Top management inside debt and corporate social responsibility? Evidence from the US

Journal Pre-proof Top Management Insider Debt and Corporate Social Responsibility? Evidence from the US Sabri Boubaker, Kaouther Chebbi, Jocelyn Grira

PII:

S1062-9769(19)30340-0

DOI:

https://doi.org/10.1016/j.qref.2019.12.001

Reference:

QUAECO 1325

To appear in:

Quarterly Review of Economics and Finance

Received Date:

26 February 2019

Revised Date:

29 September 2019

Accepted Date:

14 December 2019

Please cite this article as: Boubaker S, Chebbi K, Grira J, Top Management Insider Debt and Corporate Social Responsibility? Evidence from the US, Quarterly Review of Economics and Finance (2019), doi: https://doi.org/10.1016/j.qref.2019.12.001

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Top Management Insider Debt and Corporate Social Responsibility? Evidence from the US

Sabri Boubaker EM Normandie Business School, Métis Lab, Paris, France IRG, Paris-Est Créteil University, Créteil, France, [email protected]

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Kaouther Chebbi King Faisal University, College of Business Administration Saudi Arabia, [email protected] Jocelyn Grira Qatar University, College of Business & Economics Qatar, [email protected]

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Highlights  This study discusses the relationship between CEO inside debt and CSR.  An increase in CEO inside debt leads to high levels of CSR.  CEO inside debt is directly related to firms’ primary stakeholders: Community, Diversity, Employee Relations, Environment, and Product Characteristics.

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Abstract

This study provides evidence on the relationship between CEO inside debt and

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corporate social responsibility (hereinafter, CSR). We find that an increase in CEO inside debt leads to high levels of CSR. This finding is robust to controlling for the sensitivity of CEO equity compensation to volatility as well as to alternative measures

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of CSR. We also find that CEO inside debt is directly related to firms’ primary stakeholders (Community, Diversity, Employee Relations, Environment, and Product

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Characteristics). Our results are in line with the risk mitigation hypothesis and shed more light on CSR as a channel through which managers with more inside debt tend to respond to debtholders’ demands as their appetite for risk decreases. Keywords: Ethics in finance; Corporate social responsibility; CEO inside debt

1. Introduction 1

An extensive literature demonstrates that a variety of corporate policies and practices affect corporate engagement in social activities: for instance, the corporate governance structure influences corporate social responsibility (hereinafter, CSR) (e.g., Beltratti 2005; Cespa and Cestone 2007; Surroca and Tribo 2008; Fabrizi et al. 2014). Vice versa, CSR impacts corporate practices such as earnings management (e.g., Petrovits, 2006), investment behavior (e.g., Tondkar et al. 2010), and cash holding (e.g., Cheung 2016). In particular, recent empirical research shows that CSR activities are also concerned with executive pay practices. In fact, the 2013 joint report by the Investor Responsibility Research Center and the Sustainable Investments Institutes indicates that 43% of the Fortune 500 firms tie executive compensation to CSR.1 In the same vein, Jian and Lee (2014) and Hong et al. (2015) find that equity

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incentives designed to align CEO’s and shareholders’ interests have a negative effect on CSR. However, such equity incentives contribute to transferring wealth to shareholders at the

expenses of bondholders (e.g., Jensen and Meckling 1976). Recently, several studies have

shed light on another form of compensation that reduces shareholders-bondholders agency

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conflict, namely CEO inside debt, defined as unsecured long-term fixed claims held by

managers (John and John 1993; Sundaram and Yermack 2007; Anantharaman et al. 2010;

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Edmans and Liu 2011; Wang et al. 2010; Cassell et al. 2012; Lee et al. 2015; among others). In this paper, we extend the above line of research by investigating the role of CEO inside

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debt in influencing another important investment decision, namely, CSR investment. To the best of our knowledge, this is the first direct empirical study to examine the relation between debt-like compensations and CSR activities. Drawing on risk mitigation theory, we posit a

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positive association between CEO inside debt and CSR investment. Indeed, firms adopting CSR activities are less likely to engage in risky projects, thus meeting the default risk-

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reduction target (e.g., Gregory et al. 2014). As such, managers with more inside debt would tend to respond to debtholders’ demands through increased CSR activities. In addition, according to the strategic view of CSR as introduced by Orlitzky et al. (2011), voluntary CSR

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actions positively impact primary stakeholders’ interests and the firm’s reputation. Thus, we expect that mangers with more inside debt exert a more (less) pronounced effect on CSR dimensions that are directly related to a firm’s primary stakeholders (social issues participation).

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http://www.csrhub.com/blog/2013/05/top-companies-tie-compensation-to-sustainability.html

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Using a sample of 8222 firm-year observations representing 1655 unique U.S. firms over the 2006–2013 period, we construct a proxy for CSR activity drawn from Kinder Lydenburg Domini (KLD) data based on a firm’s engagement in social, ethical, and legal practices (e.g., Kang 2013). We obtain executive compensation data from the ExecuComp database, and we use two measures for CEO inside debt. The first metric is the CEO debt-to-equity ratio (Ceodebt), calculated as accumulated inside debt holdings divided by accumulated inside equity holdings (e.g., Wei and Yermack 2011). The second one is the relative CEO debt-toequity ratio (Relceodebt), which is computed as CEO debt-to-equity ratio divided by firm debt-to-equity ratio (e.g., Edmans and Liu 2011; Cassell et al. 2012; Anantharam et al. 2011;

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Bennett et al. 2012). We find support for our first hypothesis claiming a positive relationship between debt-like

compensation and CSR: the estimated coefficients of Ceodebt and Relceodebt are positive and statistically significant to conventional levels, indicating that an increase in CEO inside debt

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leads to higher levels of CSR. Furthermore, the analysis of individual components of CSR is consistent with our main result: a high level of CEO inside debt is also associated with five of the six dimensions used in the analysis, namely community, diversity, employee relations,

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environment, and product characteristics. Our results are robust to different subsamples and

alternative measures of CSR.

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estimation methods, additional control variables, and model specifications as well as to

In this research work, we contribute to the body of knowledge in several ways. First, this

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study provides a theoretical contribution to the existing literature examining the impact of executive compensations on CSR (e.g., Mahoney and Thorne 2005, 2006; Deckop et al. 2006; Frye et al. 2006; Fabrizi et al. 2014; Jian and Lee 2014; Hong et al. 2015) by suggesting that

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CEO compensation schemes drive managerial attention not only to firm performance but also to firm’s engagement in CSR activities. Second, it extends the literature on managerial

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incentives that usually focuses on equity-based executive pay. In fact, the literature documents that stocks and stock options motivate managers to increase firm risk (e.g., Smith and Stulz 1985; Guay 1999; Coles et al. 2006; Low 2009). We shift the attention to a different element of executive compensation and provide evidence of its association with riskdecreasing decisions. Third, this study contributes to a growing research stream on managerial debt-like compensation (e.g., Sundaram and Yermack 2007; Wei and Yermack 2011; Cassell et al. 2012; Anantharaman et al. 2013; Phan 2014; Van Bekkum 2016) by investigating the role of CEO inside debt on corporate investment and financial policies. To 3

the best of our knowledge, this is the first study documenting direct evidence of CEO inside debt’s impact on CSR decisions. Furthermore, it is the first attempt to examine the joint influence of CEO equity compensation incentives and debt compensation incentives on CSR decisions. Finally, our research adds to the literature on the drivers of CSR choices (e.g., Fombrun and Shanley 1990; Goss and Roberts 2011; McWilliams and Siegel 2001; Lee and Faff 2009; El Ghoul et al. 2011; Attig et al. 2013, among others). This study provides further empirical evidence of how shareholders-bondholders agency conflicts affect managerial decisions on adopting CSR activities. The remainder of the paper is organized as follows. Section 2 presents the theoretical

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background on the link between inside debt and CSR and the development of our

hypotheses. Section 3 presents the data and definitions of the variables used in the empirical analysis. Section 4 presents and discusses our findings. Robustness tests are presented in

2. Literature Review and Hypothesis Development

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2.1. Executive compensation and CSR

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section 5. Finally, section 6 concludes the paper.

Friedman (1970) was the first to predict that CSR has potential agency costs. In particular,

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managers could use CSR to advance their interests over those of shareholders. Several subsequent studies support evidence of such self-interest behavior by managers at the expense of shareholders (e.g., Jiraporn and Chintrakarn 2013; Brown et al. 2006; Borghesi et

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al. 2014; Cheng et al. 2014; Kruger 2015; Masulis and Reza 2015). For example, Cespa and Cestone (2007) assert that underperforming managers are prone to use CSR activities to gain

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stakeholders’ support. Prior et al. (2008) show that managers strategically make decisions to participate in CSR practices to disguise manipulation of earnings, and Barnea and Rubin (2010) find that CEOs may commit themselves to socially responsible behavior with the aim

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of enhancing their individual reputations as good global citizens, probably at the shareholders’ expense. To alleviate such agency cost, executive compensations can be designed to reward managers for maximizing shareholders’ interests (e.g., Jensen and Meckling 1976; Fama and Jensen 1983). In this sense, executive compensations could play an important role in the implementation of a firm’s social objectives (e.g., McGuire et al. 2003). The issues of executive compensation and CSR have been explored and discussed by various scholars (e.g., Belkaoui 1992; Manner 2010). One stream of research mainly concentrates on 4

CEOs’ flow compensation (e.g., McGuire et al. 2003; Mahoney and Thorne 2005; Frye et al. 2006) and argues that CSR could lead firms to keep short-term profits from being invested in projects that have no immediate payoff, which is in line with shareholders’ objectives over the interests of other stakeholders (e.g., Fama 1980). For instance, McGuire et al. (2003) use the KLD database to investigate the association between CEO incentives and firms’ social performance. They fail to find any significant association among the different components of a CEO’s compensation and strong social performance, but they do show a positive relationship between CEO’s salary and long-term incentives and weak social performance.2 In a related study, Mahoney and Thorne (2006) suggest that the results in McGuire et al. (2003) may be influenced by the U.S. institutional environment. Thus, they use a sample of 90

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publicly traded Canadian firms and find a very different result. They document a positive relationship between long-term pay amounts and firms’ CSR engagement. Going a step

further, Mahoney and Thorne (2006) show that CEO salary is associated with low CSR levels whereas CEO bonus is associated with high CSR levels. They find also a positive relation

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between CEO stock options and firms’ CSR. Their results imply that executive compensation could be an effective tool to encourage managers to invest in socially responsible projects.

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Deckop et al. (2006) and Berrone and Gomez-Mejia (2009) find similar results in the American context.

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In addition, using a sample of 597 U.S. firms over the period 2005 to 2009, Fabrizi et al. (2013) find that both monetary and non-monetary incentives affect CSR decisions. In particular, they document a negative (positive) impact of monetary (non-monetary) incentives on CSR.

