1. Introduction
Firm risk taking is a primary strategic decision (Chen et al. 2022) that has become increasingly important for researchers of strategic management. To cope with a turbulent business environment, firm executives who design and implement important business strategies inevitably confront tremendous uncertainties. The effectiveness of managerial strategies largely depends on whether those strategies incorporate and address the associated risk (Wiseman and Gomez-Mejia 1998).
We consider corporate risk to be the unpredictable variation in firm outcomes or performance. This definition is widely used in finance, economics, and management. To manage risk, firm managers may take a host of strategic actions, many of which are largely driven by managerial incentives and preferences induced by their compensation packages (Freund et al. 2021). Voluminous studies have explored the relationship between managerial incentives and risk taking, and have often found inconsistent results (Wright et al. 2007). Various components of compensation packages may encourage managers to take more or less risk (Chen and Steiner 1999; Coles et al. 2006). The inconclusive findings may be due to a lack of ex ante structure of managerial incentives (Wright et al. 2007).
Researchers have recently identified an important yet overlooked component of executive compensation packages. Firms are increasingly offering debt-like compensation (Edmans and Liu 2011; Sundaram and Yermack 2007; Wei and Yermack 2011) in the form of deferred compensation and defined benefit pensions. This type of compensation is also called “inside debt” because corporate managers are insiders and are compensated with debt of their own firms. Unlike equity-based instruments such as stocks and options, the payoff of inside debt is contingent on firm solvency (Edmans and Liu 2011). Compensation packages with inside debt tend to make managers behave like debtors and provide them with incentives (Igalens and Roussel 1999) to shy away from taking risky actions (Edmans and Liu 2011). As such, managers act conservatively in business operations and strategic decision-making and are more likely to pursue risk-reduction projects (Cassell et al. 2012; Sundaram and Yermack 2007).
In this study, we examine the relationship between managerial incentives induced by debt-like compensation and subsequent firm risk taking. We focus on the inside debt holdings of firm chief executive officers (CEOs) because CEOs are responsible for important firm strategic decisions. Building upon cumulative prospect theory (Tversky and Kahneman 1992) and instrumental stakeholder theory (Donaldson and Preston 1995; Jones 1995), we propose a theoretical framework to guide the development of our hypotheses as well as our empirical analyses. In particular, we argue that loss-averse decision makers with significant debt-like compensation tend to prefer more conservative business operations and strategic decision-making (Cassell et al. 2012; Edmans and Liu 2011; Sundaram and Yermack 2007). They do so in order to minimize their losses because inside debt offers fixed future payments and is far less risky than equity-based compensation.
Moreover, we posit that managers may choose from an array of strategic tools to achieve certain objectives with regard to risk taking, and that engaging in socially responsible activities is one viable strategy (Freeman 1984; Freeman et al. 2010). Existing studies support the role of risk-reduction in CSR activities (Kytle and Ruggie 2005; Minoja 2012). Our research model allows us to link these two streams of research and to hypothesize that managers with significant inside debt may strategically choose CSR-related strategies to achieve their risk-reduction objectives. In other words, CSR can serve as an instrument to mediate the relationship between managerial incentives induced by inside debt and firm risk taking.
Our analyses, based on a large longitudinal dataset of nonfinancial firms in the United States from 2007 to 2012, reveal that CSR strategies can integrate with core business operations. We document that CEO inside debt is negatively correlated with firm risk, and CSR activities significantly mediate the relationship between CEO inside debt and firm risk taking. Furthermore, we take a contingency perspective and identify two moderating variables, namely environmental dynamism and munificence (Goll and Rasheed 2004; McArthur and Nystrom 1991; Simerly and Li 2000), to explore the context-dependent nature of the mediation process. Strikingly, we find that environmental dynamism significantly moderates the relationship between CEO inside debt and firm involvement in CSR activities, whereas environmental munificence significantly moderates the relationship between CSR involvement and firm risk taking.
We believe our study provides several important contributions to the literature. First, in the existing literature, CSR-related strategies are largely treated as independent of a firm’s core business and operational activities. Our framework, based on cumulative prospect theory, integrates these CSR-related activities with firm strategies of risk management. Executives with a risk-reduction mindset can choose CSR as a strategic tool to reduce firm risk exposure, especially when other choices, such as reducing R&D expenditures and diversification, are not viable options. Second, although abundant empirical studies focus on the consequences of engagement in CSR-related activities, our study adds novel evidence to the literature on the antecedents of CSR-related strategies. In particular, we provide robust evidence that debt-like compensation provides executives with incentives to pursue CSR-related strategies. Third, our empirical evidence sheds further light on the complex nature of CSR-related activities. We propose that existing studies on the real effects of CSR involvement are largely mixed because engaging in CSR-related strategies is context dependent. Identifying the antecedents and contingencies is the key to understanding and evaluating the real effects of CSR-related activities.
2. Theory and Hypotheses
2.1. Inside Debt, CSR, and Firm Risk Taking
Many researchers believe that risk preference is monotonically associated with expected payoffs or utilities (Ross 2004). However, prospect theory has emerged as a leading alternative to theories based on expected utility. Empirical studies in the literature on behavioral decision theory provide strong support for the importance of loss aversion and choice of reference points in decision making (Tversky and Kahneman 1986).
As an extension of prospect theory, cumulative prospect theory (Tversky and Kahneman 1992) posits that decision-makers frame problems by using reference points to evaluate outcomes and assign more weight to losses than to gains of equivalent magnitude (Wiseman and Gomez-Mejia 1998). One recent study, for example, adopts cumulative prospect theory (Hu et al. 2022) to develop a casino gambling model of risk management. Other researchers (Trichilli et al. 2021) have applied cumulative prospect theory in banking risk management. These new developments stress a few important differences that differentiate cumulative prospect theory from other theories of risk management based on expected utility theory. First, managers use a reference point to evaluate their losses and gains. The key element of cumulative prospect theory is reference-dependent loss aversion (Tversky and Kahneman 1991), which indicates that the utility function is steeper in the negative domain than in the positive domain. Second, decision-makers do not simply focus on absolute outcomes but exhibit a risk-shifting preference between gains and losses because of the tendency toward loss aversion (Tversky and Kahneman 1986). Third, managers may overweight the small probabilities of extremely large gains and losses. Managerial compensation is tied to both short-run and long-run firm performance, and affects the personal wealth of executives. Managers need to choose certain reference points and make decisions regarding the risk exposure of their firms. These important elements of cumulative prospect theory make it particularly suitable for analyzing executive compensation and firm risk-taking behavior.
There is a growing interest in the relationship between executive compensation and managers’ attitudes toward risk, though the existing literature largely focuses on equity-based compensation (e.g., stock options) and its influence on managerial risk-taking incentives (Rajgopal and Shevlin 2002). Cumulative prospect theory provides a cognitive explanation of managerial risk-taking incentives due to the differences in framing problems (Coffee 1988), and can be effectively applied to explain how different components of executive compensation influence strategic decisions among firm managers with regard to risk (Wiseman and Gomez-Mejia 1998). Executives can evaluate their compensation by using different references points, such as cash salaries and stock options. As a form of contingent pay, stock options tie the personal wealth of executives to the wealth of the firm owners, thus encouraging managers to pursue riskier strategies and take on projects with higher variances, such as R&D expenditures and internationalization (Coffee 1988).
An emerging line of research recently identified an overlooked component of executive compensation: debt-like compensation. Debt-like compensation refers to payments to executives in the form of defined-benefit pension plans and other deferred compensation contracts (Edmans and Liu 2011; Sundaram and Yermack 2007). Unlike equity-based compensation offering varying and contingent future payments, debt-like compensation offers fixed future payoffs conditional on the solvency of the firms. If the firm becomes insolvent, the executive becomes an unsecured debtholder and receives payment according to the debt’s seniority. Therefore, these plans are very much like unsecured corporate debt, which is why debt-like compensation is also called inside debt. Moreover, debt-like compensation is pervasive, according to the Execucomp dataset (Sundaram and Yermack 2007). About 70 percent of all firms covered by Execucomp have some forms of pension and deferred compensation plans.
