CEO Incentives and the Health of Defined Benefit Pension Plans
Joy Begley*
Sandra Chamberlain*
Shuo Yang*
Jenny Li Zhang*
October 13, 2014
ABSTRACT: This paper examines how the composition of a CEO’s pay package
affects the funding levels and freezing decisions for a firm’s tax-qualified defined benefit
pension plans (QDB plans). We assume these decisions are determined by CEOs’ self-interest
which responds to how stakeholders are represented in CEO wealth. CEOs with a larger financial
interest in supplemental pension plans (SERP), in corporate equity, or in other deferred
compensation, versus their interest in the QDB plan, are predicted to be associated with greater
pension plan under-funding. We find support for this hypothesis, particularly among the
financially constrained firms. Further analysis shows that CEOs with equity wealth support
better-funded QDB plans than CEOs with more SERP. We also look at the decision to hard
freeze QDB plans for all workers. CEOs with a larger combined pension entitlement (QDB plus
SERP) are associated with fewer hard freezes. These results are consistent with CEOs fearing a
hard-freeze of the qualified plan will lead to a freeze of their SERP. Overall we contribute to
prior studies that suggest SERPs tilt executive incentives toward bond-holders. We extend these
studies by showing the interplay between controversial SERP plans that promise pension benefits
to top-level executives, and the QDB plans offered to non-management employees.
JEL Classification:
Keywords: Defined Benefit Plans, Earnings Management, Pension Plan Governance,
Data Availability: All data used in the study are available from public sources.
*
Sauder School of Business, University of British Columbia.
Joy Begley is the Ronald Cliff Professor in Accounting and Sandra Chamberlain is the Deloitte
and Touche Professor in Accounting. We are grateful to the Canadian Academic Accounting
Association who provided grant support for this project and the Social Sciences and Research
Council SSHRC for financial support of Jenny Li Zhang and Joy Begley. This paper reflects
comments received from participants at the UBCOW conference, the HKUST Accounting
Consortium, and the workshop at Arizona State University. We thank our discussant Rashad
Abdel-Khalik at the HKUST conference, and Paige Patrick at the AAA Annual Meeting. We
also thank our editor, Patricia Dechow, and two anonymous referees for their valuable
comments.
CEO Incentives and the Health of Defined Benefit Pension Plans
1.
Introduction
This paper explores how the composition of CEO wealth influences the management of
the employees’ defined benefit pension plans. This issue is important to employees because the
management’s decisions about the plan affects the security of the employees’ retirement. An
organization’s workforce can comprise a critical, intangible asset for the enterprise. Despite the
potential importance of this intangible asset, prior research exploring top management incentive
alignment with employees is relatively sparse. We use the funding status of the employees’
pension plan to capture a portion of the unobserved, implicit obligation to the employees, and we
relate this variable to the CEO’s wealth in equities, pension obligations, and other deferred
obligations.
Our work is related to prior research such as Begley and Feltham [1999] or Sundarem
and Yermack [2007]. These prior papers explore whether CEOs’ incentive packages, which
contain both debt-like claims (e.g., salary, pension, and other deferred compensation) and equity
claims (e.g., restricted stock or options), influence the magnitude of agency costs arising from
conflicts of interest between shareholders and debt-holders. This paper adds employee
stakeholders to the set of claimants. We exploit finer detail on top management pension plans
which has been available in proxy statements since 2006. Proxy statements now report the
present value of executives’ pension benefits, broken down into the portion held in the taxqualified defined benefit pension plan that is available to non-management employees (the QDB
plan) and the portion held in supplemental non-qualified plans (SERP).
The distinction between the CEO’s direct interest in the QDB plan versus SERP is a key
feature of our study. SERP interests frequently arise when CEOs are promised pensions with
1
terms similar to those of rank and file employees, but based on a much larger salary. Tax laws
limit the amount of pre-tax dollars that can be contributed on the CEO’s behalf to a tax-qualified
plan. A tax-efficient strategy is to allocate the first portion of the CEO’s pension to the taxqualified employee plan with the remainder going to SERP. However, pension funding rules
differ between these two types of pensions. While assets in the pension trust are shielded from
the firm's creditors, any assets earmarked to fund SERP obligations become general assets of the
firm upon bankruptcy. Hence the funded portion of the CEO's QDB claim is secured by the
assets in the plan, while SERP and any unfunded QDB claim are unsecured, relying on the assets
of the firm for repayment.1
CEOs typically oversee key decisions that determine the sustainability of the pension
plan, such as the level of voluntary contributions to the QDB plan or decisions to alter the terms
of the contract that determines plan benefits (e.g., freezing a plan).2 With regard to contributions,
by transferring assets from the firm to the QDB plan, the security of the CEO’s QDB claim is
enhanced, but the risk of their SERP is increased. We predict when CEOs have a larger interest
in the employee QDB plan relative to their other firm wealth (e.g., SERP), the firm’s employee
pension plan will be better funded. In addition, we speculate that incentives created by SERP
and equity interests will differ. Assets contributed to the pension plan grow tax-free, while
holding them in the firm enhances security for debt-holders (including SERP) but incurs a tax
penalty on earnings. Therefore, we expect CEOs with a greater equity interest to be more willing
to support tax-efficient funding of QDB plans than CEOs with more SERP.
