints and earnings quality. Table 3 indicates that the coefficient of firm size is—0.0131, and its interaction with low SOE rates is—0.0323. Therefore, the marginal effect of financial constraints on earnings quality becomes more negatively oriented. This counterintuitive finding accentuates the challenges faced by entities with weak political ties, compounding their difficulties in approaching capital markets and thereby intensifying the adverse impact of financial constraints on earnings quality.

In contrast, companies with moderate state ownership rates ranging from 35% to 50% emerge as the most adept at navigating the intricate balance. It can be observed from foundation year that the coefficient is −0.0019 while its interaction with moderate SOE rates is positively significant (0.0107). The marginal effect of financial constraints shrinks to a smaller number or even turns positive in this case (0.0107 > 0.0019). The nuanced participation of the government in business management at this level offers a mitigating effect on financial constraints. The findings echo the insights of Gao et al. (2023), emphasizing that moderate state ownership alleviates the negative influence of financial constraints on earnings quality. Notably, the positive interaction between foundation years and moderate state ownership rates underscores the favorable conditions conferred, minimizing the adverse effects of financial constraints on earnings quality.

Surprisingly, companies classified as SOEs with state ownership rates exceeding 50% reveal a paradoxical exacerbation of the negative relationship between financial constraints and earnings quality. With regard to dividend pay-outs, its coefficient is −0.0017 and its interaction with high SOE rates is −0.0022. Similar to low SOE rates, the marginal effect of financial constraints on earnings quality shows a larger negative sign (−0.0039), higher than the single effect of only financial constraints. Despite the enhanced access to loans, the inefficiency in the utilization of financing funds propels SOEs into heightened earnings management practices, resulting in a notable deterioration in earnings quality. The finding challenges conventional assumptions and aligns with the assertion that increased government control may inadvertently foster practices detrimental to earnings quality. In general, comparisons with the third hypothesis—“State ownership can moderate the effect of financial constraints on earnings quality”—reveal alignment with the research proposition. State ownership, with its intricate associations with financial conditions, legal protection, and government support, indeed emerges as a key moderating factor. These findings resonate with and extend the insights of Hoang et al. (2019), emphasizing the role of state ownership in shaping the relationship between earnings quality and broader corporate dynamics.

Besides, variable coefficients are unbiased and accurate only when all statistical tests of SGMM are satisfied. First, Hansen tests in 12 models are insignificant, so instrumental variables are valid. Second, first-order autoregression is significant, while second-order autoregression is insignificant. Therefore, the inclusion of the lagged dependent variable in the regression model is appropriate. Third, F tests are all significant, demonstrating that independent variables in the model are not simultaneously equal to zero and can account for earnings quality. Finally, the number of groups are much higher than the number of instruments, supporting the model’s validity.

In summary, the research finds that financial constraints decrease earnings quality. SOE rates lower than 35% and higher than 50% have a negative effect on earnings quality, while SOE rates between 35% and 50% show a positive relationship with earnings quality. Moreover, low and high SOE rates indicate a negative interaction, so they enhance the harmful effect of financial constraints on earnings quality. On the contrary, moderate SOE rates both positively influence earnings quality and positively moderate the negative relationship between financial constraints and earnings quality. Based on the results, companies should maintain the moderate state ownership level.

Conclusion

It is apparent that investors desire to be well-informed about firm operation and that earnings quality can be a well-rounded factor reflecting firm performance. Higher earnings quality means that earnings can annuitize firm intrinsic value and can be perpetuated over time. Therefore, identifying the determinants of earnings quality is critically important in the decision-making process, providing investors with useful information for their investment. The research investigates the impact of financial constraints on earnings quality to prove whether financially constrained companies practice earnings management to attract potential investors for financing funds in the capital market. Furthermore, it takes into consideration the government’s role in adjusting earnings quality. It assumes that different levels of state ownership tend to affect financial constraints differently, so it is necessary to consider the interaction effect between financial constraints and state ownership on earnings quality.

The research collects data from non-financial listed companies on Ho Chi Minh and Hanoi Stock Exchange during 2012 to 2022. It calculates the average value of four earnings characteristics to represent earnings quality, uses alternative proxies for financial constraints including firm size, foundation years, financial leverage, and dividend pay-outs, and establishes three levels of state ownership (low, moderate, and high) for the government’s intervention. The research employs SGMM technique for the dynamic approach to deal with the endogenous problem. Based on the methodology, the research finds that financial constraints encourage companies to practice earnings management, resulting in worse earnings quality. Moreover, different levels of state ownership show alternative influence on earnings quality. A lower or higher level respectively means that the government has weak and strong intervention to firm operation. Both fail to help companies enhance their earnings quality. However, a moderate rate from 35% to 50% is beneficial to earnings quality.

Based on these major findings, the research proposes two policy implications. Firstly, as for investors, this research underscores the critical importance of evaluating a company’s financial constraints when assessing earnings quality. Financial constraints emerge as a precursor to earnings management, signifying a potential red flag for investors. A nuanced understanding of a firm’s financial landscape provides valuable insights for investment decisions. Secondly, policymakers are urged to adopt a moderate intervention approach in firm operations. Excessive government control, whether too weak or too strong, proves ineffective in enhancing earnings quality. The research posits that a balanced, moderate rate of state ownership, coupled with judicious intervention, can mitigate financial constraints and bolster earnings quality. This insight carries policy implications for regulatory frameworks seeking to optimize economic performance.

