Unpacking Corporate Slack: How Excess Resources Shape Earnings Management

Abstract

This study investigates the dual role of organizational slack in shaping managerial behavior by examining its impact on earnings management. Using a large sample of 89,782 firm-year observations of US-listed firms from 1991 to 2022, we find that firms with greater slack are generally less likely to engage in earnings manipulation. We interpret these findings through the lens of the resource-based view (RBV), which provides a perspective on how slack, as a strategic resource, can influence managerial choices. Specifically, by alleviating short-term performance pressures, slack enables managers to prioritize long-term value creation over opportunistic reporting. Decomposing slack into its components reveals nuanced patterns: its effect on real earnings management is robust across firms, whereas its impact on discretionary accruals is significant primarily in firms with strong managerial incentives. Taken together, these findings enhance our understanding of how resource flexibility shapes firms’ financial reporting.

Jel Classification: G32; G34; M41

Introduction

Organizational slack is widely recognized as a double-edged sword in managerial decision-making. On the one hand, slack can buffer firms against unexpected shocks, provide flexibility for long-term investments, and support strategic adaptation; on the other hand, excessive slack may encourage inefficiency, managerial complacency, and opportunistic behavior (Bromiley, 1991; Schnatterly et al., 2018; Wiersma, 2017). As Vanacker et al. (2016) note, firms face a fundamental tension: preserve slack to ensure stability or deploy it to pursue growth; each option, however, carries potential risks. This tension extends to managerial behavior, as slack can both reduce the pressure to manipulate earnings and create opportunities for opportunistic reporting. While understanding this dual nature is essential, prior research has paid limited attention to how slack shapes financial reporting behavior.

As stated, understanding this tension is critical, yet prior research has not fully examined how slack shapes earnings management. Accordingly, our study addresses this gap by investigating whether slack contributes to earnings manipulation and highlights the potential dark side of slack for corporate governance and performance. Furthermore, by decomposing slack into its distinct components, the study reveals that different forms of slack exert unequal influence on managerial decisions, thereby offering a more nuanced and practical understanding of how resources affect financial reporting behavior.

Agency theory describes the conflicts of interest that may arise between principals and agents. The presence of slack resources contributes to the theory by suggesting that excess resources may encourage self-interested behavior. Excess resources provide managerial discretion, which may create opportunities for managers to pursue personal interests rather than the firm’s long-term goals (Lin et al., 2009). Because slack often reduces oversight and external pressures, managers gain greater discretion in allocating resources, which may encourage the pursuit of perks or self-serving rewards (Du et al., 2015) rather than investments that enhance sustainable growth (Jensen, 1994; John et al., 2017; Tan & Peng, 2003). Mismanaged slack can also manifest in organizational inefficiency (e.g., Wiersma, 2017), excessive risk-taking (Bromiley, 1991), and internal political behavior (Bourgeois & Singh, 1983). In this sense, excessive slack not only fosters complacency and managerial entrenchment but also may increase the likelihood of earnings management, as managers may attempt to maximize their bonuses and to meet earnings forecast (Beaudoin, 2008; Zhang & Gimeno, 2016) through manipulated financial reporting.

On the contrary, within the framework of the resource-based view (RBV), slack can be conceptualized as a strategic asset—a pool of resources that firms can leverage to support innovation (Marlin & Geiger, 2015a), strengthen stakeholder relationships, and sustain competitive advantage. In a previous study, Barney (1991) suggests that the accumulation of valuable, rare, inimitable, and non-substitutable (VRIN) is the main components of firm competitiveness, which link firms’ resources and sustained competitive advantage. Within the RBV framework, slack resources enrich this view by identifying that firms with VRIN resources already enjoy durable competitive positions, and slack can be interpreted as buffers. Furthermore, slack provides the necessary resources to address and manage challenges while maintaining the stability of ongoing business operations (Bourgeois, 1981), thereby reducing short-term performance pressures and lessening managers’ incentives to engage in earnings manipulation merely to meet immediate targets. By providing a buffer against short-term pressures, slack resources allow managers to focus on long-term projects, such as innovation and growth opportunities, and reinforce the firm’s competitive advantage by supporting the effective use of VRIN resources.

In this sense, slack not only insulates firms from external shocks but also facilitates strategic adaptation to changing environments. As highlighted by Mishina et al. (2024), the outcomes of slack depend largely on how managers choose to deploy it. Prior research further suggests that the effective use of slack may foster innovation (Hu et al., 2023; Terry Mousa & Chowdhury, 2014), environmental performance (Symeou et al., 2019), and organizational resilience to future uncertainties (Suzuki, 2018; Vanacker et al., 2016).

Although both organizational slack and earnings management are well studied, the relationship between the two remains underexplored. Prior research has largely examined slack’s positive role in fostering innovation and resilience (Hu et al., 2023; Suzuki, 2018; Symeou et al., 2019; Terry Mousa & Chowdhury, 2014; Vanacker et al., 2016) and its potential negative effects on efficiency and risk-taking (Bromiley, 1991; Du et al., 2015; Wiersma, 2017), although it has given little attention to how slack may shape financial reporting behavior. In particular, it is unclear whether slack exacerbates opportunities for manipulation or alleviates short-term performance pressures that drive managers toward earnings management.

Investigating this link is essential because it addresses a critical gap at the intersection of resource utilization and ethical decision-making. Understanding whether slack contributes to or constrains earnings management offers important insights into managerial behavior, corporate governance, and the conditions under which slack serves as a strategic buffer versus a liability.

To examine the impact of organizational slack on earnings management, we analyze a large sample of US-listed firms, comprising 89,782 firm-year observations from 1991 to 2022. Our results indicate that firms with greater slack are less likely to engage in earnings manipulation, a finding that is consistent with the slack resource view, which asserts that slack alleviates short-term pressures and provides financial flexibility. This flexibility enables managers to focus on long-term value creation (Tan & Peng, 2003; Wiersma, 2017), rather than resorting to short-term earnings manipulation. Moreover, these findings remain robust after potential endogeneity concerns are addressed using firm fixed effects, propensity score matching (PSM), lead–lag analysis, and two-stage least squares (2SLS).

Importantly, by decomposing slack into its constituent components, we uncover a more nuanced pattern. Specifically, we find that the effect of slack on real earnings management (REM) is consistent across firms, whereas its impact on discretionary accruals (DAEM) is significant only in firms with high managerial incentives (as measured by Delta and Vega) and in manufacturing firms. This suggests that slack constrains opportunistic behavior, particularly when managerial motivations to manipulate earnings are strong or when operational contexts facilitate manipulation.

This study makes several important contributions to accounting and corporate finance literature. First, it is among the few studies to directly examine the relationship between organizational slack and earnings management, thereby extending our understanding of how slack resources shape managerial behavior. Our findings show that firms with ample slack are less likely to engage in earnings manipulation because slack reduces short-term pressures and enables managers to prioritize long-term value creation, including innovation and capital-intensive investments. In doing so, we identify an additional benefit of slack, complementing prior research that highlights the positive effects of slack on innovation, environmental performance, and organizational resilience (e.g., Hu et al., 2023; Suzuki, 2018; Symeou et al., 2019; Terry Mousa & Chowdhury, 2014; Vanacker et al., 2016).

Second, by decomposing slack into its distinct components, we provide a clearer understanding of how different types of slack influence various forms of earnings management. Specifically, the link between slack and REM is robust and salient across firms, whereas the effect of slack on DAEM is conditional and becomes attenuated in the presence of strong managerial incentives or opportunistic behavior. These nuanced findings reinforce both RBV and agency theory.

The remainder of the study is structured as follows. Section “Literature Review and Hypotheses” reviews the relevant literature and develops the hypotheses. Section “Data and Summary Statistics” details the data collection and presents summary statistics. Section “Empirical Results” reports the main results alongside several robustness tests, and section “Conclusion” concludes with a discussion of the findings and their implications.

