Directors and officers liability insurance and the cost of debt

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Directors’ and officers’ liability insurance and the cost of debt

Chen Lin Micah Officer

Chinese University of Hong Kong Loyola Marymount University

chenlin@cuhk.edu.hk            micah.officer@lmu.edu

Rui Wang Hong Zou

City University of Hong Kong City University of Hong Kong

 rwang9@mslive.cityu.edu.hk hongzou@cityu.edu.hk

Abstract

Using hand-collected data on directors’ and officers’ liability insurance (D&O insurance), we analyze the effect of D&O insurance on the cost of debt. We find that higher levels of D&O insurance coverage are associated with higher at-issue bond yields (to maturity) and higher loan spreads. This evidence suggests that debtholders view D&O insurance coverage as increasing credit risk (potentially via moral hazard and/or information asymmetry). Further analyses show that higher levels of D&O insurance coverage are associated with greater risk taking and higher probabilities of financial restatement due to aggressive financial reporting. The greater use of D&O insurance appears to raise the cost of debt financing.

* We would like to thank conference participants at the 2011 International Conference on Corporate Finance and Financial Market in Hong Kong, and seminar participants at the University of Hong Kong and the University of International Business and Economics for helpful comments. We greatly appreciate research assistance by Joyce Li, Hoi Lo, and Chunning Ma.

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1. Introduction

Almost every public company in the U.S. and Canada carries directors’ and officers’ liability insurance (hereafter referred to as “D&O insurance”).1 A practitioner survey carried out in 2007 found that 87% of the 356 directors polled rank the availability of comprehensive D&O insurance coverage as an important consideration before agreeing to join a board.2

A typical D&O insurance policy is purchased by a company to protect its directors and officers from personal liability in the event of litigation brought by shareholders or other stakeholders (e.g., creditors) alleging “wrongdoing” in discharging their duties. Despite its popularity, D&O insurance is not without controversy and there is little evidence on how the purchase of this insurance is perceived by a company’s stakeholders. In particular, because D&O insurance insulates directors and officers from the threat of litigation and personal financial liability resulting from their decisions on behalf of the corporation,3 D&O insurance may induce moral hazard and reduce the incentive of managers to act in the best interest of stakeholders (Lin, Officer, and Zou, 2011).

In this study we investigate how debtholders perceive D&O insurance coverage by examining the impact of D&O insurance coverage on a firm’s cost of debt. Empirical research about the effect of D&O insurance on stakeholders is often hampered by the lack of data on firm-level purchases of D&O insurance, therefore in this paper we focus on Canadian public companies since disclosure of the details of D&O insurance purchases is mandatory in Canada (Chalmers, Dann, and Harford, 2002). We examine the effects of D&O insurance coverage on the cost of debt because debt capital represents an important source of corporate financing and

      

1 See Baker and Griffith (2010) for statistics in the U.S. and Egri, Gordon, and Shapiro (2006) for figures in Canada. 2 See “The Directors & Boards Survey: D&O Insurance” in Boardroom Briefing, Volume 4, No. 1, a publication of Directors & Boards Magazine and GRID Media LLC.

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creditors are important corporate stakeholders. Specifically, we examine the impact of D&O insurance on both the cost of public debt (corporate bonds) and private debt (bank loans).

Risk (particularly moral hazard) and information asymmetry are two important factors shaping debt contracts (Graham, Li, and Qiu, 2008), and D&O insurance coverage has the potential to influence both. On the one hand, the existence of D&O insurance may lower a company’s cost of debt because the coverage may lower a firm’s default risk and the insurance payout may be considered part of a company’s asset base at the time of bankruptcy (Donley and Kent, 2008). Indeed, Mayers and Smith (1982) and Core (1997) argue that D&O insurance may constitute an integral part of a company’s risk management.4

On the other hand, the existence of D&O insurance shields directors and officers from lawsuits brought by shareholders and others, thereby lowering the deterrent effect of litigation on moral hazard. Such unintended moral hazard can lead to excessive risk taking and overly optimistic financial reporting which might be beneficial to individual managers but costly to creditors. D&O insurance may, therefore, increase a firm’s cost of debt if protected directors and officers engage in more risk taking because they want to pursue their own private objectives (Jensen and Meckling, 1976), they simply have little at stake in the event of litigation, or if reduced vigilance leads to low-quality financial reporting (also potentially driven by the desire to conceal opportunistic behavior). Regardless of the cause, rational debtholders will price-protect themselves against higher default risk by demanding higher bond yields and loan spreads.

Using D&O insurance information for a sample of TSE 300 index (currently the S&P/TSX Composite Index) constituent stocks, corporate bond data from Bloomberg and Mergent’s Fixed Income Securities Database (FISD), and (syndicated) bank loan data from Loan

      

4 The effect of D&O insurance on lowering the chance of insolvency, however, is questioned by Baker and Griffith (2010) given that D&O policy limits are often small relative to firm size (though significant compared with the personal wealth of directors and officers). The mean policy limit in our sample is about 5% of the market value equity. Boyer (2005) argues that the role of D&O insurance is to provide a last-chance payment for shareholders who suffer financially due to managerial wrongdoings.

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Pricing Corporation’s (LPC) DealScan database, we find that high levels of D&O insurance coverage are associated with higher bond yields and loan spreads. These results are robust to the inclusion of various control variables and the treatment of potential endogeneity with an instrumental variable estimation. Specifically, a one-standard-deviation increase in the ratio of the D&O insurance coverage limit scaled by the market value of equity increases bond yields by about 30 basis points and loan spreads by about 20 basis points on average, with both estimates being statistically significant. The larger marginal effect of D&O insurance coverage on bond spreads compared to loan spreads is consistent with the fact that banks, as providers of private debt financing, have more control over borrowers and can monitor them more closely than public bondholders can.

Guided by debt contracting theory, we then seek to understand why providers of debt capital charge higher spreads to borrowers with higher D&O insurance coverage. Specifically, we examine two channels: the effect of D&O insurance on corporate risk taking and the effect of D&O insurance on the quality of financial reporting. First, we find that high levels of D&O insurance coverage increase firms’ total and idiosyncratic risk, strongly suggestive of greater risk taking as a channel by which D&O insurance coverage affects the cost of debt.

Second, because over 50% of the securities class actions in Canada between 1992 and 2008 involve financial restatements by the defendant company (Pritchard and Sarra, 2009), we use a hand collected dataset of earnings restatements and find that firms with higher levels of D&O insurance coverage are more likely to restate earnings. Firms with higher levels of D&O insurance coverage are also more likely to have restatements caused by prior intentional misstatements.

Taken together, our results suggest that the providers of debt capital associate higher D&O insurance coverage with higher default risk. This appears to be a rational association because higher D&O insurance seems to lead to greater risk taking and lower quality financial

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reporting that increases the information asymmetry between the borrower and lender. Our evidence is consistent with the argument that high levels of D&O insurance coverage lead to moral hazard, and that moral hazard is reflected in the terms at which lenders will provide capital to the firm.