It is clear that firm managers are awarded not only inside equity but also inside debt. According to cumulative prospect theory, this debt-like compensation offers executives a new reference point to evaluate their compensation, and the compensation mix is likely to influence managers’ decision-making, resulting in varying managerial risk-taking incentives. In particular, both inside debt and inside equity have contingencies: inside debt is contingent on firm solvency and inside equity is contingent on firm performance. Ex ante, firm managers as decision-makers will evaluate the expected outcomes for various components of their compensation packages against certain reference points. Nonetheless, the future payoff for inside debt is fixed, whereas the future payoff for inside equity varies, and is thus riskier. As a result, using their current wealth as the reference point (Wiseman and Gomez-Mejia 1998), loss-averse managers will assign different weights to inside debt and inside equity and make a choice between securing future fixed payments via conservative strategies to ensure solvency (Cassell et al. 2012) or seeking equity-based payments via riskier strategies to promote firm performance with high variances (Rajgopal and Shevlin 2002; Ross 2004). Obviously, pursuing riskier strategies not only leads to uncertain payoffs because of the high variance in the outcomes, but also increases the possibility of jeopardizing managerial job security because of the higher likelihood of not meeting the desired performance targets. When calculating the expected outcomes of their compensation packages, executives are inclined to factor inside debt into their current wealth with greater certainty because its payments are only contingent on firm solvency (Wiseman and Gomez-Mejia 1998). Indeed, empirical studies show that executives do not count on equity-based compensation with certainty and feel that losing future fixed payments is a bigger threat to their perceived wealth. As such, if we assume that decision-makers are loss-averse, we in turn assume they tend to care more about avoiding losses than about getting additional but uncertain payments (Wiseman and Gomez-Mejia 1998). Put another way, due to the higher weight placed on potential losses, corporate managers with more debt-like compensation may prefer risk-reduction strategies to safeguard their fixed future payments.
CEO inside debt is negatively associated with firm risk taking.
2.2. The Mediating Role of CSR
Stakeholder theory (Freeman 1984) has evolved as the one of the most significant theories in CSR literature (Margolis and Walsh 2003). Stakeholder theory proposes that firm managers allocate resources to engage various stakeholder groups to encourage their willful participation in the firm’s operation and production process (Harrison et al. 2010). The incorporation of the stakeholder framework in strategic decisions gives competitive advantages to firms and creates firm value (Clarkson 1995). In particular, instrumental stakeholder theory provides a practically meaningful application in analyzing business decisions (McWilliams and Siegel 2001). Instrumental stakeholder theory (Donaldson and Preston 1995; Jones 1995) views stakeholder management as a means (an instrument) to achieve certain objectives (ends). Therefore, effective stakeholder management is crucial in a company’s overall strategy, and firm managers can implement CSR-related strategies to improve strategic decision-making and create competitive advantages (Kytle and Ruggie 2005; McWilliams et al. 2006). In other words, strategic CSR reflects executives’ strategic considerations rather than just fostering good corporate citizenship (Kytle and Ruggie 2005; Porter and Kramer 2006).
In this article, we posit that various compensation packages may provide firm managers with incentives to pursue corporate social responsibility initiatives to manage firm risk, which has a meaningful impact on stakeholders (Igalens and Roussel 1999; Opoku-Dakwa et al. 2018). Specifically, firm managers with more debt-like compensation tend to engage less in risk taking and can use CSR as a strategic instrument to achieve the risk-reduction goal for the following reasons. First, according to social exchange theory (Eisenberger et al. 1986; Whitener 2001), good stakeholder management generates mutual benefits and results in strong organizational commitment and loyalty in a reciprocal way among various stakeholder groups (Harrison et al. 2010). Firms with better CSR performance can thus raise cheaper capital (Cheng et al. 2014; Ghoul et al. 2011) and attract financial resources from socially responsible investors (Hockerts and Moir 2004). In addition, CSR-related programs can help firms attract valuable human capital and reduce labor mobility, which in turn mitigates the potential risk of transferring knowledge to rivals.
Second, engaging in CSR-related activities allows firms to gain social legitimacy. Firms can take a proactive approach to meet the expectation of relevant stakeholder groups and maintain their competitive advantages if other stakeholder groups have vested interests in their firms and support their strategic decisions (Suchman 1995). Further, social legitimacy can cushion firms from unpredictable negative events (Godfrey et al. 2009), which is more valuable for a firm subject to great uncertainties (Schultz 2002).
Third, engaging in CSR activities (Godfrey et al. 2009) is voluntary. Therefore, such activities send a signal to important stakeholder groups regarding the intention to act altruistically (Mackey et al. 2007). Consequently, firms can gain moral capital that offers “insurance-like” protection (Godfrey et al. 2009; Minor and Morgan 2011). Stakeholders may negatively respond to firm actions such as recalls, which tend to decrease firm value (Luo and Bhattacharya 2009). Moral capital can effectively mitigate these unfavorable stakeholder reactions and limit the potential loss of valuable intangibles (Minor and Morgan 2011). Therefore, moral capital can be a vital resource for firms exposed to significant risk (Freeman et al. 2007).
Empirical studies document evidence supporting the argument that CSR reduces firm risk in various contexts. For example, CSR involvement can reduce the vulnerability of future cash flows, thus lowering firm risk exposure (Albuquerque et al. 2014; Cheung 2016; Luo and Bhattacharya 2009; Harjoto and Jo 2015). Other empirical studies show that CSR involvement is negatively correlated to firm credit risk and bankruptcy risk (Kytle and Ruggie 2005), and that it can prevent firms from taking excessive risk.
Therefore, drawing upon cumulative prospect theory and instrumental stakeholder theory, our research model posits that the implementation of CSR strategies can be deliberately integrated with CEO decisions on risk taking with varying levels of inside debt (Porter and Kramer 2006). We claim that CSR involvement can serve as an instrument for firm managers with debt-like compensation (i.e., inside debt) to achieve their risk-reduction objectives. Therefore, we propose the following two hypotheses:
There is a positive relationship between CEO inside debt and firm CSR involvement.
There is a negative relationship between firm CSR involvement and firm risk taking.
Note that engaging in CSR-related activities is just one of the several options for CEOs with risk-reduction incentives. Existing research shows that firms can choose to reduce their R&D expenditures (Zhou et al. 2021) or diversify their business operations (Reinholtz et al. 2021) to achieve lower levels of risk. Nonetheless, reducing inputs into R&D activities discourages future innovation, which jeopardizes the long-term growth potential of the company. Choosing to diversify their business operations may result in an inefficient internal capital market (Kabbach-de-Castro et al. 2022) and lower market valuation (Barros et al. 2024). Even with risk-reduction incentives, managers may be reluctant to cut R&D expenditures or enter into an unknown business segment. In this sense, CSR-related activities can be a good alternative way to reduce firm risk exposure. In other words, CSR involvement mediates the relationship between inside debt and firm risk taking. Therefore, we propose the next hypothesis as follows.
The relationship between CEO inside debt and firm risk taking is mediated by firm CSR involvement.
2.3. Moderating the Mediation Process: A Contingency Perspective
A large amount of the literature explores whether and to what extent environmental factors influence organizational strategies, structures, processes, and outcomes. Furthermore, the literature emphasizes that a firm’s industry presents an important contextual background for stakeholders to evaluate the strategic decision regarding compensation, CSR involvement, and risk taking (Datta et al. 2005). Prospect theory claims that perceived risk and subsequent strategic choices largely depend on the framing of the problem, which necessitates a contingency lens with which to explore the context-dependent nature of the mediated relationship between inside debt and firm risk-taking (Goll and Rasheed 2004). Therefore, taking a contingency perspective, we posit that the mediated relationship between CEO inside debt and firm risk taking is context dependent, and we propose two contextual variables: environmental dynamism and munificence (Goll and Rasheed 2004).
Environmental dynamism refers to the degree of unpredictability or instability in an organization’s industry (Dess and Beard 1984). The moderating role that environmental dynamism plays for a series of organizational variables and firm outcomes is well documented in empirical studies (Goll and Rasheed 2004).