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Recently, Hong et al. (2015), Ikram et al. (2015), and Li et al. (2015) have used hand-collected compensation data to show that offering executive compensation contracts that contain

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incentives for CSR enhances social performance outcomes. Hong et al. (2015) find that firms with better corporate governance are more likely to give executives direct incentives tied to CSR, which suggests that CSR activities are more likely to be beneficial to shareholders, as

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opposed to an agency cost. Comparably, Li et al. (2015) show that CEO power, measured by pay slice, CEO tenure, and CEO duality, is negatively associated with CSR engagement. They interpret such a result as indicating that CSR activities are value enhancing. In sum, research on the relationship between CEO compensation and CSR activities is traditionally studied in an agency theory setting (e.g., Jensen and Meckling 1976) where the 2 Long-term

incentives generally may include non-market-based long-term incentive plans, other forms of market-based compensation, and stock options (McGuire et al. 2003).

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interests of the manager are not aligned with those of the shareholders. We directly build on this research framework and aim at expanding the set of CEO incentives that may align the CEOs’ interests with those of bondholders, hence potentially shaping firms’ CSR investment choices. 2.2. CEO inside debt and CSR Agency theory argues that debt-like executive compensation schemes (pensions and deferred compensations) are designed to create an alignment of interests between managers and debtholders in order to mitigate agency problems between shareholders and creditors (e.g.,

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Jensen and Meckling 1976). A vast body of literature focuses on the economic consequences of awarding inside debt to CEOs and finds that providing CEOs with inside debt decreases their appetite for risk as they take less risky decisions that may harm debtholders. For

instance, Sundaram and Yermack (2007) use IRS filings for pension data and show that

higher inside debt incentivizes CEOs to choose investment and financial policies that are less

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risky. Wei and Yermack (2011) find an association between debt-like compensations and

risk-decreasing decisions after the Securities and Exchange Commission (SEC) mandated

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pension reporting in 2006. Empirically, Cassell et al. (2012) show that CEOs tends to manage firms more conservatively when the debt proportion of their compensation is higher,

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choosing safe investments, such as capital expenditures, over research and development (R&D) and higher working capital. In addition, Anantharaman et al. (2013) and Phan (2014) provide evidence that firms with larger CEO inside debt are associated with a lower cost of

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debt financing and risk-decreasing merger and acquisition activities, respectively, which suggests an association between inside debt and conservative management. Recently, Van Bekkum (2016) has documented a negative relationship between CEO inside debt and bank

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risk-taking decisions. Given such evidence on the link between inside debt and investment behavior, debt-like compensations align managers’ risk-taking preferences with those of

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debtholders, who favor financing decisions that decrease a firm risk, rather than with those of equity holders. CSR is a major investment decision, and increasing evidence supports the idea that CSR initiatives reduce risk. For example, Lee and Faff (2009) document a negative association between CSR scores and idiosyncratic risk. Boutin-Dufresna and Savaria (2004) report similar results in the Canadian context. Similarly, Luo and Bhattacharya (2009) find that leading (high CSR) firms are able to mitigate undesirable idiosyncratic risk. Oikonomou et al. (2010) and Albuquerque et al. (2015) find supporting evidence for a negative association between CSR and systematic risk. Erhemjamts et al. (2013) show a positive relationship 6

between CSR and capital expenditure as a proxy for investment policy.3 Bouslah et al. (2013) find that CSR involvement is related to lower financial risks. Hence, firms adopting CSR activity are less likely to engage in risky projects (Gregory et al. 2014). In addition, investing in CSR helps to build more stable relations with the community and the government, thus reducing the risk of litigation and costly sanctions that would otherwise damage a firm’s profitability in the future (e.g., McGuire et al. 1988; Peloza 2006; Sharfman and Fernando 2008; Dhaliwal et al. 2009). Vanhamme and Grobben (2009) document that the negative impact of a crisis could be countered more efficiently by firms with a long CSR history than by firms with a short CSR history. Bassen et al. (2006) show that a lack of CSR activities exposes a company to unnecessarily high risk. Taken together, the research indicates that

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higher levels of CSR tend to build a reservoir of goodwill which leads stakeholders (i.e., debtholders) to be resilient to negative information about socially responsible firms

(Bhattacharya and Sen 2004). As such, managers with more inside debt tend to respond to

debtholders’ demands through increased CSR activity. Sharfman and Fernando (2008) find

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that firms with good environmental performance have easier access to debt financing,

including high bond yields and high leverage. In a similar vein, Chen et al. (2007) show that

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non-unionized firms face higher costs of debt than their unionized peers. They interpret this as unionization lowering the tendency for shareholders to expropriate bondholders. Also, as

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argued in Chizema et al. (2019), CEO personal traits may potentially affect firms’ financial and investment choices, namely CSR investment decisions. This is consistent with conclusions in related studies like Cain and McKeon (2016) who investigate the relationship

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between corporate risk-taking and CEO personal traits, as well as conclusions in Caliskan and Doukas (2015) and Cao and Wang (2013). Consistently, McCarthy et al. (2017) show that CEO confidence relates negatively to CSR. Moreover, CEO risk aversion could potentially

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impact CSR activities as shown in Kuo et al. (2017) and Cheung (2016). Finally, agency theory seems to partially explain the relationship between CEO risk-taking, long-term

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compensation, and CSR decisions (Mahoney and Thorne, 2005, 2006). This leads to our main hypothesis: H1: There is a positive relationship between CEO inside debt and CSR.

2.3. CEO inside debt and CSR dimensions

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Capital expenditure is a non-risky project.

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CSR is a multidimensional construct (Carroll 1979). As such, the use of an aggregate CSR score may generate confounding effects among the individual dimensions of social responsibility (e.g., Griffin and Mahon 1997; Benabou and Tirole 2010; Galema et al., 2008). Following this line of reasoning, we can expect that CEO inside debt’s effect on some individual components of the CSR score might be more relevant than its effect on other components. Hillman and Keim (2001) and Attig et al. (2014) create two main groups of CSR components: those that take account of a firm’s primary stakeholders (e.g., employee relations, diversity, community, environment, and product characteristics,) and those based on social issues participation and not directly related to a firm’s primary stakeholders (e.g., human rights). They stress the importance of investing in a relationship with primary

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stakeholders to guarantee their engagement in the firm’s business activities and thus increase the firm’s competitive advantage (e.g., Chakravarthy 1986; Jones 1995; Pfeffer 1998; Attig et al. 2014). Accordingly, we expect CEO inside debt to have a greater impact on CSR

dimensions related to a firm’s primary stakeholders: for instance, investing in environmental

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concerns may be valuable to bondholders. Bauer and Hann (2010) find that proactive

environmental practices are negatively related to the cost of debt. Fang et al. (2012) show that

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firms with active alliance involvement experience are associated with a lower cost of bank loans. Also, investing in employee relations can mitigate the tendency for shareholders to

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expropriate bondholders. As a result, unionized firms face lower costs of debt than nonunionized firms (Chen et al. 2012). Also, King et al. (2015) find that unionization intensity is significantly correlated with the proportion of debt-like compensation to equity incentives.

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In contrast, debt-like compensation may have little or no effect on the aspects of social performance that are related to participation in social issues because, all else being equal, they are less likely to influence stakeholders’ contributions in the company’s activities. Thus,

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our second hypothesis is as follows:

H2: Mangers with more inside debt exert a more (less) pronounced effect on CSR dimensions that are

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directly related to a firm’s primary stakeholders (social issues participation). 3. Data and Variables 3.1. Sample construction Our sample is drawn from various sources. We obtain CSR data from the most comprehensive database in the literature, MSCI ESG STATS (formerly known as KLD

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STATS). The KLD database is provided by a global team of over 140 research analysts, and it contains yearly environmental, social, and governance (ESG) indicators for over 2600 firms. Data on executive debt-like compensations are collected from Standard and Poor’s ExecuComp database, which includes CEO compensation data for firms in the S&P 500, S&P Midcap 400, and S&P Smallcap 600 indices. The Compustat and CRSP databases offer fundamental data on companies’ financial accounts and stock returns, respectively. We merge CEO inside debt data with KLD, Compustat, and CRSP data. This procedure results in a final sample that comprises 8122 observations across 1655 firms between 2006 and 2013.4

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3.2. Regression variables 3.2.1. Measure of CSR

We obtain CSR data from the MSCI ESG STATS database (and its predecessor, Kinder,

Lydenberg, and Domini (KLD) Research & Analytics Inc.). KLD has been providing research,

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analysis, and consulting services on firms’ ESG practices since 1988. The KLD rating is the

most widely accepted CSR measure (Chatterji et al. 2009) and has been extensively used in

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the literature (e.g., Sharfman 1996; Hillman and Keim 2001; Bae et al. 2011; Bouslah et al. 2013; Servaes and Tamayo 2013; Kruger 2015). It includes strength ratings and concern

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ratings for 13 dimensions which are grouped into two major groups: qualitative issue areas and controversial business issues. Controversial business issues are alcohol, firearms, gambling, military, tobacco, and nuclear power. Qualitative issue areas include community,

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diversity, employee relations, corporate governance, environment, product characteristics, and human rights. For each qualitative issue area, KLD assigns a binary system (0/1) to a set

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of positive and negative ratings (strengths and concerns), as illustrated in Appendix A. Following previous studies (e.g., El Ghoul et al. 2011; Attig et al. 2014; Boubaker et al. 2017), we calculate a score for each qualitative issue area equal to the number of strengths minus

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the number of concerns. We then sum the qualitative issue areas scores to obtain an overall CSR score (Csr_s).