Literature Review and Hypotheses

Organizational Slack

Organizational slack refers to the surplus resources that a firm maintains beyond its immediate obligations, serving as a buffer to protect core operations from external pressures and uncertainties. Accordingly, it represents the difference between a firm’s total available resources—such as capital, labor, and time—and its liabilities (Cyert & March, 1963; Bourgeois, 1981). Far from being idle or wasteful, slack resources constitute a flexible reserve that can be redeployed to support strategic objectives. Firms may draw upon slack to fund innovation, pursue new initiatives, or respond to unforeseen challenges, highlighting its role as both a protective and an enabling resource (e.g., George, 2005; Mohr & Young, 2012).

The role of organizational slack is therefore twofold. On the one hand, slack cushions the organization against environmental volatility, allowing it to maintain stability when confronted with shocks. On the other hand, slack facilitates adaptability, as managers can redirect surplus resources toward emerging opportunities or strategic investments. In this sense, slack is not only defensive but also generative, enhancing the firm’s capacity to pursue long-term goals. However, slack is a double-edged sword, i.e., while it enables flexibility, an excess of resources can foster inefficiencies or reduce managerial discipline, leading to underutilization or misallocation of resources (e.g., Khanna & Rivkin, 2001; Weng & Yang, 2024).

Building on these insights, Bourgeois (1981) classified organizational slack into three distinct types—available slack, recoverable slack, and potential slack—each of which varies in terms of accessibility and implications for managerial decision-making. Available slack refers to readily accessible resources, such as excess cash or marketable securities (Bourgeois & Singh, 1983; Wiersma, 2017), that can be quickly deployed to meet immediate needs. While this form of slack enhances flexibility, an overabundance can encourage complacency and inefficient allocation, undermining firm performance (Lefebvre, 2024; Lepoutre & Heene, 2006). Recoverable slack, by contrast, consists of resources tied to specific budgetary allocations, such as promotional spending or consultancy fees, which can be redirected but may require managerial decisions (Wiersma, 2017). Finally, potential slack reflects the firm’s capacity to secure additional resources from external capital markets, typically measured through its debt-to-equity ratio (Marlin & Geiger, 2015b; Navarro, 1988). Unlike internal slack, potential slack may be subject to the scrutiny of creditors and investors (Geiger & Cashen, 2002; Herold et al., 2006), thus constraining managerial discretion and discouraging opportunistic practices such as earnings manipulation.

In sum, organizational slack encompasses a firm’s excess resources that may serve both protective and strategic functions. Its classification into available, recoverable, and potential slack underscores the diverse ways in which slack can shape organizational behavior, depending on its accessibility and the degree of oversight it entails. Properly managed, slack enhances resilience and adaptability; mismanaged, it risks inefficiency and weakened accountability.

Earnings Management

Schipper (1989) defines earnings management as a deliberate intervention in the financial reporting process aimed at achieving private gains. Managers may engage in earnings management to meet analysts’ expectations, secure performance-based incentives, lower the cost of capital, or present a favorable image of the company to stakeholders (e.g., Busirin et al., 2015; Goel, 2014; Healy & Wahlen, 1999; Hsu et al., 2024; Lo, 2008; Mao & Renneboog, 2015; Rani et al., 2013; Strobl, 2013). Earnings management can also be used to influence contractual outcomes that depend on reported financial figures, making it an attractive option for managers seeking to shape both market perceptions and organizational outcomes.

Earnings management is generally carried out through two primary channels, namely, adjustments in accruals within accounting discretion and deviations from normal operational activities. These practices are widely classified as DAEM and REM, respectively (Badertscher, 2011; Enomoto et al., 2015; Gunny, 2010; Healy & Wahlen, 1999; Schipper, 1989). While both methods serve the purpose of manipulating reported earnings, they differ in their mechanisms and consequences.

DAEM involves the use of accounting estimates and judgments permitted under generally accepted accounting principles (GAAP) to obscure or mask the firm’s underlying economic performance (Dechow & Skinner, 2000; Taylor & Xu, 2010). Common practices include altering provisions for bad debts, adjusting depreciation schedules, and changing the timing of asset write-offs (Tabassum et al., 2015). Because it operates within the boundaries of accounting discretion, DAEM often avoids immediate detection and allows managers to influence reported earnings without disrupting day-to-day business activities. However, the costs of this approach tend to accumulate over time. Accrual manipulation reduces the credibility and reliability of financial reporting (Dokas et al., 2025), draws greater scrutiny from auditors and regulators (Cohen & Zarowin, 2008, 2010), and can even affect major corporate transactions. For example, acquirers in mergers and acquisitions may perceive low-quality accounting information and adjust their evaluations accordingly, with some deals being discounted or terminated altogether due to the presence of DAEM (e.g., Marquardt & Zur, 2015; Martin & Shalev, 2017; Skaife & Wangerin, 2013).

In contrast, REM manipulates reported performance through actual operational decisions rather than accounting adjustments. Roychowdhury (2006) defines it as deviations from normal operational practices undertaken to mislead stakeholders into believing that financial reporting goals have been met through regular activities. Examples include cutting discretionary expenditures such as research and development, offering unusually large sales discounts, or overproducing to lower the reported costs of goods sold (Tabassum et al., 2015). Compared with accrual manipulation, REM tends to have more immediate and tangible effects. From a signaling perspective, managers may use it to convey confidence in future performance and reduce financing costs (Gunny, 2010; Habib et al., 2022). However, from an agency perspective, REM is often regarded as opportunistic behavior that weakens the reliability of financial reporting. Empirical evidence shows that such practices can inflate customer expectations for discounts, reduce long-term profitability, and weaken future cash flows (Roychowdhury, 2006). By distorting actual operations, REM increases information risk, undermines transparency, and can ultimately harm a firm’s long-term sustainability. Its prevalence is often assessed through abnormal discretionary cash flows from operations, abnormal discretionary expenses, and abnormal production costs (Roychowdhury, 2006).

In summary, both DAEM and REM aim to distort reported earnings, but they differ significantly in their methods and implications. DAEM relies on accounting discretion and is less visible in the short term; however, it undermines reporting quality and poses risks in regulatory and transactional contexts over the long term. REM, by contrast, directly affects business operations and cash flows, creating more immediate distortions that can jeopardize future competitiveness and sustainability. Taken together, these two forms of manipulation highlight the multifaceted nature of earnings management and its complex consequences for firms, investors, and regulators alike.

Organizational Slack and Earnings Management

We examine the relationship between organizational slack and earnings management through two complementary theoretical lenses—agency theory and the RBV—which generate distinct, sometimes contrasting, predictions. Explicitly aligning each hypothesis with its theoretical foundation clarifies the assumptions and mechanisms underlying our analysis.

Agency Theory

Agency theory (Jensen & Meckling, 1976) posits that conflicts of interest between managers and shareholders create incentives for managers to pursue personal objectives—such as compensation, career advancement, or reputation—rather than maximizing firm value. Under this view, slack can serve as a signal of potential managerial opportunism, particularly in environments where monitoring is weak. Managers with greater discretion may exploit slack to engage in earnings management, for example, to smooth earnings, protect discretionary budgets, or shield underperforming projects (e.g., Bourgeois, 1981; Jensen, 1986).

The Behavioral Theory of the Firm (Cyert & March, 2015) further highlights that organizations operate as political coalitions, in which slack provides managers with the resources to pursue multiple and sometimes conflicting objectives. Slack relaxes internal controls and short-term performance pressures, enabling experimentation and accommodation of internal constituencies but also reducing discipline and accountability. From this integrated perspective, slack lowers external pressure while increasing internal discretion, creating an environment conducive to managerial opportunism. In addition, empirical evidence shows that firms with weak governance, is associated with higher levels of DAEM (e.g., Jiang et al., 2008).