In this study, we propose environmental dynamism as a moderating variable because uncertainty and unpredictability may influence how executives calculate potential gains or losses and, in turn, affect their choices among various strategic options. The decision to engage in socially responsible activities is a conscious managerial choice. According to cumulative prospect theory, executives with loss-averse mindsets will evaluate both riskier strategies and less risky options to avoid the unexpected (Thaler and Johnson 1990). Moreover, the risk preferences of loss-averse decision-makers shift with the framing of the problem to avoid potential losses (Coffee 1988). At low levels of environmental dynamism, loss-averse executives tend to be conservative when fixed future payments (i.e., inside debt) are factored into their perceived current wealth. Nonetheless, when environmental dynamism is high, firm managers have more difficulty accurately assessing current and future fixed payments, as well as varying contingent payments (Simerly and Li 2000). Put another way, because of the significant unpredictability of the industry environment and the outcomes of strategic decisions (Wiseman and Gomez-Mejia 1998), executives cannot anticipate future fixed payments accurately, and they experience a nothing-to-lose sentiment toward contingent payments. Thus, loss-averse decision-makers become less sensitive to losing wealth, which weakens their risk-reduction incentives. Therefore, we propose a moderated mediation relationship as follows.
Environmental dynamism moderates the relationship between inside debt and firm risk taking. In particular, the relationship between inside debt and CSR involvement is weaker in a more dynamic environment.
Environmental munificence refers to the abundance of resources in a given industry to support firm growth. It reflects the capacity of available resources in an environment (Goll and Rasheed 2004; McArthur and Nystrom 1991) to support the sustained growth of firms within that industry. Similar to environmental dynamism, researchers have confirmed that munificence is an important contextual variable that influences strategy-performance relationships (Goll and Rasheed 2004; Kytle and Ruggie 2005; McArthur and Nystrom 1991).
Existing research has documented that CSR investments are associated with a significant cost (Harrison and Bosse 2013; McWilliams and Siegel 2001) because firms must allocate resources to satisfy the demands of various stakeholder groups. In turn, they may have to ration capital and resources or forgo some profitable opportunities. In this sense, executives must make a series of strategic decisions, including ones about CSR activities subject to limited resources. As a result, ceteris paribus, a firm in an environment with abundant resources is more likely to implement CSR strategies to create and maintain good stakeholder relationships. Moreover, in a munificent context, allocating resources to CSR-related activities does not indicate passing up other productive projects conducive to certain firm targets (Harrison and Bosse 2013). We describe the moderated mediation relationship with the following hypothesis.
Environmental munificence moderates the mediated relationship between inside debt and firm risk-taking. In particular, CEOs with more debt-like compensation will engage more in CSR, and CSR is more effective in reducing firm risk in a more munificent environment.
3. Methods
3.1. Data and Sample
To test the proposed hypotheses, we rely on four data sources to construct our sample. We first match the Standard and Poor’s Execucomp (Execucomp) dataset with the MSCI ESG KLD STATS (KLD) dataset because these two datasets contain important information about firm CSR activities and executive compensation. Because the Securities and Exchange Commission (SEC) requires full disclosure on the computation and value of deferred compensation and pension benefits from 2006 onward and the KLD data is available up to 2013, we restrict our sample period from 2007 to 2012. Following the convention (Anantharaman et al. 2014), we exclude firms in the financial industry (SIC code 6000-6999) and the utility industry (SIC code 4900-4999) from our sample because these industries are highly regulated and the objectives of regulations are to ensure that firms in these industries engage in prudential practices. Moreover, financial institutions typically have high levels of debt in their capital structure, and using debt-like compensation may not be able to properly align managers’ incentives of risk raking. We supplement the matched sample with databases from Compustat and the Center for Research in Security Prices (CSRP) to retrieve the necessary financial and stock price information. Our sample procedure yields 4675 firm-year observations with 984 unique firms.
3.2. Main Measures
Inside debt. Debt-like payment and equity-based payment in executive compensation packages may result in different managerial incentives for risk taking (Cassell et al. 2012). Decision-makers evaluate the possible gains and losses from ensuring fixed and variable future payments. Therefore, we measure inside debt by considering CEOs’ personal leverage, which takes into account the weights of debt-like payments and equity-based payments in their compensation packages. Moreover, CEOs’ risk-shifting incentives induced by inside debt also depend on the relative use of debt (i.e., firm leverage) by their own firms (Sundaram and Yermack 2007). Following the existing literature (Cassell et al. 2012; He 2015), we measure CEO debt-like compensation by comparing a CEO’s debt-to-equity ratio with the corresponding firm’s debt-to-equity ratio. Specifically, we apply the following formula, called a k-ratio (Edmans and Liu 2011):
k-ratio = (DCEO/ECEO)/(DFIRM/EFIRM)(1)
CEO inside debt (DCEO) is the sum of the present value of accumulated pension benefits and deferred compensation. A CEO’s equity compensation (ECEO) is the year-end sum of the fair market value of stock holdings and option holdings based on the Black-Scholes formula (Coles et al. 2006). DFIRM is the dollar amount of a firm’s total debt, including long-term liabilities and current liabilities, and EFIRM was the market value of the firm’s equity. We take the natural logarithm of the k-ratio to normalize its distribution. We recognize that an extremely high k-ratio may lead to excessive risk aversion because of the close connection between an executive’s personal wealth and a firm’s solvency in the long-run. In addition, regulators may put restrictions on deferred executive compensation to mitigate excess risk reduction. Therefore, k-ratio can be both skewed and censored. As a robustness check, we follow the existing research and create a dummy variable that equals one if the CEO’s personal leverage ratio is higher than the firm’s leverage ratio, and zero otherwise. We find consistent results using the alternative measure of managerial incentive induced by debt-like compensation. We do not report the results for the sake of brevity.
CSR involvement. Researchers use the KLD database extensively to quantify CSR activities and corporate social performance (CSP) in empirical research (Deng et al. 2013; El Ghoul et al. 2011; Luo et al. 2015; Manner 2010). KLD has increased its coverage of firms over time and focuses on seven broad categories: environment, community, corporate governance, human rights, employee relations, diversity, and product quality and safety. KLD assigns ratings according to a wide variety of data sources, including company filings, governmental and nongovernmental data, the general media press, and direct communications with company officers. For each category, KLD identifies a set of strengths and concerns using a binary rating. Although KLD scores are widely used in empirical studies, the scores have a few limitations. For example, the scores treat each category equally and lack standardization. It is plausible that some types of CSR activities may be more effective in reducing firm riskiness. Despite the limitations, the existing research does not reach a consensus as to what weighting scheme to use. Therefore, we assume that the effects of CSR-related activities in each category are the same on our variable of interest. For another instance, CSR scores are backward-looking and do not reflect real time decisions. Given that KLD claims that the rating reflects CSR activities at calendar year-end, we measure all other variables with a one-year lag relative to CSR_POS and CSR_NET.
In line with the existing literature (Barnea and Rubin 2010; Cheng et al. 2014; Deng et al. 2013; El Ghoul et al. 2011), we construct two variables to measure a firm’s CSR involvement. CSR_POS is the sum of all the strengths for the six dimensions, excluding corporate governance because corporate governance covers some aspects of executive compensation. We also subtract the number of concerns in the same six categories from the total number of strengths (i.e., CSR_NET). In regression analysis, we take the natural logarithm of CSR variables to normalize the distributions. Note that the CSR_NET score could be negative after we subtracted CSR concerns from CSR strengths. Therefore, we perform a linear transformation for CSR_NET by adding a positive constant to ensure that the transformed value is at least 1 before we take the natural logarithm.
Firm risk taking. We measure a firm’s risk taking by calculating its total risk using stock market return data because market measures reflect the effect of investment decisions on expected firm future cash flows and are of central important to corporate managers with value-maximizing mind-sets. This method has a superior theoretical foundation and empirical merit. Therefore, following the existing research (Cassell et al. 2012; Coles et al. 2006), we calculate firm risk as the annualized standard deviation of daily stock returns over 252 trading days for a particular firm in a given year. In addition, we perform robustness tests using firms’ idiosyncratic risk and reported qualitatively consistent results (Cassell et al. 2012).
