Managerial risk taking incentives and corporate pension policy

Journal of Financial Economics 111 (2014) 328–351

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Managerial risk taking incentives and corporate pension policy$ Divya Anantharaman a,n, Yong Gyu Lee b a b

Department of Accounting and Information Systems, Rutgers Business School, 1 Washington Park #916, Newark, NJ 07102, USA Business School, Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Jongno-gu, Seoul 110-745, South Korea

a r t i c l e in f o

abstract

Article history: Received 26 September 2012 Received in revised form 28 May 2013 Accepted 24 June 2013 Available online 31 October 2013

We examine whether the compensation incentives of top management affect the extent of risk shifting versus risk management behavior in pension plans. We find that risk shifting through pension underfunding (and, to a lesser extent, through pension asset allocation to risky securities) is stronger with compensation structures that create high wealth-risk sensitivity (vega) and weaker with high wealth-price sensitivity (delta). These findings are stronger for chief financial officers (CFOs) than for chief executive officers (CEOs), suggesting that pension policy falls within the CFO’s domain. Risk shifting through pension underfunding is also lower when the CFO’s personal stake in the pension plan is larger. Overall, these findings show that top managers’ compensation structure is an important driver of corporate pension policy. They also highlight firms within which the moral hazard concerns fueled by Pension Benefit Guaranty Corporation insurance are most relevant. & 2013 Elsevier B.V. All rights reserved.

JEL classification: G30 G32 Keywords: Defined benefit pensions Risk shifting Executive compensation Incentives

1. Introduction The employee beneficiaries of a firm’s defined benefit pension plan hold claims on the firm similar to those held by the firm’s debtholders. Beneficiaries are entitled to receive a fixed stream of cash flows starting at retirement. The firm

☆ An earlier version of this paper was titled “Governance and corporate pension policy.” We are very grateful to Elizabeth Chuk for the pre-Statements of Financial Accounting Standards 132(R) pension asset allocation data, John Graham for the marginal tax rate data, Lynn LoPucki for the UCLA-LoPucki Bankruptcy Research Database, William Schwert (the editor), Terry Shevlin (the referee), David Hollanders (discussant), Bjorn Jorgensen, Anil Verma, Johanna Weststar, Han Yi (discussant), participants at the Netspar International Pension Research Workshop 2011 and the American Accounting Association 2011 annual meeting, and many colleagues for useful comments. We thank Seokyoun Hwang, Jongkyum Kim, Kaitlin Morecraft, and Gsong Yoo for able research assistance. Part of the research for this paper was funded by 2012 Professional Staff Congress-City University of New York research award. n Corresponding author. Tel.: þ1 973 353 1313. E-mail address: [email protected] (D. Anantharaman).

0304-405X/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jfineco.2013.10.009

sponsoring the plan is required to set aside assets in a trust to fund these obligations, but if the sponsor goes bankrupt with insufficient assets to fund pension obligations, beneficiaries are bound to accept whatever reduced payouts can be made with the assets secured for the plan. Stockholders of firms approaching a state of distress, therefore, have incentives to underfund pension plans. Underfunding plans amounts to promising future benefits without funding them and is effectively a way of increasing leverage by borrowing from employees. These stockholders also have the incentive to make risky investments with plan assets. If the investments pay off and the firm survives, stockholders benefit from having to contribute less into the plan; if they do not pay off and the firm goes bankrupt, beneficiaries suffer. Thinking of beneficiaries as akin to debtholders, these are manifestations of the classic risk shifting incentives that stockholders of all leveraged firms have (Jensen and Meckling, 1976; Myers, 1977). The regulatory environment of corporate defined benefit plans in the United States exacerbates these risk shifting

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incentives. Under the Employee Retirement Income Security Act of 1974 (ERISA), most defined benefit pension plans are insured by the Pension Benefit Guaranty Corporation (PBGC). If a plan sponsor goes bankrupt with an underfunded plan, the PBGC takes over the plan and makes up the funding deficit, up to a limit. This guarantee mutes the incentives of rank-and-file beneficiaries to monitor the management of their plan, fueling the moral hazard problem. Hence, strong reasons exist to expect US defined benefit sponsors to increase plan risk (both by underfunding and by increasing plan asset risk) as they approach distress. The extant empirical evidence in this regard has, however, been surprisingly weak. While some studies find evidence of risk shifting, the majority [most recently, Rauh (2009)] find a negative association between firm risk and pension risk, consistent with risk management (or risk offsetting), not risk shifting. Given the strong theoretical predictions for risk shifting, this conflicting empirical evidence creates a puzzle. We propose one explanation for this puzzle: managerial risk aversion. While diversified stockholders have incentives to increase firm risk at the expense of debtholders, most corporate decision making is in the hands of managers, who prefer less risk than stockholders, out of concern for their reputation, undiversifiable human capital, or private benefits of control. The stockholder-manager conflict on risk could thus offset risk shifting incentives arising from the stockholder–debtholder conflict. Compensation contracting is one of the primary means of altering managerial incentives, and firms vary in the extent to which executive compensation aligns managers’ risk preferences with those of stockholders. Equity-based compensation increases the sensitivity of managers’ wealth to stock price performance (delta), and so aligns managers closer to stockholders, but could also lead managers who are underdiversified in firm-specific wealth to avoid risk. Options add convexity to managers’ payoffs and, by increasing the sensitivity of managerial wealth to firm risk (vega), can offset the risk-avoiding tendencies introduced by delta and by reputation or human capital concerns. If pension funding and investing choices are attributable at least partly to managerial incentives, we would expect to find more risk shifting in firms in which top managers have high vega. Top managers, however, also participate in the broadbased pension plans covered by ERISA (ERISA-qualified plans). If managers are concerned with safeguarding their own pensions, then their vega incentives need not necessarily exacerbate pension risk shifting, making the ultimate effect of managers’ equity incentives an interesting empirical issue. Hence, we first examine how chief executive officer (CEO) equity incentives (vega, option delta, and stock delta) affect risk shifting through pension funding and asset allocation. Pension funding and investing decisions interact closely with core financing and investing decisions. For example, required contributions to pension plans affect resources available for financing other investment opportunities (Rauh, 2006). Decisions on how to invest pension assets affect not only future cash requirements, but also the firm’s overall risk profile. Finally, ERISA-qualified pension plans offer tax-saving opportunities, making their management a key component of firm tax planning. As leaders of the

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finance function, chief financial officers (CFOs) play a key role in budgeting and managing cash flows, risk management, and tax strategy, and they could thus affect pension investing and financing. Hence, we also examine whether CFO equity incentives affect pension choices. We examine pension funding (asset allocation) with a sample of 5,748 (4,398) firm-years spanning 1999–2010. Cross-sectional tests show that firms approaching distress tend to underfund plans, after controlling for operating cash flows. Importantly, the association between firm risk and underfunding is stronger for firms whose CFOs have high vega and low option delta, suggesting that risk shifting behavior is more intense when compensation structures provide risk taking incentives to managers. To account for the endogeneity of compensation, we use lagged incentive variables. Furthermore, we employ firm, manager, and firm and manager fixed effects, to mitigate the effect of unobservable, time-invariant factors. The findings hold strongly in all fixed effects specifications but are stronger throughout for CFO than for CEO incentives. Tests of asset allocation show that firms approaching distress, in general, reduce investment in risky assets such as equities (as opposed to fixed-income investments and cash), indicating risk management and not risk shifting. While this is consistent with prior research, we find that allocations to risky assets increase when firms are not only close to distress but also have poorly funded plans, suggesting risk shifting behavior. Examining the effect of managerial incentives in cross-sectional tests, the association between firm risk and allocation to risky asset classes is again more pronounced for CFOs (but not CEOs) with high vega and low option delta. The effects persist but are only marginally significant within firms and within managers over time. We perform two further tests to establish the causal effect of compensation incentives on pension risk shifting. First, we demonstrate that the underfunding and asset allocation results are robust to instrumental variables estimation, using firm-level variables and industry-median incentives as instruments for CFO incentives, following prior literature (see, e.g., Coles, Daniel, and Naveen, 2006; Chava and Purnanandam, 2007; Liu and Mauer, 2011). Second, we exploit compensation changes brought about by the accounting rule mandating stock option expensing, to obtain relatively exogenous variation in delta and vega. While the asset allocation results are inconclusive, these tests support the causal effect of CFO equity incentives on pension underfunding. Overall, the results indicate that CFO vega incentives intensify risk shifting through pension underfunding strongly. They also intensify risk shifting through pension asset allocation to risky assets, but the effects are modest and less consistently robust. The analysis so far abstracts away from top managers’ own pensions accrued in ERISA-qualified plans, which could mute any risk taking and risk shifting incentives from their equitybased compensation. In additional analyses with a smaller sample, we find not only that pension funding, on average, is stronger, but also that the positive association between firm risk and underfunding is weaker, when top managers’ own pensions are a relatively important component of the pension plan and of their own compensation. These effects are again stronger for the CFO than the CEO. We find, however, that the effects of CFO vega persist even after controlling for managers’

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pension balances, and that they persist in subsamples with low and high executive pension balances. Nevertheless, the countervailing effect of managers’ own pensions, which correlate with stronger funding and lower risk shifting, indicates that managers’ own stake in pension plans is a key driver of pension funding policy that has heretofore been little emphasized in the literature and by regulators. This study makes three contributions. First, Rauh (2009), in the fullest extant study on pension asset allocation, finds that risk management dominates risk shifting on average and concludes that the moral hazard from PBGC insurance is not a dominant influence on corporate pension policy. We extend this interpretation in two ways. First, while we confirm that risk management dominates on average with asset allocation decisions, finding that allocations to risky assets increase in distressed firms with poorly funded plans suggests that some risk shifting exists within the most troubled sponsors. Second, the moral hazard fueled by PBGC insurance still manifests within firms in which manager-stockholder risk preferences are closely aligned. Identifying the firm-specific factors that drive risk shifting behavior is of interest to academics and regulators, especially with the deteriorating state of the PBGC’s finances.1 Second, we identify executive compensation structure as a driver of pension policy, extending prior work on defined benefit pension governance. Cocco and Volpin (2007) show in the UK setting that plans with more insider trustees are riskier. As US pension trustees are solely insiders (i.e., corporate executives), our study probes further into the extent to which these insiders are ultimately aligned with stockholders. Phan and Hegde (2013) examine another aspect of governance. They find that US managers protected by fewer antitakeover measures (and, presumably, subject to stronger stockholder monitoring) make riskier plan investments. Even though we are interested in the association between firm risk and pension risk, our results are broadly consistent in that stronger alignment with stockholders (through compensation contracting) exacerbates risk shifting. In contrast to our study, they find that stronger monitoring of managers promotes judicious risk taking that earns higher returns, which benefits both stockholders (by lowering required contributions) and employees (by improving funding security). The overall finding that top executives’ compensation structure is systematically associated with plan choices suggests a dark side to pension management in practice. Corporate executives, who usually are the trustees of ERISA plans, are charged with the fiduciary duty to manage plans solely in the interest of beneficiaries. Finding that executives’ compensation incentives affect plan outcomes, and specifically that executives’ own stake in plans associates with more funding security and conservative plan management, suggests that not all plan trustees make policy solely with the broad interest of

1 The PBGC insures pensions of 44 million workers in almost 27 thousand US plans. As of September 30, 2011, it was responsible for paying benefits to 873,000 retirees in 4,300 failed plans under its care, had a deficit of $26 billion (the highest in a decade), and had an estimated exposure of $227 billion to underfunded plans in speculative-grade firms, with a “reasonable possibility of termination”.

plan beneficiaries in mind, which appears to be at odds with fiduciary duty. Finally, we contribute to the emerging literature on the relative importance of CFO and CEO incentives for corporate outcomes (Chava and Purnanandam, 2007, 2010; Geczy, Minton, and Schrand, 2007; Jiang, Petroni, and Wang, 2010; Kim, Li, and Zhang, 2011). One consensus from this research is that CFO incentives dominate for decisions requiring specialized financial knowledge and judgment, such as finer aspects of debt structure, financial reporting, hedging, and risk management. The pension plan can be viewed as a longduration, debt-like obligation subject to substantial interest rate and other risks, requiring careful management. Finding that CFO incentives matter more than CEO incentives for pension choices is consistent with the broad theme emerging from this research, while illustrating the CFO’s role in another key area of corporate finance. 2. Background, prior research, and hypothesis development 2.1. Theory on risk shifting in pension plans A defined benefit pension plan creates an obligation similar to long-term debt, with pension beneficiaries akin to debtholders. While the firm sponsoring the plan is required to set aside assets to fund pension obligations as they fall due, beneficiaries are bound to accept whatever payments they can get if the firm goes bankrupt with an underfunded plan. Hence, the firm sponsoring the plan essentially owns the right to sell pension assets to beneficiaries at a price equal to the value of pension liabilities. This contract can be characterized as a put option on the pension assets, written by the beneficiaries, at a strike price equal to the value of the pension liabilities (Sharpe, 1976). Sharpe (1976) and Treynor (1977) show that it is value maximizing for stockholders to increase pension risk to maximize the value of this option, transferring wealth from beneficiaries to stockholders. Firms can increase pension risk by underfunding plans (increasing plan leverage) and by investing plan assets in risky instruments (increasing the plan’s underlying asset risk). Underfunding keeps plan assets low relative to liabilities, increasing the moneyness and so the intrinsic value of the pension put option, while risky investments increase the volatility of the assets on which the option is written, increasing its fair value. The institutional framework governing defined benefit pension plans in the US exacerbates these risk shifting incentives. ERISA requires corporate plans to be insured by the PBGC.2 Distressed sponsors of underfunded plans can apply for a distress termination. That is, if they prove to a bankruptcy court that they cannot remain in business unless the plan is terminated, the PBGC takes over the plan and pays benefits (up to a maximum reset by law annually), using whatever plan assets are available and using PBGC funds to make up deficits.3 As most rank-and-file beneficiaries of 2 With PBGC insurance, the put option is written by the PBGC, while beneficiaries get a certain payoff. 3 The maximum benefit guaranteed in 2009–2010 is $54,000 ($24,300) per annum at age 65 (55).