Some background on the liability risks of corporate directors and officers in Canada is appropriate. Such risks can come from shareholder litigation or lawsuits brought by other parties (e.g., creditors, regulators). Similar to that in the US, directors and officers in Canada may be sued under the corporate law for breach of fiduciary duties (i.e., duty of care and acting honestly and in good faith) or under the securities law, with the latter being the most significant source of risk (Donley and Kent, 2008). Securities lawsuits can target disclosure irregularities in the course of securities offerings (i.e., primary market liability suits) or in continuous disclosure (i.e., secondary market liability suits), and the system for handling class action securities lawsuits in Canada resembles that in the U.S. to a large extent (at least since 1992; Pritchard and Sarra, 2009).

The Canada Business Corporation Acts (CBCA) (1985), the corporate law at the federal level, allows a company to indemnify its directors and officers for legal costs via bylaws or charters as long as directors and officers have acted in good faith and in the best interests of the company. Upon the approval of a court, a Canadian company may indemnify its directors and officers for the cost of defense in shareholder derivative suits, but not settlement nor judgment. By virtue of this, Canadian public companies routinely indemnify directors and officers for legal liability (Cheffins and Black, 2006). Nevertheless, D&O insurance provides protection that is distinct from indemnification in the following ways. First, D&O insurance protects directors and officers when their company cannot indemnify them due to legal restrictions, insolvency, or when a company declines to indemnify them. This is known as Side-A coverage. Second, the so-called Side-B coverage allows a company to recover the cost it incurs in indemnifying its directors and officers. The policy limits of these types of coverage are typically equal, although

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Side-B coverage often has a deductible (an amount that must be borne by the insured company) while Side-A coverage (for the personal directors) typically does not.

Directors and officers consider D&O insurance coverage to be crucial and irreplaceable for many reasons, including the fact that the costs of settlement or judgment in derivative suits are typically covered by a D&O insurance policy (Chalmers, Dann, and Harford, 2002). Furthermore, the exclusions from D&O insurance coverage (i.e., deliberate fraud and illegal profit by directors and officers) are much narrower compared with the requirement of acting in good faith and in the best interests of the company associated with corporate indemnification (Cheffins and Black, 2006). Perhaps even more importantly, D&O policy exclusions in practice do not constitute an obstacle as they either need to be established by “final adjudication” or because plaintiff lawyers can strategically avoid referring to them in pleading (Baker and Griffith, 2010). Indeed, D&O insurers usually pay out on a policy as long as the defendants (directors and officers) do not admit to fraud or illegal profit (Baker and Griffith, 2010). Surveys of board members and executives confirm the conclusion that D&O insurance is considered valuable (and maybe even essential) in spite of the fact that companies can (and routinely do) indemnify them.5

We contribute to the literature in several ways. First, to our knowledge, our study is the first to examine how D&O insurance commonly purchased by companies affects debtholders and the pricing of debt contracts. Second, our evidence identifies a precise channel through which D&O insurance may affect firm value and, therefore, our paper also contributes to the ongoing debate over the merits of D&O insurance. Third, this paper adds to the debt contracting and financing literature that has examined the determinants and consequences of various debt contracting terms (e.g. Campello, 2006; Graham et al., 2008; Chava et al., 2009; Lin et al., 2011; Hertzel and Officer, 2011): our findings uncover a new factor (i.e., personal liability facing directors and officers) that systematically appears to affect debtholders. Fourth, our paper is also

      

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broadly related to the literature on the effects of litigation risk on corporate behavior (e.g., Lowry and Shu, 2002; Lowry, Field, and Shu, 2005; Lowry, 2009). These studies show that litigation risk affects IPO underpricing and voluntary disclosure. Our results that D&O insurance mutes the effect of litigation risk and leads to greater chances of financial restatement and greater risk taking complement this literature.

We organize the remainder of the paper as follows. In Section 2, we describe our data. Section 3 describes the key variables and summary statistics. Sections 4 and 5 report our empirical results. Section 6 concludes.

2. Data and sample selection

Our firm-level D&O insurance data is obtained for Canadian companies, because Canada mandates the disclosure of D&O insurance purchase in annual corporate filings. We focus on firms which have appeared at least once in the TSE 300 index (currently S&P/TSX Composite Index) between 2002 and 2008. We then hand collect firm-level D&O insurance information (i.e., the existence of D&O insurance, coverage, premium and deductibles) from the companies’ proxy circulars in the SEDAR database.6

We also hand collect information on corporate governance (e.g., ownership structure, board structure, and executive compensation) from annual corporate filings. Our data on bond issuance for Canadian issuers is primarily taken from Bloomberg, and is supplemented by data from the Mergent Fixed Income Securities Database. Syndicated bank loan data is extracted from LPC’s DealScan database and our loan data ends in 2008. We then match our D&O insurance information with bond data and loan data separately to arrive at the (separate) samples for the

      

6 See www.sedar.com. If a firm’s proxy circular is unavailable then we set our D&O insurance variables as missing values.

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bond and loan spread analysis.7 D&O insurance information is lagged by one year relative to bond and loan data so that the D&O insurance purchase can be assumed to be predetermined and can have its effect (if any) show up in subsequent debt contracting. Our analysis of loan spread is at the loan level: in DealScan this is referred to as a facility. In calculating bond spread at issuance, we subtract the yield of zero-coupon government bonds with the same time to maturity (from the Bank of Canada website) when the bond was issued. We exclude financial firms from our analysis, and exclude bonds/loans with missing or negative spread. Our bond sample contains a total of 377 bonds issued by 109 firms, and the loan sample comprises 615 loans drawn by 186 firms. Accounting data is taken from Compustat.

3. Variables and summary statistics

Descriptions of the variables used in our analysis are contained in Table 1. Below we describe the most important variables in detail.

[Insert Table 1 here]

3.1. D&O insurance

We follow the literature (e.g., Lin et al., 2011) and use the insurance coverage ratio as our key measure of D&O insurance. This is a continuous variable which is defined as the personal (“Side A”) coverage limit of the D&O insurance policy scaled by the market value of equity of the firm at the end of the concurrent fiscal year. We set this variable equal to zero if a firm does not have D&O insurance in a given year. Summary statistics can be found in Table 2. As can be seen from the table, about 69% of the bonds in our sample are issued by firms that purchase D&O

      

7 We first use the link file maintained by Michael Roberts to match our data to DealScan records, and then hand checked the unmatched ones.

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insurance policies to protect their directors and officers from financial liability associated with litigation (Panel A). Approximately 72% of the loans in our sample are originated by firms whose directors are protected by D&O insurance (Panel B). The personal coverage limit on average represents 4.5% (6.5%) of the issuing firm’s market value of equity in the bond (loan) sample.