Importantly, agency theory recognizes that slack can also have positive signaling effects in dynamic environments. Firms may use slack resources to pursue long-term growth and resilience, suggesting that slack does not uniformly encourage opportunism. In our analysis, however, we focus on the assumption that slack primarily amplifies managerial discretion and the potential for self-interested behavior, particularly in contexts where performance monitoring is low, and incentives for earnings management are present.

• H1 (Agency Theory): Firms with higher levels of organizational slack are more likely to engage in earnings management.

Resource-Based View

The RBV treats slack as a strategic asset—a pool of resources that can be redeployed to support innovation, strengthen stakeholder relationships, and sustain competitive advantage (Marlin & Geiger, 2015a). Unlike agency theory, RBV assumes that firms are generally in or tend toward equilibrium, and slack can be a source of long-term value creation rather than opportunistic behavior. By providing resources for real performance improvements rather than short-term financial engineering, slack enables firms to pursue strategic objectives while reducing reliance on earnings management.

While the RBV provides the theoretical foundation for understanding slack as a strategic resource, the slack resources view complements RBV by offering a perspective on how slack is used in practice (e.g., Cyert & March, 1963; Waddock & Graves, 1997). Slack can act as a buffer, helping firms absorb external shocks, invest in innovation, and focus on sustainable growth. By alleviating short-term financial pressures (e.g., Bourgeois, 1981; Latham & Braun, 2008; Liang et al., 2023; Vanacker et al., 2016), slack may reduces managers’ incentives to manipulate reported earnings and provides flexibility to prioritize long-term performance.

The integration between RBV and slack resource view highlights that slack can mitigate earnings management by providing resources that facilitate real performance improvements and reduce short-term pressures. This perspective emphasizes the strategic benefits of slack, in contrast to the opportunistic risks emphasized by agency theory.

• H2 (Resource-Based View): Firms with higher levels of organizational slack are less likely to engage in earnings management.

Financial Performance, Governance, and Slack Types

The relationship between slack and earnings management is also shaped by financial performance and governance quality. Firms with strong performance often accumulate slack and simultaneously face less pressure to manipulate earnings, raising the possibility that observed negative associations between slack and earnings management reflect underlying financial strength rather than slack itself. This endogeneity concern implies that slack’s effect on earnings management may be contingent on performance pressure and governance mechanisms, with earnings manipulation more likely when firms are near missing targets and governance is weak (John et al., 2017).

Organizational slack is heterogeneous, and different types of slack may exert distinct effects on managerial behavior (Agusti-Perez et al., 2020). Available slack, being internal and flexible, can reduce pressure to manipulate earnings but may also encourage risk-taking that increases manipulation. Recoverable slack, embedded in budgets, can be reallocated during downturns, but its opacity may enable opportunistic behavior. Potential slack, defined as access to external financing and provides flexibility (e.g., Bourgeois, 1981; Geiger & Cashen, 2002; Herold et al., 2006; Vanacker et al., 2016) that may reduce earnings management, yet debt obligations may create incentives to meet reporting targets, potentially increasing EM under certain conditions.

• H3: The effects of organizational slack on earnings management vary by slack type, such that available, recoverable, and potential slacks may either increase or decrease the likelihood of EM depending on how managers deploy and govern these resources.

Data and Summary Statistics

The data employed in this study are obtained from the Compustat Capital IQ database, which provides the comprehensive information required to construct measures of organizational slack, earnings management, and firm-specific financial characteristics. To enhance data reliability, we exclude firms with negative book equity or revenues, as such cases may represent extreme outliers that could distort the analysis. In addition, financial firms (SIC codes 6000-6999) and utility firms (SIC codes 4900-4999) are excluded, given that their financial reporting and operating environments are subject to industry-specific regulations that may bias the results. All continuous variables are winsorized at the 1st and 99th percentiles to mitigate the influence of extreme observations. Furthermore, firm-year observations with substantial missing data on key variables are removed to ensure a balanced and consistent sample. The final dataset covers the period from 1991 to 2022 and comprises 89,782 firm-year observations.

Organizational Slack Measures

Bourgeois and Singh (1983) introduced the concept of “ease-of-recovery” to classify organizational slack into three distinct forms—available slack, recoverable slack, and potential slack. This tripartite classification has been widely adopted in subsequent research because it provides both conceptual clarity and empirical tractability (e.g., Bourgeois & Singh, 1983; Bromiley, 1991; Cheng & Kesner, 1997; Chiu & Liaw, 2009; Geiger & Cashen, 2002). Consistent with this stream of literature, this study operationalizes organizational slack as follows.

First, Available Slack is measured using the current ratio (current assets divided by current liabilities), which reflects readily accessible but unutilized resources. Second, Recoverable Slack is proxied by the ratio of selling, general, and administrative expenses to sales (SG&A/sales), capturing resources currently allocated to discretionary operational activities that can be redeployed if necessary. Third, Potential Slack is measured by the equity-to-debt ratio, indicating a firm’s capacity to secure external resources through financing (Cheng & Kesner, 1997; Daniel et al., 2004).

To consolidate these dimensions into a more comprehensive indicator, we also construct a composite organizational slack index, defined as the first principal component derived from a principal component analysis (PCA) of Available Slack, Recoverable Slack, and Potential Slack. This approach follows prior studies emphasizing the advantages of capturing slack as a multidimensional construct (Janićijević et al., 2022; Marlin & Geiger, 2015a).

For robustness, the analysis further incorporates conventional financial slack measures frequently used in the literature, including Cash Slack (cash and cash equivalents scaled by total assets), Net Working Capital Slack (current assets—current liabilities scaled by total assets), and Debt Capacity Slack (1—long-term debt/total assets). These additional measures provide complementary insights, ensuring that the findings are not driven by a particular operationalization of slack.

Earnings Management Measures

We assess earnings management through two approaches, namely, DAEM and REM (i.e., real activity manipulation). Following Roychowdhury (2006), DAEM are estimated using the modified Jones model (Jones, 1991). Specifically, we estimate the following industry-year regression

$$\begin{array}{l}\frac{{\mathrm{TA}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}=a_0+a_1\frac{1}{{\mathrm{Asset}}_{i,t-1}}+a_2\frac{{\mathrm{DSales}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}\\ +a_3\frac{{\mathrm{PPE}}_{i,t-1}}{{\mathrm{Asset}}_{i,t-1}}+e_{i,t}\end{array}$$

(1)

where TA _i,t represents the total assets in year t−1, DSales_i,t is the change in sales, and PPE _i,t−1 represents gross property, plant, and equipment. DAEM is defined as deviations from the predicted values of the corresponding industry-year regression.

To measure REM, we follow Roychowdhury (2006) and Cohen et al. (2008), who identify three channels through which firms may alter real activities, namely, abnormal cash flows from operations, abnormal production costs, and reductions in discretionary expenses.

First, abnormal cash flows from operations (REM_CFO) are estimated from the following industry-year regression

$$\begin{array}{l}\frac{{\mathrm{CFO}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}=b_0+b_1\frac{1}{{\mathrm{Asset}}_{i,t-1}}+b_2\frac{{\mathrm{Sales}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}\\ +b_3\frac{{\mathrm{DSales}}_{i,t-1}}{{\mathrm{Asset}}_{i,t-1}}+e_{i,t}\end{array}$$

(2)

where CFO _i,t is cash flow from operations. An abnormal CFO is defined as the deviation between the actual and predicted values.

Second, abnormal production costs (REM_PROD) are obtained from the regression

$$\begin{array}{l}\frac{{\mathrm{Prod}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}=b_0+b_1\frac{1}{{\mathrm{Asset}}_{i,t-1}}+b_2\frac{{\mathrm{Sales}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}\\ +b_3\frac{{\mathrm{DSales}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}+b_4\frac{{\mathrm{DSales}}_{i,t-1}}{{\mathrm{Asset}}_{i,t-1}}+e_{i,t}\end{array}$$

(3)

where Prod _i,t denotes the cost of goods sold plus inventory changes. The residuals represent abnormal production costs.