Corporate governance is considered as a distinct construct from CSR, and its impact on CEO compensation has been extensively investigated in the prior literature (e.g., Core et al. 1999; Liu et al. 2014). Specifically, we follow prior research (e.g., Waddock and Graves 1997; Our sample only goes back to the year 2006 because the Securities and Exchange Commission (SEC) has required companies to disclose inside debt holdings since 2006. 4

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Johnson and Greening 1999; Chatterji et al. 2009) and exclude corporate governance in constructing our CSR metric to disentangle the effects of CSR and corporate governance. More detailed variable definitions are provided in Appendix C. 3.2.2. Measure of CEO inside debt Our key independent variable is CEO inside debt, and we proxy for it using two measures. First, we use CEO debt-to-equity ratio (Ceodebt), calculated as accumulated inside debt holdings divided by accumulated inside equity holdings. The value of CEO portfolio inside debt is computed as the sum of the aggregate balance in non-tax-qualified deferred

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compensation plans and the aggregate actuarial present value of CEO accumulated benefits under the company’s pension plans as of the fiscal year end (Wei and Yermack 2011). CEO portfolio of equity holdings comprises three components: common stock shares, unvested

restricted stocks, and the Black–Scholes value of stock options. In particular, the CEO option portfolio consists of options granted during the fiscal year and options granted in prior fiscal

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years [unexercised (unexercisable and exercisable) options (in $ millions)]. Following Core and Guay (2002) and Coles et al. (2013), we apply the modified Black–Scholes pricing

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method to value each component of the equity portfolio held by the CEO. Then, we sum them to calculate the total value of the CEO option portfolio at the end of the given fiscal

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year. We discuss the valuation methodology in more detail in Appendix B. Second, we follow prior research by specifying our second measure of CEO inside debt as the relative CEO debt-to-equity ratio (Relceodebt), computed as CEO debt-to-equity ratio divided by firm

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debt-to-equity ratio (e.g., Edmans and Liu 2011; Bennett et al. 2012; Cassell et al. 2012; Anantharam et al. 2013). Firm debt-to-equity ratio is calculated by dividing the value of total

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debt by the market value of equity. Appendix C provides detailed variable definitions. 3.2.3. Control variables

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To enable better isolation of the effect of CEO inside debt on CSR, we control for a set of firm characteristics that are deemed to affect firms’ CSR choices and are frequently encountered in the literature (Waddock and Grave 1997; Brammer et al. 2009; Kang 2013; Attig et al. 2014). (i) Volreturn measures stock return volatility and is computed as the standard deviation of stock return in the past five years. We follow Di Giuli and Kostovetsky (2014) and Boubakri et al. (2016) and control for firms’ risk measured by stock volatility. We expect that stock return volatility is negatively associated with CSR activities that reduce risk. 10

(ii) Size is defined as the natural logarithm of total sales. Since larger firms have more visibility and a larger scale of activities, a firm’s size is positively associated with its level of CSR (e.g., Chang et al. 2012). Moreover, larger companies tend to report more on their CSR practices in order to legitimize their activities as they are a subject to greater pressure in terms of responding to stakeholders’ demands (Burke et al. 1986). Accordingly, Tagesson et al. (2009) report a positive relationship between the size of the company and the extent of CSR disclosure.5 Therefore, we expect a negative relationship between Size and CSR choice. (iii) Roa measures firm profitability and is defined as the ratio of net income before extraordinary items and discontinued operations to total assets. Haniffa and Cooke (2005)

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contend that a firm’s social engagement may be influenced by profitability. Thus, profitable companies make more corporate social and environmental disclosures and legitimize their

existence through disclosing social information. As a consequence, profitability is expected to be positively associated with CSR choice.

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(iv) Mtb is the ratio of the market value of equity over book value of equity, where market value of equity is measured as the absolute value of price times the number of shares

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outstanding. Galema et al. (2008) argue that firms with a good CSR performance tend to have

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a higher market-to-book-ratio. Thus, we expect a positive association between Mtb and CSR. (v) Leverage is measured as the ratio of long-term debt to total assets. We expect a negative association between leverage and CSR since a high level of debt makes it difficult for a firm

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to continue to satisfy multiple stakeholders’ expectations through long-term-focused CSR investment (Waddock and Graves 1997).

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(vi) R&D/S is the ratio of R&D expenses to total sales. Attig et al. (2014) find that firms investing in R&D tend to adopt CSR. Thus, we expect a positive relationship between R&D

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and CSR.

(vii) ADV/S is the ratio of advertising expenses to total sales. Attig et al. (2014) find that firms investing in advertising tend to adopt CSR. Thus, we expect a positive relationship between R&D and CSR.

Waddock and Graves (1997) document that firms with more slack resources invest more in CSR (i.e., slack resources hypothesis), and Kang (2013) finds that intangible assets may affect CSR. 5

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3.3. Summary statistics Table 1 reports an overview of the descriptive statistics for the main variables used in our empirical work. Panel A provides descriptive statistics for the dependent variables: overall CSR score as well as CSR attributes. All the scores present a median equal to 0 (except for CSR number of strengths and concerns, which present a median equal to 1), which implies that the distribution of our CSR scores is relatively balanced, representing positive and negative values. The mean (median) number of CSR strengths (Csr_str_s) is 1.92 (1), and the mean (median) number of CSR concerns (Csr_con_s) is 1.60 (1), resulting in a mean (median) overall CSR score (Csr_s) of 0.32 (0). Its standard deviation is 2.803. Our statistics are similar

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to those reported by Cooper (2017), who documents an average CSR score of 0.229 with a standard deviation of 2.568 over the period 1991 to 2013. The mean CSR scores for the

community, employee relations, diversity, and environment dimensions are 0.13, 0.147, 0.058 and 0.177 respectively. The positive measures indicate that within these categories, the

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average firm has more strengths than weaknesses. In contrast, we find that the average measure is negative for the product characteristics (–0.152) and human rights (-0.016)

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dimensions.

In Panel B, we provide descriptive statistics for the main independent variable proxies, and

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our statistics highlight that the average (median) ratio of CEO inside debt to equity portfolio is 26.13% (3.91%). Similar to Wei and Yermack (2011), the distribution of CEO-firm relative debt-to-equity ratio is right-skewed with a mean and a median of 1.07 and 0.20, respectively.

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Furthermore, 27.6% of the firm-year observations are above one. These statistics are consistent with the logic of Edmans and Liu (2011) that the optimal debt ratio for the CEO should be less than that of the firm because relatively more equity compensation is needed to

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encourage CEO attempts. Descriptive statistics on firm characteristics are provided in panel C of Table 1. The mean of firm size is 7.67. On average, the sample firms are profitable, with

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a mean return on assets (Roa) of around 0.05. The mean standard deviation of stock return in the past five years (Volreturn) is 0.11. The mean market to book value of common equity (Mtb) is 2.46, and the mean leverage (Leverage) is 0.20. Such statistics are within reasonable levels and are largely in line with prior research in terms of magnitude (Jian and Lee 2014; Breuer and Rosenbach 2016). The descriptive statistics for the other firm and CEO characteristics are largely in line with those reported in the literature. [Insert Table 1 about here] 12

Table 2 reports the Pearson’s correlation between all the variables that we use in the main analysis. The correlation coefficients between overall CSR score (Csr_s) and both proxies of debt-like compensations (Ceodebt and Relceodebt) are positive. In addition, CSR is significantly related to our control variables with the expected relationships. The correlation coefficients are relatively small, which mitigates the concern that multicollinearity could affect our regression results. We further run the variance inflation factors (VIF) and find that the corresponding values are weak and do not exceed the critical value of 10, which ensures the absence of harmful multicollinearity.

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[Insert Table 2 about here] 4. Empirical Analysis 4.1. Model specification

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To gauge the effects of CEO inside debt on a firm’s CSR activity, we estimate several

specifications of the following model (subscripts are suppressed for notational convenience):

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CSR scores = β0 + β1CEO inside debt + β2 Size + β3 Roa + β4 Mtb+ β5 Volreturn+ β6 Leverage + (1)

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β7 ADV/sales+ β8 R&D/sales+ Industry dummies + year dummies +ε,

where the dependent variable is measured by the overall CSR score (Csr_s) as well as by individual components of CSR, including community (Csr_com_s), diversity (Csr_div_s),

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employee relations (Csr_emp_s), environment (Csr_env_s), human rights (Csr_hum_s), and product characteristics (Csr_pro_s). The compensation variables are Ceodebt and Relceodebt,

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which are the CEO’s debt-to-equity ratio and the relative CEO’s debt-to-equity ratio. The control variables are a set of firm characteristics that have been shown in previous research to be important determinants of CSR choice. They include Volreturn, Size, Roa, Mtb, Leverage,

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ADV/sales, and R&D/sales. We include year dummies and industry dummies, where industries are defined on the basis of two-digit SIC codes, to control for inter-temporal and industry variation in CEO compensation and CSR. Finally, we winsorize Ceodebt and Relceodebt at the 1st and 99th percentiles to mitigate the influence of outliers caused by zero or very low CEO equity portfolio and firm debt ratio in the denominator. Appendix C provides detailed definitions for each of the variables included in model.

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We estimate Equation (1) using ordinary least squares (OLS) with robust standard errors adjusted for heteroskedasticity and clustering by firm to account for the lack of independence of observations within a given firm over time. Our primary interest is focused on the estimate coefficient β1, which measures the association between CSR and debt-like compensations. In accordance with our first hypothesis H1, we expect a strong positive relationship between CEO inside debt and CSR activities. Thus, the coefficient estimate of the interaction term β1 should be significantly positive. 4.2. Regression results

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4.2.1. CEO inside debt and CSR

Table 3 reports the results of regressing overall CSR score on CEO inside debt using OLS,

with standard errors corrected for heteroskedasticity and clustering by firm. In Model 1, we

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regress overall CSR score (Csr_s) on the first proxy of CEO inside debt (Ceodebt). We find support for our first hypothesis H1 claiming a positive relationship between debt-like

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compensation and CSR activities: the estimated coefficient of Ceodebt is positive and statistically significant (at the 1% level), indicating that an increase in CEO inside debt leads to a higher level of CSR. This first result is confirmed in Model 2, which includes the second

associated with CSR activities.6

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proxy of CEO inside debt (Relceodebt). Indeed, Relceodebt is positively and significantly

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With respect to the control variables, consistent with previous findings in the literature, our results show a high level of CSR for large (Size), profitable (Roa), and high valued firms (Mtb) (e.g., Waddock and Grave 1997; Brammer et al. 2009; Hong et al. 2011; Kang 2013; Borghesi et

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al. 2014; Di Giuli and Kostovetsky 2014; Boubaker et al. 2017). In addition, firms with large advertising and R&D expenses have higher CSR scores (Attig et al. 2014), while firms with

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more debt and larger return volatility have lower CSR scores (Di Giuli and Kostovetsky 2014).

We find a positive relationship between CEO inside debt and CSR using two alternative measures of CEO inside debt including Relceodebt>1 and LnRelceodebt. 6

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Overall, both measures of CEO inside debt are consistently positively and significantly related to overall CSR score, providing support to the prediction of our first hypothesis (H1) that debt-like compensation increases CSR engagement. [Insert Table 3 about here] 4.2.2. CEO inside debt and the compounds of CSR Table 4 presents the results from estimating the impact of CEO inside debt on the following six attributes included in the CSR rating: community (Csr_com_s) in models 1 and 2, employee relations (Csr_emp_s) in models 3 and 4, diversity (Csr_div_s) in models 5 and 6,

and product characteristics (Csr_pro_s) in models 11 and 12.