Moderating variables: environmental dynamism and munificence. In this study, we propose two moderating variables, namely environmental dynamism and munificence, for the mediated relationship between inside debt and firm risk taking. Researchers document objectively measured industry characteristics (Dess and Beard 1984) as contingent variables in a wide array of settings related to firm decisions and outcomes (Goll and Rasheed 2004; Lin et al. 2009; Simerly and Li 2000). We define munificence as the industrial capacity to support sustained growth. It reflects the availability of resources critical for firm growth in a particular industry (McArthur and Nystrom 1991). A munificent environment provides sufficient resources and business opportunities for firms to thrive. To operationalize munificence, we focus on industry growth because industries with growing demand for products and services allow firms to grow in a sustainable way. For example, if the estimated yearly change (b1) of industry sales is 0.18, the industry grows at 18% in terms of its sales. The rapid growth in a given industry signifies more available resources and opportunities.
Following the existing literature (Goll and Rasheed 2004; Lin et al. 2009; Simerly and Li 2000), we focus on three-digit SIC codes and adopt the following equation to perform regression analysis:
Yt = b0 + b1t + ε(2)
where Y = industry sales, t = year, and ε = residuals. We obtain industry sales from Compustat by summing annual net sales for all firms in a particular three-digit SIC industry segment for a given year. We run the same regression for each three-digit SIC industry with a 10-year rolling window. Our approach allows for both cross-sectional and time-series variations in environmental dynamism across different industries.We retrieve the regression slope coefficients (b1) from Equation (2) and divide the numbers by the average industry sales to construct our measure of environmental munificence. Note that higher environmental munificence scores indicate greater industry capacity to support firm growth within the industry.
We define environmental dynamism as the unpredictability of change in an organization’s external environment. Such uncertainty may affect how a firm chooses to limit its risk exposure. To operationalize environmental dynamism, we investigate the yearly change of industry sales. Note that the standard error of the sampling distribution of estimated β coefficient represents the unpredictability of the industry’s growth. Further, if the standard error of b1 is 0.25, the predictability of the growth rate in that industry is quite low because the constructed confidence interval tends to be wide. Therefore, instead of focusing on the estimated coefficient, we record the standard error of the estimated coefficient and scale the value by industry mean to ensure comparability across different industries. Specifically, we regress industry sales by year (Equation (2)), record the standard errors of the regression slope (b1), and divide the standard error by the average industry sales for the same time window as our measure of dynamism. Note that higher environmental dynamism scores indicate higher uncertainty and unpredictability in a particular industry.
Other control variables. In our regression analysis, we enter a set of control variables to capture various firm characteristics that may influence firm risk-taking and engagement in CSR activities. Specifically, we calculated firm size as the natural logarithm of firm sales (Manner 2010). Discretionary slack is defined as firm undistributed cash flow, which is viewed as a valuable strategic asset that enables a firm to invest in technological advancements and other business opportunities (Kim et al. 2014). We measure discretionary slack as operating income before depreciation, minus total income taxes, changes in deferred taxes from the previous year, gross interest expense on total debt, total preferred dividend requirement on cumulative preferred stock, dividends paid on noncumulative preferred stock, and the total dollar amount of dividends declared on common stock. We then scale discretionary slack by firm total sales. Leverage is the ratio of the book value of long-term debt to the book value of assets (Barnea and Rubin 2010; Sundaram and Yermack 2007). We measured firm growth potential as the market-to-book ratio. We gauged firm performance by sale growth, which is the percentage change in sales from year t-1 to year t (Cassell et al. 2012; Coles et al. 2006). In addition, following the existing research, we include the G-index to capture the strengths of corporate governance, which is calculated based on 24 management-favoring provisions provided by the Investor Responsibility Research Center (IRRC). A higher G-index indicates more management power and fewer shareholder rights.
To control for other strategic choices that influence firm risk taking, we include firm R&D intensity, measured as R&D expenditures scaled by firm sales (Sundaram and Yermack 2007). We also include a sales-based Herfindahl index (HHI) to proxy firm diversification using Compustat segment data. We used 1 minus the HHI index in our analysis to ensure that the relationship between the degree of diversification and the measure is positive.
Certain CEO characteristics may affect both the contractual design of compensation packages and firm decisions on risk taking and engaging in CSR activities. We controlled for several variables capturing certain CEO characteristics, including CEO age, CEO tenure, and an indicator for CEO gender (1 for female CEOs). CEO turnover was an indicator that took the value 1 if the identity of the CEO changes, zero otherwise (Coles et al. 2006).
3.3. Analytic Framework
To analyze the mediation process (Baron and Kenny 1986), we employed a systematic framework as follows.
Y = β10 + β11 X + Controls + ε1 (3)
Me = β20 + β21 X + Controls + ε2(4)
Y = β30 + β31 X + β32 Me + Controls + ε3(5)
Specifically, Y is the outcome variable that is firm risk, as detailed earlier. X is the treatment variable (i.e., CEO inside debt), and Me is the mediator (i.e., CSR_POS or CSR_NET). Equation (3) tests the direct relationship between CEO inside debt and firm risk-taking. Equation (4) tests the relationship between inside debt and the mediating variable. Equation (5) tests the complete mediation process. According to hypotheses 1–4, a significant mediation process requires that β11 (H1), β21 (H2), and β32 (H3 and H4) be significant. Moreover, a full mediation process requires an insignificant β31, whereas a partial mediation process requires that β31 be significant but smaller than β11.
Further, we adopt a system of three equations proposed by Muller et al. (2005) and Preacher et al. (2007) to analyze the moderated mediation effect.
Y = β40 + β41 X + β42 Mo + β43 X Mo + Controls + ε4(6)
Me = β50 + β51 X + β52 Mo + β53 X Mo + Controls + ε5(7)
Y = β60 + β61 X + β62 Mo + β63 X Mo + β64 Me +β65 Me Mo + Controls + ε6(8)
Specifically, Mo is the moderator (environmental dynamism in H4 and environmental munificence in H5). A significant moderated mediation indicates that either or both of the following two sets of conditions needs to be satisfied.
Set 1:
β41 ≠ 0. A significant β41 reveals an overall treatment effect on the outcome.
β43 = 0. An insignificant β43 indicates that the overall treatment effect on the outcome does not depend on the moderator.
β53 ≠ 0. A significant β53 indicates that the treatment effect on the mediator depends on the moderator.
β64 ≠ 0. A significant β64 indicates that the mediator has a direct effect on the outcome.
Set 2:
β41 ≠ 0. A significant β41 reveals an overall treatment effect on the outcome.
β43 = 0. An insignificant β43 indicates that the overall treatment effect on the outcome does not depend on the moderator.
β65 ≠ 0. A significant β65 indicates that the effect of the mediator on the outcome depends on the moderator.
β51 ≠ 0. A significant β51 reveals the treatment effect on the mediator.
Note that in the test of the moderated mediation process, we introduce the interaction terms. To avoid multicollinearity issues, we demean all the variables involved in the interaction terms. We use robust standard errors to accommodate heteroskedasticity (White 1980). For all regression models, we include industry fixed effects to control for time-invariant industry characteristics. We also capture changing risk-taking preferences because our sample period covered the subprime mortgage crisis via the financial crisis dummy variable, which equaled one for 2008, 2009, and 2010, and zero otherwise.
4. Results
Table 1 presents summary statistics and a pairwise correlation matrix for the variables in the regression analysis. Correlation coefficients in bold are significant at 5% level. We winsorized all variables at the 1 percent and 99 percent levels to alleviate the influence of extreme values. Because it takes time for certain strategies to achieve their desired outcomes, we lagged our CSR measures for one period, and lagged the measure of inside debt for two periods. We cautiously examined the correlations as well as the variance inflation factors in the regression analysis and concluded that multicollinearity is not a major concern. Our control variables in summary statistics were also consistent with the existing research (Wei and Yermack 2011). Moreover, the pairwise correlations showed that CEO inside debt was positively correlated with CSR investment, and was negatively correlated with firm risk taking.