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corporate defined benefit plans have a substantially guaranteed benefit, they have little incentive to monitor corporate pension policy, aggravating the moral hazard problem. While the PBGC has strong incentives to monitor, it has limited control over the sponsor in solvency, has low priority in bankruptcy, and cannot charge fully riskadjusted insurance premiums. When PBGC insurance premiums are not fully risk-adjusted, it becomes value maximizing for stockholders to increase plan risk, so as to maximize the difference between the value and the cost of the PBGC put option (Sharpe, 1976). The guaranteed pension combined with the absence of risk-adjusted premiums intensifies stockholders’ ability and incentive to engage in risk shifting with the pension plan. Plan sponsors do, however, face some regulatory constraints on risk shifting. First, the PBGC moved in 1986 from a flat-rate insurance premium to a flat plus variable rate per dollar of unfunded benefits. Adjusting premiums for underfunding reduces the value of the PBGC option, but premiums are still not adjusted for the plan sponsor’s creditworthiness or plan asset risk, over which ERISA does not place many restrictions. Second, the PBGC requires mandatory contributions and a minimum funding level, reducing flexibility with funding decisions. However, plan sponsors still enjoyed some discretion over funding. Underfunded sponsors could amortize funding shortfalls over long periods, were not required to make additional contributions when plans were at least 90% funded, and were sometimes exempt from contributing for many years when they had previously contributed above the minimum (Congressional Research Service, 2006). These and other weaknesses in the regulatory framework prompted the passage of the Pension Protection Act of 2006 (PPA), which tightens funding requirements.4 With the progressive tightening of insurance and funding norms, whether distressed plan sponsors still have the incentives and ability to underfund plans becomes an empirical question. 2.2. Empirical evidence on risk shifting in pension plans Stockholders of relatively safe and profitable firms are likely to have little incentive to increase pension risk, compared with firms close to distress. In distressed firms, stockholders reap the benefits (through reduced contributions) if pension risks pay off, whereas beneficiaries lose if they do not, as the plan can be put onto the PBGC in a distress termination. Therefore, if risk shifting considerations are an important driver of pension policy, we would expect distressed firms to take more risk with their plans, compared with safe and healthy firms. Many empirical studies have tested this prediction. Evidence on this association between firm risk and pension funding is mixed. Bodie, Light, Morck, and Taggart (1985) and Coronado and Liang (2003) find that firms close to distress have lower pension funding, consistent with risk shifting, while Friedman (1983), Francis and Reiter (1987), and Petersen (1996) find the opposite. 4 PPA funding rules (phased in from 2008) raise required funding from 90% to 100% of liabilities, require funding shortfalls to be amortized over seven years, and introduce a new concept of at-risk plans that are subject to stricter funding requirements.

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Evidence on the association between firm risk and pension asset allocation is broadly consistent with plan sponsors in poor financial condition reducing allocations to riskier asset classes such as equities, consistent with risk management and not risk shifting behavior (Friedman, 1983; Amir and Benartzi, 1999; Rauh, 2009). As risk shifting theory is often quoted as a main driver of pension risk taking, the lack of empirical evidence to support it remains a puzzle.5

2.3. The effect of managers’ equity incentives on pension policy Well-diversified stockholders prefer managers to accept all positive net present value (NPV) projects available, regardless of risk. But managers, unlike stockholders, have undiversifiable human capital invested in their employers, which they risk losing if the firm goes bankrupt (Amihud and Lev, 1981; Hirshleifer and Thakor, 1992). They also have private benefits of control, such as the ability to extract perquisites, divert corporate resources for personal use, or simply enjoy a quiet life, which again disappear in insolvency (Eckbo and Thorburn, 2003; John, Litov, and Yeung, 2008). The desire to protect their reputations, human capital, and private benefits of control could induce managers to act conservatively. Accordingly, manager-controlled firms are found to be more conservative than stockholder-controlled firms (Amihud and Lev, 1981; Agrawal and Mandelker, 1987; Saunders, Strock, and Travlos, 1990; Eissdorfer, 2008; Laeven and Levine, 2009). Structuring managers’ compensation appropriately can mitigate these conflicts. Increasing the sensitivity of managerial wealth to stock price performance (delta) aligns managers’ interests closer to stockholders’, but it could also make managers so under-diversified that they forgo risky but positive NPV projects. Stockholders can still give a risk-avoiding manager the incentive to take risks by making his wealth a convex function of firm performance, usually by compensating with options (Guay, 1999). Options increase the sensitivity of managerial wealth to firm risk (vega), incentivizing risk taking. A rich literature shows that high vega leads to riskier investing and financing choices, such as more leverage, more research and development, and less diversification (see, e.g., Knopf, Nam, and Thornton, 2002; Rajgopal and Shevlin, 2002; Coles, Daniel, and Naveen, 2006). If managerial incentives affect pension strategy, we would expect risk shifting to be stronger when managers’ compensation structures align their risk taking preferences closer to stockholders’. As firms can increase pension risk either by increasing plan leverage (by underfunding plans) or by increasing plan asset risk (by 5 Rauh (2009) provides one explanation. If managers invest pension assets in risky investments that do not pay off and the firm goes bankrupt, the unfunded liability can be put onto the PBGC. However, if the investments do not pay off but the firm survives, it is left having to make large cash contributions to the plan, which could mean sacrificing other investment opportunities or defaulting on nonpension debt. If these costs are sufficiently high, then risk management could be optimal from stockholders’ perspective.

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investing plan assets in equity-like instead of bond-like instruments), we would expect risk shifting through pension underfunding and asset allocation to be stronger in firms whose managers have high vega. However, the ERISA-qualified pension plans we are interested in typically extend coverage to all employees, from the rank-andfile to top executives. If senior executives have substantial balances accrued under these plans, and if these balances are an economically significant part of their overall compensation, then the desire to secure these balances could first, lead executives to make conservative pension policies and, second, exert a countervailing effect on any risk shifting tendencies.6 These countervailing effects could be particularly pronounced for highly paid top managers, as their promised pension (typically a function of careeraverage or final salary) is likely to exceed the maximum guaranteed by the PBGC. If managers’ decision making is driven by the incentive to safeguard their own pensions, then high vega need not necessarily exacerbate risk shifting. Given these possibilities, the effect of managerial equity incentives on risk shifting becomes an interesting empirical question. We state our hypotheses as follows, in alternative form: Hypothesis 1 (H1). Risk shifting through pension underfunding is stronger in firms whose managers have high vega. Hypothesis 2 (H2). Risk shifting through pension asset allocation to equities is stronger in firms whose managers have high vega. The effects of delta are more ambiguous ex ante, as delta affects the manager’s incentive to maximize stockholder value, as well as his risk aversion. On the one hand, to the extent that high-delta managers are more risk averse, they could choose less risk as they are highly exposed to firm-specific risk in their own portfolios (Coles, Daniel, and Naveen, 2006; Chava and Purnanandam, 2010). On the other hand, if high-delta managers, by virtue of being strongly aligned with stockholders, choose higher NPV projects and these projects tend to be risky, then delta could be positively associated with risk.7 2.4. Who drives pension financing and investing decisions? While the arguments for why compensation incentives affect pension choices are clear, empirically, pension choices will be affected only by incentives of those individuals who have authority over plan management. In the US, the pension trust is fully an asset of the sponsoring corporation, and trustees are typically corporate executives (Cocco and Volpin, 6 Consistent with this intuition, Asthana (2009) finds that plans with more benefits accrued toward highly compensated executives tend to be better funded, on average. Asthana (2009) does not speak to asset allocation, or to risk shifting through underfunding or asset allocation, which is the focus of our study. 7 For example, if plan sponsors have high NPV investment opportunities that require cash (that would otherwise be diverted to fund pension plans) and high-delta managers maximize investments in such projects, then high delta could correspond to greater underfunding, or to higher asset allocation to equities, if managers employ a return-seeking strategy to minimize required cash contributions.

2007).8 An examination of company websites and Securities and Exchange Commission (SEC) filings for a small group of sample firms suggests that pension decisions, including appointment of trustees, are an internal corporate matter, typically the responsibility of a pensions, retirement, or benefits committee that exists at the sub-board level, which often has some review and oversight from the board (Table A1).9 As trustees and internal committee members are likely to report eventually to the sponsor’s senior management, we posit that top managers’ incentives are germane to pension decisions. We first examine CEO incentives, as the CEO has overall responsibility for corporate decisions. However, defined benefit pension liabilities are commonly viewed as a form of long-duration debt and an integral part of the firm’s capital structure (see, e.g., Berner, Boudreau, and Peskin, 2006; Carroll and Niehaus, 1998). Managing various risk exposures from the firm’s debt (in case of pension debt by funding the plan or adjusting the risk profile of plan assets) is a key responsibility of CFOs (Graham and Harvey, 2001). Practitioner surveys also indicate that internal pension committees often include CFOs.10 Hence, we also examine the association of CFO incentives with pension policy. While Chava and Purnanandam (2010) suggest that CEOs delegate finer aspects of corporate policies to CFOs, pension management could be an issue of broader importance, if pension assets and liabilities are economically very significant. Therefore, whether the CEO’s or CFO’s incentives are more important here is

8 Here, US law diverges from UK law, where some trustees are appointed by the plan sponsor while others are appointed by active and retired plan beneficiaries, leading to a mix of senior management and employee-representatives on the board. Cocco and Volpin (2007) characterize the US system, where corporate executives form 100% of the board of trustees, as a limiting case of the UK system. 9 We check SEC filings and websites for one hundred randomly selected sample firms, to understand their pension governance structure, with findings summarized in Table A1. One-third of these firms disclose the existence of an internal retirement or benefits committee in charge of pension plans, mostly at the sub-board level. In total, 61 firms mention plan monitoring in the responsibilities of some board committee: the Finance Committee (12 firms, Group 1), the Compensation Committee (30 firms, Group 2), or both (16 firms, Group 3), and a separate Pensions Committee (3 firms, Group 4). The Finance and Compensation committees do not directly appoint pension trustees but instead are often responsible for appointing the internal committee that then appoints trustees and makes investment decisions. Finance Committee duties usually include reviewing funding and investing, and Compensation Committee duties include reviewing plan design. Overall, these data suggest that direct management of plans (appointing trustees, fund managers, etc.) is the responsibility of internal personnel, with some review at the board level, and that there is board-level review of funding or investing in some firms (Groups 1, 3, and 4) but not others (Group 2). This view of pension governance is supported by anecdotal descriptions of pension management. Kujaca (1996, pp. 63–64) describes: The board of directors “often sets up a committee composed of board members to help oversee the pension fund’s investment activities… They often delegate responsibility to company employees. When such delegation occurs, the pension investment committee takes on an advisory role… To assume the responsibilities delegated by board members, the company will typically establish a committee of senior executives… This committee often includes a very senior person, perhaps even the vice-chairmen of the company.” 10 See, for example, http://www.executiveboard.com/blogs/trendsin-pension-risk-management.

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ultimately an empirical question, and we do not offer predictions on their relative importance. 3. Research design We use the following empirical specification to test for risk shifting behavior: depvar ¼ α0 þ α1  distress þ α2  incentive þ α3  distress incentive þ Σγ j  controlj þ ε

ð1Þ

Analyses are at the firm-year level. We test H1 and H2 with underfunding and %equity respectively as the dependent variable (depvar). Underfunding is measured by (pension liabilities minus fair value of pension assets), divided by pension liabilities at the end of the year, so high underfunding indicates poorly funded plans, and negative underfunding indicates overfunded plans. %equity is the proportion of pension assets invested in equities, as opposed to lower-risk investments such as fixed-income securities or cash. We measure closeness to distress (distress) using the structural default model of Black and Scholes (1973) and Merton (1974) (BSM), which models equity as a call option on the firm’s assets.11 Following the recent implementation of BSM by Campbell, Hilscher, and Szilagyi (2008), we construct for each firm-year a distance-to-default (dd) metric, a measure of the difference between firm asset value and face value of debt, scaled by the standard deviation of asset value. dd represents the number of standard deviations by which firm value must fall for the firm to default. Higher dd corresponds to safer firms. Distress is dd multiplied by  1 and so is increasing in default probability. Incentive captures vega, option delta, and stock delta of CEOs and CFOs, calculated following Core and Guay (2002). To mitigate endogeneity, we use lagged values of all incentives. Vega measures the change in the executive’s personal portfolio in response to a 0.01 change in stock return volatility. As Guay (1999) reports that option vega is much higher than stock vega, we measure overall vega of the CEO and CFO using vega from his option portfolio (lagceov, lagcfov). Delta is the change in the executive’s portfolio in response to a 1% change in stock price. We measure the executive’s delta from his option portfolio (lagceod, lagcfod) and from his stock portfolio (lagceosd, lagcfosd) separately. We estimate four empirical models for each dependent variable. Model 1 contains only distress and control variables. Model 2 includes CEO incentives. In Model 3, we replace CEO incentives with CFO incentives. Model 4 uses both CEO and CFO incentives. This approach mitigates any potential multicollinearity problems arising from correlation between CEO and CFO incentives (Chava and Purnanandam, 2010). If plan sponsors take more risk with plans as they approach distress, we expect the coefficient on distress to be 11 Hillegeist, Keating, Cram, and Lunstedt (2004) show that BSM model is more informative about default than accounting-based measures such as the Altman (1968) Z-score, as BSM incorporates equity market value, which captures information from sources other than financial statements, as well as asset volatility, which is crucial to default prediction. The disadvantages of BSM relate to its simplifying assumptions and to the fact that markets need not efficiently impound all public information about the probability of default.

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positive and significant. If vega exacerbates risk shifting, we expect the coefficient on distress  lagceov or distress  lagcfov, or both, to be positive and significant. Conversely, if delta mitigates risk shifting, the interaction of distress with CEO or CFO delta, or both, will be negative and significant. However, while delta could induce managerial risk aversion, the relation between delta and pension policies can also be an outcome of the incentive-alignment effects of delta. We control for firm size (size) and book-to-market ratio (bm). Lower bm indicates greater investment opportunities, and stockholders in such firms not only grant higher delta and vega (Core and Guay, 1999; Guay, 1999), but also have more to lose from excessive risk taking (see, e.g., Keeley, 1990). We control for the marginal tax rate (mtr) estimated by Graham (1996); high mtr implies stronger incentives to fund plans and invest in higher-taxed fixed-income assets (Black, 1980; Thomas, 1988; Frank, 2002). We control for cash flow from operations (ocf) and standard deviation thereof [s(ocf)], as distressed firms could underfund plans not necessarily to exploit the PBGC option, but simply because they are too cashconstrained to fund them (Coronado and Liang, 2003). Moving from firm to plan-related measures, we control for plan size [the natural logarithm of the fair value of plan assets log(fvpa)] and current and lagged plan asset returns (returns, lagreturns), as returns are strongly associated with allocation to riskier assets (Rauh, 2009). Distressed firms manipulate actuarial assumptions—by choosing a higher discount rate, for example—to improve reported plan funding (e.g., Amir and Gordon, 1996; Asthana, 1999). Hence, in underfunding models, we control for the chosen discount rate (discountrate). The %equity models include additional controls: a crude measure of pension duration (duration), as plans with younger participants (and longer duration) invest more in equities to hedge against future salary increases (Sundaresan and Zapatero, 1997; Rauh, 2009); underfunding, as underfunded plans could have stronger incentives to increase asset risk, and underfunding  distress, as managers of underfunded plans could have even stronger incentives to risk-shift in distressed firms.12 Furthermore, firms can boost earnings by choosing a higher expected rate of return (ERR) assumption on pension assets and by investing in riskier assets to justify the higher ERR. As Bergstresser, Desai, and Rauh (2006) find that firms preparing for acquisitions and firms close to critical earnings thresholds exhibit this behavior, we include indicator variables for each scenario (acquisition, meetbenchmark) in %equity models. We include year- and industry-level fixed effects in all specifications. Table B1 defines variables in detail. 4. Sample and descriptive statistics 4.1. Sample selection The initial sample contains all firm-years with accounting and pension data available from the Compustat Fundamental and Pension annual data files. The calculation of 12 As duration of pension liabilities is not required disclosure, we capture it with the ratio of annual pension service cost to the sum of service cost and interest cost. High service cost plans have more participants actively working and accruing benefits and hence longer duration, compared with low service cost plans.