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environment (Csr_env_s) in models 7 and 8, human rights (Csr_hum_s) in models 9 and 10,

The positive and significant association between CEO inside debt and the dimensions of

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community, employee relations, diversity, environment, and product characteristics lends

support to the finding of Hillman and Keim (2001) that CEO inside debt is relevant for CSR

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dimensions which are directly related to firms’ primary stakeholders. This suggests that managers with a high level of CEO inside debt try to meet various primary stakeholders’ expectations (e.g., bondholders) in regard to corporate social performance which could lead

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to sustainable competitive advantages. In line with such an argument, CEO inside debt does not appear to have an impact on the dimension of human rights as its estimated coefficient is not statistically significant across both Ceodebt and Relceodebt. Indeed, improving human

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rights practices helps a company to enhance its reputation for being a socially responsible firm, but such activities are less interesting for bondholders and do not have any effect on

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financing decisions. In contrast, the significant negative link between CEO inside debt and the product characteristics score (Csr_pro_s) is undoubtedly due to the components of this score. For instance, the strengths of product characteristics include R&D and innovation

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expenditures which require a high level of risk. As such, CEO inside debt dampens managerial risk-taking incentives, leading to riskier investments being substituted with safer investments. In this context, Cassell et al. (2012) provide evidence of a negative relation between inside debt holdings and R&D expenses. They document that firms with higher CEO inside debt invest less in R&D projects (i.e., risky projects). Taken together, the results of our analysis of the individual components of CSR suggest that managers with more CEO inside debt matter most for CSR investments that are directly 15

related to the firm’s primary stakeholders. These investments mostly reflect the discretionary (not altruistic) behavior of a firm that is looking to increase its competitive advantage through efficient management of insufficient resources in order to contribute to stakeholders’ satisfaction. [Insert Table 4 about here] 5. Robustness Checks 5.1. Alternative estimation methods

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In Table 5, we follow Petersen (2009), Gow et al. (2010), Attig et al. (2014), and Benlemlih and Bitar (2016) and use different estimation methods to control for cross-sectional and serial

dependence, namely, the White procedure to correct the heteroskedasticity of the standard errors (Model 1), a generalized linear model estimation (Model 2), the Fama-MacBeth

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procedure in Model 3, a quantile regression procedure (Model 4), the Newey-West

procedure to correct autocorrelation among the residuals (Model 5), and two-way clustering

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by firm and year (Model 6).

Importantly, the estimated coefficients on Ceodebt and Relceodebt load significantly positively

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on CSR in all these regressions, indicating that our main evidence on the positive association between CEO inside debt and CSR is unaffected by the use of different estimation methods.

5.2. Endogeneity

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[Insert Table 5 about here]

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Notwithstanding our insightful results on the CEO inside debt-CSR relationship, it is worth noting that our results could be driven by potential concerns pertaining to the endogeneity of

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CEO inside debt. In particular, our research is likely to suffer from reverse causality between CEO inside debt and CSR. Additionally, our results may be biased due to the presence of omitted variables that affect both CEO inside debt and CSR. To address this issue, we use two approaches, namely the instrumental variable approach and the Heckman self-selection model.

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5.2.1. Instrumental variable approach Our first approach to addressing endogeneity is based on a two-stage instrumental variable approach. Anantharam et al. (2011) and Cassell et al. (2012) document that variation in state personal income taxes generates variation in the amount of CEO inside debt as CEOs pay taxes on deferred compensation and pension benefits only when they receive them. Hence, the deferral of tax payments is one main goal of deferred compensation plans, and CEOs have greater incentives to defer larger amounts of compensation when they expect to have lower marginal tax rates in the future, ceteris paribus (Chason 2006). In this context, we instrument for CEO inside debt using the tax rates of the state since states impose different

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marginal tax rates on personal income.7

An important feature of state marginal tax rates on personal income is that they satisfy the conditions for a valid instrument in the CEO inside debt regression equation. Indeed, they are correlated with CEO inside debt. Thus, there should be no relationship between

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executive personal tax rates and CSR other than through the level of inside debt, and we address this concern with formal tests of weak instruments and instrument validity (i.e.,

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exogeneity). We follow Belkhir and Boubaker (2013) and supplement our instrumental validity approach with three different tax rates as instruments, namely, the maximum tax

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rate for wages (Wage_tax_rate), the maximum tax rate for long-term capital gains (Gain_tax_rate), and the maximum mortgage subsidy rate (Mortg_tax_rate).

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Table 6 reports the results of the first-stage (models 1 and 2) and second-stage (models 3-8) regressions. In the first step, we regress CEO inside debt on three instruments and control variables from the baseline model. We find that CEO inside debt is positively and

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significantly affected by Wage_tax_rate and Gain_tax_rate. In addition, the association between the state mortgage subsidy rate (Mortg_tax_rate) and CEO inside debt is also

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negative and statistically significant across CEO inside debt measures, suggesting that a CEO is likely to prefer present compensation due to the deduction of mortgage interest expenses from the taxable income. These results are similar to those reported by Belkhir and Boubaker (2013). The F-statistics range from 49.93 to 56.29, which alleviates the concern that our

We obtain the state tax rates from http://www.nber.org/~taxsim/state-rates/. These rates are calculated using the TAXSIM model (Freenberg and Coutts 1993). 7

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coefficient estimators are biased because of weak instruments (Bound et al. 1995; Staiger and Stock, 1997). In the second stage, we use the fitted value of the Ceodebt and Relceodebt variables from the first-stage regression as the test variable. The regression results from the second stage according to the 2SLS, LIML, and GMM approaches reinforce our earlier findings across both proxies of CEO inside debt. They show that the impact of the predicted value of Ceodebt (Relceodebt) is positive and statistically significant at the 1% level, suggesting that CEOs holding large amounts of inside debt are likely to prefer to adopt CSR investment.

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[Insert Table 6 about here] 5.2.2. Heckman self-selection

To further address the endogeneity issue, we consider a Heckman (1979) selection approach

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that corrects for self-selection bias. In the first step (selection equation), we estimate a probit model that regresses a dummy variable, which takes the value of one if the company has a

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strictly positive Ceodebt (Relceodebt) and zero otherwise, on the three instruments discussed previously (Wage_tax_rate, Gain_tax_rate, and Mortg_tax_rate) and the control variables from the baseline model. In the second-stage regression (outcome equation), we consider the CSR

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proxy as the dependent variable and CEO inside debt as the interest variable, and we include control variables as well as the self-selection parameter (measured as the inverse Mills ratio) estimated from the first-stage regression. The Heckman (1979) two-stage self-selection model

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continues to suggest that high levels of CEO inside debt increase CSR investment.

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[Insert Table 7 about here]

5.3. Additional control variables

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The results presented so far in our analysis show that firms are more likely to engage in CSR activities when their CEOs hold large amounts of inside debt. In this section, we address the question of whether our results are driven by other dimensions of CSR. For this purpose, and to mitigate the omitted variable problem, we separately add to our model various control variables that have been shown in the literature to influence CSR rating. Particularly, we control for firm characteristics, including age, cash, dividends, and z-score, and CEO characteristics, including vega, age, tenure and gender. The results of this sensitivity analysis 18

are reported in Table 8. The results show that, in all cases, our previous findings for the variables Ceodebt and Relceodebt, and the control variables included in Equation (1), remain qualitatively unchanged, suggesting that controlling for other variables does not alter the role of CEO inside debt on the choice of CSR investment. Interestingly, older firms are more likely than younger firms to invest strategically in CSR. Moreover, more cash and dividend paying firms are associated with a higher KLD score, which is in line with previous literature (Borghesi et al. 2014; Di Giuli and Kostovetsky 2014; Attig et al. 2014; Boubaker et al. 2017). Thus, z-score (a proxy for credit risk) does not appear

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to explain KLD scores. More importantly, we find that the coefficient of the variable Vega is positive and statistically significant at the 1% level. Our result is close to that of Ikram et al. (2016) and Dunbar et al.

(2016), who provide evidence that greater equity incentives, as measured by the sensitivity of equity compensation to volatility (Vega), are also associated with higher CSR investments.

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Hence, the positive effect of CEO debt-like compensation incentives on CSR is independent of the positive effect of CEO equity compensation incentives on CSR, suggesting that the

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factors driving both relations are separate. In addition, when we add CEO characteristics, including CEO age, tenure, and gender, as control variables, we find that the only significant

(2014).

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[Insert Table 8 about here]

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CEO characteristic is female, which is consistent with the results of Di Giuli and Kostovetsky

5.4. Alternative proxies for CSR

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We check the sensitivity of our findings to using alternative proxies for the CSR variable. We replace the dependent variable with three alternative proxies. The first proxy is CSR

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strengths (positive CSR policies) (Csr_str_s), and the second one is CSR concerns (negative CSR policies) (Csr_con_s). We use this disaggregation because regrouping social strengths and weaknesses into a single CSR score may obscure distinct constructs (Mattingly and Berman 2006), neglect cross-sectional variation in CSR behavior (Chatterji et al., 2009), cause countervailing effects, and mask some important differences (Kim et al. 2012). In addition, we include corporate governance area in the calculation of our overall CSR measure (Csr_gov_s), and we rerun our basic regression. The results are reported in Table 9.

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In models 1, 2, 3, and 4 of Table 9, we estimate the coefficient on CEO inside debt (Ceodebt and Relceodebt) separately for the KLD Strengths score and the KLD Concerns score. Strengths and concerns reflect different levels of risk: socially responsible firms (i.e., firms associated with high-CSR strengths) are associated with a lower risk than socially irresponsible firms (i.e., firms associated with high-CSR concerns) (Oikonomou et al. 2012; Mishra and Modi 2012; Bouslah et al. 2013). We find that debt-like compensation is associated both with more strengths (higher KLD Strengths score) and fewer concerns (higher KLD Concerns score). The coefficients on Ceodebt and Relceodebt are more than twice as large for strengths as for concerns. This result is intuitive since a CEO holding inside debt seeks to lessen the downside risk of negative cases (concerns) by investing in strengths. In

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this context, Goss and Roberts (2011) document that firms with CSR strengths benefit from a reduction in their cost of debt, while firms with CSR concerns are penalized by less attractive loan contract terms (more precisely, a higher cost of bank loans).

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The estimated coefficients on Ceodebt and Relceodebt load positively and significantly on the overall CSR measure, including the corporate governance dimension (Csr_gov_s), which indicates that the results are not sensitive to this correction and are qualitatively consistent

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[Insert Table 9 about here]

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with the results in Table 3.

5.5. Alternative sample compositions

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In robustness tests for various sample compositions, we first follow Liu et al. (2014) and Benlemlih and Bitar (2016), among others, and exclude utility firms—that is, those with SIC

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codes between 4900 and 4999—because they are regulated entities. The results are reported in models 1 and 2 of Table 10, and our core evidence of the positive effect of CEO inside debt

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on CSR is virtually unaffected. Second, we follow Han and Pan (2015) and rerun our main regression on manufacturing firms only. The new sample contains 3336 (2829) firm–year observations. The results are reported in Table 10 (specifications (3) and (4)). Our core evidence of the positive effect of CEO inside debt on CSR is virtually unaffected. [Insert Table 10 about here]

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Finally, we assess the impact of the global financial crisis on the relationship between CEO inside debt and CSR. The results reported in Table 11 show that our findings still hold after controlling for such a financial shock. [Insert Table 11 about here] 6. Conclusion Our research work investigates the effect of CEO inside debt on CSR decisions. CEO inside debt holding is unsecured, and unfunded firm liabilities expose CEOs to the same risk of loss in insolvency as unsecured corporate debt. Agency theory argues that debt-like

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compensation aligns the interests of managers and debtholders and incentivizes managers to run firms in a way that cater for debtholders’ wealth by reducing managerial risk-taking. We posit that managers with large inside debt holdings foster financing decisions that reduce

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firm risk.