4.1. Mediation Analyses
Table 2 presents the basic regression results testing the proposed mediation process set forth in H1, H2, H3 and H4. In particular, columns 1–3 in Table 2 focus on CSR_POS, and columns 4–6 focus on CSR_NET. Note that *, **, *** indicate significance levels at 10%, 5% and 1%, respectively. The results reported in column 1 suggests that CEO inside debt is significantly and negatively correlated with firm risk (p < 0.05). The findings shown in column 2 indicate that firm managers with higher levels of CEO inside debt are more likely to engage in CSR activities (p < 0.01). In column 3, we tested the mediating role of CSR in the relationship between inside debt and firm risk taking. We document a significant and negative coefficient of CSR_POS after controlling for inside debt. Moreover, the magnitude of the coefficient of inside debt was smaller than the coefficient reported in column 1. Collectively, the results reported in columns 1–3 reveal that CSR_POS partially mediated the relationship between inside debt and firm risk taking. In addition, we performed a formal Sobel test (Luo et al. 2015; Sobel 1982, 1986) to assess the statistical and economic significance of the mediation process. In columns 4–6, we repeat our analysis by focusing on CSR_NET and document qualitatively similar findings. Our results indicate that CSR_POS (CSR_NET) mediated around 21 (14) percent of the overall treatment effect (p < 0.01). The literature suggests that firm managers can reduce risk exposure by engaging in diversification, hedging, and reducing R&D expenses, among other options. In other words, conducting CSR-related activities is just one of the options available to managers with debt-like compensation. Therefore, it is not surprising that we observed a partial mediation effect. Nonetheless, given that managers have different strategic tools for risk management, the mediation effects indicated by the Sobel test are not trivial. Note that the magnitude of the mediation effect was weaker when we used CSR_NET to proxy a firm’s involvement in socially responsible activities, which makes sense because the beneficial effect of engaging in CSR-related activities on firm risk taking can be offset by the externality of concerns by various stakeholder groups (Chava 2014). Put another way, firms mainly engage in those CSR-related activities that can improve corporate social performance, which, in turn, helps to reduce firm risk exposure. However, concerns of corporate social performance jeopardize firm managers’ efforts to reduce firm risk. The seven percent difference in terms of estimated coefficients for CSR_POS and CSR_NET is exactly what we expected to observe. Taken as a whole, our results as reported in Table 2 lend strong support for hypotheses 1–4.
We then ran a host of tests to ensure the robustness of our findings. Note that firms design optimal compensation contracts to properly align managerial incentives to best serve the interests of their shareholders. Therefore, inside debt can be endogenously determined, and this endogeneity issue may bias our estimation results. We adopted the instrumental variable approach. Following Anantharaman et al. (2014) and He (2015), we used state individual tax rate as the instrument, which would not affect firm risk taking directly but tended to affect a firm’s preference for using debt-like compensation. We obtained consistent results using the instrument variable approach.
There have been intensive debates on the use of KLD indexes as proxies for firm involvement in CSR-related activities or corporate social performance (Sharfman 1996; Hart and Sharfman 2015). Although researchers have confirmed that “KLD social performance ratings data are measuring at least part of the same CSP construct…”, these measures are not without challenges (Sharfman 1996). One concern is regarding the ordinal nature of CSR scores. Following the existing research (Pavelin and Porter 2008), we used an ordered Probit model instead of the ordinary least square (OLS) linear model specification.
Another concern around using KLD indexes is related to the validity of summing up the scores of strengths or subtracting scores of concerns from scores of strengths, which implicitly treats all subcategories of CSR ratings equally. To address these issues of comparability and the normality of data, researchers tend to add weights to different sub-categories to derive a set of continuous measures to better gauge firm CSP (Hart and Sharfman 2015). Despite the fact that this approach does not eliminate the problem and that there is no consensus as to the weights for each subcategory, following the existing literature (Clarkson 1995; Waldman et al. 2006), we first made a distinction between primary stakeholders (e.g., employees, suppliers, customers, and public agencies) and secondary stakeholders (e.g., actors without formal transactions with the organizations). We then assigned a value of 2 (−2) for the strengths (concerns) of primary stakeholders and assigned a value of 1 (−1) for the strengths (concerns) of secondary stakeholders. This weighting scheme allowed us to focus more on the important items in the KLD ratings that are related to important stakeholder relationships.
Using both the ordered Probit model and weighted CSR scores, we found qualitatively similar results. Note that we used a system of linear model specifications to test the mediating effect of CSR (Preacher et al. 2007) and gauge the economic significance of mediation through the Sobel test (Sobel 1982). Recent studies (MacKinnon et al. 2002; Woody 2011) have evaluated the soundness of the Sobel test, revealing that it is rather conservative and has lower explanatory power. In other words, it is likely that the Sobel test fails to reject the null hypothesis when the alternative is true (Type II error). To mitigate this concern, we followed Zhang (2025) and implemented an adjusted joint significance test to construct a Sobel-type confidence interval for the mediation effect of a single mediator. As shown in Table 2, this data-adjusted approach yields an adjusted 95% confidence interval of the Sobel-type mediated percentage, which allows us to better understand the mediation process. Both the lower bound and upper bound of the confidence interval are positive, which confirms a significant mediation process.
In addition, we included several other controls in our model specifications. Although inside debt can provide risk-reduction incentives to firm executives, engaging in CSR-related activities is just one of the strategic options from which managers can choose. Therefore, we controlled firm R&D intensity and diversification to ensure the robustness of our findings (Sundaram and Yermack 2007). Moreover, the effectiveness of firm compensation policies also depends on executives who design and implement strategies for risk management. In other words, even if a firm has a consistent compensation policy, the managerial incentives may be different for different CEOs, and changes in incentives may result in changes in policy and risk (Coles et al. 2006). As such, following Coles et al. (2006), we added an indicator variable (i.e., CEO turnover), which takes value 1 if the identity of the CEO changes, and zero otherwise. The inclusion of the abovementioned controls did not change our results in a material way. We found that CEO turnover is positively associated with firm risk taking. It is plausible that firms replace their CEOs with the expectation of higher future returns, which tend to be associated with higher risk taking.
4.2. Moderated Mediation Analyses
We further investigated two moderating variables that are likely to alter the mediation process. In Table 3, we focus on environmental dynamism as the moderator (Tosi and Slocum 1984). We hypothesized that the proposed mediation process would vary across different levels of industry dynamism. Note that *, **, *** indicate significance levels at 10%, 5% and 1%, respectively. The results in column 1 of Table 3 indicate a significant and negative overall treatment effect of CEO inside debt on firm risk-taking (β41 = −0.014, p < 0.01) but no overall moderating effect of environmental dynamism. As shown in column 2, we found that the overall positive effect of CEO inside debt on CSR involvement depends on different levels of environmental dynamism (β53 = −0.024, p < 0.05). Furthermore, we show in column 3 that the relationship between CSR engagement and firm risk taking did not depend on environmental dynamism. Therefore, our results as reported in columns 1–3 reveal that environmental dynamism moderated the relation between inside debt and CSR involvement, which supports hypothesis 4. Likewise, in columns 4–6, we repeat our test by focusing on CSR_NET, obtaining consistent results. Thus, hypothesis 4 is supported.
Similarly, Table 4 shows our analysis of moderated mediation for environmental munificence. Note that *, **, *** indicate significance levels at 10%, 5% and 1%, respectively. Column 1 of Table 4 shows an overall treatment effect of inside debt on firm risk-taking (β41 = −0.014, p < 0.01). The insignificant coefficient of β43 suggests that there was no overall moderating effect of environmental munificence. As shown in column 2, we found that CEO inside debt was significantly positively correlated with firm CSR involvement. In column 3, we show that the coefficient (β65) of the interaction term of CSR engagement and environmental munificence was significantly negative, which suggests that environmental munificence moderated the relationship between CSR involvement and firm risk-taking. In columns 4–6, we repeat our analyses by focusing on CSR_NET and document similar results.
In addition, we demonstrate a significant coefficient of the interaction term of munificence and inside debt in column 2 (β53 = −0.149, p < 0.10). We also found a significant coefficient of CSR involvement, as shown in column 3 (β64 = −0.010, p < 0.01). This finding indicates that munificence also moderated the relationship between inside debt and CSR involvement. However, this effect was not significant when we replicated our analysis with CSR_NET, as shown in columns 4–6.