Third, abnormal discretionary expenses (REM_DISX) are estimated as follows

$$\frac{{\mathrm{Disx}}_{i,t}}{{\mathrm{Asset}}_{i,t-1}}=b_0+b_1\frac{1}{{\mathrm{Asset}}_{i,t-1}}+b_2\frac{{\mathrm{Sales}}_{i,t-1}}{{\mathrm{Asset}}_{i,t-1}}+e_{i,t}$$

(4)

where Disx _i,t is the sum of SG&A expenses, research and development (R&D) expenditures, and advertising costs. Abnormal discretionary expenses are defined as deviations from predicted levels.

Therefore, REM is the sum of REM_CFO, REM_PROD, and REM_DISX. This aggregate measure captures the extent to which a firm manipulates its financial performance through real operational decisions.

Control Variables

We control for several firm characteristics that prior research has determined to influence earnings management. Firm size is included because larger firms attract greater scrutiny but possess more resources and flexibility that may facilitate manipulation (John et al., 2017). Tobin’s Q proxies for growth opportunities, as firms with higher valuations face stronger market pressures and thus greater incentives to manage earnings (Nguyen et al., 2024). Return on Assets captures operating performance, since highly profitable firms are generally less motivated to manipulate earnings, whereas less profitable firms may do so to meet benchmarks (Roychowdhury, 2006). R&D Intensity is controlled because R&D expenditures increase earnings volatility and allow for greater managerial discretion in reporting (Shust, 2015). Leverage reflects debt-related monitoring and covenant pressures that may simultaneously constrain and motivate earnings management (Anagnostopoulou & Tsekrekos, 2017). Tangibility, measured as property, plant, and equipment over total assets, is included because tangible asset intensity affects financing constraints and reporting choices. Finally, Market Competition is industry competition and captured using the Herfindahl–Hirschman index (HHI) following Hou and Robinson (2006). Market competition is considered as weaker competitive positions can encourage earnings manipulation (Datta et al., 2013), whereas stronger competition may function as an external governance mechanism limiting managerial discretion (Giroud & Mueller, 2011).

To address unobserved heterogeneity, our baseline specification includes both industry- and year-fixed effects. Industry fixed effects capture time-invariant sectoral characteristics, such as accounting practices, regulatory environments, and competitive structures, whereas year fixed effects account for macroeconomic shocks common to all firms each year. Although firm fixed effects are employed as a robustness check, industry fixed effects are more appropriate in this context. This is because certain types of slack, particularly Potential Slack, are closely tied to firms’ capital structures, which tend to be relatively stable over time and do not exhibit substantial within-firm variation. As a result, the firm fixed effects risk absorbing part of the variation in slack that is theoretically relevant to its effect on earnings management. By contrast, systematic differences across industries in capital structure, investment intensity, and asset allocation make industry fixed effects better suited for isolating meaningful variations in organizational slack that influence earnings management practices.

Summary Statistics

Panel A of Table 1 presents the summary statistics for the slack measures, earnings management variables, and control variables employed in this study. Detailed definitions of all variables are provided in the Appendix. To reduce skewness and approximate a normal distribution, slack variables are logarithmically transformed. The mean values for Available Slack, Recoverable Slack, and Potential Slack are 0.5719, −1.4039, and 0.9983, respectively, with corresponding standard deviations of 0.7397, 0.984, and 1.7579. To mitigate the influence of extreme observations, all continuous variables are winsorized at the 1st and 99th percentiles.

Table 1.

Summary Statistics.

Panel A
			Obs.	M	SD	P25	P50	P75
DAEM			89,782	0.1553	2.0101	−0.0838	0.0306	0.2509
REM			89,782	−0.0057	0.0830	−0.0226	−0.0010	0.0137
REM_PROD			89,782	−0.0188	0.4672	−0.1681	−0.0159	0.1212
REM_CFO			89,782	−0.1029	0.6080	−0.2292	−0.0563	0.0547
REM_DISX			89,782	0.2560	1.8330	−0.0994	0.1157	0.4610
Organizational Slack			76,070	0.0175	1.1831	−0.7641	−0.0973	0.6681
Available Slack			89,782	0.5719	0.7397	0.1670	0.5841	1.0104
Recoverable Slack			89,782	−1.4039	0.9840	−1.9822	−1.4072	−0.8937
Potential Slack			89,782	0.9983	1.7579	−0.0547	0.7241	1.7757
Firm Size			89,782	5.3522	2.3565	3.6227	5.3472	7.0564
Tobin’s Q			89,782	2.0975	1.9540	1.0858	1.4723	2.2606
Return on Assets			89,782	0.0229	0.2880	0.0118	0.1004	0.1576
R&D Intensity			89,782	0.2546	1.2699	0.0000	0.0000	0.0528
Leverage			89,782	0.2760	0.2075	0.1077	0.2445	0.3995
Tangibility			89,782	0.2796	0.2308	0.0968	0.2104	0.4031
Market Competition			89,782	0.8001	0.1568	0.7418	0.8503	0.9098
Panel B
	z	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) DAEM	1.000
(2) REM	−0.212***	1.000
(3) REM_PROD	−0.099***	0.515***	1.000
(4) REM_CFO	−0.203***	0.901***	0.280***	1.000
(5) REM_DISX	0.176***	−0.633***	−0.059***	−0.561***	1.000
(6) Organizational Slack	−0.004	−0.047***	−0.161***	−0.028***	−0.026***	1.000
(7) Available Slack	0.027***	−0.054***	−0.037***	−0.064***	0.037***	0.776***	1.000
(8) Recoverable Slack	−0.024***	0.006*	−0.224***	0.055***	−0.056***	0.500***	0.034***	1.000
(9) Potential Slack	−0.005	−0.034***	−0.064***	−0.034***	−0.011***	0.810***	0.420***	0.192***
(10) Firm Size	0.010***	−0.105***	−0.036***	−0.123***	0.048***	−0.241***	0.053***	−0.403***
(11) Tobin’s Q	0.010***	0.021***	−0.031***	0.025***	−0.006*	0.242***	−0.020***	0.370***
(12) Return on Assets	0.036***	−0.203***	−0.168***	−0.209***	0.020***	−0.162***	0.147***	−0.575***
(13) R&D Intensity	0.009***	0.086***	0.165***	0.054***	0.077***	0.227***	0.158***	0.377***
(14) Leverage	0.002	0.037***	0.044***	0.040***	0.005	−0.675***	−0.362***	−0.106***
(15) Tangibility	0.025***	0.020***	0.014***	0.031***	−0.021***	−0.390***	−0.320***	−0.320***
(16) Market Competition	0.004	−0.035***	−0.009***	−0.041***	0.046***	0.064***	0.017***	0.117***
	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)
(9) Potential Slack	1.000
(10) Firm Size	−0.182***	1.000
(11) Tobin’s Q	0.167***	−0.210***	1.000
(12) Return on Assets	−0.045***	0.440***	−0.402***	1.000
(13) R&D Intensity	0.109***	−0.133***	0.225***	−0.438***	1.000
(14) Leverage	−0.856***	0.094***	−0.061***	−0.034***	−0.060***	1.000
(15) Tangibility	−0.210***	0.123***	−0.157***	0.149***	−0.118***	0.218***	1.000
(16) Market Competition	0.059***	−0.056***	0.074***	−0.082***	0.106***	−0.021***	−0.008**	1.000

Note: This table presents descriptive statistics and a pairwise correlation matrix for the principal variables utilized in this study. Panel A shows that the sample consists of approximately 89,782 firm-year observations over the period 1991–2022. To mitigate the influence of extreme observations, all continuous variables are winsorized at the 1st and 99th percentiles. Variable definitions and measurement details are provided in Appendix 1. Panel B reports the pairwise correlations among the key variables employed in this study.

Statistical significance is denoted by superscripts *, **, and ***, corresponding to the 10%, 5%, and 1% significance levels, respectively.