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distress requires stock return volatility from the Center for Research in Security Prices (CRSP). CEO and CFO incentives are calculated from ExecuComp.13 We drop firms that have less than $1 million in either market capitalization or sales. This results in a sample of 5,784 firm-year observations from 1,030 unique firms, spanning 1999–2010, for the underfunding tests (H1). The sample starts in 1999, as actual returns are required disclosure only from 1998, per Statements of Financial Accounting Standards (SFAS) 132, and we require one year–lagged returns. For asset allocation tests (H2), the sample is 4,398 firm-year observations from 923 unique firms, spanning 1999–2010. Compustat data on asset allocations are available only from 2003, as SFAS 132(R), which required asset allocation disclosure, was effective only for fiscal years ending December 2003 or later. Pre-2003 asset allocations are, however, available for a limited sample of firms, through the Pensions & Investments (P&I) survey of the largest one thousand US plan sponsors, many of which are corporations. We augment Compustat data with P&I data, which we obtain for 1999–2002, and so obtain a longer panel for %equity tests. 4.2. Descriptive statistics Table 1, Panel A, describes dependent variables. All continuous variables are winsorized at 1% and 99%. Mean (median) underfunding is 15.8% (18.4%) of plan liabilities, with interquartile range of 4.7%–30.8%. %equity has mean (median) of 60.3% (63%) and interquartile range of 55%– 69%. Funding status and equity allocations shift downward after 2001 and 2008 (untabulated).14 Panel B (C) describes firm (plan) characteristics. Mean (median) dd is 7.77 (6.95), and the 5th (25th) percentile is 1.88 (4.42); most sample firms hence have low default risk (distress, the measure used in tests, is dd   1). Untabulated statistics show that mean (median) firm assets are $13.7b ($3.7b), and pension assets are $285m ($274m). The mean (median) plan is 14% (9%) of firm assets; pensions are hence economically substantial to corporate balance sheets. Panel D describes executive compensation. The mean (median) CEO vega (lagceov) is $229,000 ($91,000) and CFO vega (lagcfov) is $51,000 ($20,000). The mean (median) CEO option delta is $363,000 ($134,000), compared with $74,000 ($27,000) for CFOs. The mean (median) CEO has lower delta from stock than from options, of $302,000 ($70,000), as does the CFO, with $30,000 ($12,000). Median CEO incentives are four to five times larger than CFO incentives. However, the expectation that CFO incentives affect pension decisions comes not from any expectation that CFOs have high equity incentives as such, but from the fact that pension decisions 13 We identify CEOs with ExecuComp’s annual CEO indicator and CFOs by examining executive titles (TITLEANN) for “CFO,” “chief financial officer,” “finance,” “vice president-finance,” “treasurer,” or “controller.” When more than one executive per firm-year is identified, we use the highest-paid executive. 14 The fact that equity allocations decline following market crashes suggests that rebalancing to target allocations does not happen period by period, due to behavioral inertia, transaction costs, etc. (Rauh, 2009). One possible driver of the shift in %equity is SFAS 158, effects of which we discuss in Section 7.4.

relate more naturally to the CFO’s expertise.15 The CFO-to-CEO incentive ratio also shows considerable variation. If this ratio reflects the CFO’s relative importance, it shows some variation across firms in how important CFOs are (Jiang, Petroni, and Wang, 2010). In tests, we standardize all compensation measures by subtracting the sample mean and scaling by the sample standard deviation, allowing easy assessment and comparisons of economic significance. Table 2 provides correlations between key variables. Underfunded plans invest less in equities, consistent with prior research. Smaller plans in smaller firms that are closer to distress and have volatile cash flows tend to be more underfunded. Surprisingly, cash flows are positively associated with underfunding. Correlations with %equity paint a picture consistent with risk management rather than risk shifting: firms further away from distress, with growth prospects and stable cash flows, invest in equities. All incentives are negatively associated with underfunding, but CEO and CFO vega are positively associated with equity investment. Vega and delta are higher in large firms with growth opportunities, consistent with prior research. CEO and CFO incentives are strongly but not perfectly correlated with each other, with Pearson correlations ranging from 0.31 to 0.68, showing some independent variation in each executive’s incentives. 5. The effect of managers’ equity incentives on pension financing and investing 5.1. The effect of equity incentives on pension funding Table 3 presents tests of H1. Column 1 presents Model 1, the basic underfunding model without incentives. Distress is strongly positively associated with underfunding, consistent with risk shifting. While inconsistent with many prior studies, this is consistent with Coronado and Liang (2003), possibly because they use a BSM-based measure that is closest to distress. The regulatory environment, therefore, still leaves plan sponsors with sufficient discretion to underfund plans when advantageous to do so. In Model 2, we find that all CEO incentives are insignificant, both in the main effect and interacted with distress. In Model 3, however, distress  lagcfov is positive and strongly significant, consistent with the expectation that the positive association between firm risk and underfunding is exacerbated in firms with high CFO vega. Conversely, distress  lagcfod is negative and significant, indicating that risk shifting is attenuated with high CFO option delta. Distress  lagcfosd is marginally positively significant, contrary to the effect of distress  lagcfod.16 15 While CFO incentives appear small relative to CEO incentives, CFO incentives in our sample are similar to or larger than those in recent work (Jiang, Petroni, and Wang, 2010; Chava and Purnanandam, 2010; Kim, Li, and Zhang, 2011) that finds a significant effect of CFO incentives on various corporate outcomes. CFO vega (delta) is also nontrivial with respect to annual CFO cash compensation, 8% (18%) thereof. 16 We interpret the positive coefficient on distress  lagcfosd as consistent with higher inside equity ownership leading to stronger risk shifting, by creating stronger manager-stockholder alignment in general. However, the effect of CFO stock delta in almost every other instance is significantly negative or insignificant, consistent more with high delta inducing conservative managerial choices.

D. Anantharaman, Y.G. Lee / Journal of Financial Economics 111 (2014) 328–351

335

Table 1 Descriptive statistics. This table provides descriptive statistics for dependent variables (Panel A), firm characteristics (Panel B), pension plan characteristics (Panel C), chief executive officers’ (CEOs’) and chief financial officers’ (CFOs’) incentives and compensation structure (Panel D), and their stake in Employee Retirement Income Security Act of 1974 (ERISA)-qualified pension plans (Panel E). All statistics are for the sample of 5,748 firm-year observations over 1999–2010, except distributions of %equity, acquisition, meetbenchmark, and duration, which are for 4,398 observations over 1999–2010, and distributions of underfundingERISA, ceopen/tot, cfopen/tot, ceopen/sal, and cfopen/sal, which are for 2,451 observations over 2006–2010. underfunding is pension liabilities minus the fair value of pension assets, divided by pension liabilities. %equity is the proportion of pension assets invested in equity securities. underfundingERISA is underfunding in ERISA-qualified pension plans. dd is the distance-to-default. size is the natural logarithm of total assets. bm is the book value of equity divided by market value of equity. mtr is the before-financing marginal tax rate. ocf is operating cash flow before pension contributions, scaled by total assets. s(ocf) is the standard deviation of ocf for the current and past four years. acquisition is an indicator for firms involved in mergers and acquisitions during the year. meetbenchmark is an indicator for firms that could have prevented losses with a 50 bps increase in assumed return on pension assets. log(fvpa) is the natural logarithm of fair value of pension assets. discountrate is the discount rate actuarial assumption. returns (lagreturns) are the current (lagged) actual rate of return on pension assets. duration is annual pension service cost divided by the sum of service cost and pension interest cost. lagceov (lagcfov) is CEO (CFO) lagged vega, or the change in the CEO (CFO) option portfolio value for a 0.01 change in stock return volatility. lagceod (lagcfod) is CEO (CFO) lagged option delta, or the change in CEO (CFO) option portfolio value for a 1% change in stock price. lagceosd (lagcfosd) is CEO (CFO) lagged stock delta, or the change in CEO (CFO) stock portfolio value for a 1% change in stock price. CEO and CFO incentives are in thousands of dollars. ceopen/tot (cfopen/tot) is the CEO (CFO) ERISA-qualified pension balance (at-risk portion only) divided by the firm’s total ERISAqualified pension balance. ceopen/sal (cfopen/sal) is the CEO (CFO) estimated annual ERISA-qualified pension payout (at-risk portion only) divided by their current annual base salary. Variable

Median

Standard deviation

5th Percentile

10th Percentile

25th Percentile

75th Percentile

90th Percentile

95th Percentile

Panel A: Dependent variables underfunding 0.158 %equity 0.603 underfundingERISA 0.185

0.184 0.630 0.190

0.247 0.152 0.213

 0.305 0.300  0.149

 0.141 0.420  0.045

0.047 0.550 0.073

0.308 0.693 0.306

0.416 0.750 0.412

0.500 0.790 0.488

Panel B: Firm characteristics dd 7.767 size 8.333 bm 0.731 mtr 0.319 ocf 0.077 s(ocf) 0.041 acquisition 0.155 meetbenchmark 0.004

6.953 8.207 0.751 0.350 0.070 0.032 0.000 0.000

4.657 1.580 0.236 0.078 0.067 0.034 0.362 0.060

1.877 5.895 0.319 0.103  0.017 0.006 0.000 0.000

2.669 6.351 0.392 0.199 0.004 0.010 0.000 0.000

4.418 7.213 0.557 0.345 0.033 0.018 0.000 0.000

10.189 9.429 0.912 0.351 0.114 0.053 0.000 0.000

13.635 10.418 1.009 0.358 0.163 0.082 1.000 0.000

16.813 11.040 1.073 0.371 0.203 0.109 1.000 0.000

Panel C: Pension characteristics log(fvpa) 5.654 5.613 discountrate 0.062 0.060 returns 0.055 0.091 lagreturns 0.049 0.083 duration 0.310 0.311

1.866 0.008 0.129 0.131 0.152

2.462 0.052  0.223  0.227 0.008

3.296 0.054  0.134  0.141 0.106

4.385 0.058  0.019  0.033 0.218

6.984 0.068 0.135 0.135 0.403

8.092 0.075 0.193 0.191 0.500

8.765 0.078 0.226 0.224 0.563

391.21 91.77 0.39 629.67 130.29 0.37 854.88 52.12 0.64

3.42 0.00 0.00 4.63 0.00 0.00 2.10 0.00 0.00

9.15 0.10 0.03 12.32 0.21 0.03 6.13 0.00 0.00

31.61 5.86 0.13 43.84 7.76 0.11 22.53 2.43 0.06

238.91 53.30 0.40 374.09 80.18 0.40 200.04 35.06 0.38

588.27 122.76 0.67 929.49 193.92 0.67 550.38 79.43 0.73

983.48 202.77 0.93 1613.41 300.27 0.95 1295.71 125.16 1.09

0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000

0.028 0.005 5.676 3.930

0.157 0.070 11.980 17.083

0.315 0.149 17.216 24.494

Panel D: Managerial lagceov lagcfov lagcfov/lagceov lagceod lagcfod lagcfod/lagceod lagceosd lagcfosd lagcfosd/lagceosd

Mean

equity incentives 229.21 90.67 50.75 19.87 0.34 0.24 363.15 133.54 74.16 27.09 0.32 0.23 301.65 69.81 30.49 11.69 0.35 0.19

Panel E: Managerial stake in ERISA-qualified pension plans ceopen/tot (%) 0.072 0.000 0.317 cfopen/tot (%) 0.028 0.000 0.109 ceopen/sal (%) 3.966 0.000 8.926 cfopen/sal (%) 4.624 0.000 11.313

Model 4 includes CEO and CFO incentives. While distress  lagceov is negative and marginally significant, contrary to expectation, interactions of CFO vega and CFO option delta with distress remain significant and economically larger than the effects of CEO incentives. Overall, risk shifting through underfunding is stronger when CFOs have high vega, consistent with H1. Coefficients on control variables, broadly similar across models,

are consistent with prior research. The adjusted R2 ranges from 50.9% to 51.4%. To evaluate the economic significance of CFO incentives, consider the slope estimates on distress (0.004), lagcfov (0.024), and distress  lagcfov (0.004) in Model 4. At the 95th percentile of distress ( 1.877), when the firm is relatively close to default, a shift in lagcfov from the 50th percentile (standardized value¼  0.336) to the 95th

336 Table 2 Correlations. This table presents the correlations between the main variables. Pearson (Spearman) correlations are reported below (above) the diagonal. All correlations are computed with the maximum possible observations. Correlations in italics are significant at the 10% level. Correlations in bold are significant at o 5% level. The variable names are represented by numbers due to space limitations. See Table B1 for variable definitions.

underfunding (1) %equity (2) distress (3) size (4) bm (5) mtr (6) ocf (7) s(ocf) (8) log(fvpa) (9) returns (10) lagreturns (11) lagceov (12) lagceod (13) lagceosd (14) lagcfov (15) lagcfod (16) lagcfosd (17) underfundingERISA (18) ceopen/tot (19) cfopen/tot (20) ceopen/sal (21) cfopen/sal (22)

(1)

(2)

(3)

(4)

(5)

(6)

1.00  0.08 0.09  0.15 0.02 0.02  0.08 1.00  0.18  0.04  0.12 0.02 0.04  0.14 1.00  0.05 0.52  0.13  0.13  0.03  0.02 1.00 0.13 0.06 0.01  0.10 0.51 0.12 1.00  0.14  0.04 0.08  0.17 0.12  0.19 1.00 0.04 0.03  0.24  0.15  0.48 0.18 0.07  0.06 0.01  0.31  0.16  0.09  0.33 0.06  0.07 0.70  0.05 0.04  0.18 0.15  0.27 0.05  0.13 0.02  0.23 0.09  0.17 0.04  0.04 0.02  0.05 0.05  0.16 0.41  0.16 0.11  0.04 0.06  0.17 0.37  0.21 0.12  0.04  0.01  0.09 0.19  0.05 0.08  0.07 0.04  0.17 0.38  0.17 0.10  0.06 0.04  0.18 0.34  0.23 0.10  0.03 0.01  0.09 0.35  0.12 0.08 0.98  0.07 0.27  0.20 0.07 0.02  0.13 0.11  0.03 0.15 0.08  0.01  0.12 0.07  0.04 0.12  0.04  0.01  0.15 0.09  0.05 0.25 0.07 0.02  0.14 0.05  0.05 0.18  0.05 0.01

(7)

(8)

0.05 0.04  0.24  0.15  0.50 0.14 1.00 0.20  0.14 0.00  0.03 0.07 0.14 0.04 0.07 0.14 0.08 0.08  0.13  0.05  0.13  0.03

0.09  0.04 0.06  0.37  0.18  0.02 0.21 1.00  0.23 0.00  0.03  0.08  0.05  0.04  0.09  0.07  0.08 0.14  0.16  0.12  0.17  0.12

(9)

(10)

(11)

(12)

(13)