5. Conclusions and Discussion
In this study, building on cumulative prospect theory and instrumental stakeholder theory, our aim was to understand debt-like compensation and managerial incentives for risk taking. Further, we argued that CSR can be a viable instrument for executives with significant debt-like compensation to achieve their strategic objectives of risk reduction. In particular, we adopted cumulative prospect theory to explain why CEOs granted significant inside debt tend to take on less risky projects. We then argued that CSR involvement has instrumental value because executives can use it to reduce firm risk. Thus, CSR involvement mediates the relationship between inside debt and firm risk taking. Further, we argued that the abovementioned mediation process is context dependent, and we identified two moderating variables: environmental dynamism and munificence. To test our hypotheses, we compiled a large longitudinal dataset using Execucomp, KLD, and Compustat from 2007 to 2012. The empirical analyses lent strong support to our hypotheses.
5.1. Implications for Research
Given that executive incentives influence managerial decisions that entail different levels of risk, prior research has extensively examined the relationship between managerial incentives induced by compensation and firm risk taking. This study investigated an overlooked component (i.e., debt-like compensation) in managerial pay and its effect on risk taking. More importantly, while the empirical investigations on firm decisions to engage in CSR activities were largely fragmented and disconnected from core business and operations (Porter and Kramer 2006), we argued that CSR-related strategies were not isolated decisions and could strategically integrate with other major managerial decisions (Porter and Kramer 2006). In other words, CSR involvement can serve as an instrument (means) to allow CEOs with debt-like compensation to achieve their risk-reduction objective (ends). In addition, drawing on multiple lines of research, we showed that the abovementioned mediation process is context dependent. We thereby offered new insights for the literature on CSR through a contingency lens and provided an analytical framework for related firm strategic decisions.
5.2. Managerial and Policy Implications
The results of our study have important managerial implications. Firms design various types of compensation packages to align managerial incentives. Our research indicates that debt-like compensation induces managers to behave conservatively. With other available tools that strategically reduce firm risk risking, managers with significant inside debt may choose to engage in CSR when other choices are not effective or efficient (Edmans and Liu 2011). Our study also shows a robust link between CSR and firm core operations and strategies. Moreover, decision-makers have to pay attention to contextual conditions. Resource configuration and allocation across multiple projects, including CSR, is subject to perceived risk due to unpredictable external environments. The effectiveness of CSR strategies also depends on how well the external environment can support the sustained growth of firms within a particular industry. Therefore, we believe corporate managers can use our analytic framework to better understand the relationship between managerial incentives and risk taking as well as the integration of other strategic tools to achieve objectives of risk management.
In addition, this research offers informative insights for policymakers. Debt-like compensation provides incentives for CEOs and other executives to engage in more conservative risk-taking activities. Ideally, the design of compensation packages needs to induce the correct amount of risk-taking incentives. Nonetheless, the components of pension and other deferred compensation in the package raise concerns about the complexity in monitoring and regulating such compensation structures. Furthermore, the policymakers need to consider the need for transparency in financial disclosure to ensure that investors understand the nature of compensation packages and make informed decisions.
5.3. Limitations and Future Research
Although our study adds novel evidence and generates critical implications for academic researchers, practitioners, and policymakers, we recognize that this research is not without its limitations. For example, we do not have data on managers’ personal wealth to create a true personal debt-equity ratio. Moreover, these managers may use their personal wealth to hedge in the market to undo the leverage imposed by compensation packages. In another case, we focused on external environments as contextual conditions. It is plausible that the decision to engage in CSR activities also depends on international contextual variables. Companies may choose other pathways to achieve desired levels of risk exposure, and these different pathways may be simultaneously determined. Nevertheless, these limitations reveal venues for future research. It is plausible that firms may engage in CSR-related activities to limit their risk exposure when they face constraints of adopting alternative options, such as reductions in R&D and diversification. Our research points out the importance of the structure of managerial incentives and the necessity to further explore how executives integrating CSR strategies into core business operations can be fruitful.
Conceptualization, D.Y. and H.W.; methodology, M.W., Y.S. and X.Z.; software, M.W.; validation, Y.S. and X.Z.; formal analysis, M.W. and H.W.; writing—original draft preparation, H.W.; writing—review and editing, D.Y. and H.W. All authors have read and agreed to the published version of the manuscript.
Data is contained within the article.
The authors declare no conflicts of interest.
Footnotes
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Summary Statistics and Pairwise Correlation Matrix.
| Variable | Mean | St Dev | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Firm risk | 0.34 | 0.14 | 1 | |||||||||||||||||
| 2 | CSR_POS | 2.59 | 3.43 | −0.27 | 1 | ||||||||||||||||
| 3 | CSR_NET | 0.38 | 2.81 | −0.23 | 0.70 | 1 | |||||||||||||||
| 4 | Inside debt | 0.65 | 0.81 | −0.13 | 0.18 | 0.13 | 1 | ||||||||||||||
| 5 | Firm size | 7.93 | 1.35 | −0.30 | 0.62 | 0.28 | 0.14 | 1 | |||||||||||||
| 6 | Discretionary slack | 0.011 | 0.078 | −0.08 | 0.18 | 0.14 | 0.11 | 0.07 | 1 | ||||||||||||
| 7 | Leverage | 0.25 | 0.15 | 0.05 | 0.04 | −0.04 | −0.29 | 0.07 | −0.09 | 1 | |||||||||||
| 8 | Market-to-book ratio | 3.00 | 13.40 | −0.04 | 0.06 | 0.06 | 0.04 | 0.04 | 0.03 | 0.04 | 1 | ||||||||||
| 9 | Sales growth | 0.10 | 1.27 | −0.01 | −0.03 | −0.02 | −0.03 | 0.00 | 0.01 | 0.05 | 0.02 | 1 | |||||||||
| 10 | G-index | 9.35 | 2.50 | −0.07 | 0.07 | 0.07 | 0.17 | 0.07 | −0.04 | 0.00 | 0.01 | −0.01 | 1 | ||||||||
| 11 | R&D intensity | 23.62 | 37.84 | −0.01 | 0.06 | 0.10 | −0.02 | −0.10 | −0.08 | −0.07 | 0.01 | 0.02 | −0.08 | 1 | |||||||
| 12 | Diversification | 0.51 | 0.55 | −0.10 | 0.11 | −0.01 | 0.10 | 0.23 | 0.14 | 0.10 | 0.02 | −0.01 | 0.21 | −0.16 | 1 | ||||||
| 13 | CEO age | 55.92 | 6.45 | −0.06 | 0.05 | 0.00 | 0.11 | 0.11 | 0.14 | 0.00 | −0.01 | 0.01 | 0.02 | −0.06 | 0.11 | 1 | |||||
| 14 | CEO tenure | 7.49 | 6.91 | 0.00 | −0.08 | −0.06 | −0.03 | −0.05 | −0.08 | 0.03 | 0.00 | 0.04 | −0.10 | −0.01 | 0.00 | 0.39 | 1 | ||||
| 15 | Female CEO | 0.03 | 0.17 | −0.01 | 0.07 | 0.08 | 0.02 | 0.02 | 0.04 | 0.04 | −0.01 | 0.00 | −0.05 | 0.03 | 0.04 | −0.06 | −0.10 | 1 | |||
| 16 | CEO turnover | 0.04 | 0.20 | 0.00 | −0.01 | 0.01 | 0.03 | 0.00 | 0.03 | 0.00 | −0.01 | 0.00 | 0.02 | 0.00 | 0.00 | 0.10 | 0.06 | −0.01 | 1 | ||
| 17 | Dynamism | 1.28 | 0.93 | 0.07 | −0.11 | −0.16 | 0.02 | −0.07 | −0.08 | 0.03 | −0.02 | −0.01 | −0.01 | −0.07 | 0.14 | 0.03 | 0.01 | −0.04 | −0.02 | 1 | |
| 18 | Munificence | 0.05 | 0.05 | 0.07 | 0.01 | −0.09 | −0.03 | 0.05 | 0.09 | −0.06 | −0.03 | 0.01 | −0.06 | 0.06 | −0.06 | −0.03 | 0.02 | −0.03 | 0.00 | −0.18 | 1 |
Mediation Analysis (Note that *, **, *** indicate significance levels at 10%, 5% and 1%, respectively).