Panel B Table 1 reports the pairwise correlations among slack measures, earnings management, and control variables. Preliminary analysis indicates that most correlations between slack and earnings management are negative and statistically significant, suggesting that firms with greater slack tend to engage in lower levels of earnings management. Nevertheless, regression analyses are necessary to further explore the direction and magnitude of these relationships.

Empirical Results

The Effect of Organizational Slack on Earnings Management

To investigate the impact of organizational slack—comprising Available Slack, Recoverable Slack, and Potential Slack—on earnings management, we estimate the following regression model

$$\begin{array}{l}\mathrm{DAEM}/{\mathrm{REM}}_{i,t+1}=a_0+a_1{\mathrm{Slack}}_{i,t}\\ +b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}\end{array}$$

(5)

where DAEM represents discretionary accrual-based earnings management, and REM denotes real earnings management in the subsequent year t + 1. The variable Slack captures Available Slack, Recoverable Slack, and Potential Slack measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals, respectively.

Panel A of Table 2 presents the overall impact of organizational slack on earnings management. The coefficients of slack are negative and statistically significant at the 1% level, providing robust preliminary support for Hypothesis 2 (RBV). In terms of economic significance, a one standard deviation increase in organizational slack reduces DAEM by 0.0311, which is equivalent to approximately 20% of its mean, but only 1.5% of its overall variation. For REM, a one-standard-deviation increase in slack decreases REM by roughly the same magnitude as its mean and represents approximately 7% of its total variation. These results indicate that organizational slack not only has a statistically significant effect but also a meaningful effect relative to the average levels of earnings management, thus underscoring its role as a buffer that mitigates managerial incentives to manipulate reported performance.

Table 2.

The Impact of Organizational Slack on Earnings Management.

Panel A
	DAEMt+1	DAEMt+1	DAEMt+1	REMt+1	REMt+1	REMt+1
	(1)	(2)	(3)	(4)	(5)	(6)
Organizational Slack	−0.0340*** (0.008)		−0.0263** (0.011)	−0.0010*** (0.000)		−0.0048*** (0.000)
Firm Size		−0.0297*** (0.004)	−0.0292*** (0.004)		−0.0005*** (0.000)	−0.0008*** (0.000)
Tobin’s Q		0.0153*** (0.006)	0.0127** (0.006)		0.0002(0.000)	−0.0009*** (0.000)
Return on Assets		0.3022*** (0.053)	0.4081*** (0.059)		−0.0583*** (0.002)	−0.0523*** (0.003)
R&D Intensity		−0.0147(0.012)	0.0119(0.028)		0.0017*** (0.001)	0.0016(0.001)
Leverage		0.0766* (0.040)	−0.0494(0.059)		0.0006(0.002)	−0.0185*** (0.002)
Tangibility		0.6163*** (0.052)	0.5558*** (0.055)		−0.0036** (0.002)	−0.0100*** (0.002)
Market Competition		−0.0342(0.045)	0.0202(0.045)		−0.0092*** (0.002)	−0.0082*** (0.002)
Constant	0.1509*** (0.007)	0.1154*** (0.044)	0.0976** (0.047)	−0.0089*** (0.000)	0.0051*** (0.002)	0.0153*** (0.002)
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	64,899	74,785	64,899	64,899	74,785	64,899
Adj. R2	0.060	0.063	0.063	0.116	0.126	0.137
Panel B
	DAEMt+1	DAEMt+1	DAEMt+1	REMt+1	REMt+1	REMt+1
	(1)	(2)	(3)	(4)	(5)	(6)
Available Slack	−0.0068(0.014)			−0.0024*** (0.001)
Recoverable Slack		−0.0764*** (0.013)			−0.0104*** (0.001)
Potential Slack			−0.0349*** (0.009)			−0.0014*** (0.000)
Constant	0.1212*** (0.047)	0.0178(0.047)	0.1870*** (0.051)	0.0071*** (0.002)	−0.0034* (0.002)	0.0118*** (0.002)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	74,646	69,370	69,947	74,646	69,370	69,947
Adj. R2	0.063	0.061	0.065	0.127	0.135	0.130
Panel C
	DAEMt+1	DAEMt+1	DAEMt+1	REMt+1	REMt+1	REMt+1
	(1)	(2)	(3)	(4)	(5)	(6)
Available Slack	−0.0067(0.014)			−0.0024*** (0.001)
Recoverable Slack		−0.0749*** (0.014)			−0.0105*** (0.001)
Potential Slack			−0.0346*** (0.009)			−0.0014*** (0.000)
DAEM				−0.0007*** (0.000)	−0.0010*** (0.000)	−0.0004(0.000)
REM	0.3281** (0.167)	0.1969(0.183)	0.2925* (0.168)
Constant	0.1185** (0.047)	0.0175(0.047)	0.1830*** (0.051)	0.0072*** (0.002)	−0.0034* (0.002)	0.0119*** (0.002)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	74,646	69,370	69,947	74,646	69,370	69,947
Adj. R2	0.063	0.061	0.065	0.127	0.135	0.130

Note: This table presents the relationship between organizational slack and earnings management, measured by discretionary accruals (DAEM) and real earnings management (REM). The empirical model is: $DAEM/RE{M}_{i,t+1}=a_0+a_1{\mathrm{Slack}}_{i,t}+b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ . where DAEM represents discretionary accrual-based earnings management and REM denotes real earnings management in the subsequent year t + 1. The variable Slack include Organizational Slack, Available Slack, Recoverable Slack, and Potential Slack measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

These findings suggest that, rather than facilitating opportunistic behavior, slack functions as a buffer that enables firms to absorb external shocks, invest in innovation, and pursue long-term strategic objectives. By alleviating short-term financial constraints (e.g., Bourgeois, 1981; Cyert & March, 1963; Vanacker et al., 2016), slack may reduce managerial incentives to manipulate reported earnings to meet immediate performance targets.

As discussed in Hypothesis 3, the effects of slack on earnings management may vary according to the type of slack. Consistent with Agusti-Perez et al. (2020), the impact of resources on firm outcomes depends on the specific nature of the resource. To examine this heterogeneity, Panel B of Table 2 decomposes organizational slack into its components: Available Slack, Recoverable Slack, and Potential Slack. The results indicate that most slack components significantly reduce both DAEM and REM. The only exception is Available Slack, which does not have a statistically significant effect on DAEM. These findings highlight the importance of distinguishing between different types of slack when assessing their influence on earnings management.

REM and DAEM are often treated as substitutes, and in some cases, the level of DAEM is adjusted based on the extent of REM (e.g., Zang, 2012). To account for this interplay, Panel C of Table 2 reports robustness checks in which one type of earnings management is included as a control variable while the other serves as the dependent variable, following the approach of Kim et al. (2012). The results remain robust; that is, the coefficients of Available Slack, Recoverable Slack, and Potential Slack continue to be negative and statistically significant at the 1% level, reaffirming the negative relationship between organizational slack and earnings management and providing further support for Hypothesis 2.

Extended Tests on the Impact of Organizational Slack on Earnings Management

To further investigate the impact of organizational slack on REM, we decompose REM into three components, namely, REM_CFO, REM_PROD, and REM_DISX. The regression model in Equation (5) is applied to each component, and the results are reported in Table 3.

Table 3.

Disaggregated Analysis of Real Earnings Management.