 0.32  0.14  0.25  0.03  0.03 0.03 0.19 0.14 0.07 0.07  0.11  0.23  0.19  0.25  0.28 0.70 0.08 0.07 0.57 0.51  0.04  0.10  0.05  0.23  0.32 0.05 0.02  0.01 0.12 0.12  0.12 0.00  0.04 0.12 0.21  0.23  0.01  0.04  0.14  0.09 1.00 0.13 0.11 0.45 0.37 0.10 1.00  0.06 0.03  0.01 0.07  0.12 1.00 0.02 0.08 0.31 0.02 0.01 1.00 0.90 0.25  0.02 0.07 0.87 1.00 0.07  0.01 0.04 0.16 0.24 0.32 0.01 0.03 0.68 0.58 0.26  0.03 0.08 0.60 0.66 0.25  0.04 0.05 0.22 0.27  0.28  0.18  0.23  0.07  0.10 0.19 0.03 0.03 0.09 0.06 0.20 0.03 0.06 0.06 0.05 0.34 0.04 0.04 0.15 0.12 0.29 0.04 0.07 0.10 0.09

(14)

(15)

 0.04  0.02 0.00 0.04 -0.15  0.23 0.42 0.45  0.16  0.22 0.13 0.11 0.10 0.12  0.08  0.13 0.22 0.35 0.00 0.02 0.06 0.03 0.43 0.70 0.51 0.64 1.00 0.35 0.09 1.00 0.15 0.89 0.31 0.37  0.08  0.09 0.11 0.06 0.04 0.12 0.12 0.12 0.06 0.15

(16)

(17)

(18)

(19)

(20)

(21)

(22)

 0.02  0.03 0.96  0.03  0.06  0.09  0.07 0.04  0.03  0.03  0.06 0.01 0.02 0.03  0.24  0.11 0.20 0.05 0.05  0.04  0.03 0.40 0.42  0.21  0.05  0.07 0.11 0.09  0.29  0.13 0.06 0.12 0.08 0.05  0.02 0.10 0.09  0.06  0.02  0.06 0.01  0.01 0.20 0.08 0.05  0.11  0.05  0.08  0.02  0.08  0.12 0.10  0.07  0.02  0.10  0.05 0.28 0.29  0.30  0.15  0.15 0.17 0.18  0.01 0.01  0.24 0.01  0.01 0.01 0.01 0.08 0.06  0.17  0.01 0.01 0.01 0.04 0.61 0.34  0.08  0.07  0.08 0.02 0.02 0.70 0.40  0.09  0.07  0.08  0.01 0.01 0.39 0.55  0.04  0.01  0.03 0.02  0.02 0.92 0.59  0.07  0.07  0.06 0.03 0.04 1.00 0.63  0.08  0.07  0.05 0.01 0.04 0.45 1.00  0.08  0.05  0.02 0.03 0.08  0.10  0.11 1.00  0.07  0.09  0.08  0.17 0.04 0.08  0.14 1.00 0.40 0.36 0.14 0.13 0.17  0.14 0.42 1.00 0.26 0.43 0.08 0.13  0.16 0.95 0.45 1.00 0.51 0.16 0.20  0.14 0.41 0.97 0.48 1.00

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Variable

D. Anantharaman, Y.G. Lee / Journal of Financial Economics 111 (2014) 328–351

337

Table 3 Do managerial equity incentives affect risk shifting through pension underfunding? Cross-sectional tests. This table presents regressions of pension funding status on financial distress and managerial equity incentives. The dependent variable underfunding is pension liabilities minus pension assets, divided by pension liabilities. distress is distance-to-default multiplied by minus 1. lagceov (lagcfov) is CEO (CFO) lagged vega, or the change in the CEO (CFO) option portfolio value for a 0.01 change in stock return volatility. lagceod (lagcfod) is CEO (CFO) lagged option delta, or the change in CEO (CFO) option portfolio value for a 1% change in stock price. lagceosd (lagcfosd) is CEO (CFO) lagged stock delta, or the change in CEO (CFO) stock portfolio value for a 1% change in stock price. CEO and CFO incentives are standardized by subtracting the sample mean and scaling by the sample standard deviation. All coefficients on CEO and CFO incentives are multiplied by one thousand for ease of presentation. Control variables are defined in Table B1. All models include industry fixed effects based on two-digit Standard Industrial Classification codes and year fixed effects. Robust tstatistics adjusted for firm-level clustering are reported in parentheses. nnn, nn, and n denote significance at the 1%, 5%, and 10% level respectively. (1) Variable distress lagceov [  1,000] lagceod [  1,000] lagceosd [  1,000] distress  lagceov [  1,000] distress  lagceod [  1,000] distress  lagceosd [  1,000] lagcfov [  1,000] lagcfod [  1,000] lagcfosd [  1,000] distress  lagcfov [  1,000] distress  lagcfod [  1,000] distress  lagcfosd [  1,000] size bm mtr ocf s(ocf) log(fvpa) discountrate returns lagreturns Adjusted R2 N

Coefficient 0.005

0.057  0.007  0.034  0.135  0.094  0.077  3.640  0.438  0.335 0.509 5,748

(2) t-Statistic (4.17)nnn

(8.85)nnn (  0.29) (  0.86) (  2.11)nn (  0.70) (  14.85)nnn (  3.54)nnn (  15.39)nnn (  12.92)nnn

(3)

Coefficient

t-Statistic

0.004  1.590  11.500  11.000 0.105  1.400 0.356

(4.02)nnn (  0.11) (  0.83) (  1.24) (0.10) (  1.28) (0.34)

0.060  0.012  0.027  0.144  0.101  0.078  3.546  0.440  0.334 0.512 5,748

percentile (standardized value ¼1.657) translates into an increase in underfunding of 3.3%.17 At the 95th percentile of distress, a 50th–95th percentile shift in CFO vega, accompanied by a 95th–50th percentile shift in CFO delta, translates into a 6.9% increase in underfunding. 5.2. Addressing endogeneity with fixed effects specifications of underfunding Delta and vega could be determined by unobserved firmspecific factors (e.g., monitoring technology, culture) and manager-specific factors (e.g., personality, risk tolerance, human capital) that also drive pension policies. In fact, Coles and Li (2010) find that manager and firm fixed effects together account for about 78% (66%) of the explained variation in delta (vega). Therefore, in Table 4, we replicate Table 3 but with fixed effects at the firm level (Panel A) and at the manager level and firm-and-manager (i.e., for each unique combination of firm and manager) level (Panel B). As expected, fixed effects add considerable explanatory power 17 The predicted value of underfunding at the 95th percentile of distress and the 50th percentile of lagcfov is {0.004   1.877þ 0.024   0.336þ0.004   1.877   0.336}¼  1.3%. Predicted value of underfunding at the 95th percentile of distress and the 95th percentile of lagcfov is {0.004   1.877þ 0.024  1.657þ 0.004   1.877  1.657} ¼ 2%. This gives rise to the 2% minus  1.3% ¼ 3.3% increase in underfunding.

(9.20)nnn (  0.48) (  0.67) (  2.24)nn (  0.75) (  15.02)nnn (  3.44)nnn (  15.61)nnn (  13.00)nnn

Coefficient

(4) t-Statistic

0.005

(4.17)nnn

13.900  22.700  1.490 2.350  3.270 1.120 0.060  0.013  0.032  0.140  0.097  0.077  3.476  0.439  0.336 0.512 5,748

(1.06) (  1.75)n (  0.22) (2.33)nn (  3.27)nnn (1.69)n (9.26)nnn (  0.49) (  0.80) (  2.16)nn (  0.72) (  14.80)nnn (  3.38)nnn (  15.55)nnn (  12.96)nnn

Coefficient

t-Statistic

0.004  18.900 2.130  10.200  2.540 0.697 0.263 24.000  21.500 0.223 4.020  3.610 0.982 0.062  0.015  0.028  0.146  0.103  0.078  3.432  0.439  0.332 0.514 5,748

(4.03)nnn (  1.10) (0.12) (  1.14) (  1.92)n (0.51) (0.26) (1.50) (  1.32) (0.03) (3.01)nnn (  2.68)nnn (1.74)n (9.50)nnn (  0.58) (  0.71) (  2.27)nn (  0.77) (  15.06)nnn (  3.34)nnn (  15.63)nnn (  12.98)nnn

in all models. The main and interactive effects of CFO vega and option delta remain strongly significant throughout. Interestingly, distress  lagceov becomes significant with firm fixed effects but not with manager fixed effects. CEO incentives, therefore, appear to have some effect, but not consistently across specifications. Overall, these models alleviate to some degree the concerns of omitted, time-invariant, firmor manager-specific factors driving the results. 5.3. The effect of equity incentives on asset allocation to risky asset classes Table 5 presents results of testing H2. Model 1, in contrast to underfunding models, shows that distressed firms invest less in equities, consistent with risk management (Rauh, 2009). Poorly funded plans, however, invest more in equities. The interactive term distress  underfunding is positive and strongly significant, indicating that while distressed firms in general invest less in equities, distressed firms with poorly funded plans invest more in equities.18 Firm and plan status hence appear to interact, 18 With Model 1 coefficients, a 50th–95th percentile shift in distress translates to a 3.5% decrease in %equity for a very overfunded firm at the 5th percentile of underfunding (  30.5%). This change obtains from: {  0.003   1.877þ0.15   0.305 þ0.013   1.877   0.305}  {  0.003   6.953 þ0.15   0.305 þ0.013   6.953   0.305} ¼  3.5%.

338

Table 4 Do managerial equity incentives affect risk shifting through pension underfunding? Fixed effects tests. This table presents regressions of pension funding status on financial distress and managerial equity incentives. The dependent variable underfunding is pension liabilities minus pension assets, divided by pension liabilities. distress is distance-to-default multiplied by minus 1. lagceov (lagcfov) is CEO (CFO) lagged vega, or the change in the CEO (CFO) option portfolio value for a 0.01 change in stock return volatility. lagceod (lagcfod) is CEO (CFO) lagged option delta, or the change in CEO (CFO) option portfolio value for a 1% change in stock price. lagceosd (lagcfosd) is CEO (CFO) lagged stock delta, or the change in CEO (CFO) stock portfolio value for a 1% change in stock price. CEO and CFO incentives are standardized by subtracting the sample mean and scaling by the sample standard deviation. All coefficients on CEO and CFO incentives are multiplied by one thousand for ease of presentation. Control variables are defined in Table B1. All models in Panel A include firm fixed effects and year fixed effects. In Panel B, columns 1–3 include manager and year fixed effects, while columns 4–5 include firm-and-manager fixed effects (i.e., for each unique combination of firm and manager) and year fixed effects. FE denotes fixed effects. t-Statistics based on robust standard errors are reported in parentheses. nnn, nn, and n denote significance at the 1%, 5%, and 10% level, respectively. Panel A: Firm fixed effects tests (1)

distress lagceov [  1,000] lagceod [  1,000] lagceosd [  1,000] distress  lagceov [  1,000] distress  lagceod [  1,000] distress  lagceosd [  1,000] lagcfov [  1,000] lagcfod [  1,000] lagcfosd [  1,000] distress  lagcfov [  1,000] distress  lagcfod [  1,000] distress  lagcfosd [  1,000] size bm mtr ocf s(ocf) log(fvpa) discountrate returns lagreturns Adjusted R2 N

Coefficient

t-Statistic

0.002

0.074 0.008 0.007 0.033  0.173  0.204  4.388  0.371  0.267 0.634 5,748

Coefficient

nnn

(3.46)

t-Statistic (3.65) (2.01)nn ( 2.42)nn ( 3.83)nnn (2.30)nn ( 2.23)nn ( 2.81)nnn

t-Statistic nnn

0.002

22.000 27.300  2.090 2.340  2.470 0.198 0.077 0.003 0.008 0.038  0.174  0.205  4.374  0.372  0.264 0.636 5,748

(13.61)nnn (0.19) (0.48) (1.19) ( 2.59)nnn ( 34.32)nnn ( 9.53)nnn ( 20.31)nnn ( 15.18)nnn

0.078 0.002 0.010 0.036 0.170 0.206 4.309 0.373 0.265 0.636 5,748

(4)

Coefficient

nnn

0.002 13.900 16.100 15.300 1.410 1.340 0.875

(13.09)nnn (0.67) (0.34) (1.08) (  2.63)nnn (  34.13)nnn (  9.69)nnn (  20.19)nnn (  15.27)nnn

(3)

(3.55)

(3.14)nnn (  3.95)nnn (  0.58) (3.69)nnn (  3.84)nnn (0.62) (13.51)nnn (0.25) (0.39) (1.24) (  2.65)nnn ( 34.26)nnn (  9.67)nnn ( 20.22)nnn (  15.12)nnn

Coefficient

t-Statistic

0.002 1.970 2.630 14.700 0.174 0.158 0.929 18.700  23.400 1.180 2.000 2.120 0.426 0.079 0.001 0.009 0.037 0.168 0.206 4.326 0.373 0.264 0.636 5,748

(3.63)nnn (0.23) (  0.32) (  3.55)nnn (0.22) (  0.21) (  2.89)nnn (2.17)nn (  2.77)nnn (0.32) (2.43)nn (  2.58)nn (1.29) (13.71)nnn (0.05) (0.45) (1.22) (  2.56)nn (  34.39)nnn (  9.57)nnn (  20.25)nnn (  15.09)nnn

Panel B: Manager fixed effects and firm-and-manager fixed effects tests (1) CEO FE Variable distress lagceov [  1,000] lagceod [  1,000] lagceosd [  1,000] distress  lagceov [  1,000] distress  lagceod [  1,000] distress  lagceosd [  1,000] lagcfov [  1,000]

Coefficient 0.002 12.600 16.800 4.740 0.633 0.891 1.010

(2) CFO FE t-Statistic nnn

(3.33) (1.83)n ( 2.60)nnn ( 0.96) (1.05) ( 1.54) ( 3.05)nnn

Coefficient 0.002

18.700

(3) CFO FE t-Statistic nnn

(4.07)

(2.60)nnn

Coefficient 0.002  2.330  5.280  11.300  0.163  0.252  0.434 18.100

(4) CEO-firm FE t-Statistic nnn

(4.09) (  0.27) (  0.62) (  2.49)nn (  0.21) (  0.32) (  1.31) (2.03)nn

Coefficient 0.002 13.100  17.100 4.610 0.724 0.974 0.993

(5) CFO-firm FE

t-Statistic nnn

(3.13) (1.93)n ( 2.67)nnn ( 0.95) (1.22) ( 1.71)n ( 3.03)nnn

Coefficient

t-Statistic

0.002  2.330  8.890  10.400 0.185  0.502  0.491 19.400

(2.78)nnn ( 0.27) ( 1.08) ( 2.41)nn (0.25) ( 0.68) ( 1.54) (2.29)nn

D. Anantharaman, Y.G. Lee / Journal of Financial Economics 111 (2014) 328–351

Variable

(2)

lagcfod [  1,000] lagcfosd [  1,000] distress  lagcfov [  1,000] distress  lagcfod [  1,000] distress  lagcfosd [  1,000] size bm mtr ocf s(ocf) log(fvpa) discountrate returns lagreturns Adjusted R2 N