| Independent Variables | Dependent Variables | |||||
|---|---|---|---|---|---|---|
| Firm Risk | CSR_POS | Firm Risk | Firm Risk | CSR_NET | Firm Risk | |
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Inside debt | −0.011 *** | 0.073 *** | −0.010 *** | −0.011 *** | 0.024 *** | −0.010 *** |
| [−4.716] | [5.116] | [−4.252] | [−4.716] | [4.149] | [−4.267] | |
| CSR_POS | −0.015 *** | |||||
| [−5.221] | ||||||
| CSR_NET | −0.049 *** | |||||
| [−6.709] | ||||||
| Firm size | −0.026 *** | 0.395 *** | −0.020 *** | −0.026 *** | 0.070 *** | −0.023 *** |
| [−17.801] | [48.776] | [−10.468] | [−17.801] | [18.283] | [−14.023] | |
| Discretionary slack | −0.001 ** | 0.012 *** | −0.001 ** | −0.001 ** | 0.011 ** | −0.001 ** |
| [−2.232] | [2.630] | [−2.131] | [−2.032] | [2.167] | [−2.199] | |
| Leverage | 0.103 *** | 0.143 * | 0.105 *** | 0.103 *** | 0.000 | 0.103 *** |
| [6.919] | [1.891] | [7.118] | [6.919] | [0.000] | [6.984] | |
| Market-to-book ratio | −0.001 | 0.001 ** | −0.001 | −0.001 | 0.001 *** | −0.001 |
| [−1.245] | [2.095] | [−1.163] | [−1.245] | [2.698] | [−1.039] | |
| Sales growth | −0.000 | −0.121 | −0.002 | −0.000 | −0.041 * | −0.002 |
| [−0.029] | [−1.632] | [−0.228] | [−0.029] | [−1.852] | [−0.256] | |
| G-index | −0.001 | −0.003 | −0.001 | −0.001 | 0.005 ** | −0.001 |
| [−0.598] | [−0.821] | [−0.666] | [−0.598] | [2.159] | [−0.315] | |
| R&D intensity | −0.001 ** | 0.002 *** | −0.001 | −0.001 ** | 0.001 *** | −0.001 |
| [−1.962] | [3.416] | [−1.268] | [−1.962] | [4.154] | [−0.915] | |
| Diversification | 0.004 | −0.106 *** | 0.002 | 0.004 | −0.044 *** | 0.001 |
| [1.014] | [−5.162] | [0.566] | [1.014] | [−5.032] | [0.400] | |
| CEO age | 0.014 | −0.008 | 0.014 | 0.014 | −0.074 * | 0.011 |
| [0.795] | [−0.075] | [0.788] | [0.795] | [−1.829] | [0.598] | |
| CEO tenure | −0.004 * | −0.047 *** | −0.005 * | −0.004 * | −0.013 ** | −0.005 * |
| [−1.675] | [−3.397] | [−1.947] | [−1.675] | [−2.351] | [−1.929] | |
| Female CEO | 0.008 | 0.242 *** | 0.012 | 0.008 | 0.107 *** | 0.013 |
| [0.704] | [3.863] | [1.009] | [0.704] | [4.026] | [1.153] | |
| CEO turnover | 0.014 | −0.033 | 0.013 | 0.014 | 0.000 | 0.014 |
| [1.538] | [−0.581] | [1.484] | [1.538] | [0.023] | [1.558] | |
| Constant | 0.426 *** | −2.205 *** | 0.393 *** | 0.426 *** | 2.119 *** | 0.529 *** |
| [5.884] | [−5.414] | [5.383] | [5.884] | [13.041] | [7.288] | |
| Year effect | Y | Y | Y | Y | Y | Y |
| Industry effect | Y | Y | Y | Y | Y | Y |
| Observations | 3793 | 3793 | 3793 | 3793 | 3793 | 3793 |
| Adjusted R-squared | 0.462 | 0.458 | 0.466 | 0.462 | 0.223 | 0.470 |
| Sobel test | p < 0.01 | p < 0.01 | ||||
| Indirect effect | −0.0011 | −0.0012 | ||||
| Direct effect | −0.0097 | −0.0096 | ||||
| Total effect | −0.0108 | −0.0108 | ||||
| Mediated total effect (%) | 10.08% | 10.92% | ||||
| 95% Confidence interval | [0.0303, 0.1604] | [0.0375, 0.1933] | ||||
Moderated mediation analyses: the effect of environmental dynamism (Note that *, **, *** indicate significance levels at 10%, 5% and 1%, respectively).
| Independent Variables | Dependent Variables | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Firm Risk | CSR_POS | Firm Risk | Firm Risk | CSR_NET | Firm Risk | |||||||
| (1) | (2) | (3) | (4) | (5) | (6) | |||||||
| Inside debt | β41 | −0.014 *** | β51 | 0.097 *** | β61 | −0.013 *** | β41 | −0.014 *** | β51 | 0.049 *** | β61 | −0.013 *** |
| [−3.898] | [4.189] | [−3.577] | [−3.898] | [4.422] | [−3.617] | |||||||
| Dynamism | β42 | −0.004 * | β52 | −0.043 *** | β62 | −0.005 * | β42 | −0.004 * | β52 | −0.009 | β62 | −0.007 |
| [−1.762] | [−2.723] | [−1.700] | [−1.762] | [−1.607] | [−0.450] | |||||||
| Dynamism × Inside debt | β43 | 0.003 | β53 | −0.024 ** | β63 | 0.003 | β43 | 0.003 | β53 | −0.020 *** | β63 | 0.002 |
| [1.302] | [−2.104] | [1.211] | [1.302] | [−2.638] | [1.087] | |||||||
| CSR_POS | β64 | −0.014 *** | ||||||||||
| [−3.345] | ||||||||||||
| Dynamism × CSR_POS | β65 | −0.001 | ||||||||||
| [−0.311] | ||||||||||||
| CSR_NET | β64 | −0.034 *** | ||||||||||
| [−2.854] | ||||||||||||
| Dynamism × CSR_NET | β65 | 0.001 | ||||||||||
| [0.137] | ||||||||||||
| Firm size | −0.025 *** | 0.388 *** | −0.019 *** | −0.025 *** | 0.068 *** | −0.022 *** | ||||||
| [−16.801] | [46.331] | [−9.897] | [−16.801] | [17.978] | [−13.611] | |||||||
| Discretionary slack | −0.001 ** | 0.013 *** | −0.001 ** | −0.001 ** | 0.009 ** | −0.001 ** | ||||||
| [−2.305] | [3.874] | [−2.351] | [−2.205] | [2.070] | [−2.129] | |||||||
| Leverage | 0.076 *** | 0.141 * | 0.079 *** | 0.076 *** | −0.017 | 0.075 *** | ||||||
| [5.204] | [1.807] | [5.430] | [5.204] | [−0.589] | [5.112] | |||||||
| Market-to-book ratio | −0.001 | 0.001 ** | −0.001 | −0.001 | 0.001 *** | −0.001 | ||||||
| [−1.242] | [2.144] | [−1.096] | [−1.242] | [3.192] | [−1.008] | |||||||
| Sales growth | −0.001 | −0.100 | −0.003 | −0.001 | −0.032 | −0.002 | ||||||
| [−0.104] | [−1.419] | [−0.274] | [−0.104] | [−1.590] | [−0.201] | |||||||
| G-index | −0.002 ** | 0.002 | −0.002 ** | −0.002 ** | 0.007 *** | −0.001 * | ||||||
| [−2.137] | [0.524] | [−2.115] | [−2.137] | [4.040] | [−1.855] | |||||||
| R&D intensity | −0.000 *** | 0.003 *** | −0.000 *** | −0.000 *** | 0.001 *** | −0.000 *** | ||||||
| [−3.264] | [3.797] | [−3.032] | [−3.264] | [4.074] | [−3.020] | |||||||
| Diversification | −0.008 ** | −0.051 ** | −0.009 ** | −0.008 ** | −0.025 *** | −0.009 ** | ||||||
| [−2.242] | [−2.510] | [−2.409] | [−2.242] | [−2.755] | [−2.492] | |||||||
| CEO age | 0.016 | −0.050 | 0.015 | 0.016 | −0.058 | 0.014 | ||||||
| [0.886] | [−0.484] | [0.854] | [0.886] | [−1.424] | [0.781] | |||||||
| CEO tenure | −0.007 *** | −0.041 *** | −0.007 *** | −0.007 *** | −0.014 ** | −0.007 *** | ||||||
| [−2.686] | [−2.972] | [−2.901] | [−2.686] | [−2.537] | [−2.838] | |||||||
| Female CEO | 0.011 | 0.231 *** | 0.014 | 0.011 | 0.100 *** | 0.014 | ||||||
| [0.897] | [3.649] | [1.187] | [0.897] | [3.775] | [1.166] | |||||||
| CEO turnover | 0.015 * | −0.021 | 0.015 * | 0.015 * | −0.000 | 0.015 * | ||||||
| [1.712] | [−0.378] | [1.656] | [1.712] | [−0.005] | [1.713] | |||||||
| Constant | 0.437 *** | −2.112 *** | 0.405 *** | 0.437 *** | 2.062 *** | 0.507 *** | ||||||
| [6.033] | [−5.146] | [5.561] | [6.033] | [12.615] | [6.762] | |||||||
| Year effect | Y | Y | Y | Y | Y | Y | ||||||
| Industry effect | Y | Y | Y | Y | Y | Y | ||||||
| Observations | 3793 | 3793 | 3793 | 3793 | 3793 | 3793 | ||||||
| Adjusted R-squared | 0.486 | 0.473 | 0.489 | 0.486 | 0.257 | 0.489 | ||||||
Moderated mediation analyses: the effect of environmental munificence (Note that *, **, *** indicate significance levels at 10%, 5% and 1%, respectively).