	REM_PRODt+1	REM_CFOt+1	REM_DISXt+1	REM_PRODt+1	REM_CFOt+1	REM_DISXt+1	REM_PRODt+1	REM_CFOt+1	REM_DISXt+1
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Available Slack	−0.0144*** (0.003)	−0.0190*** (0.004)	0.0327** (0.013)
Recoverable Slack				−0.1916*** (0.003)	−0.0397*** (0.004)	−0.1131*** (0.012)
Potential Slack							−0.0150*** (0.002)	−0.0091*** (0.003)	0.0046(0.008)
Constant	−0.0068(0.010)	0.0341** (0.014)	0.0950** (0.041)	−0.2090*** (0.010)	−0.0064(0.013)	−0.0237(0.040)	0.0297** (0.012)	0.0672*** (0.015)	0.0628(0.045)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	74,646	74,646	74,646	69,370	69,370	69,370	69,947	69,947	69,947
Adj. R2	0.045	0.152	0.123	0.126	0.147	0.113	0.047	0.155	0.132

Note: This table presents the relationship between organizational slack and real earnings management components. The empirical model is: $RE{M}_{i,t+1}=a_0+a_{1}Slac{k}_{i,t}+b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ . where REM is decomposed into abnormal production costs (REM_PROD), abnormal sales activities (REM_CFO), and abnormal discretionary expenses (REM_DISX) in the subsequent year t + 1. The variable Slack includes Available Slack, Recoverable Slack, and Potential Slack measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

Columns (1)–(3) present the effects of Available Slack, Columns (4)–(6) report the effects of Recoverable Slack, and Columns (7)–(9) display the effects of Potential Slack. Across most specifications, the coefficients are negative and statistically significant, indicating that higher levels of slack are associated with lower levels of each type of REM.

These findings align with those of Tan and Peng (2003) and Wiersma (2017), who highlight that firms with greater cash reserves or slack enjoy increased financial flexibility, thereby reducing the pressure to manipulate earnings for short-term performance. In addition, firms with greater Potential Slack are better able to access external financing, which is subject to external monitoring, thereby further diminishing incentives for earnings manipulation (Herold et al., 2006). Overall, these results suggest that organizational slack—particularly Potential Slack—reduces the likelihood of REM by enhancing financial flexibility and promoting external accountability.

To check the robustness of our results, Table 4 repeats the analysis using common financial slack measures often used in prior studies. These include Cash Slack (cash and cash equivalents divided by total assets), Net Working Capital Slack (current assets minus current liabilities, divided by total assets), and Debt Capacity Slack (1 − long-term debt/total assets). Using these alternative measures allows us to examine whether the results are dependent on how slack is defined. The findings remain consistent with our baseline results, exhibiting a negative relationship between financial slack and earnings management.

Table 4.

Robustness Tests With Alternative Slack Proxies.

	DAEMt+1	DAEMt+1	DAEMt+1	REMt+1	REMt+1	REMt+1
	(1)	(2)	(3)	(4)	(5)	(6)
Cash Slack	−0.0823(0.065)			−0.0109*** (0.003)
Net Working Capital Slack		−0.0613(0.046)			−0.0023(0.002)
Debt Capacity Slack			−0.3808*** (0.058)			−0.0128*** (0.002)
Constant	0.0997*** (0.034)	0.1076*** (0.038)	0.3626*** (0.053)	−0.0016(0.001)	−0.0031** (0.002)	0.0053** (0.002)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	74,785	74,785	74,748	74,785	74,785	74,748
Adj. R2	0.063	0.063	0.064	0.127	0.126	0.127

Note: This table presents the relationship between organizational slack and earnings management using alternative proxies of slack, namely Cash Slack, Net Working Capital Slack, and Debt Capacity Slack. The empirical model is $DAEM/RE{M}_{i,t+1}=a_0+a_1{\mathrm{Slack}}_{i,t}+b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ , where DAEM represents discretionary accrual-based earnings management and REM denotes real earnings management in the subsequent year t + 1. The variable Slack includes Cash Slack, Net Working Capital Slack, and Debt Capacity Slack measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

To further strengthen the robustness and comprehensiveness of our results, we employ alternative proxies for financial reporting quality, namely, the earnings smoothness measures proposed by Leuz et al. (2003). Table 5 reports robustness tests of the relationship between organizational slack and earnings management using this alternative measure. The findings presented in Table 6 indicate that most of the coefficients for Available Slack, Recoverable Slack, and Potential Slack are negative and statistically significant, thus reinforcing the conclusion that firms with greater slack are generally less likely to engage in earnings manipulation.

Table 5.

Earnings Smoothness as an Alternative Proxy for Earnings Management.

	Smoothnesst+1	Smoothnesst+1	Smoothnesst+1	Smoothnesst+1	Smoothnesst+1	Smoothnesst+1	Smoothnesst+1	Smoothnesst+ 1
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Available Slack	−0.1921*** (0.010)				−0.0883*** (0.011)
Recoverable Slack		0.1631*** (0.009)				0.1166*** (0.017)
Potential Slack			−0.0199*** (0.006)				−0.0033(0.006)
Organizational Slack				−0.0397*** (0.008)				−0.0269*** (0.009)
Constant	1.7468*** (0.034)	1.7213*** (0.035)	1.6199*** (0.037)	1.5968*** (0.035)	2.3581*** (0.071)	2.4522*** (0.075)	2.1915*** (0.073)	2.2557*** (0.075)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	No	No	No	No	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	No	No	No	No
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	74,646	69,370	69,947	64,899	73,302	68,104	68,573	63,600
Adj. R2	0.080	0.081	0.068	0.071	0.614	0.619	0.612	0.613

Note: This table presents the relationship between organizational slack and earnings management, employing the earnings smoothness measure of Leuz et al. (2003) as an alternative proxy. The empirical model is: ${\mathrm{Smoothness}}_{i,t+1}=a_0+a_{1}Slac{k}_{i,t}+b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ . where Smoothness is the standard deviation of profitability (net income scaled by total assets) divided by the standard deviation of cash flows from operations scaled by total assets in the subsequent year t + 1. The variable Slack includes Organizational Slack, Available Slack, Recoverable Slack, and Potential Slack measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry or firm fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

Table 6.

Robustness Analysis Using Firm Fixed Effects.

	DAEMt+1	DAEMt+1	DAEMt+1	REMt+1	REMt+1	REMt+1
	(1)	(2)	(3)	(4)	(5)	(6)
Available Slack	0.0141(0.022)			−0.0026*** (0.001)
Recoverable Slack		−0.0788** (0.031)			−0.0085*** (0.001)
Potential Slack			−0.0312** (0.013)			0.0000(0.001)
Constant	0.3038** (0.130)	0.3583*** (0.136)	0.3402** (0.134)	0.0041(0.005)	−0.0125** (0.005)	0.0003(0.005)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	73,302	68,104	68,573	73,302	68,104	68,573
Adj. R2	0.080	0.077	0.079	0.154	0.159	0.154

Note: This table presents the relationship between organizational slack and earnings management using a firm fixed effect. The empirical model is: $DAEM/RE{M}_{i,t+1}=a_0+a_1{\mathrm{Slack}}_{i,t}+b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ . where DAEM represents discretionary accrual-based earnings management and REM denotes real earnings management in the subsequent year t + 1. The variable Slack includes Available Slack, Recoverable Slack, and Potential Slack measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent firm fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

Interestingly, Recoverable Slack is positively related to earnings smoothness. This is because Recoverable Slack is often linked to discretionary resources, such as extra operating expenses or temporarily unused assets, that managers can control. Unlike Available Slack and Potential Slack, which mainly act as financial buffers, Recoverable Slack gives managers more flexibility to adjust spending. For example, a manager could reduce R&D or marketing expenses in 1 year and increase them in another to make earnings appear more stable over time. In this way, Recoverable Slack can sometimes make it easier for managers to smooth earnings, showing that not all types of slack automatically limit earnings management.

Endogeneity Concerns

In this section, we address potential endogeneity concerns through a series of tests. A key concern is the possibility of omitted variables, where earnings management may be driven by firm characteristics that are time-invariant or relatively stable over time. To account for this, we include firm fixed effects, which control for unobserved firm-level heterogeneity—such as organizational culture, internal control systems, and long-standing risk preferences—that could simultaneously influence both slack accumulation and earnings management practices. Failing to account for these unobservables could otherwise bias the estimated relationship between organizational slack and earnings management.