0.057 0.002 0.012 0.053 0.173 0.166 5.754 0.413 0.261 0.584 5,748

(9.41)nnn ( 0.19) ( 0.60) (1.79)n ( 2.51)nn ( 29.31)nnn (  12.73)nnn ( 23.23)nnn ( 15.32)nnn

 25.600  2.310 1.730  2.170 0.290 0.057 0.004 0.005 0.036  0.171  0.138  5.023  0.418  0.275 0.538 5,748

( 3.59)nnn ( 0.59) (2.77)nnn ( 3.40)nnn (0.88) (9.25)nnn (0.29) (0.22) (1.17) ( 2.42)nn (  28.79)nnn (  10.73)nnn (  23.48)nnn (  16.01)nnn

20.400 0.176 1.670  1.790 0.383 0.059 0.002 0.005 0.038  0.170  0.138  4.986  0.420  0.276 0.538 5,748

(  2.30)nn (0.04) (2.05)nn (  2.18)nn (1.12) (9.48)nnn (0.13) (0.24) (1.24) (  2.41)nn ( 28.82)nnn (  10.65)nnn ( 23.59)nnn (  16.06)nnn

0.072 0.000 0.014 0.050 0.186 0.197 5.739 0.393 0.252 0.590 5,748

(10.78)nnn (0.04) ( 0.67) (1.68)n ( 2.72)nnn ( 30.32)nnn (  12.79)nnn ( 22.09)nnn ( 14.98)nnn

 18.300 0.056 1.820  1.770 0.408 0.071 0.005  0.001 0.040  0.075  0.196  5.442  0.386  0.252 0.562 5,748

( 2.16)nn (0.01) (2.36)nn ( 2.26)nn (1.27) (10.24)nnn (0.34) ( 0.06) (1.38) ( 1.06) (  29.56)nnn ( 12.19)nnn (  22.37)nnn (  15.42)nnn

D. Anantharaman, Y.G. Lee / Journal of Financial Economics 111 (2014) 328–351

339

and some evidence exists of risk shifting behavior, but only within the most troubled plan sponsors. The moral hazard effect of PBGC insurance is likely to be strongest in such firms, as sponsors have a realistic option to put the plan onto the PBGC only when they are distressed and have underfunded plans that they are unable to continue supporting. In Model 2, the effects of CEO vega are insignificant. In Models 3 and 4, however, CFO vega (option delta) interacted with distress is positively (negatively) significant, both with and without controls for CEO incentives. Therefore, even though the association between distress and %equity is negative on average, it becomes more positive for firms with high-vega CFOs, indicating that risk management is mitigated (and risk shifting is stronger) with high vega. Overall, the positive association between CFO vega and risk shifting through pension asset allocation is consistent with H2. To understand the economic significance of CFO incentives, consider the coefficients on distress ( 0.003), lagcfov (0.026), and distress  lagcfov (0.002) in Model 4. At the 95th percentile of distress (  1.877), a shift in lagcfov from the 50th to 95th percentile (standardized value ¼  0.336 to 1.657) translates into a 4.2% increase in %equity.19 Among control variables, firms with volatile cash flows invest less in equities, consistent with risk management. High tax rate firms invest more in equities, contrary to the tax arbitrage theory (e.g., Black, 1980) but suggesting that profitable firms (which tend to have high mtr) invest in equities. Longer-duration plans invest more in equities. Investment returns are strongly positively associated with %equity, as in Rauh (2009), suggesting that managers do not rebalance portfolios when equity markets are performing well. The earnings management indicators acquisition and meetbenchmark are insignificant. The adjusted R2 ranges from 16.9% to 17.2%.20 5.4. Addressing endogeneity with fixed effects specifications of asset allocation Table 6 presents %equity models with firm fixed effects (Panel A), and manager fixed effects and firm-andmanager fixed effects (Panel B). While CEO incentives are insignificant, CFO incentives show some effects, albeit not (footnote continued) The same shift in distress translates into a 1.8% increase in %equity for a very underfunded firm at the 95th percentile of underfunding (50%). 19 Another way to understand the economic significance of CFO incentives is to consider the effect on %equity from a given shift in distress, for a low-vega, high-delta firm and for a high-vega, low-delta firm. A 50th-95th percentile shift in distress translates into a 0.9% decrease in %equity for a firm at (25th percentile of CFO vega, 75th percentile of CFO delta), and reverses to a 0.5% increase in %equity for a firm at (75th percentile of CFO vega, 25th percentile of CFO delta), indicating a shift from risk management to risk shifting. The online Appendix C provides step-by-step calculations of these effects. 20 We replicate %equity models with a broader definition of risky assets, which includes equities, real estate, and “other” assets (which are often alternative assets such as hedge funds, private equity, venture capital, and real assets). The proportion of plan assets in other assets has been increasing steadily over the sample period. We find substantially similar results to those documented and so do not tabulate them.

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D. Anantharaman, Y.G. Lee / Journal of Financial Economics 111 (2014) 328–351

Table 5 Do managerial equity incentives affect risk shifting through pension asset allocation? Cross-sectional tests. This table presents regressions of pension asset allocation on financial distress and managerial equity incentives. The dependent variable %equity is the proportion of pension assets invested in equity securities. distress is distance-to-default multiplied by minus 1. underfunding is pension liabilities minus pension assets, divided by pension liabilities. lagceov (lagcfov) is CEO (CFO) lagged vega, or the change in the CEO (CFO) option portfolio value for a 0.01 change in stock return volatility. lagceod (lagcfod) is CEO (CFO) lagged option delta, or the change in CEO (CFO) option portfolio value for a 1% change in stock price. lagceosd (lagcfosd) is CEO (CFO) lagged stock delta, or the change in CEO (CFO) stock portfolio value for a 1% change in stock price. CEO and CFO incentives are standardized by subtracting the sample mean and scaling by the sample standard deviation. All coefficients on CEO and CFO incentives are multiplied by one thousand for ease of presentation. Control variables are defined in Table B1. All models include industry fixed effects based on two-digit Standard Industrial Classification codes and year fixed effects. Robust t-statistics adjusted for firm-level clustering are reported in parentheses. nnn, nn, and n denote significance at the 1%, 5%, and 10% level respectively. (1) Variable distress underfunding distress  underfunding lagceov [  1,000] lagceod [  1,000] lagceosd [  1,000] distress  lagceov [  1,000] distress  lagceod [  1,000] distress  lagceosd [  1,000] lagcfov [  1,000] lagcfod [  1,000] lagcfosd [  1,000] distress  lagcfov [  1,000] distress  lagcfod [  1,000] distress  lagcfosd [  1,000] size bm mtr ocf s(ocf) log(fvpa) duration returns lagreturns acquisition meetbenchmark Adjusted R2 N

Coefficient  0.003 0.150 0.013

 0.015  0.004 0.117 0.009  0.379 0.010 0.074 0.155 0.067 0.004  0.024 0.169 4,398

(2)

(3)

t-Statistic

Coefficient

t-Statistic

(  2.62)nnn (4.10)nnn (4.23)nnn

 0.003 0.146 0.013  0.986 7.640  15.300 0.332  0.189  1.200

(  2.54)nn (3.99)nnn (4.15)nnn (  0.10) (0.87) (  1.86)n (0.36) (  0.23) (  1.74)n

(  2.62)nnn (  0.18) (3.12)nnn (0.16) (  2.70)nnn (1.97)nn (2.34)nn (4.38)nnn (2.26)nn (0.54) (  1.07)

 0.016 0.001 0.118 0.003  0.373 0.010 0.074 0.156 0.066 0.004  0.026 0.171 4,398

very robust. In firm fixed effects models (Panel A), distress  lagcfov is positive and marginally significant at 10%, but only when controlling for CEO incentives (Model 4). In Panel B, distress  lagcfov is always positive and significant at 10% or less, with p-values between 4–6%, and distress  lagcfod is negative and marginally significant at the 10% level. Overall, higher CFO vega does correspond to greater risk shifting, but the effects are marginal at best, within firms over time. 5.5. 2SLS estimation of funding status and asset allocation tests To further address the endogeneity of compensation incentives, we perform two-stage least squares (2SLS) estimation of funding status and asset allocation models, with second-stage results tabulated in Table 7. We rely on prior literature for instruments for CFO delta and vega: CFO age, CFO tenure, and firm age (Coles, Daniel, and Naveen, 2006; Liu and Mauer, 2011), and industry-median incentives (Chava and Purnanandam, 2007) under the assumption that the unobserved aspect of pension risk

(  2.53)nn (0.04) (3.13)nnn (0.05) (  2.68)nnn (1.93)n (2.35)nn (4.43)nnn (2.22)nn (0.54) (  1.12)

Coefficient

(4) t-Statistic

Coefficient

t-Statistic

 0.003 0.149 0.013

(  2.53)nn (4.08)nnn (4.19)nnn

18.600  16.900 0.816 2.050  1.960  0.299  0.016  0.001 0.120 0.004  0.374 0.010 0.073 0.158 0.069 0.004  0.025 0.169 4,398

(1.46) (  1.51) (0.15) (2.02)nn (  2.16)nn (  0.67) (  2.71)nnn (  0.06) (3.18)nnn (0.08) (  2.67)nnn (2.00)nn (2.32)nn (4.49)nnn (2.32)nn (0.55) (  1.08)

 0.003 0.146 0.013  14.000 23.300  17.200  0.977 1.250  1.220 25.600  29.200 6.310 2.420  2.500 0.028  0.016 0.002 0.120 0.001  0.369 0.009 0.073 0.163 0.070 0.004  0.026 0.172 4,398

(  2.48)nn (3.98)nnn (4.09)nnn (  1.17) (2.21)nn (  1.99)nn (  0.89) (1.20) (  1.73)n (1.74)n (  2.17)nn (1.01) (1.95)n (  2.22)nn (0.06) (  2.64)nnn (0.09) (3.19)nnn (0.02) (  2.64)nnn (1.90)n (2.31)nn (4.64)nnn (2.39)nn (0.51) (  1.12)

shifting does not have an industry component.21 We instrument interactions of distress with CFO incentives with the product of distress and aforementioned instruments (Wooldridge, 2002). As shown in Table 7, the Hausman test rejects the consistency of ordinary least squares (OLS), indicating that instrumental variables estimation is required. While we do not tabulate the six first stage results for brevity, the highly

21 We perform some tests to evaluate this assumption. As our main coefficient of interest is distress  lagcfov, the particular concern is industry-related factors that are correlated both with CFO vega and with the relation of distress to pension choices. One such factor is productmarket competition, which affects compensation (e.g., Cunat and Guadalupe, 2005) and also increases risk management, by increasing the costs of financial distress (Purnanandam, 2008). Similarly, growth opportunities affect compensation and also make firms less likely to riskshift. We rerun the baseline tests with controls for competition and, more important, with the interaction of distress with competition. While the baseline model already controls for growth opportunities (bm), we also interact bm with distress. The additional controls are insignificant, and baseline results on CFO incentives are almost unchanged. Results are detailed in the online Appendix C.

D. Anantharaman, Y.G. Lee / Journal of Financial Economics 111 (2014) 328–351

significant (at o1% level) p-value shown from the underidentification test suggests that the instruments are relevant in the first stage. As we have more instruments than endogenous regressors, we also run the Hansen J-test of over-identifying restrictions. As shown, we fail to reject the null hypothesis that the surplus instruments are uncorrelated with the error term from the second stage, adding some confidence about instrument validity. Even though coefficient magnitudes in 2SLS estimation are much smaller than in OLS, the fitted value of distress  cfov (distress  cfod) is highly positively (negatively) significant, consistent with the baseline results. 5.6. Using exogenous variation in incentives from the passage of SFAS 123(R) The Financial Accounting Standards Board (FASB) issued SFAS 123(R), requiring firms to expense stock option grants, in 2004. Expensing stock options reduces current earnings, and the requirement to expense grants was widely believed to reduce firms’ incentives to use them to compensate employees. Consistently, option grants as a proportion of total compensation decreased, and restricted stock grants increased, over the period during which the rule went into effect (Carter, Lynch, and Tuna, 2007). We use the change in vega and delta occurring around the passage of SFAS 123(R) to obtain exogenous variation in these incentives, to identify causal effects, following Chava and Purnanandam (2010). We compute the change in CFO vega and delta over 2002 to 2006, and make the identifying assumption that the change in incentives over this period that occurred in response to the rule is exogenous to pension policies.22 We first check if CFO incentives change significantly. The statistics reported in Table 8, Panel A, confirm that the average CFO portfolio vega decreases significantly post-SFAS 123(R), while stock delta increases significantly, consistent with a shift from options to stock grants. The number of new options granted, as well as the vega of new grants, also decreases significantly. We then estimate the following model, with a reduced set of controls (size, bm, and ln(fvpa)) to minimize loss of sample (Δ indicates the change over 2002–2006): Δdepvar ¼ β0 þ β1  Δdistress þβ2  Δincentive þ β3 Δdistress  Δincentive þ Σδj  Δcontrolj þ m

ð2Þ

Panel B presents results of the underfunding and %equity models, run on the sample of 396 and 145 firms, respectively, with available data. In the underfunding model, Δdistress is insignificant, but Δcfov and Δdistress  Δcfov are both positive and significant at the 10% level or less, consistent with expectation. In the %equity model, however, cfov is insignificant, in both the main and interactive effects, even though signs are positive as expected. The requirement for data to calculate changes over 2002–2006 reduces the sample size drastically, so, while these results complement the main 22 We choose 2006 as the post-SFAS 123(R) period as the standard was effective for fiscal years beginning after June 15, 2005—i.e., the fiscal year 2006 and onward for most firms. We choose 2002 as the pre-SFAS 123(R) comparison period [similar to Chava and Purnanandam (2010)], to allow for the fact that many firms voluntarily started to expense stock options prior to the actual mandate.