3.2. Cost of debt

Following the literature (e.g. Campello et al., 2008; Graham et al., 2008; Lin et al., 2011), we use two commonly-used variables to measure the cost of debt. The first measure (Bond spread) is compiled from Bloomberg, and is defined as the difference between the issue’s offering yield and the yield of a treasury bond with the same maturity. We measure this spread at issue for all bonds issued by firms in our sample during our sample period. The mean of the bond spread in Table 2, Panel A is 268 basis points. The second measure (Loan spread) is obtained from DealScan, and we use the all-in-drawn spread as the cost of the bank loan. This measure is defined as the spread over the London Interbank Offered Rate (LIBOR) or LIBOR equivalent on a loan plus associated loan origination fees. Therefore, it is an all-inclusive measure of the loan price (Lin et al., 2011). As with the bond sample, we measure this spread for all loans taken out by firms in our sample during our sample period and is measured at origination of the loan. The average loan spread in Table 2, Panel B is 182 basis points.

[Insert Table 2 here]

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To examine the impact of D&O insurance on the cost of debt, we follow the literature (e.g., Campbell and Taksler, 2004; Graham et al., 2008; Lin et al., 2011) and control for other firm-specific and contract-firm-specific factors that might affect cost of debt. All firm-firm-specific variables enter our regressions with a one-year lag from the year in which the bond or loan is originated: this ensures that these characteristics are at least exogenous in time. For robustness, some of our regressions also include industry and year fixed effects to attempt to capture heterogeneity between bonds/loans that is unrelated to observable firm/debt characteristics.

Regarding firm characteristics, we control for firm size (measured using assets), market-to-book ratio, profitability, asset tangibility, cash-flow volatility, and leverage. Firm size decreases information asymmetry problems in credit markets and, as a consequence, likely reduces the cost of debt. Asset tangibility increases recovery rates in default and therefore should also be negatively associated with cost of debt. Moreover, profitable, low-leverage firms and firms with stable cash flows are less likely to default and these characteristics are therefore expected to be associated with lower cost of debt (Lin et al., 2011). For the market-to-book ratio, our prediction is less clear. On the one hand, as a proxy for growth opportunities the market-to-book ratio might indicate a higher likelihood of risk shifting activities. On the other hand, it could proxy for additional value (over liquidation) that is left for creditors in distress (Graham et al., 2008).

As discussed in the literature (e.g. Shleifer and Vishny, 1997), corporate governance mechanisms might attenuate the agency costs of debt. Therefore, we also control for board characteristics and ownership structure in our regressions. With respect to board characteristics, we control for the proportion of outside directors and CEO-Chairman duality, the most widely used measures of board structure (see a recent survey by Adams, Hermalin, and Weisbach, 2010). Regarding the ownership structure, we include two indicator variables to control for dual-class share structure and blockholders. Dual-class share structures enable corporate insiders to exercise effective control over a company with a relatively small direct stake in the cash-flow rights (Lin

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et al., 2011). In such firms, corporate insiders have incentives to expropriate minority shareholders and creditors, through various tunneling and self dealing activities. 8 Many of these activities increase default risk and loss given default and, as a consequence, the cost of debt. Blockholders also play an important governance role as they are more informed than retail investors, and have strong incentives and capabilities to devote resources to monitoring (Shleifer and Vishny, 1997). We hand-collect blockholding data from proxy circulars, and our indicator variable captures the existence of a shareholder with greater than 10% of the firm’s shares.

We also control for contract-specific characteristics that might affect cost of debt. Specifically, in the bond spread regressions, we control for the size of the bond issuance and the type of the bond (callable or convertible). In the loan spread regressions, we control for loan size and the presence of a performance pricing clause (Asquith et al., 2005). Moreover, we include indicator variables to control for loan type (term loans or revolvers) and purpose (working capital or general corporate purposes, refinancing, acquisition, commercial paper backup, or others), and we also include indicator variables for the borrower’s S&P credit-rating category (firms without ratings are the omitted indicator variable).

3.4. Univariate analysis

Before conducting regression analysis in the following section, we first look at univariate statistics to see whether the broad patterns of the data are consistent with our hypothesis about the relation between D&O insurance and the cost of debt. We split the sample into two groups based on median D&O insurance coverage and compare the mean values of cost of debt (bond spreads and loan spreads) between the low- and high-coverage groups. The results are presented in Table 3.

      

8 These activities include outright theft, diverting firm resources for their own use, executive perquisites, expropriation of corporate opportunities, committing funds to unprofitable projects that provide private benefits, transferring assets and profits out of companies, loan guarantees using the firm’s assets as collateral, and other self-dealing financial transactions (Shleifer and Vishny, 1997; La Porta et al., 1999; Johnson, et al., 2000; Djankov et al., 2008; Lin et al., 2011).

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[Insert Table 3 here]

As can be seen from the table, the means of both bond and loan spreads are consistent with the hypothesis that high D&O insurance coverage is perceived by debtholders as being associated with higher borrower risk. Specifically, we find that firms with above-median D&O insurance coverage have significantly higher cost of debt, on average, than do firms with below-median coverage. The average bond (loan) spread for firms in the high-coverage group is 284 (222) basis points while the average bond (loan) spread for firms in the low-coverage group is 253 (142) basis points. These differences are statistically significant at the 10% for bond spreads and the 1% level for loan spreads. Taken together, these univariate comparisons provide initial evidence confirming a relation between D&O insurance coverage and the cost of debt.

4. Empirical results

4.1. The effect of D&O insurance coverage on bond spreads

In this section, we use regression analysis to examine the effects of D&O insurance on bond spreads. The main empirical model we estimate is as follows:

Bond spread = f (D&O insurance coverage, Borrower characteristics, Governance measures,

Bond characteristics, Industry and time effects) (1)

In Eq. (1), the dependent variable is bond spread, defined as the difference between the issuer’s offering yield and the yield of a treasury bond with the same maturity. The bond spread is measured in percentage points. The key independent variable of interest is the D&O insurance coverage ratio. Other independent variables include controls for borrower characteristics, board

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structure, ownership structure, bond characteristics, as well as borrower industry and year fixed effects. The empirical results are presented in Table 4, which contains five regressions. The first specification controls for a set of borrower characteristics and credit rating dummies, while the second regression adds controls for bond characteristics. The third adds controls for a set of board and ownership structure variables. The fourth regression adds year fixed effects and the fifth adds firm fixed effects (in place of industry fixed effects). The inclusion of firm fixed effects helps eliminate time invariant unobserved characteristics that might affect the cost of debt.9

[Insert Table 4 here]

As can be seen from the table, we find strong evidence that D&O insurance is positively associated with bond spreads, as indicated by the positive and statistically significant coefficients on the D&O insurance coverage ratio across all specifications. A one-standard-deviation increase in the D&O insurance coverage ratio increases bond spreads by 29 to 42 basis points on average (based on the coefficient point estimates in columns 4 and 5), all else equal. Relative to the unconditional average bond spread of 268 basis points, this represents a substantial (11 - 15%) increase. Hence, the effect of D&O insurance on bond spreads is both economically and statistically significant.

Regarding the control variables, the empirical results are largely consistent with prior literature. Specifically, we find that larger borrower firm size, higher market to book ratio, and higher profitability tend to be associated with significantly lower bond spreads. We also find some evidence that a larger fraction of outside directors on the board is associated with lower

      

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bond spreads (in column 4) and that larger bond issues are associated with higher spreads (in columns 2 and 3).