The results, reported in Table 6, continue to show a negative relationship between organizational slack and earnings management, indicating that firms with greater liquidity are less likely to engage in earnings manipulation. While the magnitude of the effect is somewhat weaker after controlling for firm fixed effects, the relationship remains economically and statistically significant. This attenuation may reflect the fact that firm fixed effects absorb part of the variation in slack, particularly in firms where slack levels change little over the sample period.

Second, we address potential selection bias, which may occur if firms self-select into treatment based on their slack levels; that is, firms with certain characteristics (e.g., more resources or higher growth potential) are more likely to exhibit slack and thus be included in the treated group, which could otherwise bias the estimation. To mitigate this concern, we apply the PSM method of Rosenbaum and Rubin (1983). Firms are classified into treatment and control groups according to their slack. Specifically, treated firms (Treatment Available Slack, Treatment Recoverable Slack, and Treatment Potential Slack = 1) are defined as those in the top 20% of the respective slack measures (Available Slack, Recoverable Slack, and Potential Slack), whereas untreated firms (Available Slack, Treatment Recoverable Slack, and Treatment Potential Slack = 0) constitute the remainder of the sample. Matched control firms are identified from the untreated group using 1:1 nearest-neighbor matching and 1:2 nearest-neighbor matching. The propensity score is estimated using a probit model that incorporates a comprehensive set of control variables, including Firm Size, Tobin’s Q, Return on Assets, R&D Intensity, Leverage, Tangibility, Market Competition, and industry- and year fixed effects. This specification ensures that treated and matched firms are comparable along observable dimensions.

The results of the PSM analysis are consistent and support our hypothesis. ¹ Overall, the findings indicate that firms with high levels of slack (treated firms) are less likely to engage in earnings management. Importantly, the effect is strong and statistically significant for REM but weaker and statistically insignificant for DAEM. One plausible explanation for this asymmetry is that REM is more directly influenced by a firm’s operational and financial flexibility. Firms with abundant slack can rely on internal resources to meet performance targets, reducing the need to alter their operating decisions (e.g., sales practices, production, and discretionary spending). By contrast, DAEM is largely accounting-driven and can be executed without immediate resource constraints, making it less sensitive to the availability of slack. Furthermore, DAEM is more easily detected by auditors and regulators, which may discourage its use irrespective of a firm’s slack position (Roychowdhury, 2006). Together, these findings suggest that organizational slack primarily mitigates earnings manipulation through operational channels rather than accounting adjustments.

Next, we conduct a lead–lag analysis to further strengthen our identification strategy and address potential concerns of reverse causality. Specifically, one might argue that earnings management could influence future slack levels rather than the other way around. For example, firms engaging in earnings manipulation may accumulate or deplete slack resources in subsequent years as a result of their reporting choices. To mitigate this concern, we re-estimate the baseline model by shifting the dependent variable forward and examining the effect of slack on earnings management in years t + 1, t + 2, and t + 3.

The results indicate that most coefficients remain negative and statistically significant across different lead specifications. ² This pattern provides two important insights. First, it suggests that the observed relationship is not merely contemporaneous but persists over future periods, which is consistent with the interpretation that slack reduces the incentives and pressures to manipulate earnings. Second, it mitigates concerns that the baseline results are driven by endogeneity, as reverse causality would likely weaken or reverse the estimated effect at longer horizons.

Taken together, the lead–lag analysis strengthens the robustness of our findings and reinforces the conclusion that firms with greater slack are less likely to engage in earnings management.

Thus far, we have employed multiple strategies to mitigate endogeneity concerns that could bias our results. Nevertheless, potential endogeneity of organizational slack may still remain, as it is not randomly distributed across firms, but rather it may be strategically accumulated in response to anticipated performance, capital market pressures, or managerial discretion. Moreover, unobserved firm characteristics, such as managerial ability, risk preferences, and organizational culture, could simultaneously influence both slack accumulation and the propensity to engage in earnings management.

To address these concerns more rigorously, we conduct a 2SLS analysis, using industry-level cash flow norms as an instrument for firm-level slack. Industry-level cash flow is defined as the mean value of firms’ operating cash flows within the same industry in year t. Industry cash flow norms are strongly correlated with firm slack, satisfying the relevance condition, while remaining exogenous to firm-specific incentives to manage earnings, thereby meeting the exclusion restriction. This instrumental variable approach allows us to more convincingly isolate the causal effect of organizational slack on earnings management.

Table 7 presents the results of the 2SLS analysis. Consistent with expectations, industry-level cash norms exert a positive effect on firms’ slack resources (Columns 1–3). In the second stage, we use the predicted values of slack from stage one to replicate the analysis reported in Table 3. The results indicate that slack is generally associated with reduced earnings management, particularly REM (Columns 7–9). While the relationship between slack and DAEM becomes statistically insignificant (Columns 4–6), this may reflect the inherently higher discretion and measurement noise in DAEM compared with real activities. Overall, the evidence supports a negative association between slack and earnings management, suggesting that firms with greater slack tend to engage in less earnings manipulation.

Table 7.

Robustness of Organizational Slack and Earnings Management: 2SLS Approach.

	Stage 1			Stage 2
	Available slackt+1	Recoverable slackt+1	Potential slackt+1	DAEMt+1	DAEMt+1	DAEMt+1	REMt+1	REMt+1	REMt+1
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Industry Cash Flow	0.7521*** (0.099)	0.5700*** (0.113)	0.0973(0.191)
$\widehat{\mathrm{Available}\mathrm{Slac}k}$				−0.1787(0.388)			−0.2922*** (0.015)
$\widehat{\mathrm{Recoverable}\mathrm{Slac}k}$					−0.2358(0.512)			−0.3856*** (0.019)
$\widehat{\mathrm{Potential}\mathrm{Slac}k}$						−1.3814(3.000)			−2.2587*** (0.113)
Constant	0.6599*** (0.021)	−1.0422*** (0.023)	2.6174*** (0.040)	0.2522(0.297)	−0.1115(0.498)	3.7501(7.891)	0.2287*** (0.011)	−0.3660*** (0.019)	5.9479*** (0.298)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	74,651	69,585	69,118	74,785	74,785	74,785	74,785	74,785	74,785
Adj. R2	0.287	0.516	0.537	0.063	0.063	0.063	0.131	0.131	0.131

Note: This table reports 2SLS estimates of the effect of organizational slack on earnings management, using industry-level cash holdings as an instrument variable. The empirical model is: Stage 1: $Slac{k}_{i,t+1}=a_0+a_{1}IndustryCashFlo{w}_{i,t}+b^{\mathrm{"}}{Z}_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ . Stage 2: $DAEM/RE{M}_{i,t+1}=a_0+a_1\widehat{Slack\_(i,t)}+b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ , where DAEM represents discretionary accrual-based earnings management and REM denotes real earnings management in the subsequent year t + 1. The variable Slack includes Organizational Slack, Available Slack, Recoverable Slack, and Potential Slack measured in year t. Z is a vector of the control variables described above. Instrumental variable is Industry Cash Flow. Industry Cash Flow is defined as the mean value of firms’ operating cash flows within the same industry in year t. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

Additional Analyses: Mediating Mechanisms and Heterogeneous Effects

Thus far, our findings indicate that firms with greater slack tend to engage in less earnings management. In this section, we briefly explore the underlying mechanisms driving this relationship. As discussed in the hypothesis development section, the RBV and slack resource view suggest that slack alleviates pressures that typically induce earnings manipulation, promoting stability and a long-term orientation. In other words, slack functions as a buffer, enabling firms to withstand external shocks, invest in innovation, and pursue long-term objectives.

To examine these mechanisms, we assess the effect of organizational slack on long-term investments measured by capital intensity and R&D intensity. Table 8 shows that slack has positive and statistically significant effects on both capital intensity and R&D intensity, as indicated by the coefficients of Available Slack, Recoverable Slack, and Potential Slack. This suggests that firms with more slack tend to allocate more resources to long-term investments, which is consistent with our theoretical expectations.