341

tests by more cleanly identifying the effects of CFO incentives, they are subject to the caveat of low testing power. They buttress causal inference of the effect of CFO incentives on risk shifting through pension underfunding, but are inconclusive with respect to asset allocation. 6. The effect of managers’ own stake in pension plans on risk shifting The ERISA-qualified pension plans we are interested in typically extend coverage to most or all employees, from rankand-file employees to top managers. If top managers have balances accrued under these plans, and if these balances are a significant part of their overall compensation, then their actions could be affected by the desire to safeguard their own pensions. Top managers, in particular, have much to lose if a pension plan is put on to the PBGC, as their promised pension (typically a function of salary over their final years at the firm) is likely to exceed the maximum annual pension payout guaranteed by the PBGC. Therefore, if top managers’ incentives to secure their own pensions affect pension policy, we would expect risk shifting through pension underfunding and asset allocation to be weaker when top managers have large pension balances. To test this prediction, we construct two alternative measures of the importance of top managers’ own pensions. First, we measure the size of the CEO’s or CFO’s own pension balance, relative to the overall pension plan balance (ceopen/ tot, cfopen/tot). Second, we attempt to capture the importance of the pension in the managers’ own compensation package. To do so, we first estimate the annual pension payout expected post-retirement from each executive’s accrued pension balance (with the procedure described in Table B1) and scale it by the executive’s current salary (ceopen/sal, cfopen/ sal).23 In both measures, we specifically capture the at-risk portion of the pension (i.e., pensions in excess of the maximum guaranteed by the PBGC) in the numerator. We then augment our baseline models by incorporating each of these two measures in turn, and interacting them with distress. This analysis requires data on CEOs’ and CFOs’ individual balances in pension plans, which are required disclosure only for fiscal years ending December 2006 or after, per Regulation S-K. Therefore, the sample used in this analysis is reduced to observations from 2006 and later.24 We identify CEO or CFO pension balances in 23 We transform the accumulated pension balance into an estimated annual payout to obtain a flow measure of pensions, which we can then compare with salary. While estimating importance of pensions in the executive’s compensation package, we compare them with salary, because one objective of pension plans is to provide the executive a replacement salary in retirement, and because scaling pension payouts by total (cash- and equity-based) compensation induces a mechanical correlation with delta and vega incentives. 24 We also use post-Regulation S-K disclosures to produce a cleaner measure of underfunding related only to ERISA plans (underfundingERISA). Defined benefit pensions for top executives of US firms come from two distinct sources. First, these executives participate in the broad-based, tax-qualified, ERISA-regulated pension plans in which substantially all firm employees participate. These plans are our focus. Second, many firms accrue additional benefits for top executives through Supplemental Executive Retirement Plans (SERPs). SERPs are outside the ambit of ERISA, are not subject to any requirements, restrictions, or guarantees, and are

342

Table 6 Do managerial equity incentives affect risk shifting through pension asset allocation? Fixed effects tests. This table presents regressions of pension asset allocation on financial distress and managerial equity incentives. The dependent variable %equity is the proportion of pension assets invested in equity securities. distress is distance-to-default multiplied by minus 1. underfunding is pension liabilities minus pension assets, divided by pension liabilities. lagceov (lagcfov) is CEO (CFO) lagged vega, or the change in the CEO (CFO) option portfolio value for a 0.01 change in stock return volatility. lagceod (lagcfod) is CEO (CFO) lagged option delta, or the change in CEO (CFO) option portfolio value for a 1% change in stock price. lagceosd (lagcfosd) is CEO (CFO) lagged stock delta, or the change in CEO (CFO) stock portfolio value for a 1% change in stock price. CEO and CFO incentives are standardized by subtracting the sample mean and scaling by the sample standard deviation. All coefficients on CEO and CFO incentives are multiplied by one thousand for ease of presentation. Control variables are defined in Table B1. All models in Panel A include firm fixed effects and year fixed effects. In Panel B, columns 1–3 include manager fixed effects and year fixed effects, while columns 4–5 include firm-and-manager (i.e., for each unique combination of firm and manager) fixed effects and year fixed effects. FE denotes fixed effects. t-Statistics based on robust standard errors are reported in parentheses. nnn, nn, and n denote significance at the 1%, 5%, and 10% level respectively. Panel A: Firm fixed effects tests

Variable distress underfunding distress  underfunding lagceov [  1,000] lagceod [  1,000] lagceosd [  1,000] distress  lagceov [  1,000] distress  lagceod [  1,000] distress  lagceosd [  1,000] lagcfov [  1,000] lagcfod [  1,000] lagcfosd [  1,000] distress  lagcfov [  1,000] distress  lagcfod [  1,000] distress  lagcfosd [  1,000] size bm mtr ocf s(ocf) log(fvpa) duration returns lagreturns acquisition meetbenchmark Adjusted R2 N

(2)

Coefficient  0.001 0.083 0.009

0.007  0.036  0.007 0.019  0.121  0.011  0.047 0.120 0.059  0.006  0.002 0.088 4,398

t-Statistic

Coefficient

nn

(  2.07) (3.24)nnn (4.05)nnn

t-Statistic ( 1.98) (3.27)nnn (3.99)nnn ( 0.52) (1.13) (0.28) (0.01) ( 0.49) ( 0.34)

0.002 0.028 0.007 0.011 0.129 0.010 0.046 0.121 0.054 0.005 0.002 0.086 4,398

(4)

Coefficient

nn

0.001 0.084 0.009 4.030 8.700 1.340 0.009 0.331 0.121

(0.89) (  2.28)nn (  0.30) (0.52) (  1.50) (  1.26) (  2.05)nn (5.15)nnn (2.82)nnn (  1.20) (  0.07)

(3)

(0.31) ( 1.75)n ( 0.29) (0.29) ( 1.59) ( 1.15) ( 1.98)nn (5.16)nnn (2.58)nnn ( 1.09) ( 0.10)

t-Statistic nn

 0.001 0.083 0.009

(  2.04) (3.24)nnn (3.97)nnn

4.510 2.550  6.300 1.010  0.892  0.384 0.006  0.031  0.006 0.015  0.126  0.011  0.044 0.121 0.055  0.005  0.002 0.084 4,398

(0.54) (0.31) (  1.56) (1.38) (  1.23) (  1.11) (0.82) (  1.90)n (  0.27) (0.41) (  1.56) (  1.30) (  1.93)n (5.15)nnn (2.62)nnn (  1.08) (  0.08)

Coefficient

t-Statistic

 0.001 0.085 0.009 12.200 13.600 4.030  0.983 0.513 0.014 12.700  5.980  6.680 1.590  1.240  0.368 0.004  0.027  0.008 0.012  0.132  0.011  0.043 0.122 0.054  0.005  0.002 0.082 4,398

( 2.06)nn (3.32)nnn (4.03)nnn ( 1.25) (1.41) (0.80) ( 1.09) (0.58) (0.04) (1.21) ( 0.59) ( 1.58) (1.65)n ( 1.30) ( 1.03) (0.47) ( 1.67)n ( 0.31) (0.31) ( 1.62 ( 1.24) ( 1.88)n (5.20)nnn (2.56)nn ( 1.04) ( 0.10)

Panel B: Manager fixed effects and firm-and-manager fixed effects tests

Variable distress underfunding distress  underfunding

Coefficient  0.003 0.089 0.009

(1)

(2)

(3)

(4)

(5)

CEO FE

CFO FE

CFO FE

CEO-firm FE

CFO-firm FE

t-Statistic nnn

(  3.33) (3.18)nnn (3.93)nnn

Coefficient  0.002 0.068 0.007

t-Statistic nn

( 2.23) (2.45)nn (3.14)nnn

Coefficient  0.002 0.073 0.008

t-Statistic nn

( 2.29) (2.63)nnn (3.25)nnn

Coefficient  0.002 0.097 0.009

t-Statistic nnn

( 2.94) (3.49)nnn (3.89)nnn

Coefficient  0.002 0.070 0.007

t-Statistic ( 2.80)nnn (2.49)nn (3.10)nnn

D. Anantharaman, Y.G. Lee / Journal of Financial Economics 111 (2014) 328–351

(1)

(0.07) ( 0.93) ( 0.42) (0.15) ( 1.74)n ( 0.93) ( 2.16)nn (5.72)nnn (3.03)nnn ( 1.37) (0.01) 0.001  0.016  0.010 0.006  0.159  0.009  0.053 0.139 0.064  0.007 0.000 0.079 5,748 (  0.79) (  0.09) (  0.36) (0.16) (  1.93)n (  2.08)nn (  2.15)nn (5.94)nnn (3.06)nnn (  1.22) (  0.01)  0.007  0.002  0.009 0.006  0.176  0.019  0.053 0.144 0.065  0.006 0.000 0.048 5,748

lagceov [  1,000] lagceod [  1,000] lagceosd [  1,000] distress  lagceov [  1,000] distress  lagceod [  1,000] distress  lagceosd [  1,000] lagcfov [  1,000] lagcfod [  1,000] lagcfosd [  1,000] distress  lagcfov [  1,000] distress  lagcfod [  1,000] distress  lagcfosd [  1,000] size bm mtr ocf s(ocf) log(fvpa) duration returns lagreturns acquisition meetbenchmark Adjusted R2 N

2.800 4.600 9.060  0.038  0.291 0.117

(0.35) (0.59) (1.55) (  0.05) (  0.43) (0.29)

6.120 0.731  0.898 1.420  1.270  0.127  0.020  0.004 0.022 0.042  0.097 0.017  0.003 0.117 0.055  0.005  0.022 0.096 5,748

(0.71) (0.09) ( 0.20) (1.93)n ( 1.72)n ( 0.37) ( 2.35)nn ( 0.20) (0.90) (1.10) ( 1.04) (2.59)nnn ( 0.12) (4.87)nnn (2.58)nn ( 1.07) ( 0.81)

 10.500 17.400 4.030  0.949 0.869  0.060 13.600  10.500  1.330 2.000  1.860  0.094  0.021  0.003 0.021 0.036  0.099 0.018  0.003 0.121 0.056  0.005  0.023 0.094 5,748

( 1.03) (1.70)n (0.66) ( 1.04) (0.95) ( 0.14) (1.24) ( 0.98) ( 0.29) (2.06)nn ( 1.93)n ( 0.26) ( 2.47)nn ( 0.14) (0.87) (0.93) ( 1.06) (2.60)nnn ( 0.14) (5.04)nnn (2.61)nnn ( 1.05) ( 0.84)

3.580 3.270 7.790  0.048  0.213 0.150

(0.45) (0.42) (1.34) ( 0.07) ( 0.32) (0.38)

 5.690 12.600 5.300  0.695 0.698 0.046 9.390  5.950  1.780 1.820  1.700  0.172 0.001  0.023 0.004 0.034  0.052  0.019  0.048 0.127 0.060  0.008  0.023 0.051 5,748

( 0.56) (1.25) (0.90) ( 0.78) (0.78) (0.11) (0.87) ( 0.57) ( 0.40) (1.91)n ( 1.79)n ( 0.49) (0.14) ( 1.33) (0.15) (0.89) ( 0.55) ( 1.80)n ( 1.93)n (5.28)nnn (2.82)nnn ( 1.71)n ( 0.88)

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ERISA-qualified pension plans manually from proxy statements. From Table 1, Panel E, the average CEO’s (CFO’s) atrisk pension is 0.07% (0.03%) of the total pension balance, and the average annual at-risk payout is 4% (4.6%) of current salary. Medians are all zero. From Table 2, pension funding is stronger and equity investment is higher when CEO and CFO pensions are larger. Table 9, Panel A, presents underfunding results. In CEOonly models (columns 1–2), distress  ceopen/sal is negative and significant, indicating that risk shifting through underfunding is mitigated when the pension is a more important part of the CEO’s compensation. In CFO-only models (columns 3–4), cfopen/tot is negative and significant, consistent with Asthana (2009), as also is cfopen/sal. Both measures are also negative and significant when interacted with distress. Therefore, pension funding is stronger, and risk shifting through underfunding is weaker, when the CFO’s pension is larger. With both CEO and CFO pension measures together (columns 5–6), only CFO measures remain significant.25 In %equity models (Panel B), both CEO and CFO pension measures are insignificant. Across all models, the effects of CFO equity incentives remain significant and broadly consistent with Tables 3–6, suggesting that the effect of CFO pensions is largely independent of the effect of CFO equity incentives. As CFO pensions directly affect risk shifting, any increase in CFO vega might not necessarily exacerbate risk shifting when CFO pension balances are large. We examine this conjecture in Table 10, which presents results of estimating the funding and asset allocation models from Table 9, separately within subsamples of low and high cfopen/tot.26 In underfunding models, distress  lagcfov (distress  lagcfod) is significantly positive (negative) in both subsamples. In %equity models, distress  lagcfov is positive but insignificant in both subsamples. The insignificant results might be attributable to reduced testing power from the smaller samples. Overall, the effects of CFO vega do not appear to be significantly different across subsamples of low versus high CFO pensions, suggesting

(footnote continued) typically unfunded. The pension obligation reported on corporate balance sheets includes both types of balances. As Regulation S-K requires disclosure of balances plan by plan for all Named Executive Officers (NEOs), we are able to remove the aggregated NEO SERP balance from total reported pension obligations, to construct underfundingERISA. All Section 6 tests are run with underfundingERISA instead of underfunding. However, executives other than NEOs could have SERP balances, which we are unable to remove. In additional tests, we replicate the results in Tables 3–6 with similar measures of underfunding from only ERISA plans. We approximate this in the pre-2006 sample by assuming that the relative proportions of the total pension obligation from ERISA plans versus SERPs stay constant over time. These results are presented in the online Appendix C. 25 Consider coefficients from Column 5 on distress (0.004), cfopen/tot (  0.037), and distress  cfopen/tot (  0.003). At the 95th percentile of distress (  1.433), a shift in cfopen/tot from the 50th percentile (standardized value¼  0.259) to the 90th percentile (standardized value ¼ 0.378) translates into a 2% decrease in underfunding, while a shift to the 95th percentile (standardized value¼ 1.111) translates into a 4.5% decrease, indicating large effects for the small subsample of plans dominated by top executive balances. 26 Results of partitioning by cfopen/sal are very similar and, hence, are not tabulated.

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Table 7 Do managerial equity incentives affect pension risk shifting? Instrumental variables (IV) estimation. This table presents the second-stage results from a two-stage least squares estimation of funding status and asset allocation tests, with CFO incentives cfov, cfod, and cfosd instrumented in the first stage. First-stage estimates are not provided for brevity. Instruments are industry medians (based on two-digit Standard Industrial Classification codes for each year) of these incentives as well as firm age (number of years the firm has been in Compustat), CFO tenure, and CFO age. CFO tenure and age are from ExecuComp, with missing values supplemented by hand-collection from proxies. We instrument distress  cfov, distress  cfod, and distress  cfosd with the product of distress and aforementioned instruments. All coefficients on (unstandardized) CFO incentives are multiplied by one thousand for ease of presentation. See Table B1 for variable definitions. Robust t-statistics adjusted for heteroskedasticity are reported in parentheses next to the coefficient estimates. nnn, nn, and n denote significance at the 1%, 5%, and 10% level respectively, based on two-tailed t-tests. The null hypothesis in the Hausman test is that ordinary least squares estimates are consistent; rejection of the null indicates that IV techniques are required. The null hypothesis in the under-identification test is that the instruments are uncorrelated with the endogenous regressors. The null hypothesis in the over-identifying restrictions test is that the instruments are jointly valid, i.e., uncorrelated with the error term from the second-stage regression. Dependent variable ¼ underfunding

Dependent variable ¼ %equity

Variable

Coefficient

t-Statistic

distress

0.006 0.682

(5.14)nnn (1.61)

 0.002 0.775

(  1.65)n (2.22)nn

 0.804  0.087 0.105  0.081  0.006

(  3.00)nnn (  0.27) (2.55)nn (  3.24)nnn (  0.28)

0.068  0.043  0.032  0.140  0.071  0.076  3.354  0.424  0.329

(10.41)nnn (  1.92)n (  1.04) (  2.68)nnn (  0.86) (  26.82)nnn (  4.87)nnn (  13.11)nnn (  10.39)nnn

 0.375 0.198 0.059  0.049 0.019 0.157 0.014  0.031 0.039 0.128  0.050  0.333 0.012

(  1.47) (0.88) (1.96)nn (  2.30)nn (1.12) (5.66)nnn (4.99)nnn (  5.65)nnn (1.92)n (3.96)nnn (  1.00) (  3.65)nnn (3.81)nnn

0.167 0.064 0.076 0.008  0.033

(4.61)nnn (1.98)nn (3.84)nnn (1.19) (  1.50)

Fitted value of: cfov [  1,000] cfod [  1,000] cfosd [  1,000] distress  cfov [  1,000] distress  cfod [  1,000] distress  cfosd [  1,000] underfunding distress  underfunding size bm mtr ocf s(ocf) log(fvpa) discountrate returns lagreturns duration acquisition meetbenchmark R2 p-Value for Hausman test for endogeneity p-Value for under-identification test: Kleibergen-Paap LM statistic p-Value for over-identifying restrictions test: Hansen J-statistic N

that while CFO pensions directly affect risk shifting, the effect of CFO equity incentives is not necessarily conditional on CFO pensions. Overall, the finding that pension funding is more secure, and that risk shifting through underfunding is mitigated, when executives—particularly the CFO—have large unsecured stakes in the plans, is striking for at least two reasons. First, ERISA limits pensions for any individual (to $195,000 in 2010). While this caps the economic significance of ERISA pensions relative to total executive compensation, the cross-sectional variation in the at-risk portion is still substantial enough to generate significant effects. Second, executives, who are the trustees of ERISA plans, have a fiduciary responsibility to manage plan assets in beneficiaries’ best interest. Even though we find that asset allocation is not associated with executives’ own stake in the plans, finding that plans are better-funded overall and less subject to risk shifting concerns when executives’ own stakes are larger suggests that selfinterest affects plan funding, to some degree.