Using a large sample of 1655 unique U.S. firms and 8122 firm-year observations between

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2006 and 2013, and after controlling for determinants of CSR suggested in the literature, we find that CSR investments are higher in firms when CEOs have larger amounts of debt-like compensation. This result provides strong evidence for our first hypothesis and suggests that

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CEOs with high level of inside debt holdings are more likely to collude with bondholders by favoring investments that decrease firms’ risk, such as CSR.

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Furthermore, CEO inside debt is shown to be positively associated with five of the six dimensions used in the analysis, namely, community, diversity, employee relations, the environment, and product characteristics. The only dimension associated with low levels of

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CEO inside debt is product characteristics; this is most likely due to the integration of R&D and innovation expenditures, which are risky investments, in the calculation of this score. In

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additional tests, we check the robustness of our results to different estimation methods, additional control variables, alternative measures of CSR, and alternative sample compositions. The results continue to hold. This study examines the effect of debt-like compensations on the adoption of CSR activities for firms covered by the KLD database and located in the United States. To generalize our main findings, future research should extend the framework of the relationship between

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CEO inside debt and CSR by considering other forms of debt-based compensation, different social performance ratings, and other countries/regions.

Compliance with Ethical Standards We declare that we have no conflict of interest. The research doesn’t involve Human Participants and/or Animals.

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Conflict of Interest and Authorship Conformation Form Please check the following as appropriate: o All authors have participated in (a) Conformation Form

Conflict of Interest and Authorship

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Please check the following as appropriate:

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o All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

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o This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

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o This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.

APPENDIX B

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Stock option valuation (Coles et al. 2013) FSA 123 R (Revised Standard No. 123) issued by the FASB (Financial Accounting Standards Board) specifies new rules regarding the accounting format for equity-based compensations. They state that stock option awards must be expensed on the basis of the fair value at the grant date. Since 2006, firms have started reporting under the new format. They adopt their own choice of option valuation method. Thus, ExecuComp reports a stock option’s fair value, calculated by the firms of their choices, rather than providing its own valuation which is comparable across firms. Therefore, we use the Black-Scholes (1973) formula to calculate a 22

option’s valuation. The Black-Scholes model features the input of six assumptions: the market price per share, the strike price, the term of the grant, the estimated risk-free rate, the estimated stock price, and the estimated dividend yield. We follow the estimation process in Coles et al. (2013). ExecuComp provides the number of vested, unvested, and unearned options of each tranche, and their corresponding expiration date and exercise price. We estimate future stock volatility as the most recent 60-month stock return volatility. We use the most recent three-year average dividend yield as the estimate of the future dividend yield. Stock returns and dividend yield are winsorized at the 1st or 99th percentile values. The market price per share at the time of the grant is assumed to be equal to the strike price per share. The Federal Reserve provides Treasury constant maturities for 1, 2, 3, 5, 7, and 10-

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year Treasury securities. We interpolate the rates to obtain the risk-free rates for 4, 6, 8, and 9 years. We use the 10-year rate if the option maturity term is over 10 years.

Following the above-described method, we extend Coles et al.’s (2013) estimation by

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including the fiscal years 2011, 2012, and 2013.

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34

Table 1: Descriptive statistics N

Mean

STD

5th 25th 75th 95th Median percentile percentile percentile percentile

0.567 1.219 1.431 0.909 0.268 0.662 2.887 1.748 2.803

0 -1 -2 -1 0 -1 0 0 -3

0 0 -1 0 0 0 0 0 -1

Panel A: CSR scores 8122 8122 8122 8122 8122 8122 8122 8122 8122

0.130 0.147 0.058 0.154 -0.016 -0.152 1.928 1.608 0.320

Panel B: CEO inside debt proxies Ceodebt Relceodebt Relceodebt>1 (1947) LnRelceodebt

8122 0.214 7044 1.077 7044 0.276

0.350 1.874 0.447

0 0 0

0 0 0

4856 -0.744

1.759

-4.067

-1.713

0.051 5.539 -0.064 0.676 0 0 0 4 0.004 0 0.198

0.079 6.600 0.014 1.323 0.051 0 0 12 0.024 0 0.526

0.055 1.524 0.101 33.367 0.211 0.029 0.120 10.589 0.118 0.055 0.362

re

0.117 7.703 0.048 2.963 0.209 0.012 0.039 21.081 0.112 0.015 0.728

lP

8122 8122 8122 8122 8122 8122 8122 8122 7960 8042 8122

na

Volreturn Size Roa Mtb Leverage ADV/sales R&D/sales Firm age Cash Dividend Z-score

0 0 1 0 0 0 3 2 1

1 3 3 2 0 1 8 5 6

0.039 0.208 0

0.267 1.181 1

1.283 7.182 1

-0.451

0.676

1.276

0.108 7.504 0.047 2.040 0.182 0 0 20 0.074 0.002 0.809

0.139 8.642 0.087 3.278 0.311 0.011 0.028 32 0.159 0.017 1

0.218 10.473 0.170 7.762 0.534 0.059 0.194 36 0.349 0.056 1

40.107 53 6.238 0

124.927 58 10.825 0

499.802 65 22.915 0

-p

Panel C: Firm characteristics

0 0 0 0 0 0 1 1 0

ro of

Csr_com_s Csr_emp_s Crs_div_s Csr_env_s Csr_hum_s Csr_pro_s Csr_str_s Csr_con_s Csr_s

Panel D: CEO characteristics 8122 8122 6930 8122

119.928 53.664 8.085 0.039

ur

CEO vega CEO age CEO tenure CEO female (318)

243.457 6.904 6.953 0.194

0 43 0.863 0

8.691 49 3.236 0

Jo

Notes: This table presents summary statistics for the variables used in our regressions. The sample comprises 8122 observations covering 1655 unique U.S. firms for the period spanning 2006 to 2013. The list of variable definitions and sources are provided in Appendix C.

35

1 0.155*** 0.151*** 0.179***

Size

0.449***

Roa Leverage Mtb

0.043*** 0.057*** 0.090***

ADV/sales 0.129*** R&D/sales 0.066***

1 0.225*** 1 0.172***

1 0.216***

0.275*** 0.529*** -0.017 0.098*** 0.078***

0.002 0.146*** 1 0.186*** 1 *** * 0.254 0.021 Notes: This table presents Pearson correlation coefficients between CEO inside debt and other control variables. The sample comprises 8122 observations covering the period spanning 2006 to 2013. The list of variable definitions and sources are provided in Appendix C. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

Jo

ur

na

lP

re

-p

0.034*** -0.073***

1

36

R&D/sale s

ADV/sale s

Mtb

Leverage

Roa

Size

Volreturn

1 0.690*** 1 1 0.243*** 0.236*** 0.229*** 0.243*** 0.438*** 0.202*** 0.143*** 0.157*** 0.166*** 0.213*** 0.068*** 0.082*** 0.174*** 0.141*** *** *** -0.078 -0.037 -0.014 -0.191*** -0.026** 0.135***

ro of

Csr_s Ceodebt Relceodebt Volreturn

Relceodeb t

Csr_s

Ceodebt

Table 2: Correlation coefficients between variables

Table 3: The impact of CEO inside debt and CSR Variable Intercept

Expected Sign

Ceodebt

+

Relceodebt

+

(1) -8.689*** (-15.82) 0.237*** (2.82)

(2) -9.895*** (-14.47)

Jo

ur

na

lP

re

-p

ro of

0.153*** (2.92) Volreturn -0.265 -0.359 (-0.35) (-0.44) Size + 0.952*** 0.933*** (16.96) (15.78) Roa + 0.121 0.189** (1.48) (2.07) Mtb + 0.227** 0.209** (2.49) (2.09) Leverage -0.205 -0.037 (-1.01) (-0.17) R&D/sales + 1.436** 1.933** (2.32) (2.48) ADV/sales + 7.871*** 10.209*** (3.92) (4.11) Year_FE Yes Yes Industry_FE Yes Yes Sample Size 8122 7044 Adjusted R² 0.2424 0.2472 Notes: This table reports the results from regressing overall CSR score on CEO inside debt and other control variables over the period 2006 to 2013 for the 8122 firm-year observations of the sample. Models 1 and 3 regress overall CSR score on CEO inside debt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings). Models 1 and 2 regress overall CSR score on Ceodebt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings) and Relceodebt (measured as CEO debt-to-equity ratio divided by firm debt-to-equity ratio), respectively. The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales (ADV/sales). Appendices A and B outline the definitions for all the regression variables. All financial variables are winsorized at the 5% level. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Heteroskedasticity robust t-statistics corrected for clustering at the firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

37

oo

f

Table 4: The impact of CEO inside debt and individual components of CSR Ceodebt Relceodebt Csr_emp_s

Csr_div_s

Csr_env_s

Csr_hum_s

Csr_pro_s

Csr_com_s

Csr_emp_s

Csr_div_s

Variables

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Intercept

-1.087*** (-14.79) 0.032** (2.44)

-2.284*** (-16.41) 0.050** (2.08)

-4.558*** (-15.03) 0.199*** (4.86)

-0.907*** (-13.42) 0.019* (1.81)

0.046 (0.93) -0.007 (-0.86)

0.842*** (9.65) -0.046*** (-3.11)

-1.280*** (-11.37)

-1.993*** (-5.35)

0.020** (2.19) 0.157 (0.50) 0.200*** (9.22) -0.43 (-1.10) 0.103** (2.46) -0.091 (-0.97) 0.945** (2.33) 0.071 (0.11) Yes Yes 7044 02304

Ceodebt Relceodebt

Mtb Leverage R&D/sales ADV/sales Year_FE Industry_FE Sample Size Adjusted R²

-0.063 (-0.63) 0.122*** (19.43) 0.014 (1.27) 0.028** (2.26) 0.006 (0.24) 0.029 (0.66) 0.410* (2.03) Yes Yes 8122 0.2331

0.057 (1.10) -0.016*** (-2.80) -0.002 (-0.25) 0.002 (0.19) -0.009 (-0.50) 0.042* (1.84) -0.104 (-0.84) Yes Yes 8122 0.0356