4.2. The effect of D&O insurance coverage on loan spreads

The results in the previous section show that D&O insurance coverage exerts a significant impact on bond spreads. In this section, we examine the effects of D&O insurance on the cost of bank loans, another important type of corporate debt. The main empirical model we estimate is as follows:

Loan spread = f (D&O insurance coverage, Borrower characteristics, Governance measures,

Loan characteristics, Industry and time effects). (2)

In Eq. (2), the dependent variable is the all-in-drawn loan spread measured in percentage points. The key independent variable of interest is the D&O insurance coverage ratio. Other independent variables are similar to those in Table 4 (except for the loan characteristics, such as loan type and loan purpose indicator variables). The empirical results are presented in Table 5, which presents five specifications that roughly mirror those in Table 4.

[Insert Table 5 here]

As can be seen from the table, the empirical results are consistent with our previous findings. Across all model specifications, we find a positive and significant relation between the D&O insurance coverage ratio and loan spreads, indicating that firms whose directors have greater D&O coverage pay higher borrowing costs in the syndicated loan market. Specifically, a one-standard-deviation increase in insurance coverage ratio increases loan spreads by 18 to 20 basis points on average (based on the coefficient point estimates in columns 4 and 5), ceteris

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paribus. Considering the average loan spread is 182 basis points, this represents about a 10% increase. Therefore, the effect of D&O insurance on loan spreads is both economically and statistically significant. Taken together, our bond and loan spreads evidence suggests that D&O insurance exerts a significant impact on cost of debt, whether such capital is raised in the bond or syndicated loan market.

With respect to the control variables in Table 5, the empirical results are largely consistent with our expectations (and the results in Table 4). For instance, we find that firm size and market to book ratio are negatively associated with bank loan spreads. Furthermore, cash flow volatility is positively associated with loan spreads in some specifications. A higher degree of tangibility is also associated with a lower loan spreads in some regressions, suggesting that the collateral recovery rate is an important consideration in loan pricing. Despite the relatively small sample size, Table 5 also exhibits strong evidence that highly levered firms pay higher loan spreads and that larger loans are priced at lower spreads.

4.3. D&O insurance and cost of debt: Instrumental-variables

Potential endogeneity is a source of concern for many corporate finance studies. Relative to other studies in the literature, it is less of a concern in this setting because bond and loan spreads are set by the firms’ creditors and/or by competitive forces in credit markets (i.e., these are observed outcomes and not firm choice variables). Moreover, we have taken steps to alleviate concerns arising from reverse-causality (by lagging the independent variables) and omitted variables (by using an extensive set of controls, including firm fixed effects).

Nevertheless, it is still possible that some unobserved firm-specific characteristics might affect both D&O insurance coverage and the cost of debt. Although it’s difficult to completely solve this endogeneity problem, we attempt to further address this issue using an instrumental variable approach. Following the recent literature (Adams et al., 2010; Lin et al., 2011), we use

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the industry median insurance coverage ratio as an instrument for the firm’s D&O coverage ratio. The choice of the instrument variable is based on the following reasons. First, firms in the same region and same industry might compete for a small pool of managerial talent in the local labor market, and so a firm’s managerial compensation package (e.g. including D&O insurance coverage) is likely to be highly dependent on the compensation packages offered by competitors in the same industry (or region) (Adams et al., 2010). Second, firms in the same industry face similar business risks, and therefore the potential risk of shareholder litigation often exhibits industry patterns (Hertzel and Officer, 2011; Lin et al., 2011).10 Therefore, a firm’s D&O insurance policy might be driven by the trend in the industry to hedge potential business and litigation risks. More importantly, the key observation is that the industry median insurance coverage ratio might affect a firm’s D&O insurance policy, but, a priori, it is less likely that the median industry insurance coverage ratio would directly affect the firm’s cost of debt (exclusion restriction). Under this premise, the industry median insurance coverage ratio can be used as an instrument for the firm’s D&O coverage ratio.

Table 6 reports the results of our two-stage least squares instrumental variable regressions. In column 1, the dependent variable is the bond spread (as in Table 4), while in column 2 we present results with the loan spread as the dependent variable (as in Table 5). The key instrumental variable is the industry median D&O insurance coverage ratio based on three-digit SIC codes. The first-stage IV regressions also include all the control variables from prior tables (for the bond and loan spread regressions, respectively). The F-statistics for the first stage regressions indicate that the coefficients on the instruments are jointly significantly different from zero at the 1% level. We also calculate Shea’s (1997) partial R2’s from the first-stage regressions. These R2’s both exceed the suggested (“rule of thumb”) hurdle of 10%. These tests suggest that our instrument is relevant in explaining the variation of the potentially endogenous regressor (D&O insurance coverage). For brevity, we only present the second stage regression

      

10 For instance, high-tech firms might be more likely to become litigation targets than other firms are in certain periods (Core, 2000).

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results in Table 6.11

[Insert Table 6 here]

As can be seen from the table, the empirical results are robust in these instrumental variables specifications. The coefficients on the D&O insurance coverage ratio variable remain positive and statistically significant in both the bond and loan spread regressions. If endogeneity is a concern in this setting, it does not appear to be driving our empirical results.

5. Economic mechanisms: firm risk taking and financial restatements

Our evidence suggests that firms with greater D&O insurance coverage tend to have a higher cost of debt. In this section, we seek to understand the economic mechanisms through which D&O insurance coverage might affect the cost of debt. As widely documented in the literature, information asymmetry and excessive risk taking are important concerns of creditors.12 We explore two possibilities: 1) whether directors and officers protected by D&O insurance are more likely to engage in more risk taking; and 2) whether D&O insurance leads to low-quality financial reporting (potentially as a result of weaker due diligence, reduced vigilance, or outright financial misstatement). These factors directly, and adversely, affect the position of creditors, and as such may be viable mechanisms by which D&O insurance coverage influences the cost of debt.

5.1. D&O insurance coverage and firm risk taking

      

11 The first stage regression results are available from the authors by request.

12 For instance, Graham et al. (2008) find a significant and positive effect of financial restatement on bank loan pricing. Lin et al. (2011) find that credit risk and information opacity are associated with significantly higher cost of borrowing.

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The sample used in this analysis contains firms for which we have D&O insurance data and daily stock return data from Thomson Reuters Datastream. This intersection gives us a sample of 2,569 firm-years. Following the literature (e.g., Coles et al., 2006), we use two variables to measure firm risk. Total risk is defined as the natural logarithm of the annualized variance of daily stock returns (in percentage) over the fiscal year. Idiosyncratic risk is defined as natural logarithm of the annualized variance of the residuals from a market model using the TSE 300 index return as the market return. We lag the D&O insurance coverage ratio by one year relative to these firm risk proxies to allow for causality in time.