0.499 0.07 0.00 0.13 5,606

Coefficient

t-Statistic

0.128 0.00 0.00 0.21 4,345

7. Robustness checks, additional tests, and discussion of findings We perform the following robustness tests and additional analyses, results of which are tabulated in the online Appendix C. 7.1. The relative importance of CFO versus CEO incentives: validity checks The effect of CEO incentives on pension choices is weak at best, while CFO incentives exert a robust effect. As there was no affirmative requirement to disclose CFO compensation until Regulation S-K, CFO incentives were available only for firms in which the CFO is one of the five highestpaid officers. As this could bias the sample toward firms in which the CFO is more important, we rerun the tests for all firms with CEO incentives available, without requiring CFO incentives. CEO vega remains insignificant. Hence, while we cannot rule out the possibility that the CFOs in our

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Table 8 Do managerial equity incentives affect pension risk shifting? Changes around Statements of Financial Accounting Standards (SFAS) 123(R). Panel A presents a comparison of CFO incentives between the pre-SFAS 123(R) period (2002) and post-SFAS 123(R) period (2006) for the sample used in the regression analysis. Panel B presents regression results of changes in pension underfunding and pension equity investment on changes in managerial equity incentives. The prefix Δ indicates the change in the variable between 2002 and 2006. In Panel A, cfo_grant is the number of options granted to CFOs; cfov_ng is the vega of new grants to CFOs; and cfod_ng is the option delta of new grants to CFOs. CFO incentives in Panel B are standardized by subtracting the sample mean and scaling by the sample standard deviation. See Table B1 for definitions of other variables. Robust t-statistics adjusted for heteroskedasticity are reported in parentheses. nnn, nn, and n denote significance at the 1%, 5%, and 10% level respectively. Panel A: Comparison of CFO incentives between the pre- and post-SFAS 123(R) periods

Variable

Pre-SFAS 123(R) (N¼ 396) Mean

cfov cfod cfosd cfo_grant cfov_ng cfod_ng

75.76 81.62 29.27 66.09 19.56 16.56

Post-SFAS 123(R) (N¼ 396) Mean 48.90 90.22 50.55 40.87 7.18 14.32

Difference ( ¼ Post–Pre)

t-Statistic for difference

 26.86 8.60 21.28  25.22  12.38  2.24

(  3.44)nnn (0.93) (4.12)nnn (  4.71)nnn (  6.08)nnn (  1.13)

Panel B: Regression results Dependent variable ¼Δunderfunding Variable intercept Δdistress Δcfov [  1,000] Δcfod [  1,000] Δcfosd [  1,000] Δdistress  Δcfov [  1,000] Δdistress  Δcfod [  1,000] Δdistress  Δcfosd [  1,000] Δsize Δbm Δlog(fvpa) Δunderfunding Δdistress  Δunderfunding Adjusted R2 N

Coefficient  0.016  0.001 40.910  31.620  23.370 3.760  3.190  1.390 0.086 0.121  0.231

0.253 396

sample are more powerful than average, the lack of importance of CEO incentives, at least, is not an artifact of requiring CFO data.27 We run a further placebo test, replacing CEO or CFO incentives with average incentives of all other NEOs, and find no effect. This adds confidence that CFO effects manifest due to CFOs’ role in managing pension plans, as opposed to being driven by firm-level correlated omitted variables, sample selection biases, or other spurious factors. 7.2. Interpreting the relative importance of CFO versus CEO incentives One key finding in recent literature is that CFO incentives are particularly important for finer aspects of capital 27 After the advent of SEC’s Regulation S-K, firms are required to disclose CFO compensation. Therefore, if sample selection bias exists, it should predominate in the pre-Regulation S-K sample. When running all tests separately within subperiods of the sample, CEO incentives do not affect risk shifting significantly in the predicted direction in any subperiod. CFO incentives affect risk shifting significantly both in the 2003– 2005 subperiod (which is pre-Regulation S-K) and the 2006–2010 subperiod (which is post-Regulation S-K). These results are available in the online Appendix C. We conclude that the relative importance of CFO incentives is unlikely to be driven by sample selection biases.

t-Statistic (  1.09) (  0.95) (2.14)nn (  1.49) (  2.18)nn (1.87)n (  1.35) (  1.28) (4.10)nnn (3.43)nnn (  8.86)nnn

Dependent variable ¼ Δ%equity Coefficient 0.042 0.001 25.640  2.780  5.640 2.870  1.850  2.120  0.050 0.145 0.007  0.200  0.028 0.002 145

t-Statistic (1.43) (0.48) (0.81) (  0.07) (  0.41) (0.85) (  0.50) (  1.40) (  1.34) (1.75)n (0.11) (  1.13) (  1.22)

structure: CFO (as opposed to CEO) incentives matter most for debt-maturity choice (Chava and Purnanandam, 2010) and the proportion of floating-to-fixed rate debt (Chava and Purnanandam, 2007), both of which are important aspects of interest rate risk management. Survey evidence further suggests that risk management activities, such as the hedging of interest rate and foreign currency risks through derivatives, as well as active trading based on a view of the market, are the CFO’s domain (Geczy, Minton, and Schrand, 2007). CFO incentives also dominate in financial reporting choices (Jiang, Petroni, and Wang, 2010; Ge, Matsumoto, and Zhang, 2011; Kim, Li, and Zhang, 2011). One broad theme emerging from these findings is that CFOs play a key role in decisions involving specialized financial knowledge and judgment.28 Defined benefit pensions are commonly viewed (by academics, credit rating agencies, and CFOs themselves) as a long-duration, “high-risk form of debt whose interest

28 This is not to imply that CFOs do not play a role in other decisions. While Chava and Purnanandam (2010) find that CEO incentives dominate in broader decisions (e.g., determining overall leverage), other research (Bertrand and Schoar, 2003; Frank and Goyal, 2007) finds that CFO fixed effects provide as much explanatory power, if not more, as CEO fixed effects, even in such broader financing and investing choices.

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Table 9 Do top executives’ own pensions affect pension risk shifting? This table presents results of testing whether executives’ own pensions from Employee Retirement Income Security Act of 1974 (ERISA) plans affect risk shifting through underfunding and asset allocation in these plans. The dependent variable in Panel A (Panel B) is underfundingERISA (%equity). underfundingERISA is underfunding in ERISA-qualified plans only, defined in the same way as underfunding, but using total pension liability balance minus the aggregated pension balance in non-ERISA plans for all Named Executive Officers. ceopen/tot (cfopen/tot) is the CEO (CFO) ERISA pension balance (at-risk portion only) scaled by the firm’s total ERISA pension balance. ceopen/sal (cfopen/sal) is the CEO (CFO) estimated annual ERISA pension payout (at-risk portion only) scaled by current annual salary. CEO and CFO equity incentives and pension measures are standardized by subtracting the sample mean and scaling by the sample standard deviation. All coefficients on CEO and CFO equity incentives and pension measures are multiplied by one thousand for ease of presentation. See Table B1 for definitions of other variables. All models use industry fixed effects based on two-digit Standard Industrial Classification codes and year fixed effects. Coefficient estimates on control variables, year, and industry fixed effects are not reported for brevity. Robust t-statistics adjusted for firm-level clustering are reported in parentheses. nnn, nn, and n denote significance at the 1%, 5%, and 10% level respectively, based on two-tailed t-tests. Panel A: Do executives’ pensions affect risk shifting through pension underfunding? (Dependent variable ¼underfundingERISA) (2)

(3)

Variable

Coefficient

t-Statistic

Coefficient

t-Statistic

distress lagceov lagceod lagceosd distress  lagceov distress  lagceod distress  lagceosd ceopen/tot distress  ceopen/tot ceopen/sal distress  ceopen/sal lagcfov lagcfod lagcfosd distress  lagcfov distress  lagcfod distress  lagcfosd cfopen/tot distress  cfopen/tot cfopen/sal distress  cfopen/sal Controls Adjusted R2 N

0.004 4.800  14.400  4.480 0.101  1.190  0.009  10.461  0.838

(2.83)nnn (0.36) (  1.14) (  0.56) (0.10) (  1.23) (  0.01) (  1.17) (  0.64)

0.004 15.200  18.100  2.960 1.020  1.590 0.154

(2.88)nnn (1.21) (  1.46) (  0.36) (1.04) (  1.70)n (0.23)

 16.964  1.863

(  1.54) (  2.21)nn

Coefficient 0.004

22.700  26.500  8.150 1.790  2.880 0.324  32.014  2.793

Yes 0.414 2,474

Yes 0.414 2,457

(4) t-Statistic

Coefficient

(2.90)nnn

(5) t-Statistic (2.95)nnn

0.004

(1.73)n (  1.96)nn (  0.93) (1.85)n (  2.83)nnn (0.47) (  3.78)nnn (  2.35)nn

Yes 0.423 2,474

26.100  26.600  11.200 2.250  2.960 0.173

(1.98)nn (  1.96)nn (  1.28) (2.37)nn (  2.94)nnn (0.26)

 14.870  2.077 Yes 0.418 2,466

(  1.81)n (  3.14)nnn

(6)

Coefficient

t-Statistic

0.004  21.500 8.210  3.900  2.400 1.400  0.164 8.776 1.307

(2.87)nnn (  1.32) (0.53) (  0.51) (  1.75)n (1.08) (  0.29) (1.16) (1.00)

37.600  31.800  7.600 3.580  3.850 0.399  37.024  3.411

(2.28)nn (  1.94)n (  0.82) (2.56)nn (  2.77)nnn (0.56) (  3.95)nnn (  2.62)nnn

Yes 0.422 2,474

Coefficient

t-Statistic

0.004  7.670 3.500  0.143  1.110 0.883 0.095

(2.89)nnn (  0.48) (0.23) (  0.02) (  0.85) (0.70) (0.16)

 9.783  0.829 34.400  29.900  11.400 3.330  3.670 0.169

(  0.92) (  0.84) (2.06)nn (  1.81)n (  1.24) (2.42)nn (  2.65)nnn (0.24)

 11.220  1.683 Yes 0.417 2,451

(  1.72)n (  2.31)nn

Panel B: Do executives’ pensions affect risk shifting through pension asset allocation? (Dependent variable¼ %equity) (1) Variable underfundingERISA distress underfundingERISA  distress lagceov lagceod lagceosd

Coefficient 0.185  0.002 0.014 11.400 12.000  12.600

(2) t-Statistic nnn

(3.51) (  1.40) (2.85)nnn (0.87) (1.12) (  1.46)

Coefficient 0.192  0.002 0.014 1.400 15.600  12.600

(3) t-Statistic nnn

(3.69) (  1.42) (2.87)nnn (0.11) (1.37) (  1.47)

Coefficient 0.187  0.002 0.014

(4) t-Statistic nnn

(3.53) (  1.44) (2.77)nnn

Coefficient 0.181  0.002 0.014

(5) t-Statistic nnn

(3.45) (  1.36) (2.68)nnn

Coefficient 0.193  0.002 0.015  5.850 28.400  12.600

(6) t-Statistic nnn

(3.63) (  1.41) (2.89)nnn (  0.30) (1.93)n (  1.41)

Coefficient

t-Statistic

0.192  0.002 0.014  21.900 34.700  13.400

(3.67)nnn (  1.29) (2.80)nnn (  1.29) (2.31)nn (  1.49)

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(1)

(1.03) (1.04) 6.741 0.809 Yes 0.164 2,311

(  0.68) (  1.10) (1.87)n (  1.90)n (0.34) (2.31)nn (  1.55) (  0.33)  7.110  0.911 35.500  31.700 2.900 3.350  2.190  0.228

(0.37) (0.22) 2.435 0.141 Yes 0.153 2,326

Yes 0.166 2,333

(1.81)n (  1.24) (  0.17) (1.96)n (  1.38) (  0.79) (  0.45) (  0.70)  4.369 0.478

Yes 0.153 2,333 Yes 0.162 2,317

(  0.15) (0.59) (  1.63)

Yes 0.164 2,333

distress  lagceov distress  lagceod distress  lagceosd ceopen/tot distress  ceopen/tot ceopen/sal distress  ceopen/sal lagcfov lagcfod lagcfosd distress  lagcfov distress  lagcfod distress  lagcfosd cfopen/tot distress  cfopen/tot cfopen/sal distress  cfopen/sal Controls Adjusted R2 N

0.490 0.443  1.320  2.228 2.004

(0.46) (0.45) (  1.66) (  0.20) (1.20)

 0.151 0.596  1.250

26.300  15.800  1.810 2.040  1.280  0.549 8.085 0.862

(1.83)n (  1.22) (  0.23) (1.98)nn (  1.35) (  0.81) (1.25) (0.63)

25.800  16.100  1.350 1.990  1.300  0.531

 1.440 1.700  1.210  10.747 1.152

28.400  29.200 3.480 2.820  1.960  0.153 15.815 1.176

(  0.88) (1.16) (  1.41) (  0.71) (0.62)

(1.44) (  1.75)n (0.40) (1.84)n (  1.41) (  0.22) (1.35) (0.77)

(  1.74)n (1.43) (  1.39)  2.610 2.100  1.190

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rate is often beyond the company’s direct control” (CFO Research Services, 2011, p. 7; Towers Watson, 2010). Accompanying such a long-term debt-like obligation are many of the issues described above. For example, movements in interest rates cause fluctuations in estimated present values of plan liabilities, in turn creating uncertainty in cash outlays needed to meet funding requirements. Some firms use derivatives to hedge these risks; others borrow when interest rates are low, fund the plan, and invest in fixed-income investments matched by duration to plan liabilities, to immunize the plan from interest rate and equity market exposure. All these decisions involve financial knowledge and fit naturally within the scope of areas where CFOs have been shown to be important. Furthermore, the many discretionary choices of assumptions in pension accounting—which we expect CFOs to be responsible for, as part of their overall responsibility for financial reporting—interact closely with funding and asset allocation decisions. Therefore, viewing pensions as an integral component of capital structure, our findings are consistent with the emerging consensus that CFOs play a key role in managing the more technical, complex aspects of debt structure and the many risk exposures thereof. 7.3. Who else matters in pension governance? The role of other potential trustees If there is board-level oversight of pension policy, then the equity incentives of the board could also affect pension choices. We run all tests incorporating the average equity ownership of all outside board members, and interactions thereof with distress. Inferences on CEO and CFO incentives are unchanged, while board equity ownership and its interaction with distress are both insignificant, suggesting that board ownership does not drive pension decisions in practice. However, the smaller sample, and lack of data to estimate delta and vega incentives for directors, makes these inferences only tentative and an interesting area for future research. Employee beneficiaries are also potentially important actors in pension governance. While individual beneficiaries are limited in their ability to monitor, employees organized into unions could emphasize funding security. We capture union coverage at the industry level (union) and repeat all tests controlling for union, distress  union, and separately within subsamples of high and low union. While union and distress  union are not significant, we find the interesting result that the effect of CFO incentives on risk shifting through pension underfunding (but not on %equity) persists only when union coverage is low. If union presence indicates external monitoring by an organized workforce, the effect of CFO incentives on funding is driven by firms in which such external monitoring is weak.