-0.518*** (-3.93) -0.142*** (-17.95) 0.011 (-0.71) -0.0744*** (-4.81) -0.182*** (-4.93) 0.067 (1.64) -0.417 (-1.63) Yes Yes 8122 0.1135

e-

0.509 (1.22) 0.583*** (21.21) 0.148*** (3.28) 0.154*** (3.39) 0.167 (1.60) 0.147 (0.81) 4.531*** (4.39) Yes Yes 8122 0.3239

Pr

Roa

0.201 (0.95) 0.208*** (16.70) -0.055** (-2.01) 0.103*** (3.73) -0.175*** (-3.23) 0.767*** (4.56) 0.218 (0.59) Yes Yes 8122 0.2278

na l

Size

-0.021 (-0.21) 0.137*** (20.15) 0.025* (1.88) 0.057*** (4.17) -0.029 (-1.19) 0.157*** (4.15) 1.828*** (6.92) Yes Yes 8122 0.1181

Jo ur

Volreturn

pr

Csr_com_s

0.015** (2.18) -0.008 (-0.07) 0.141*** (18.98) 0.036** (2.41) 0.060*** (3.89) -0.033 (-1.12) 0.200*** (4.02) 2.447*** (6.53) Yes Yes 7044 0.1223

Csr_env_s

Csr_hum_s

Csr_pro_s

(9)

(10)

(11)

(12)

-4.521*** (-8.43)

-1.096*** (-9.87)

-0.062 (-0.56)

0.293 (1.36)

0.127*** (5.30) 0.721 (1.65) 0.587*** (20.08) 0.213*** (4.40) 0.153*** (3.14) 0.254** (2.20) 0.202 (0.97) 5.198*** (4.29) Yes Yes 7044 0.3275

0.011* (1.87) -0.068 (-0.63) 0.122*** (18.31) 0.015 (1.25) 0.030** (2.27) 0.015 (0.54) 0.069 (1.19) 0.599** (2.19) Yes Yes 7044 0.2387

-0.005 (-0.94) 0.034 (0.58) -0.018*** (-2.76) -0.005 (-0.45) 0.001 (-0.12) -0.007 (-0.35) 0.052** (2.00) -0.158 (-0.87) Yes Yes 7044 0.0405

-0.015*** (4.98) -0.693*** (-4.73) -0.159*** (-18.06) -0.021 (-1.20) -0.082*** (-4.80) -0.199*** (-4.52) 0.117** (2.23) -0.111 (-0.33) Yes Yes 7044 0.1180

Notes: This table reports the results from regressing individual components of CSR on CEO inside debt and other control variables over the period 2006 to 2013 for the 8122 firm-year observations of the sample. Models 1 to 6 regress individual components of CSR on Ceodebt, measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings, and the control variables. Individual components of CSR are community score (in Model 1), employee relations score (in Model 2), diversity score (in Model 3), environmental performance score (in Model 4), human rights score (in Model 5), and product characteristics score (in Model 6). Models 7 to 12 regress individual components of CSR on Relceodebt, measured as CEO debt-to-equity ratio divided by firm debt-to-equity ratio, and control variables. Individual components of CSR are community score (in Model 7), employee relations score (in Model 8), diversity score (in Model 9), environmental performance score (in Model 10), human rights score (in Model 11), and product characteristics score (in Model 12). The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales (ADV/sales). Appendices A and B outline the definitions for all the regression variables. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Robust t-statistics corrected for clustering at the firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

38

Volreturn Size Roa Mtb Leverage R&D/sales ADV/sales

-0.265 (-0.46) 0.952*** (37.28) 0.121* (1.84) 0.227*** (3.52) -0.205 (-1.49) 1.436*** (5.99) 7.871*** (8.40) Yes Yes 8122 0.242

Jo ur

Year_FE Industry_FE Sample Size Adj. R²/R2

-0.265 (-0.54) 0.952*** (32.68) 0.121** (2.00) 0.227*** (3.65) -0.205* (-1.69) 1.436*** (4.77) 7.871*** (7.48) Yes Yes 8122 0.242

0.153*** (5.16) -0.359 (-0.66) 0.933*** (29.62) 0.189*** (2.78) 0.209*** (3.05) -0.037 (-0.27) 1.933*** (4.57) 10.209*** (7.24) Yes Yes 7044 0.247

(4) -6.299*** (-21.94)

0.153*** (5.56) -0.356 (-0.56) 0.933*** (32.56) 0.189*** (2.60) 0.209*** (2.96) -0.037 (-0.23) 1.933*** (6.97) 10.209*** (8.81) Yes Yes 7044 0.247

(5) -7.475*** (-4.87) 0.153*** (3.25)

-0.340 (-0.51) 0.888*** (4.60) 0.129** (2.46) 0.196 (1.58) -0.414** (-2.43) 1.495*** (10.26) 8.049*** (7.32) Yes Yes 8122 0.226

oo

(3) -6.432*** (-2543) 0.237*** (4.47)

Quantile

(6) -8.308*** (-4.97)

(7) -5.547*** (-19.48) 0.189*** (3.47)

pr

Relceodebt

(2) -9.895*** (-20.87)

0.130*** (4.19) 0.098 (0.13) 0.869*** (4.29) 0.192** (3.02) 0.145 (1.08) -0.266 (-1.21) 2.239*** (5.12) 10.747*** (6.00) Yes Yes 7044 0.228

e-

Ceodebt

(1) -8.689*** (-28.15) 0.237*** (4.21)

Fama Macbeth

na l

Intercept

GLM

Pr

White Variables

f

Table 5. The impact of CEO inside debt and CSR: Alternative estimations and standard errors

-0.211 (-0.37) 0.751*** (26.55) 0.169*** (2.65) 0.285*** (7.51) -0.009 (-0.12) 0.691* (1.94) 5.241*** (3.61) Yes Yes 8122 0.096

(8) -5.393*** (-14.64)

0.108*** (3.28) -0.521 (-0.70) 0.726*** (17.83) 0.178*** (2.95) 0.261*** (3.13) 0.060 (0.35) 1.682*** (5.78) 9.389*** (4.55) Yes Yes 7044 0.091

Newey-West

Clustering by firm and year

(9) -6.432*** (-15.79) 0.237*** (2.97)

(11) -9.722*** (-5.89) 0.237*** (3.08)

-0.265 (-0.38) 0.952*** (18.92) 0.121 (1.56) 0.227*** (2.65) -0.205 (-1.09) 1.436*** (2.74) 7.871*** (4.38) Yes Yes 81220 0.242

(10) -6.299*** (-14.45)

0.153*** (3.20) -0.359 (-0.47) 0.933*** (17.54) 0.189** (2.17) 0.209** (2.22) -0.037 (-0.18) 1.933*** (2.73) 10.209*** (4.52) Yes Yes 7044 0.247

-0.265 (-0.33) 0.952*** (4.71) 0.121 (1.49) 0.227 (1.54) -0.205 (-1.16) 1.436** (2.56) 7.871*** (3.76) Yes Yes 8122 0.242

(12) -9.895*** (-5.61)

0.153*** (2.84) -0.359 (-0.37) 0.933*** (4.35) 0.189** (2.07) 0.209 (1.26) -0.037 (-0.20) 1.933** (2.58) 10.209*** (3.60) Yes Yes 7044 0.247

Notes: This table reports the results from regressing overall CSR score on CEO inside debt and other control variables over the period 2006 to 2013 for the 8122 firm-year observations of the sample. The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales (ADV/sales). We show the heteroskedasticity-consistent standard errors based on the White procedure (Models 1 and 2), a generalized linear model (Models 3 and 4), a fixed effect regression (Models 5 and 6), a quantile regression (Models 7 and 8), a Newey-West estimation procedure (Models 9 and 10), and a two-way clustering standard errors regression. All models include industry and year fixed effects. Appendices A and B outline the definitions for all the regression variables. All financial variables are winsorized at the 5% level. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Heteroskedasticity robust t-statistics corrected for clustering at the year and firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively .

39

Variables

(1) 0.092 (0.48)

Intercept

(2) -0.649 (-1.62)

Ceodebt (Fitted) Relceodebt (Fitted)

Second Stage 2SLS (3) -5.318*** (-3.91) 2.773*** (2.94)

0.041** (2.51) 0024* (1.82) -0.006** (-2.05) -2.696*** (-8.60) 0.198*** (14.90) -0.122*** (-3.62) 0.053 (1.61) -1.539*** (-15.97) -0.393*** (-3.65) -1.093** (-2.15) Yes 5613 0.178 0.1439 56.2947***

Pr

Mortg_tax_rate

0.026*** (2.85) 0.016* (1.81) -0.010** (-2.27) -1.291*** (-9.68) 0.057*** (9.30) 0.121*** (8.60) 0.158*** (11.00) -0.009 (-0.31) -0.166*** (-3.85) -0.912*** (-6.48) Yes 6308 0.134 0.1234 49.9357***

Volreturn Size

R&D/sales

Jo ur

ADV/sales

na l

Roa

f (6) -5.478*** (-3.39)

GMM (7) -5.347*** (-11.15) 2.638*** (3.92)

1.193*** (3.78)

(8) -5.544*** (-15.12)

0.884*** (3.37)

e-

Gain_tax_rate

Leverage

LIML (5) -5.261*** (-3.90) 2.973*** (2.97)

0.848*** (3.55)

Wage_tax_rate

Mtb

(4) -5.596*** (-3.42)

pr

First stage

oo

Table 6: Addressing endogeneity: Instrumental variable approach

2.382* (1.68) 0.746*** (5.28) -0.161** (-2.06) -0.209 (-1.48) -0.169** (-2.15) 1.605*** (5.11) 11.868*** (6.53) Yes 6308 0.043

0.812 (0.53) 0.728*** (5.10) 0.302*** (2.94) 0.170 (1.38) 1.052 (1.50) 1.866*** (3.66) 12.113*** (4.21) Yes 5613 0.190

2.642* (1.78) 0.734*** (5.29) -0.185** (-2.19) -0.242 (-1.63) -0.167** (-2.11) 1.636*** (501) 12.039*** (6.59) Yes 6308 0.092

1.766 (0.82) 0.659*** (4.42) 0.345*** (2.79) 0.152 (1.18) 1.582 (1.49) 1.993*** (3.28) 12.409*** (4.13) Yes 5613 0.115

2.203 (1.27) 0.752*** (9.25) -0.148 (-0.87) -0.192 (-0.91) -0.174 (-0.96) 1.580*** (3.72) 11.728*** (6.95) Yes 6308 0.065

0.877 (0.57) 0.714*** (6.56) 0.303*** (2.97) 0.152* (1.84) 1.090 (1.34) 1.834*** (3.78) 12.161*** (7.12) Yes 5613 0.183