Before getting into the regression analysis, we first conduct univariate tests to identify the general patterns in the data. We split the sample at the in-sample median of the D&O insurance coverage ratio and compare the mean values of our risk proxies between these two groups. The results are presented in Panel A of Table 7. As can be seen from the table, univariate comparisons show that firms with higher D&O insurance coverage tend to take more risk. We find that firms with an above-median D&O insurance coverage ratio tend to have significantly higher total and idiosyncratic risk, on average, than do firms with below-median coverage, and the differences are statistically significant at the 1% level. These univariate tests provide evidence that is consistent with our evidence on the cost of debt: firms with high D&O insurance coverage seem to take more risk and this is reflected in the higher cost of borrowing.

[Insert Table 7 here]

We conduct regression analyses with total risk and idiosyncratic risk as dependent variables. The specifications control for firm characteristics such as size, market to book ratio, profitability, sales growth, leverage, R&D spending, and capital expenditure. Moreover, we control for risk-taking incentives generated by executive compensation. As explained in the

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literature (e.g. Jensen and Meckling, 1976; Smith and Stulz, 1985; Guay, 1999), both the slope and the convexity of the relation between the stock price and CEO wealth affect managerial risk taking incentives. We therefore control for the delta (sensitivity of CEO wealth to changes in stock price) and vega (sensitivity of CEO wealth to changes in stock price volatility) along with managerial cash compensation (salary, bonus, and other annual compensation) in our regressions. Specifically, delta is defined as the change in the value of a CEO’s stock options and stockholdings for a $1 increase in the stock price. Vega is defined as the change in the value of a CEO’s stock options and stockholdings for a 1% change in stock return volatility. All measures of delta and vega are expressed in thousands of dollars, and all control variables are lagged by one year relative to the risk measures.

As can be seen from the table, the regression results confirm the findings in the univariate tests. The regressions in columns 1 and 2 use total risk as the dependent variable, while the empirical results based on idiosyncratic risk are presented in columns 3 and 4. In columns 2 and 4, we include firm, but not industry, fixed effects (and vice versa in columns 1 and 3). The D&O insurance coverage ratio is significantly positively associated with both total risk and idiosyncratic risk in all specifications in Table 7. In terms of the control variables, we find that larger and more profitable firms tend to exhibit lower risk (however measured), as one might expect. CEO vega is positively associated with total and idiosyncratic risk in columns 1 and 3, consistent with the notion that firm risk-taking is influenced by the CEO’s incentive compensation.

5.2. D&O insurance coverage and financial restatements

To examine whether D&O insurance leads to low-quality financial reporting, we construct financial restatement indicator variables. We first download the annual report for each firm-year in our D&O insurance sample from the SEDAR database. We set the restatement

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variables to missing for a firm-year when there is no usable annual report. We then search in the downloaded annual reports for the keyword “restat”. If the annual report for a firm-year has no reference of the keyword, the firm-year’s restatement indicator is set equal to zero. Restatements arising from accounting standard changes and other normal restatements required for the comparability of financial statements (e.g., due to discontinued operations, mergers, and acquisitions) are not regarded as a restatement (i.e., the restatement indicator is set to zero). Otherwise, we follow Hennes, Leone, and Miller (2008) to determine whether the restatement is intentional or unintentional (i.e., due to a careless error). Hennes et al. (2008) argue that it is critically important to distinguish restatements arising from intentional manipulation vs. unintentional mistakes. In our sample, this process results in 3,166 usable observations, each of which is a firm-year with D&O insurance coverage and restatement indicator data.

[Insert Table 8 here]

We first examine univariate differences between the restatement indicator variables in Panel A of Table 8. Specifically, we split the sample using the in-sample median of the D&O insurance coverage ratio and compare the mean values of our restatement indicator variables between these two groups. As can be seen in Panel A, these simple comparisons are consistent with the idea that low-quality financial reporting is one channel through which D&O insurance coverage adversely affects the position of creditors (and hence increases the cost of borrowing). Specifically, we find that firms with high D&O coverage tend to have a significantly higher incidence of earnings restatement, on average, than do firms with low D&O coverage. For instance, on average 8.9% of firm-years have an earnings restatement for firms with high D&O coverage while only 2.2% of firm-years for firms with low D&O insurance coverage are affected by a restatement. More troubling, this difference appears to be driven by differences in intentional restatements (last row of Panel A). The differences are statistically significant at 1%

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level.

To explore this issue more rigorously, we examine the effect of D&O insurance coverage on the incidence of earnings restatement using a Probit regression controlling for various firm (size, market to book ratio, leverage, profitability, and stock return over prior year) and corporate governance (outside board member ratio, CEO duality, dual class share structure, and the presence of a blockholder) control variables. The empirical results are presented in Panel B of Table 8. Marginal effects derived from the Probit coefficients are reported. Columns 3 and 6 are based on an IV approach (with three-digit industry median D&O insurance coverage and the control variables used as instruments, as in Table 6). The D&O insurance coverage ratio and all control variables are lagged by one year relative to the restatement indicator variables.

The regression results largely confirm our findings in the univariate tests. Specifically, we find that the D&O insurance coverage ratio is positively associated with the incidence of earnings restatement and the incidence of intentional earnings restatement, as indicated by the positive and statistically significant coefficients across all model specifications. Overall, these results bolster our previous findings and help explain the link between D&O insurance coverage and the cost of debt: higher D&O coverage appears to be associated with greater risk-taking (Table 7) and lower-quality financial reporting (Table 8), and both attributes adversely affect the position of creditors.

6. Conclusion

Using a unique hand-collected dataset of corporate purchases of D&O insurance by Canadian firms, we examine whether D&O insurance coverage affects the cost of debt. We find that higher levels of D&O insurance coverage are associated with higher at-issue bond yields to maturity and higher loan spreads, suggesting that debtholders perceive D&O insurance coverage as impairing their ability to monitor and/or control firms. Further analyses show that greater risk taking and lower-quality financial reporting (resulting in a higher probability of financial

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restatement) might explain why debtholders appear to penalize firms that carry higher levels of D&O insurance coverage. Taken together, our results are consistent with the argument that D&O insurance coverage may generate (unintended) moral hazards that are harmful to debtholders. Our study provides the first evidence suggesting how debtholders view the prevalent use of D&O insurance and adds to our understanding of the effect of D&O insurance on the conduct of directors and officers. It also identifies a new factor that is taken into account by debtholders in debt contracting and pinpoints a channel through which D&O insurance may affect firm value.

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Table 1: Variable definitions

Variables Definitions

D&O insurance information

Insurance (1/0) Indicator variable equal to one if the firm purchases D&O insurance in the fiscal year; zero otherwise.

Coverage amount The limit on the personal coverage of D&O insurance in millions of US dollars (US$m).

Insurance coverage ratio Limit on the personal coverage of the D&O insurance/market value of equity at the fiscal year end (winsorized at the 1st and 99th percentile).

Bond characteristics

Bond spread The difference between a bond’s offering yield and the yield of treasury bonds (with the same maturity) (winsorized at the 1st and 99th percentile).

Bond size Offering amount of bond (US$m).