Table 8.

Organizational Slack and Long-Term Investments.

	Capital intensityt+1			R&D intensityt+1
	(1)	(2)	(3)	(4)	(5)	(6)
Available Slack	0.1984*** (0.006)			0.0479*** (0.006)
Recoverable Slack		0.1619*** (0.006)			0.0232*** (0.004)
Potential Slack			0.0767*** (0.004)			0.0133*** (0.004)
Constant	2.7036*** (0.021)	3.0055*** (0.022)	2.6498*** (0.024)	−0.0206(0.013)	0.0232** (0.010)	−0.0214(0.013)
Control Variables	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	72,707	67,612	68,243	74,646	69,370	69,947
Adj. R2	0.677	0.683	0.683	0.693	0.543	0.702

Note: This table reports the effect of organizational slack on long-term investments, measured by capital intensity and R&D intensity. The empirical model is: $C\mathrm{apital}\mathrm{Intensity}\mathrm{or}\mathrm{R}\&DIntensit{y}_{i,t+1}=a_0+a_1{\mathrm{Slack}}_{i,t}+b\prime Z_{i,t}+{\vartheta }_i+m_t+e_{i,t}$ . where dependent variables are Capital Intensity and R&D Intensity in the subsequent year t + 1. The variable Slack includes Organizational Slack, Available Slack, Recoverable Slack, and Potential Slack measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

The effect of organizational slack on earnings management depends largely on managerial incentives, which we measure using Delta and Vega (Coles et al., 2006; Core & Guay, 2002). Delta reflects how sensitive a manager’s wealth is to changes in the stock price, while Vega reflects how sensitive a manager’s wealth is to changes in the stock price volatility. Managers with high Delta or Vega typically have strong reasons to influence reported performance because earnings affect how competent they appear, their compensation, and their internal standing.

However, when these highly incentivized managers operate in firms with ample slack, the situation changes. Slack reduces short-term pressure and provides managers with more flexibility to meet goals without relying on accrual-based earnings manipulation. In other words, although high-incentive managers usually have strong motives to manage earnings, slack gives them alternative ways to maintain stable performance, making DAEM less necessary. This explains why we find that slack significantly reduces DAEM only when managerial incentives are strong.

In contrast, when managerial incentives are weak—low Delta and Vega—managers have little personal benefit from manipulating earnings to begin with. Earnings management is already low because motivation is low. As a result, adding slack does not meaningfully change their behavior, which is why the slack–DAEM relationship becomes statistically insignificant in these firms.

Table 9 supports this interpretation. For DAEM, slack matters only when incentives are strong. For REM, the negative relationship with slack is similar across all firms because slack directly affects operational decisions—such as production, sales, and R&D—that drive real activities manipulation. Thus, slack reduces REM more universally.

Table 9.

Heterogeneous Effects of Organizational Slack on Earnings Management by Managerial Incentives.

	High delta		Low delta
	DAEMt+1	REMt+1	DAEMt+1	REMt+1
	(1)	(2)	(3)	(4)
Organizational Slack	−0.0325*** (0.012)	−0.0048*** (0.000)	0.0027(0.025)	−0.0045*** (0.001)
Constant	0.0543(0.053)	0.0162*** (0.002)	0.2994*** (0.107)	0.0081** (0.003)
Control Variables	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Obs.	50,026	50,026	14,873	14,873
Adj. R2	0.062	0.128	0.072	0.176
	High vega		Low vega
	DAEMt+1	REMt+1	DAEMt+1	REMt+1
	(1)	(2)	(3)	(4)
Organizational Slack	−0.0295** (0.012)	−0.0047*** (0.000)	−0.0111(0.025)	−0.0053*** (0.001)
Constant	0.0334(0.053)	0.0156*** (0.002)	0.3472*** (0.108)	0.0097*** (0.004)
Control Variables	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Obs.	49,567	49,567	15,332	15,332
Adj. R2	0.061	0.128	0.074	0.174

Note: This table examines how the effect of organizational slack on earnings management varies with managerial incentives, proxied by Delta and Vega (Coles et al., 2006). DAEM represents discretionary accrual-based earnings management and REM denotes real earnings management in the subsequent year t + 1. The independent variable is Organizational Slack and measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

Overall, slack helps curb opportunistic behavior, but mainly among managers who otherwise have strong incentives to manipulate earnings. Slack does not create incentives; it changes the tools managers use to meet their objectives.

In Table 10, we also find that the relationship between slack and DAEM becomes statistically insignificant in manufacturing firms. This may reflect the higher operational complexity and stricter regulatory oversight in manufacturing, which limit managers’ ability or incentive to manipulate accruals. In contrast, the effect of slack on REM remains consistent across industries, suggesting that slack influences operational decisions regardless of the sector.

Table 10.

Organizational Slack and Earnings Management: Manufacturing vs. Non-Manufacturing Firms.

	Manufacturing		Others
	DAEMt+1	REMt+1	DAEMt+1	REMt+1
	(1)	(2)	(3)	(4)
Organizational Slack	−0.0197(0.017)	−0.0055*** (0.001)	−0.0307** (0.014)	−0.0043*** (0.001)
Constant	0.3150*** (0.064)	0.0159*** (0.002)	−0.2711*** (0.071)	0.0161*** (0.003)
Control Variables	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Obs.	35,473	35,473	29,426	29,426
Adj. R2	0.107	0.193	0.075	0.117

Note: This table examines whether the effect of organizational slack on earnings management differs between manufacturing firms (SIC codes 2000-3999) and non-manufacturing firms. DAEM represents discretionary accrual-based earnings management and REM denotes real earnings management in the subsequent year t + 1. The independent variable is Organizational Slack and is measured in year t. Z is a vector of the control variables described above. Standard errors are clustered at both the firm and year levels, and the terms ϑ_i, μ _t , and ε_i,t represent industry fixed effects, year fixed effects, and residuals.

Statistical significance is denoted by *, **, and ***, corresponding to the 10%, 5%, and 1% levels, respectively.

Conclusion

This study examines the impact of organizational slack on earnings management using data from US-listed firms between 1991 and 2022. Our results consistently show that organizational slack generally reduces the likelihood of earnings manipulation. These findings are robust across multiple tests, including alternative measures of slack and earnings management, various lead–lag specifications, firm-, industry-, and year fixed effects, PSM, and 2SLS estimation. Firms with higher levels of slack enjoy greater financial flexibility, which alleviates pressure to manipulate earnings and supports long-term investment decisions, as evidenced by higher capital intensity and R&D intensity.

We acknowledge that endogeneity cannot be fully resolved in this study. Although we use multiple approaches to mitigate potential biases—including alternative variable constructions, lead–lag specifications, fixed effects, and 2SLS—these methods cannot entirely rule out the possibility that unobserved factors simultaneously affect both organizational slack and earnings management. In particular, our instrumental variable, based on industry-level cash flow norms, may still be correlated with industry-specific differences in earnings management practices, which could violate the exclusion restriction. For this reason, we interpret our results as evidence of a robust negative association between slack and earnings management, rather than a definitive causal effect. We suggest that future research leverage quasi-natural experiments or exogenous shocks to more rigorously identify causal mechanisms.

We also find that the effect of slack on REM is consistent across firms, whereas its impact on DAEM is significant only in firms with high managerial incentives (as measured by Delta and Vega) and in manufacturing firms. This highlights that slack constrains opportunistic behavior, particularly when managers have strong motivations to manipulate earnings or when operational conditions make manipulation feasible.

From a managerial perspective, our findings underscore the strategic value of financial slack. Maintaining sufficient slack resources allows managers to reduce short-term pressures, make more informed long-term investment decisions, and avoid engaging in earnings manipulation, ultimately enhancing firm stability and supporting sustainable performance.