Year and Industry_FE Sample Size Adjusted R2 Partial R2 of excluded instruments F-test of excluded instrument Wald Chi2 664.02*** 627.02*** 1382.58*** 1099.86*** 1476.90*** 1467.84*** Notes: This table presents results of the instrumental variable regressions. Models 1 and 2 show the first-stage regression where the dependent variables are Ceodebt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings) and Relceodebt (measured as CEO debt-to-equity ratio divided by firm debt-to-equity ratio). Models 3 to 8 present the results from the second-stage regressions (2SLS, LIML, GMM). The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales(ADV/sales). Appendices A and B outline the definitions for all the regression variables. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Heteroskedasticity robust t-statistics corrected for clustering at the firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

40

Table 7: Addressing endogeneity: Heckman self-selection Variables Intercept

Selection equation (1)

(2)

Outcome equation (3) (4)

-7.142*** (-8.71)

-7.591*** (-8.75)

Ceodebt

-0.958 (-0.92) 0.319*** (4.63)

Relceodebt

-1.638 (-1.64)

0.169*** (4.45)

0.181*** 0.197*** (4.60) (4.73) Gain_tax_rate 0.077** 0.081** (2.02) (2.00) Mortg_tax_rate -0.065*** -0.068*** (-5.22) (-4.99) Volreturn -1.778*** -1.694*** 0.365 -0.301 (-4.65) (-4.22) (0.34) (-0.28) Size 0.499*** 0.519*** 0.480*** 0.518*** (26.13) (24.56) (5.12) (5.62) Roa 0.169*** 0.134*** 0.064 0.199* (3.80) (2.79) (0.55) (1.72) Mtb 0.349*** 0.287*** -0.167 0.006 (7.93) (6.00) (-1.40) (0.05) Leverage 0.242** -0.065 -0.605** 0.093 (2.51) (-0.57) (-2.11) (0.29) R&D/sales -5.240*** -4.744*** 15.036*** 12.946*** (-15.33) (-12.42) (9.60) (8.84) ADV/sales -5.014*** -5.869*** 25.359*** 27.244*** (-7.03) (-7.15) (11.81) (12.02) Year_FE Yes Yes Yes Yes Industry_FE Yes Yes Yes Yes 6308 5613 6308 5613 Sample Size 0.231 0.137 0.079 0.136 Adjusted R2 Notes: This table reports the results of the Heckman two-step treatment effect model used to correct the selfselection in CEO inside debt over the period 2006 to 2013. Models 1 and 3 regress overall CSR score on CEO inside debt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings). Models 1 and 2 regress Ceodebt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings) and Relceodebt (measured as CEO debt-to-equity ratio divided by firm debtto-equity ratio), respectively, on the instruments and control variables. Models 3 and 4 regress the overall CSR score on Ceodebt and Relceodebt, respectively. The selection (Ceodebt and Relceodebt) equations use Ceodebt dummy and Relceodebt dummy as the dependent variables that take the value of 1 if the firm has a strictly positive Ceodebt and Relceodebt, respectively, and 0 otherwise. We employ three instruments: (1) the maximum tax rate for wages (Wage_tax_rate), (2) the maximum tax rate for long term capital gains (Gain_tax_rate), and (3) the maximum mortgage subsidy rate (Mortg_tax_rate). The outcome equation regresses our main measure of CSR on CEO inside debt and the control variables. The outcome equation also controls the inverse Mills ratio (INV_MILLS) estimated from the selection equation. The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales(ADV/sales). Appendices A and B outline the definitions for all the regression variables. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Robust t-statistics corrected for clustering at the year and firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

Jo

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na

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re

-p

ro of

Wage_tax_rate

41

f

Table 8: Additional control variables Dividend

z-score

Intercept

-8.747*** (-15.83)

-9.929*** (-16.09)

-8.601*** (-15.78)

-8.619*** (-15.69)

Ceodebt

0.211** (2.53)

0.215** (2.50)

0.232*** (2.76)

0.238*** (2.83)

CEO Characteristics Vega CEO Age -7.815*** (-13.86) 8.268*** (-13.18) 0.298*** 0.148*** (3.49) (2.92)

Gender

Dividend

z-score

CEO Characteristics Vega Age

-8.683*** (-15.94)

-9.867*** (-14.79)

-9.857*** (-14.38)

-8.894*** (-15.05)

-9.846*** (-14.37)

-9.049*** (-12.38)

0.142*** (2.70) 0.009* (1.70) -0.216 (-0.26) 0.911*** (15.18) 0.174* (1.92) 0.188* (1.87) -0.027 (-0.12) 2.002** (2.51) 10.336*** (4.16)

0.139*** (2.64) 1.201* (1.75) -0.496 (-0.62) 0.948*** (15.93) 0.219** (2.31) 0.226** (2.27) -0.004 (-0.02) 1.801** (2.37) 10.525*** (4.36)

0.144*** (2.76) 1.307** (2.19) -0.235 (-0.29) 0.941*** (15.85) 0.210** (2.29) 0.207** (2.05) -0.148 (-0.63) 1.936** (2.49) 11.188*** (4.49)

0.152*** (2.92) -0.093 (-1.03) -0.403 (-0.50) 0.933*** (15.79) 0.192** (2.10) 0.208** (2.08) -0.039 (-0.18) 1.935** (2.49) 10.203*** (4.11)

0.175*** (3.35) 0.001*** (4.41) -0.324 (-0.40) 0.804*** (12.93) 0.202** (2.23) 0.195* (1.94) -0.011 (-0.05) 1.779** (2.40) 9.240*** (3.73)

Tenure

Gender

9.397*** (-12.75)

9.857*** (-14.65)

0.161*** 0.166*** (3.06) (2.97) Additional_Control 0.009* 1.161** 1.008** -0.086 0.001*** -0.010 -0.012 1.393*** -0.011 -0.014 (1.67) (1.96) (2.19) (-0.99) (4.64) (-1.49) (-1.45) (4.11) (-1.45) (-1.49) Volreturn -0.097 -0.403 -0.149 -0.291 -0.202 -0.211 -0.150 -0.211 -0.253 -0.207 (-0.13) (-0.54) (-0.20) (-0.39) (-0.27) (-0.28) (0.834) (-0.28) (-0.31) (-0.23) Size 0.928*** 0.968*** 0.959*** 0.951*** 0.818*** 0.964*** 0.889*** 0.951*** 0.947*** 0.863*** (16.46) (17.16) (17.03) (16.96) (14.12) (17.18) (14.52) (17.10) (16.05) (13.33) Roa 0.110 0.156* 0.139* 0.122 0.118 0.135 0.129 0.115 0.205** 0.208** (1.35) (1.83) (1.69) (1.48) (1.45) (1.65) (1.47) (1.41) (2.25) (2.11) Mtb 0.205** 0.248*** 0.226** 0.225** 0.204** 0.244*** 0.152 0.225** 0.227** 0.138 (2.23) (2.72) (2.47) (2.46) (2.21) (2.66) (1.55) (2.51) (2.25) (1.28) Leverage -0.195 -0.126 -0.256 -0.225 -0.209 -0.189 0.294 -0.177 -0.011 -0.035 (-0.96) (-0.64) (-1.25) (-1.11) (-1.04) (-0.95) (-1.31) (-0.88) (-0.05) (-0.15) R&D/sales 1.506** 1.317** 1.449** 1.445** 1.286** 1.411** 1.709** 1.451** 1.903** 2.092** (2.37) (2.20) (2.32) (2.32) (2.20) (2.31) (2.26) (2.35) (2.48) (2.16) ADV/sales 8.054*** 7.741*** 8.419*** 7.889*** 7.095*** 7.811*** 8.657*** 7.463*** 10.144** 11.149** (4.00) (3.84) (4.08) (3.93) (3.57) (3.93) (3.76) (3.69) * * (4.12) (4.02) Year_FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry_FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sample Size 8122 8122 8122 8122 8122 8012 6930 8122 7044 7044 7044 7044 7044 6944 6041 Adjusted R² 0.2433 0.2432 0.2441 0.2425 0.2540 0.2457 0.2305 0.2515 0.2480 0.2481 0.2494 0.2473 0.2579 0.2503 0.2337 Notes: This table presents the regression results after adding various control variables, one at a time, to our model (Equation (1)). In models 1, 2, and 3, we regress overall CSR score on Ceodebt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings). In models 4, 5, and 6, we regress overall CSR score on Relceodebt (measured as CEO debt-to-equity ratio divided by firm debt-to-equity ratio). The sample includes the 8122 firm-year observations over the period 2006 to 2013. The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm Leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales(ADV/sales). Appendices A and B outline the definitions for all the regression variables. Unreported industry controls are

0.144*** (2.81) 1.438*** (3.78) -0.395 (-0.48) 0.929*** (15.95) 0.184** (2.03) 0.205** (2.08) -0.015 (-0.07) 1.927** (2.51) 10.052** * (4.04) Yes Yes 7044 0.2563

Jo ur

na l

Pr

e-

Relceodebt

6.552*** (-10.55) 0.259*** (2.88)

pr

Variables

Tenure

Relceodebt Firm Characteristics Firm age Cash

oo

Ceodebt Firm Characteristics Firm age Cash

-9354*** (-11.55)

0.225*** (2.75)

42

Table 9: Inside debt and CSR: Alternative measures of CSR Csr_Con_s (3) -2.145*** (-5.53) 0.172*** (2.61)

(4) -1.234*** (0.185)

(6) -9.641*** (-10.31)

0.216*** 0.03** 0.178*** (4.30) (2.22) (3.17) Volreturn 0.845 1.223 1.110** 1.165*** -0.919 -1.216 (1.13) (1.51) (2.06) (0.447) (-1.13) (-1.39) Size 1.482*** 1.535*** 0.529*** 0.335*** 0.847*** 0.827*** (25.37) (24.47) (12.41) (0.020) (14.05) (13.13) Roa 0.202*** 0.297*** 0.081 -1.096*** 0.113 0.210** (2.63) (3.43) (1.39) (0.215) (1.26) (2.13) Mtb 0.432*** 0.488*** 0.205*** -0.0001 0.159 0.146 (5.13) (5.15) (3.21) (0.0004) (1.61) (1.34) Leverage 0.113 0.373 0.318* 0.404*** -0.379 -0.162 (0.57) (1.51) (1.82) (0.147) (-1.63) (-0.65) R&D/sales 0.606 0.799 -0.829*** -1.301*** 1.274** 1.843** (1.43) (1.59) (-2.85) (0.314) (2.33) (2.55) ADV/sales 7.452*** 8.890*** -0.419 -1.369 8.451*** 11.131*** (4.13) (3.69) (-0.38) (1.051) (3.77) (3.99) Year_FE Yes Yes Yes Yes Yes Yes Industry_FE Yes Yes Yes Yes Yes Yes 8122 7044 8122 7044 8122 7044 Sample Size 0.4108 0.4152 0.2533 0.2719 0.2204 0.2308 Adjusted R² Notes: This table presents the results of a set of robustness tests using alternative proxies of CEO inside debt and CSR. In models 1 and 2, we regress overall CSR score on alternative proxies of CEO inside debt, namely, Relceodebtsup1 (a dummy variable which is equal to one when the relative ratio is greater than one, and zero otherwise) and Log(Relceodebt). In models 3 and 4, the dependent variable is Csr_str. In models 5 and 6, the dependent variable is Csr_con, and in models 7 and 8, the dependent variable is overall CSR score including the dimension corporate governance. The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales). and ratio of advertising expenses to total sales (ADV/sales). Appendices A and B outline the definitions for all the regression variables. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Robust t-statistics corrected for clustering at the year and firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