Convertible (1/0) Indicator variable equal to one for convertible bonds; zero otherwise. Callable (1/0) Indicator variable equal to one for callable bonds; zero otherwise.

Loan characteristics

Loan spread The all-in-drawn spread over LIBOR charged by the bank for the loan facility.

Loan size Loan (facility) amount (US$m).

Performance pricing (1/0) Indicator variable equal to one if the loan facility uses performance pricing; zero otherwise.

Loan type dummies Indicator variables for loan type (term loan, revolver greater than one year, revolver less than one year, and 364-day facility).

Loan purpose dummies Indicator variables for loan purpose (including corporate purposes, working capital, debt repayment, acquisition, backup line for commercial paper, and others).

Firm characteristics

Assets Book value of total assets.

Market-to-book (Fiscal-year-end market value of equity + book value of liabilities)/ total assets.

Profitability Operating income after depreciation/sales (winsorized at the 1st and 99th percentile).

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Leverage (Long-term debt + debt in current liabilities)/total assets (winsorized at the 1st and 99th percentile).

Cash flow volatility Standard deviation of annual cash flows from operations over the ten fiscal years prior to debt origination, scaled by the total assets excluding cash and cash equivalents (winsorized at the 1st and 99th percentile).

Tangibility Net property, plant and equipment/total assets (winsorized at the 1st and 99th percentile).

Credit rating dummies Indicator variables for each category of S&P firm ratings (AAA, AA, A, BBB, BB, B or worse).

Total risk Natural logarithm of the annualized variance of daily stock returns (in percentage) over the fiscal year.

Idiosyncratic risk Natural logarithm of the annualized variance of the residuals from the market model (the market return is the return to the TSX 300 index). Return on assets (ROA) Income before extraordinary items/total assets (winsorized at the 1st

and 99th percentile).

Sales growth Ln(Salest/Salest-1) (winsorized at the 1 st

and 99th percentile).

R&D Research and development expenditure/total assets (winsorized at the 1st and 99th percentile).

CAPEX Net capital expenditure/total assets (winsorized at the 1st and 99th percentile).

Stock return over prior year Stock returns over the previous fiscal year (calculated using

compounded daily stock returns) net of the daily compounded return to the S&P/TSX Composite Index over the same period, measured in percentage (winsorized at the 1st and 99th percentile).

Earnings restatement variables

Restatement indicator Indicator variable equal to one if there is an earnings restatement in a fiscal year; zero otherwise. Restatements arising from accounting standard changes and other normal restatements for the comparability of financial statements (e.g., due to discontinued operations, mergers and acquisitions) are not regarded as restatement.

Intentional restatement indicator Indicator variable equal to one if the earnings restatement is due to intentional misstatement; zero otherwise. Following Hennes et al. (2008), intentional restatement refers to restatement arising from irregularities (as opposed to errors that are unintentional).

Governance variables

Blockholder (1/0) Indicator variable equal to one if there is at least one blockholder owning more than 10% of the firm’s shares; zero otherwise.

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Duality (1/0) Indicator variable equal to one if the positions of CEO and board chairman are occupied by the same person; zero otherwise. Outside directors The proportion of outside directors on the board.

Dual-class (1/0) Indicator variable equal to one if the company has a dual-class share structure; zero otherwise.

Cash compensation Natural log of (1+salary+bonus+other annual compensation). CEO delta CEO’s portfolio sensitivity to a $1 increase in the stock price (in

thousands) (winsorized at the 1st and 99th percentile).

CEO vega CEO’s portfolio sensitivity to a 1% change in stock-return volatility (in thousands) (winsorized at the 1st and 99th percentile).

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Table 2: Summary statistics

This table presents summary statistics for the variables used in our D&O insurance and cost of debt analysis. Variable definitions are in Table 1.

Panel A: Summary statistics of the firm and bond characteristics (Bond sample)

Variable Mean Std. dev.

Percentiles

N 25th 50th 75th

Insurance (1/0) 0.690 0.463 0 1 1 377

Coverage amount (US$m) 67.930 87.287 0 43.750 100.033 377

Insurance coverage ratio 0.045 0.115 0 0.012 0.039 377

Bond spread (basis points) 268.180 176.881 137.429 220.588 376.043 377

Bond size (US$m) 318.995 276.993 125 250 400 377

Convertible (1/0) 0.160 0.367 0 0 0 350 Callable (1/0) 0.837 0.370 1 1 1 350 Ln(assets) 8.062 1.456 6.922 8.100 9.301 377 Market-to-book 1.433 0.518 1.099 1.297 1.645 374 Profitability 0.108 0.350 0.048 0.144 0.241 372 Leverage 0.312 0.176 0.209 0.299 0.386 373

Cash flow volatility 0.034 0.033 0.013 0.024 0.046 356

Tangibility 0.575 0.249 0.405 0.598 0.786 377

Blockholder (1/0) 0.402 0.491 0 0 1 376

Duality (1/0) 0.911 0.286 1 1 1 358

Outside directors 0.823 0.113 0.750 0.857 0.917 375

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Panel B: Summary statistics of the firm and loan characteristics (Loan sample)

Variable Mean Std. dev.

Percentiles

N 25th 50th 75th

Insurance (1/0) 0.720 0.449 0 1 1 615

Coverage amount (US$m) 51.288 72.145 0 25 75 615

Insurance coverage 0.065 0.141 0 0.020 0.056 615

Loan spread (basis points) 181.808 125.299 75 150 250 615

Loan size (US$m) 450.107 796.363 75 200 498.750 615

Performance pricing (1/0) 0.239 0.427 0 0 0 615

Ln(assets) 5.267 1.347 4.317 5.298 6.212 615

Market-to-book 1.532 0.683 1.109 1.365 1.732 595

Profitability 0.090 0.208 0.040 0.093 0.167 599

Leverage 0.289 0.191 0.150 0.284 0.405 606

Cash flow volatility 0.047 0.055 0.016 0.031 0.053 582

Tangibility 0.488 0.263 0.293 0.480 0.692 608

Blockholder (1/0) 0.555 0.497 0 1 1 598

Duality (1/0) 0.886 0.318 1 1 1 595

Outside directors 0.792 0.129 0.722 0.833 0.889 597

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Table 3: Costs of debt for subsamples

This table compares average bond and loan spreads between groups based on the insurance coverage ratio. The low (high) coverage group is defined as having insurance coverage ratio below (above) the in-sample median of the insurance coverage ratio distribution. The difference tests are based on t-tests. *, **, ***: statistically significantly different from zero at the 0.10, 0.05 and 0.01 level (two-tailed), respectively. Variable definitions are in Table 1.