| Independent Variables | Dependent Variables | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Firm Risk | CSR_POS | Firm Risk | Firm Risk | CSR_NET | Firm Risk | |||||||
| (1) | (2) | (3) | (4) | (5) | (6) | |||||||
| Inside debt | β41 | −0.014 *** | β51 | 0.066 *** | β61 | −0.011 *** | β41 | −0.014 *** | β51 | 0.029 ** | β61 | −0.012 *** |
| [−3.364] | [3.374] | [−3.155] | [−3.364] | [2.529] | [−3.017] | |||||||
| Munificence | β42 | 0.031 | β52 | −0.241 | β62 | 0.069 | β42 | 0.031 | β52 | −0.328 ** | β62 | 0.717 ** |
| [0.585] | [−0.758] | [1.276] | [0.585] | [−2.467] | [2.249] | |||||||
| Munificence × Inside debt | β43 | 0.007 | β53 | 0.149 * | β63 | 0.013 | β43 | 0.007 | β53 | 0.128 * | β63 | 0.001 |
| [0.148] | [1.817] | [0.283] | [0.148] | [1.740] | [0.030] | |||||||
| CSR_POS | β64 | −0.010 *** | ||||||||||
| [−2.901] | ||||||||||||
| Munificence × CSR_POS | β65 | −0.084 ** | ||||||||||
| [−1.981] | ||||||||||||
| CSR_NET | β64 | −0.017 | ||||||||||
| [−1.631] | ||||||||||||
| Munificence × CSR_NET | β65 | −0.312 ** | ||||||||||
| [−2.246] | ||||||||||||
| Firm size | −0.025 *** | 0.389 *** | −0.019 *** | −0.025 *** | 0.069 *** | −0.023 *** | ||||||
| [−17.077] | [45.115] | [−10.206] | [−17.077] | [18.160] | [−14.126] | |||||||
| Discretionary slack | −0.001 ** | 0.013 *** | −0.001 ** | −0.001 ** | 0.010 ** | −0.001 ** | ||||||
| [−2.189] | [4.105] | [−2.337] | [−2.189] | [2.185] | [−2.174] | |||||||
| Leverage | 0.070 *** | 0.186 ** | 0.067 *** | 0.070 *** | −0.003 | 0.064 *** | ||||||
| [4.858] | [2.449] | [4.717] | [4.858] | [−0.115] | [4.462] | |||||||
| Market-to-book ratio | −0.001 | 0.001 ** | −0.001 | −0.001 | 0.001 *** | −0.001 | ||||||
| [−1.258] | [2.477] | [−1.128] | [−1.258] | [3.267] | [−1.079] | |||||||
| Sales growth | −0.001 | −0.135 * | −0.002 | −0.001 | −0.046 * | −0.002 | ||||||
| [−0.077] | [−1.731] | [−0.258] | [−0.077] | [−1.959] | [−0.193] | |||||||
| G-index | −0.002 ** | 0.002 | −0.002 ** | −0.002 ** | 0.005 *** | −0.002 ** | ||||||
| [−2.303] | [0.402] | [−2.545] | [−2.303] | [2.947] | [−2.330] | |||||||
| R&D intensity | −0.000 *** | 0.003 *** | −0.000 *** | −0.000 *** | 0.001 *** | −0.000 *** | ||||||
| [−3.357] | [3.465] | [−3.060] | [−3.357] | [4.434] | [−3.022] | |||||||
| Diversification | −0.010 *** | −0.053 *** | −0.009 *** | −0.010 *** | −0.035 *** | −0.010 *** | ||||||
| [−2.775] | [−2.644] | [−2.706] | [−2.775] | [−3.948] | [−2.732] | |||||||
| CEO age | 0.015 | −0.075 | 0.013 | 0.015 | −0.096 ** | 0.011 | ||||||
| [0.823] | [−0.725] | [0.717] | [0.823] | [−2.373] | [0.598] | |||||||
| CEO tenure | −0.008 *** | −0.036 *** | −0.008 *** | −0.008 *** | −0.013 ** | −0.008 *** | ||||||
| [−3.037] | [−2.626] | [−3.210] | [−3.037] | [−2.290] | [−3.192] | |||||||
| Female CEO | 0.008 | 0.265 *** | 0.012 | 0.008 | 0.090 *** | 0.011 | ||||||
| [0.666] | [4.205] | [0.964] | [0.666] | [3.415] | [0.924] | |||||||
| CEO turnover | 0.017 * | −0.029 | 0.017 * | 0.017 * | 0.006 | 0.018 ** | ||||||
| [1.855] | [−0.521] | [1.867] | [1.855] | [0.294] | [1.968] | |||||||
| Constant | 0.433 *** | −2.106 *** | 0.407 *** | 0.433 *** | 2.223 *** | 0.479 *** | ||||||
| [6.021] | [−5.149] | [5.730] | [6.021] | [13.648] | [6.553] | |||||||
| Year fixed effect | Yes | Yes | Yes | Yes | Yes | Yes | ||||||
| Industry fixed effect | Yes | Yes | Yes | Yes | Yes | Yes | ||||||
| Observations | 3793 | 3793 | 3793 | 3793 | 3793 | 3793 | ||||||
| Adjusted R-squared | 0.494 | 0.471 | 0.500 | 0.494 | 0.238 | 0.500 | ||||||
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Abstract
In this study, we focus on managerial incentives provided by debt-like compensation and further investigate whether and to what extent such managerial incentives may affect CEOs’ decisions on risk management. Building on cumulative prospect theory and instrumental stakeholder theory, we propose that CEOs tend to have risk-reduction incentives if they are paid with debt in their own firms, and that firm engagement in corporate social responsibility (CSR) activities can mediate the relationship between debt-like compensation and firm risk taking. In addition, we posit that the mediated relationship between CEO debt-like compensation and firm risk taking is contingent, and we propose environmental dynamism and munificence as two such contingencies that moderate the mediated process. Using a large longitudinal dataset of nonfinancial U.S. firms, we document strong supportive evidence for these hypotheses.
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Details
; Xinting Zhen 4
1 School of Economics and Management, East China Normal University, Shanghai 20062, China;
2 Department of Statistics, The Ohio State University, Columbus, OH 43201, USA;
3 Stuart School of Business, Illinois Institute of Technology, Chicago, IL 60016, USA;
4 Department of Business Administration and Accounting, Saint Michael’s College, Burlington, VT 05446, USA;