Jo ur

na l

Pr

Relceodebt

Csr_Gov_s (5) -8.381*** (-14.21) 0.263*** (2.88)

pr

Ceodebt

(2) -10.898*** (-10.76)

e-

Variables Intercept

Csr_Str_s (1) -10.833*** (-19.87) 0.409*** (4.58)

oo

f

based on the two-digit code of the Standard Industrial Classification. Heteroskedasticity robust t-statistics corrected for clustering at the firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively

43

Table 10: Alternative sample compositions

Ceodebt Relceodebt Volreturn Size Roa Mtb Leverage R&D/sales ADV/sales Year_FE Industry_FE Sample Size

0.036 (0.07) 0.988*** (33.31) 0.136** (2.22) 0.239*** (3.79) -0.158 (-1.33) 1.414*** (4.73) 7.555*** (7.27) Yes Yes 7698 0.2499

(2) -9.122*** (-26.95)

0.123*** (4.01) 0.044 (0.08) 0.976*** (30.36) 0.193*** (2.78) 0.207*** (2.95) -0.017 (-0.12) 1.883*** (4.50) 9.709*** (6.93) Yes Yes 6621 0.2542

(3) -7.426*** (-17.90) 0.192** (2.10)

-3.151*** (-3.85) 0.962*** (18.80) 0.238** (2.37) 0.079 (0.76) -0.406 (-1.36) 1.481*** (4.40) 16.716*** (8.56) Yes Yes 3336 0.2481

(4) -7.584*** (-16.50)

0.163*** (3.56) -3.076*** (-3.19) 0.930*** (16.62) 0.342*** (3.03) 0.030 (0.26) -0.026 (-0.08) 1.869*** (4.30) 17.955*** (8.21) Yes Yes 2829 0.2593

ro of

(1) -9.822*** (-21.65) 0.179*** (2.81)

Intercept

Manufacturing firms

-p

Excluding utilities Variables

Jo

ur

na

lP

re

Adjusted R² Notes: This table presents the regression results after adding various control variables, one at a time, to our model (Equation (1)). In models 1, 2, and 3, we regress overall CSR score on Ceodebt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings). In models 4, 5, and 6, we regress overall CSR score on Relceodebt (measured as CEO debt-to-equity ratio divided by firm debt-to-equity ratio). The sample includes the 8122 firm-year observations over the period 2006 to 2013. The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales (ADV/sales). Appendices A and B outline the definitions for all the regression variables. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Heteroskedasticity robust t-statistics corrected for clustering at the firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

44

Table 11: The impact of CEO inside debt on CSR – The effect of the global financial crisis Crisis period Variables

(1) -7.078*** (-8.19) 0.183*** (2.68)

Intercept Ceodebt

(2) -6.960*** (-7.50)

(3) -12.374*** (-22.74) 0.282*** (2.68)

(4) -12.743*** (-20.77)

Crisis Insidedebt*Crisis Volreturn Size Roa Mtb Leverage R&D/sales ADV/sales Year_FE Industry_FE Sample Size

0.221 (0.23) 0.588*** (8.42) 0.080 (0.77) 0.034 (0.29) -0.301 (-1.08) 1.269** (2.07) 8.532*** (3.20) Yes Yes 4528 0.1241

0.442 (0.41) 0.542*** (7.29) 0.128 (1.10) -0.025 (-0.20) -0.103 (-0.36) 1.463** (2.01) 11.252*** (3.14) Yes Yes 3894 0.1257

0.171*** (3.02)

-0.691 (-0.83) 0.182* (1.77) 0.500*** (4.51) 1.422*** (25.63) -0.162 (-0.68) 1.614** (2.25) 6.687*** (3.39) Yes Yes 3594 0.3675

-0.465 (-0.53) 0.265** (2.30) 0.515*** (4.17) 1.444*** (24.36) -0.062 (-0.22) 2.830** (2.12) 8.562*** (3.34) Yes Yes 3150 0.3660

ro of

0.135** (3.76)

-p

Relceodebt

Out of Crisis period

Jo

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lP

re

Adjusted R² Notes: This table presents the regression results after adding various control variables, one at a time, to our model (Equation (1)). In models 1, 2, and 3, we regress overall CSR score on Ceodebt (measured as CEO accumulated inside debt holdings divided by CEO accumulated inside equity holdings). In models 4, 5, and 6, we regress overall CSR score on Relceodebt (measured as CEO debt-to-equity ratio divided by firm debt-to-equity ratio). The sample includes the 8122 firm-year observations over the period 2006 to 2013. The control variables are size (Size), return on assets (Roa), market to book value of common equity (Mtb), standard deviation of stock return in the past five years (Volreturn), firm leverage (Leverage), ratio of research and development expenses to total sales (R&D/sales), and ratio of advertising expenses to total sales (ADV/sales). Appendices A and B outline the definitions for all the regression variables. Unreported industry controls are based on the two-digit code of the Standard Industrial Classification. Heteroskedasticity robust t-statistics corrected for clustering at the firm level are presented in parentheses. *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

45

Appendix A Qualitative Issue Areas We consider six qualitative issue areas: community, diversity, employee relations, environment, human rights, and product characteristics. Each area has a set of strengths and concerns, as detailed below. We calculate a score for each area equal to the number of strengths minus the number of concerns. We also calculate an overall CSR score equal to the sum of all areas’ scores.

Concerns

Strengths

Community

Investment controversies

Charitable giving

Negative economic impact

Innovative giving

Indigenous people’s relations Tax disputes

ro of

Areas

Non-U.S. charitable giving Support for housing

Other concern

Support for education

Indigenous people’s relations

-p

Volunteer programs Other strength

Diversity

Controversies

CEO

Jo

ur

Employee relations

na

lP

Other concern

re

Non-representation

Environment

Promotion Board of directors Work/life benefits Women and minority contracting Employment of the disabled Gay and lesbian policies Other strength

Union relations

Union relations

Health and safety concern

No-layoff policy

Workforce reductions

Cash profit sharing

Retirement benefits concern

Employee involvement

Other concern

Retirement benefits strength Health and safety strength Other strength

Hazardous waste

Beneficial products and services

Regulatory problems

Pollution prevention

Ozone-depleting chemicals

Recycling

Substantial emissions

Clean energy

Agricultural chemicals

Communications

Climate change

Property, plant, and equipment

46

Human rights

Other concern

Other strength

South Africa

Positive record in South Africa

Northern Ireland

Indigenous people’s relations

Burma concern

strength

Mexico

Labor rights strength

Labor rights concern

Other strength

Indigenous people’s relations concern Other concern Product safety

Quality

Marketing/contracting concern

R&D/innovation

Antitrust

Benefits to economically

ro of

Product characteristics

Other concern

disadvantaged

Other strength

Corporate governance

High compensation

Limited compensation

Ownership concern

Ownership strength

Transparency strength

-p

Accounting concern Political accountability concern

Political accountability strength

Transparency concern

Other strength

Jo

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Other concern

47

Appendix C Variable definitions Variable Panel A. CSR Variables Csr_com_s Csr_div_s Csr_emp_s Csr_env_s Csr_hum_s

Source

Number of Strengths - Number of Concerns (com_str_num -com_con_num) Number of Strengths - Number of Concerns (div_str_num - div_con_num) Number of Strengths - Number of Concerns (emp_str_num -emp_con_num) Number of Strengths - Number of Concerns (env_str_num - env_con_num) Number of Strengths - Number of Concerns (hum_str_num - hum_con_num) Number of Strengths - Number of Concerns (pro_str_num - pro_con_num) The sum of strength scores for community, diversity, employment, environment, human rights, and product components (com_str_num + div_str_num + emp_str_num + env_str_num + hum_str_num + pro_str_num).

MSCI ESG STATS

The sum of concern scores for community, diversity, employment, environment, human rights, and product components (com_con_num + div_con_num + emp_con_num + env_con_num + hum_con_num + pro_con_num). Csr_str_s - Csr_con_s

As above

As above As above As above As above As above

ro of

Csr_pro_s

Definition

Csr_str_s

Panel B. CEO-related Variables Ceodebt

lP

Csr_s

re

-p

Csr_con_s

As above

As above

CEO debt-to-equity ratio is a CEO’s portfolio of inside debt divided by his/her equity holdings.

ExecuComp + CRSP

Relative CEO debt-to-equity ratio is calculated by dividing CEODEBT by the firm’s debt-to-equity ratio.

ExecuComp + CRSP + Compustat

CEO tenure is the number of years that the current CEO has served in the company. CEO gender

ExecuComp

Age Vega

CEO age The sensitivity of a CEO’s stock option portfolio to stock returns volatility.

As above As above

Wage_tax_rate

The maximum tax rate for wages imposed in the state.

National Bureau of Economics Research (NBER)

na

Relceodebt

Jo

Gender

ur

Tenure

As above

48

Gain_tax_rate

The maximum tax rate for long-term capital gains.

National Bureau of Economics Research (NBER)

Mortg_tax_rate

The maximum mortgage subsidy rate.

National Bureau of Economics Research (NBER)

Natural log of market value (CSHO*PRCC_F) at the end of fiscal year t-1 for firm i. Return on assets (NI/lag(AT)) in year t for firm i. Market-to-book ratio (CSHO*PRCC_F/SEQ) in year t-1 for firm i. Leverage for firm i, measured as the sum of debt in current liabilities and total long-term debt (DLC + DLTT) at the end of year t-1 divided by year t-1 total assets. Ratio of research and development expenses to total sales. Ratio of research and advertising expenses to total sales. Standard deviation of stock return in the past 5 years.

Compustat

Panel C. Firm-level variables Size Roa Mtb

As above As above

ro of

Leverage

As above

As above As above CRSP

re

-p

R&D/S ADV/S Volreturn

Altman’s (1968) Z-score = 6.56 × (working capital/total assets) + 3.26 × (retained earnings/total assets) + 6.72 × (earnings before interest and taxes/total assets) + 1.05 × (book value of firm/book value of total liabilities). Cash and marketable securities. Common dividends deflated by net assets.

lP

Z_score

As above As above

Jo

ur

na

Cash Dividend

Compustat

49