Variable

All Firms in the low

coverage group (L)

Firms in the high coverage group (H)

Difference (L-H)

Mean Std. dev. N Mean N Mean N

Panel A: Bond sample

Insurance coverage ratio 0.045 0.115 377 0.002 189 0.088 188 -

Bond spread (basis points) 268.180 176.881 377 252.504 189 283.940 188 -31.437*

Panel B: Loan sample

Insurance coverage ratio 0.065 0.141 615 0.005 307 0.125 308 -

Loan spread (basis points) 181.808 125.299 615 141.522 307 221.963 308 -80.440***

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Table 4: Bond spread regressions

This table shows the results from OLS regressions of the bond spread on insurance coverage ratio. Bond spread is measured in percentage points. Standard errors (clustered at the firm level) that are robust to both cross-sectional heteroskedasticity and within-firm serial correlation are used in computing t-statistics (in square brackets). *, **, ***: statistically significantly different from zero at the 0.10, 0.05 and 0.01 level (two-tailed), respectively. The coefficients on the constant, year, firm, credit rating, and industry dummies are omitted for brevity. Variable definitions are in Table 1.

(1) (2) (3) (4) (5)

Insurance coverage ratio 3.880*** 4.031*** 3.447*** 2.496*** 3.694***

[5.172] [5.985] [4.009] [4.214] [3.967] Ln(assets) 0.083 -0.125 -0.063 -0.277** -0.519*** [0.905] [-1.222] [-0.496] [-2.455] [-3.045] Market-to-book -0.458** -0.533*** -0.658*** -0.588*** -0.342 [-2.523] [-2.841] [-3.049] [-3.005] [-1.041] Profitability -0.874** -0.742** -0.542 -0.998*** -0.828** [-2.084] [-2.047] [-1.457] [-3.339] [-2.176] Leverage -0.291 -0.268 -0.444 -0.455 -0.240 [-0.395] [-0.391] [-0.689] [-0.781] [-0.237]

Cash flow volatility 6.234 7.091 9.151* 3.889 5.691

[1.070] [1.360] [1.705] [0.976] [0.897] Tangibility -0.208 -0.180 -0.286 -0.042 0.446 [-0.584] [-0.470] [-0.537] [-0.099] [1.011] Ln(bond size) 0.411*** 0.393** -0.132 0.094 [2.937] [2.578] [-1.136] [0.783] Convertible (1/0) -0.429 -0.507 -0.561 -0.746 [-1.104] [-1.241] [-1.445] [-1.054] Callable (1/0) -0.128 -0.095 0.386* 0.269 [-0.473] [-0.344] [1.690] [1.132] Outside directors -2.060 -2.353** -1.133 [-1.489] [-2.202] [-0.600] Blockholder (1/0) 0.271 0.326 [1.059] [1.644] Duality (1/0) 0.289 0.393 [0.820] [1.574] Dual-class (1/0) -0.176 0.114 [-0.492] [0.433] Control for

Credit rating dummies Yes Yes Yes Yes Yes

Firm effects No No No No Yes

Industry effects Yes Yes Yes Yes No

Year effects No No No Yes Yes

Adjusted R2 0.412 0.439 0.443 0.643 0.788

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Table 5: Loan spread regressions

This table shows the results from OLS regressions of loan spread on insurance coverage ratio. Loan spread is measured in percentage points. Standard errors (clustered at the firm level) that are robust to both cross-sectional heteroskedasticity and within-firm serial correlation are used in computing t-statistics (in square brackets). *, **, ***: statistically significantly different from zero at the 0.10, 0.05 and 0.01 level (two-tailed), respectively. The coefficients on the constant, year, firm, credit rating, loan type, loan purpose, and industry dummies are omitted for brevity. Variable definitions are in Table 1.

(1) (2) (3) (4) (5) Insurance coverage ratio 1.347** 1.277** 1.288** 1.268** 1.389***

[2.235] [2.165] [2.235] [2.456] [3.258] Ln(assets) -0.399*** -0.298*** -0.225*** -0.203*** -0.182 [-8.708] [-4.406] [-3.765] [-3.215] [-1.308] Market-to-book -0.194** -0.161** -0.112 -0.123 -0.214** [-2.557] [-2.076] [-1.441] [-1.588] [-2.240] Profitability -0.537 -0.416 -0.395 -0.601 -0.492 [-1.451] [-1.200] [-1.042] [-1.597] [-0.782] Leverage 0.855** 0.873** 1.133*** 1.079*** 0.101 [2.482] [2.534] [3.527] [3.060] [0.194]

Cash flow volatility 1.642 1.821* 1.694 1.744* 1.761

[1.614] [1.794] [1.609] [1.672] [0.930] Tangibility -0.564* -0.620* -0.436 -0.349 0.964 [-1.702] [-1.892] [-1.497] [-1.129] [1.252] Ln(loan size) -0.141** -0.166*** -0.193*** -0.180*** [-2.401] [-3.159] [-3.259] [-2.701] Performance pricing (1/0) -0.127 -0.121 -0.008 0.073 [-1.214] [-1.063] [-0.077] [0.588] Outside directors 0.014 -0.926 [0.031] [-1.252] Blockholder (1/0) -0.144 [-1.377] Duality (1/0) -0.188 [-1.040] Dual-class (1/0) 0.004 [0.023] Control for

Credit rating dummies Yes Yes Yes Yes Yes

Loan type No No Yes Yes Yes

Loan purpose No No Yes Yes Yes

Firm effects No No No No Yes

Industry effects Yes Yes Yes Yes No

Year effects No No No Yes Yes

Adjusted R2 0.381 0.392 0.465 0.490 0.698

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Table 6: Instrumental variables specifications

This table shows the results from second-stage regressions of bond and loan spreads on instruments for insurance coverage ratio. Bond and loan spread is measured in percentage points. Insurance coverage (%) is instrumented with fitted values from a first-stage regression on industry median insurance coverage (%) based on 3-digit SIC codes and the control variables from prior tables. Shea’s partial R2 is a measure of IV relevance. 1st-stage F-test is the test of excluded IV in the first-stage regression. Standard errors (clustered at the firm level) that are robust to both cross-sectional heteroskedasticity and within-firm serial correlation are used in computing t-statistics (in square brackets). *, **, ***: statistically significantly different from zero at the 0.10, 0.05 and 0.01 level (two-tailed), respectively. The coefficients on the constant and the various fixed effects are omitted for brevity. Variable definitions are in Table 1.