7.4. Accounting and pension regulation shifts over the sample period The sample is set in a period of changing pension reporting rules. First, SFAS 132(R) (effective 2003) required sponsors to

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Table 10 Do large chief financial officer (CFO) pension balances mitigate the effect of CFO equity incentives? This table presents the specification from column 3 of Table 9, estimated separately on subsamples with low and high cfopen/tot respectively. cfopen/tot is the CFO’s own Employee Retirement Income Security Act of 1974 (ERISA) pension balance (at-risk portion only) scaled by the firm’s total ERISA pension balance. underfundingERISA is defined in the same way as underfunding, but using total pension liability balance minus the aggregated pension balance in non-ERISA plans for all Named Executive Officers. CFO equity incentives and pension measures are standardized by subtracting the sample mean and scaling by the sample standard deviation. All coefficients on CFO equity incentives and pension measures are multiplied by one thousand for ease of presentation. See Table B1 for definitions of other variables. All models use industry fixed effects based on two-digit Standard Industrial Classification codes and year fixed effects. Coefficient estimates on control variables, year, and industry fixed effects are not reported for brevity. Robust t-statistics adjusted for firm-level clustering are reported in parentheses. nnn, nn, and n denote significance at the 1%, 5%, and 10% level respectively, based on two-tailed t-tests. Coefficients on cfopen/tot and distress  cfopen/tot are not estimable for the low cfopen/tot subsample, as cfopen/tot is uniformly zero for this subsample. Dependent variable ¼ %equity

Dependent variable ¼ underfundingERISA Low cfopen/tot Variable underfundingERISA distress underfundingERISA  distress lagcfov [  1,000] lagcfod [  1,000] lagcfosd [  1,000] distress  lagcfov [  1,000] distress  lagcfod [  1,000] distress  lagcfosd [  1,000] cfopen/tot [  1,000] distress  cfopen/tot [  1,000] Controls Adjusted R2 N

Coefficient

t-Statistic

High cfopen/tot Coefficient

0.004

(2.56)nn

0.004

33.914  34.733 1.624 2.253  3.070 1.722 – – Yes 0.419 1,661

(2.17)nn (  1.89)n (0.14) (1.86)n (  2.45)nn (1.62) – –

31.568  34.196  24.504 4.040  5.338  1.447  54.243  0.676 Yes 0.615 813

disclose asset allocation, albeit crudely. As nondisclosure of asset allocation previously allowed sponsors to use higher ERRs than justified by underlying assets, sponsors responded by increasing allocation to high expected return assets, to better justify their ERRs in the more transparent regime post2003 (Chuk, 2013). Hence, in %equity models, we control for each sponsor’s “unexplained ERR” (portion of past ERR unexplained by asset allocations) as estimated by Chuk (2013), which predicts asset allocation shifts around SFAS 132(R). In a second shift, FASB’s Staff Position (FSP) SFAS 132 (R)(1) (effective 2009) required more detailed breakdowns of assets, in response to increasing amounts of assets being disclosed opaquely as “other assets.” As the FSP’s consequences could be similar to SFAS 132(R), we control for its impact with the proportion of assets disclosed opaquely preFSP and an index of overall transparency in asset allocation disclosure pre-FSP, with the intuition that the economic consequences of the FSP are likely to be higher for firms that used to give poor disclosure of plan assets. Another pension accounting shift is SFAS 158 (effective 2006), which first required sponsors to recognize pension under- (over-) funded status as a liability (asset) on corporate balance sheets. Consequently, many sponsors shifted to fixed-income investments to reduce balance sheet volatility (Amir, Guan, and Oswald, 2010). Per Amir, Guan, and Oswald (2010), in %equity models, we control for all cross-sectional determinants of the shift from equities to fixed-income that are not included in the baseline model: the ratio of pension assets or liabilities to total assets, firm leverage, and dividend yield. The results are robust to these additional controls. Balance sheet recognition of funding status also increased manipulation of actuarial assumptions, so as to improve

t-Statistic

(1.58) (1.40) (  1.89)n (  1.97)nn (2.29)nn (  3.15)nnn (  1.38) (  3.99)nnn (  0.36)

Low cfopen/tot

High cfopen/tot

Coefficient

t-Statistic

Coefficient

t-Statistic

0.150  0.002 0.012 18.207 0.324  10.196 0.709 0.449  1.524 – – Yes 0.224 1,539

(2.33)nn (  1.35) (1.91)n (1.14) (0.02) (  0.98) (0.51) (0.34) (  1.71)n – –

0.220  0.001 0.009 24.944  19.958  0.486 1.585  0.964  0.323 4.797  0.347 Yes 0.260 794

(2.33)nn (  0.28) (1.04) (0.69) (  0.99) (  0.03) (0.67) (  0.63) (  0.34) (0.45) (  0.18)

reported funding status (Fried, 2010). As this manipulation could obscure the true funding status, as it also does the distress-underfunding relation, we estimate a hypothetical funding status that undoes sponsors’ actual choices of assumptions and replaces them with more neutral benchmarks (Hann, Lu, and Subramanyam, 2007). Replicating the underfunding tests with this estimate of the non-discretionary funding status yields results similar to the baseline results. Finally, the PPA (effective 2006), by tightening funding requirements, could reduce sponsors’ ability to underfund plans. As Congress eventually passed a series of measures to soften PPA provisions post-crisis, its impact need not manifest in our period.29 Adding controls for (or removing) industries with differential treatment under the PPA (airlines, defense contractors) does not affect underfunding results. We also rerun all tests separately for subperiods of the sample, partitioning roughly on the accounting and pension regulation shifts. While CFO incentives do not affect risk shifting in the earliest subperiod (1999–2002), possibly due to the smaller sample available then, they affect risk shifting significantly in both 2003–2005 and 2006–2010 subperiods, which straddle most of the regulatory shifts we describe. Later years saw widening funding deficits and increasing distress risk, which could have improved testing power, by creating more meaningful variation in plan and sponsor status.

29 These measures are Worker, Retiree, and Employer Recovery Act (2008), Preservation of Access to Care for Medicare Beneficiaries and Pension Relief Act (2010), and Moving Ahead for Progress in the 21st Century Act (2012).

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7.5. Replicating results with alternative measures of firm distress We replicate the tests with three alternative measures of distress: (1) an accounting ratio-based approach with the Altman (1968) Z-score, (2) the hazard model approach of Shumway (2001) and Campbell, Hilscher, and Szilagyi (2008), with the latter’s “best model,” and (3) the Bharath and Shumway (2008) “naïve” alternative to the distanceto-default measure. Funding status results are very robust across these measures. Asset allocation results, while less robust and more specification-dependent, are broadly consistent with the baseline results.

8. Summary and conclusions

349

price sensitivity (delta). Conversely, risk shifting by underfunding plans is weaker when top managers have a larger stake in the plans that is at risk if the plan fails. These findings are stronger for CFOs than for CEOs, suggesting that pension policy falls within the CFO’s domain in most firms. These results highlight an important driver of corporate pension policy in the US. They also identify plans in which risk shifting behavior manifests most strongly and the moral hazard fueled by PBGC insurance is of particular concern. Understanding further the governance factors that determine pension risk shifting, particularly through asset allocation practices (which remain little understood), is an interesting and important area for future research, especially at this time of deteriorating PBGC finances. Appendix A

We find that managers’ equity incentives affect the extent of risk shifting versus risk management behavior in defined benefit pension plans. Risk shifting by underfunding plans (and, to a lesser extent, by investing plan funds in risky asset classes) is stronger when top managers have high wealth-risk sensitivity (vega) and weaker when they have high wealth-

See Table A1. Appendix B See Table B1.

Table A1 Structure of pension governance at one hundred sample firms. These data are for a random sample of one hundred sample firms. We read their pension footnotes in Securities and Exchange Commission (SEC) Form 10-K filings, as well as corporate governance disclosures on company websites, to understand their pension governance structure. SEC filings or corporate websites indicate that 33 firms have Retirement Committees, Benefits Committees, or Investment Committees and that 30 committees are internal (subboard level). Group 1: Finance Committee only

Group 2: Compensation Committee only

Group 3: Both committees Finance Committee

Some review or oversight of pension plans included in committee charter Responsibilities of the committee: Appoint trustees Appoint members of the retirement, benefits, or investment committee Monitor investments Monitor funding Monitor actuarial assumptions Review plan design

12

30

– 4

– 13

– 4

10 7 2 1

5 2 1 10

11 6 3 1

Group 4: Pension Committee

All firms

Compensation Committee 16

3

61

– 2

2 –

2 23

2 3 — 13

3 3 3 2

31 21 9 27

Table B1 Variable definitions. This table defines variables used. The prefix lag indicates that the variable is lagged by a year. Names in block capitals are Compustat variable names. Variable

Definition

Dependent variables underfunding Pension liabilities (PBPRO) minus fair value of pension assets (PPLAO), divided by pension liabilities. %equity Proportion of pension assets invested in equity securities (PNATE). underfundingERISA (pbproERISA  PPLAO)/pbproERISA, where pbproERISA is PBPRO minus balances accrued by all Named Executive Officers under nonERISA-qualified plans (balances are from ExecuComp, manually identified as belonging to non-ERISA-qualified plans by reading proxies). ERISA is the Employee Retirement Income Security Act of 1974. Key independent variables dd Distance-to-default calculated similar to Campbell, Hilscher, and Szilagyi (2008). The annualized standard deviation of daily stock returns is computed using Center for Research in Security Prices data over the entire fiscal year, as opposed to the annualized three-month rolling standard deviation as in Campbell, Hilscher, and Szilagyi (2008). One-year Treasury bill rates are obtained from Compustat. One-year Treasury bills were eliminated in 2001 and reintroduced in 2008. For this period, we use the average of six-month and two-year rates.

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Table B1 (continued ) Variable distress ceov (cfov)

ceod (cfod) ceosd (cfosd) ceopen/sal (cfopen/sal)

ceopen/tot (cfopen/tot) Control variables size bm mtr ocf s(ocf) log(fvpa) discountrate returns duration acquisition meetbenchmark

Definition dd multiplied by  1. Change in the chief executive officer (CEO) [chief financial officer (CFO)] option portfolio value for a 0.01 change in stock-return volatility (in thousands of dollars), calculated per Core and Guay (2002) assumptions for 2005 and earlier, and directly from ExecuComp for 2006 and later. Change in the CEO (CFO) option portfolio value for a 1% change in stock price (in thousands of dollars) calculated per Core and Guay (2002) assumptions for 2005 and earlier, and directly from ExecuComp for 2006 and later. Change in the CEO (CFO) stock portfolio value for a 1% change in stock price (in thousands of dollars) calculated per Core and Guay (2002) assumptions for 2005 and earlier, and directly from ExecuComp for 2006 and later. CEO (CFO) estimated annual post-retirement payout from ERISA-qualified pensions (at-risk portion only)/Current annual base salary. To estimate the numerator, we (i) estimate the total annual pension payout annpayout with the following relation: pbpro (for each beneficiary) ¼ annpayout/{1/(1 þ r)TTR þ1/(1 þ r)TTR þ 1 þ ⋯þ 1/(1 þr)TTD}, where pbpro is the individual’s accrued pension balance from ExecuComp (manually identified from proxies as relating to ERISA-qualified plans), r is the discount rate from Compustat, and TTR (TTD) is time to retirement (time to death). TTR is estimated by assuming retirement age of 65 minus current age. For executives past retirement age, we set TTR ¼1. TTD is time to death, set to gender-specific life expectancy, from the Centers for Disease Control and Prevention National Vital Statistics Reports. annpayout, the only unknown, is inferred given all the other parameters. (ii) The at-risk portion of annpayout, annpayoutATRISK ¼annpayout minus maximum benefit guaranteed by Pension Benefit Guaranty Corporation (PBGC) each year, from http://www.pbgc.gov/wr/benefits/guaranteed-benefits/ maximum-guarantee.html. CEO’s (CFO’s) pbproATRISK/pbpro ERISA . pbpro ATRISK is the at-risk portion of the CEO’s (CFO’s) ERISA-qualified pension balance and is estimated by using the assumptions from step (i) of the procedure outlined above, i.e., pbproATRISK ¼ annpayoutATRISK/ {1/(1þ r)TTR þ1/(1 þ r)TTR þ 1 þ ⋯þ 1/(1 þr)TTD}. Natural logarithm of total assets (AT). Book value of equity (CEQ)/Market value of equity (PRCC_F  CSHO). Simulated before-financing marginal tax rate from John Graham. Missing values are estimated with the Graham and Mills (2008) procedure. Cash flows from operations (OANCF) before pension contributions (PBEC), divided by beginning total assets. Standard deviation of ocf for the current and previous four years. Natural logarithm of fair value of plan assets (PPLAO, millions of dollars). Discount rate actuarial assumption (PBARR). Actual returns from plan assets (PBARAT/PPLAO). Ratio of annual pension service cost (PPSC) to the sum of service cost and interest cost (PPIC). Indicator set to one if the firm is involved in a merger or acquisition during the year (identified from SALE_FN) and zero otherwise. Indicator set to one if it is possible to prevent negative net income with 50 basis point increase in assumed return and set to zero otherwise, as defined in Bergstresser, Desai, and Rauh (2006).

Appendix C. Supplementary materials Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j. jfineco.2013.10.009.

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