While this research sheds light on critical aspects of financial constraints, state ownership, and earnings quality, it is not without limitations. The focus on the Vietnamese context raises questions about generalizability, prompting future studies to explore diverse emerging markets. Additionally, the examination of state ownership can benefit from a more nuanced exploration of governance mechanisms and specific policy interventions. Future research endeavors might delve deeper into the mechanisms through which moderate state ownership levels mitigate financial constraints. A qualitative exploration of company-specific strategies and government interventions can provide a richer understanding of the dynamics at play. Furthermore, a longitudinal perspective could unravel the evolving impact of state ownership on earnings quality over extended periods.

As a matter of fact, after Doi Moi Policy in 1986, the state economy has to give ground to the market economy, leading to the era of private sectors dominated by corporate enterprises. Their prosperous development has significantly changed our mindset on the dominating role of the state economy. In reality, what has been happening in recent decades demonstrates that its extensive participation is no longer suitable for driving the economy due to social and economic shifts. John Maynard Keynes, who devoted all his life to supporting the government’s intervention into the economy, once said, “When the facts change, I change my mind. What do you do, Sir?”

Footnotes

Appendix

Table A2.

Estimated Results Using Fixed Effects Model.

$E Q_{it}$	Firm size—FC1			Foundation years—FC2			Financial leverage—FC3			Dividend payouts—FC4
	Low	Medium	High	Low	Medium	High	Low	Medium	High	Low	Medium	High
	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate	SOE rate
Constant	0.141***	0.169***	0.0557*	−0.0246	−0.101***	−0.0921***	0.405***	0.347***	0.355***	0.415***	0.365***	0.374***
	(0.0394)	(0.0284)	(0.0292)	(0.0300)	(0.0237)	(0.0245)	(0.0180)	(0.0111)	(0.0112)	(0.0152)	(0.00887)	(0.00897)
$Liquidit y_{it}$	0.00870***	0.00746***	0.00869***	0.00774***	0.00763***	0.00777***	0.00935***	0.00915***	0.00930***	0.00645***	0.00631***	0.00656***
	(0.000777)	(0.000732)	(0.000777)	(0.000744)	(0.000738)	(0.000746)	(0.000790)	(0.000782)	(0.000790)	(0.000737)	(0.000728)	(0.000736)
$Profitabilit y_{it}$	−8.83e-06	−2.62e-06	−6.70e-06	−7.32e-06	−4.17e-06	−4.53e-06	−1.63e-05*	−1.31e-05	−1.26e-05	−1.23e-05	−9.46e-06	−1.03e-05
	(8.26e-06)	(7.72e-06)	(8.30e-06)	(7.84e-06)	(7.78e-06)	(7.88e-06)	(8.37e-06)	(8.28e-06)	(8.37e-06)	(7.70e-06)	(7.61e-06)	(7.70e-06)
$Revenu e_{it}$	1.69e-05***	9.6e-06***	1.7e-05***	1.2e-05***	1.1e-05***	1.2e-05***	2.1e-05***	2.0e-05***	2.1e-05***	1.4e-05***	1.3e-05***	1.4e-05***
	(1.99e-06)	(1.90e-06)	(1.99e-06)	(1.92e-06)	(1.90e-06)	(1.92e-06)	(1.99e-06)	(1.98e-06)	(1.99e-06)	(1.86e-06)	(1.84e-06)	(1.85e-06)
$Audi t_{it}$	0.0601***	0.00170	0.0850***	0.00239	0.0107	0.0297***	0.0696***	0.0719***	0.0973***	0.0455***	0.0473***	0.0681***
	(0.0108)	(0.0100)	(0.0104)	(0.0107)	(0.00999)	(0.0104)	(0.0110)	(0.0102)	(0.0106)	(0.0101)	(0.00944)	(0.00981)
$F C_{it}$	−0.0468***	−0.0255***	−0.0541***	−0.0221***	−0.0223***	−0.0222***	−0.00010**	−0.0001***	−0.0001***	−0.0039***	−0.0044***	−0.0039***
	(0.00606)	(0.00447)	(0.00465)	(0.00115)	(0.00104)	(0.00106)	(4.25e-05)	(3.19e-05)	(3.22e-05)	(0.000256)	(0.000180)	(0.000183)
$SO E_{it}$	−0.115***	0.245***	−0.0457	−0.0986***	0.165***	−0.0452	−0.0575***	0.147***	−0.0661***	−0.0490***	0.145***	−0.0571***
	(0.0405)	(0.0489)	(0.0431)	(0.0259)	(0.0343)	(0.0292)	(0.0152)	(0.0170)	(0.0157)	(0.0136)	(0.0159)	(0.0140)
$F C_{it} \times SO E_{it}$	−0.0110*	0.0199**	0.00237	−0.00116	0.00114	−0.000234	−2.96e-05	−1.87e-05	−2.54e-06	−0.000329	0.00198***	−0.0011***
	(0.00639)	(0.00791)	(0.00682)	(0.000837)	(0.00124)	(0.000943)	(2.91e-05)	(5.16e-05)	(3.26e-05)	(0.000326)	(0.000403)	(0.000395)
Observations	3,962	3,962	3,962	3,962	3,962	3,962	3,962	3,962	3,962	3,962	3,962	3,962
R ² within	0.143	0.249	0.144	0.218	0.231	0.214	0.108	0.127	0.110	0.246	0.262	0.248
R ² between	0.0006	0.0017	0.0033	0.00467	0.0049	0.00325	0.00133	0.000365	0.00357	0.000311	0.0000769	0.00541
R ² overall	0.0105	0.0426	0.016	0.00000436	0.00000303	0.0000495	0.00524	0.00475	0.00781	0.0543	0.0488	0.0726
F statistics	79.9	78.93	80.18	133.3	143.2	129.7	57.99	69.42	58.9	155.7	169.6	156.9
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Source. Calculated by the author.

Standard errors in parentheses: *p < .1. **p < .05. ***p < .01

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nguyen Cong Thanh

Minh Ly Duc

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

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