Bond spread Loan spread

Insurance coverage ratio 2.413** 1.227**

[2.148] [2.205] Ln(assets) -0.286** -0.214*** [-2.419] [-3.345] Market-to-book -0.610*** -0.142* [-2.908] [-1.710] Profitability -0.979*** -0.597 [-3.401] [-1.516] Leverage -0.424 1.077*** [-0.724] [2.925]

Cash flow volatility 4.042 1.745*

[0.960] [1.733] Tangibility -0.071 -0.344 [-0.165] [-1.091] Blockholder (1/0) 0.335 -0.132 [1.594] [-1.232] Duality (1/0) 0.402 -0.185 [1.409] [-0.995] Outside directors -2.408** 0.055 [-2.115] [0.114] Dual-class (1/0) 0.114 0.018 [0.429] [0.095] Ln(bond size) -0.144 [-1.184] Convertible (1/0) -0.578 [-1.489] Callable (1/0) 0.402* [1.759] Ln(loan size) -0.193*** [-3.263] Performance pricing (1/0) -0.009 [-0.082] Control for

Credit rating dummies Yes Yes

Loan type No Yes

Loan purpose No Yes

Industry and year effects Yes Yes

1st-stage Shea’s partial R2 0.604 0.578

1st-stage F-test (p-value) 0.000 0.000

Adjusted R2 0.635 0.483

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Table 7: D&O insurance coverage and firm risk-taking

Panel A presents the summary statistics for firm risk measures and t-tests for differences in means for low- and high-insurance-coverage groups. The low- (high-) coverage group is defined as having insurance coverage ratio below (above) the median of the insurance coverage ratio distribution in the sample with insurance coverage and risk data. Panel B shows the results from regressing firm risk measures on the insurance coverage ratio. Standard errors (clustered at the firm level) that are robust to both cross-sectional heteroskedasticity and within-firm serial correlation are used in computing t-statistics (in square brackets). *, **, ***: statistically significantly different from zero at the 0.10, 0.05 and 0.01 level (two-tailed), respectively. The coefficients on the constant, year, and industry dummies are omitted for brevity. Variable definitions are in Table 1.

Panel A: Risk proxies by coverage groups

Variable All Firms in the low

coverage group (L)

Firms in the high coverage group (H)

Difference (L-H)

Mean Std. dev. N Mean N Mean N

Insurance coverage ratio 0.078 0.168 2,569 0.006 1,285 0.150 1,284 -

Total risk 7.578 1.065 2,569 7.441 1,285 7.716 1,284 -0.275***

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Panel B: Regression results

Total risk Idiosyncratic risk

(1) (2) (3) (4) Insurance coverage ratio 1.191*** 0.825*** 1.178*** 0.831***

[6.680] [4.709] [6.579] [4.641]

Ln(assets) -0.237*** -0.031 -0.285*** -0.087**

[-10.482] [-0.766] [-12.977] [-2.248]

Market-to-book -0.034* -0.029* -0.055*** -0.053***

[-1.836] [-1.766] [-2.973] [-3.299] Return on assets (ROA) -1.193*** -0.567*** -1.221*** -0.613***

[-8.257] [-4.233] [-8.432] [-4.687] Sales growth 0.067 0.007 0.052 -0.001 [1.643] [0.179] [1.292] [-0.021] Leverage -0.008 0.306** 0.045 0.317** [-0.055] [2.448] [0.329] [2.230] R&D 0.183 0.399 0.017 0.300 [0.460] [1.016] [0.045] [0.800] CAPEX -0.023 0.106 -0.079 0.086 [-0.082] [0.302] [-0.282] [0.242] Cash compensation -0.015 -0.018 -0.014 -0.015 [-1.269] [-1.055] [-1.217] [-0.913] CEO delta 0.001 0.001 0.001 -0.001 [1.613] [1.145] [0.762] [-0.245] CEO vega 0.003*** 0.001 0.003*** 0.001 [3.612] [0.005] [4.148] [0.115] Control for

Firm fixed effects No Yes No Yes

Industry effects Yes No Yes No

Year effects Yes Yes Yes Yes

Adjusted R2 0.550 0.415 0.576 0.418

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Table 8: D&O insurance coverage and the incidence of earnings restatement

Panel A presents summary statistics for the incidence of earnings restatement and t-tests for differences in means for low- and high-insurance-coverage groups. The low- (high-) coverage group is defined as having insurance coverage ratio below (above) the median of the insurance coverage ratio distribution in the sample with insurance coverage and restatement data. Panel B shows the results from Probit regressions of the incidence of earnings restatement on the insurance coverage ratio. Column (3) and (6) use the IV approach, where insurance coverage is instrumented with fitted values from a first-stage regression on the on 3-digit SIC code industry median insurance coverage ratio and the control variables from prior tables. Shea’s (1997) partial R2 is a measure of IV relevance. 1st-stage F-test is the test of excluded IV in the 1st-stage regression. Standard errors (clustered at the firm level) that are robust to both cross-sectional heteroskedasticity and within-firm serial correlation are used in computing t-statistics (in square brackets). *, **, ***: statistically significantly different from zero at the 0.10, 0.05 and 0.01 level (two-tailed), respectively. Marginal effects from the Probit regressions are reported. The marginal effect of an indicator variable is calculated as the discrete change in the expected value of the dependent variable as the indicator variable changes from zero to one. The coefficients on the constant, year and industry dummies are omitted for brevity. Variable definitions are in Table 1.

Panel A: Earnings restatements by insurance coverage groups

Variable All Firms in the low

coverage group (L)

Firms in the high coverage group (H)

Difference (L-H)

Mean Std. dev. N Mean N Mean N

Insurance coverage ratio 0.064 0.129 3,166 0.004 1,583 0.125 1,583 -

Restatement dummy 0.056 0.229 3,166 0.022 1,583 0.089 1,583 -0.067***

(38)

Panel B: Probit regressions of the incidence of earnings restatement on D&O coverage

Restatement dummy Intentional restatement dummy

(1) (2) (3) (4) (5) (6) Insurance coverage ratio 0.137*** 0.141*** 0.159*** 0.125*** 0.127*** 0.147***

[5.638] [5.613] [4.216] [5.610] [5.576] [4.241] Ln(assets) 0.004 0.005 0.004 0.002 0.003 0.002 [1.255] [1.489] [1.121] [0.679] [0.838] [0.477] Market-to-book -0.002 -0.002 -0.003 -0.004 -0.004 -0.005 [-0.419] [-0.372] [-0.615] [-1.013] [-0.911] [-1.113] Leverage 0.056** 0.056** 0.060** 0.053** 0.053** 0.057** [2.245] [2.118] [2.225] [2.243] [2.120] [2.217]

Return on assets (ROA) -0.026 -0.029 -0.035 -0.031 -0.034 -0.040

[-1.037] [-1.074] [-1.247] [-1.349] [-1.437] [-1.583]

Sales growth -0.005 -0.005 -0.006 -0.010 -0.010 -0.011

[-0.464] [-0.472] [-0.564] [-0.991] [-0.995] [-1.096] Stock return over prior year -0.001* -0.001* -0.001** 0.001 0.001 0.001

[-1.859] [-1.870] [-2.019] [-1.028] [-1.068] [-1.191] Outside directors -0.009 -0.009 -0.001 -0.001 [-0.244] [-0.243] [-0.035] [-0.034] Blockholder (1/0) 0.001 0.002 0.005 0.006 [0.050] [0.194] [0.557] [0.686] Duality (1/0) 0.008 0.009 0.013 0.013 [0.558] [0.565] [1.064] [1.075] Dual-class (1/0) -0.010 -0.011 -0.004 -0.005 [-0.812] [-0.886] [-0.352] [-0.415] Control for

Industry effects Yes Yes Yes Yes Yes Yes

Year effects Yes Yes Yes Yes Yes Yes

1st-stage Shea’s partial R2 0.399 0.399

1st-stage F-test (p-value) 0.000 0.000

Adjusted R2 0.085 0.084 0.075 0.090 0.089 0.079

Figure

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References

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