1
In bankruptcy, some unfunded QDB entitlements may be protected by the Pension Benefit Guaranty Corporation
(PBGC), a self-funded insurance plan for defined benefit plans. But the upper limit on this protection is modest. In
2013, the maximum pension benefit is set at $4,790 per month for employees retiring at age 65.
2
Cocco and Volpin [2007] use UK pension data to show that when there are more insiders overseeing the pension
trust, plan assets are invested in riskier instruments. Anantharaman and Lee [2014] assert that in the US, oversight
of the pension trust tends to be entirely in the hands of insiders (page 330).
2
Similar to Anatharaman and Lee [2014], (hereafter AL 2014), we find that pensions are
healthier when the CEO has more wealth in the QDB plan. Among CEO wealth components,
both SERP and equity claims correlate with more underfunding of QDB. Notably, across
different model specifications, the most consistent and strongest predictor of plan underfunding
(relative to QDB wealth) is SERP wealth.
We go on to examine how CEO wealth-based incentives impact corporate decisions
regarding the continuation of the employee QDB plan. Consistent with general trends in the
economy, many firms in our sample decide to freeze their QDB plan thereby preventing new
and existing hires from accruing future benefits under the plan. We focus on hard freezes (in
which the plan obligation ceases to accrue additional benefits). Note that executive SERP often
arises because the QDB plan cannot absorb the entire pension. This link between the two plans
means that hard freezes of the QDB plan are likely to be associated with freezing of SERP. If so,
the CEO will be reluctant to freeze the employee plan. Consistent with this view, we find that
CEOs with larger pension interests (including both their QDB and their SERP) are less likely to
be associated with hard freezes.
We believe our analysis makes several contributions to prior literatures. First, our paper
provides new theory and extends the evidence about the relation between executives’ incentives
and defined benefit plan health as measured by funding. Prior research by AL 2014 is the first
to show that executive equity and QDB incentives relate to pension plan underfunding. Their
results for equity hold primarily for CFOs and not for CEOs; they find both executives’ QDB
incentives predict underfunding. However, this prior study omits to consider the larger part of
the executive’s pension compensation (i.e., SERP). We argue SERP interests create a
particularly interesting tension for firms that have QDB plans. Specifically, by providing
3
executives with SERP claims (because tax-limitations prevent accommodating the CEO in the
tax-qualified QDB plan), compensation committees create the potential for a diverging of
interests among all pension claimants (executives versus employees.) This hypothesis can only
be explored if the researcher incorporates SERP into the research design, as we do in the current
study. In the context of the AL 2014 study, SERP wealth is an omitted variable with unknown
consequences to their conclusions.
Perhaps the most striking difference in our conclusions versus those in AL [2014] is that
our study finds QDB funding holds for CEOs, and not for CFOs; AL 2014 finds the opposite.
We cannot fully resolve these differences across the two studies. Nevertheless we believe our
perspective on funding decisions and CEO incentives is valid and our analysis adds new
evidence to this prior study, albeit using a different research design.
Our paper also contributes to an emerging literature that pulls apart inside debt to isolate
the distinct incentives attached to different components. Anantharam et al. [2011] does so in the
context of private debt contracts to show that SERP wealth appears to align executive interests
with private debt, while deferred wealth and QDB do not. Cadman and Vincent [2014] decompose SERP into top-hat plans and, more standard, defined benefit plans. They find that total
compensation is higher for firms that have more top-hat wealth and they suggest that top-hat
wealth indicates more powerful executives.3 Our paper extends this literature by examining the
relations among various components of inside debt to pension health.
Finally, our paper contributes to the literature on plan freezes such as Comprix and
Muller III [2011]. Comprix and Muller III [2011] link hard freezes of pension plans to
conservative pension assumptions that inflate reported pension obligations. They conjecture that
inflating the pension obligation improves the corporation’s bargaining position with employees.
3
We have not separated SERP into these two components.
4
Like Munnell et al. [2007], Comprix and Muller III [2011] assume firm management is in
conflict with the employees over the future of the pension plan. We include measures of
executive incentives in the freeze prediction model to examine this idea in more detail. We show
that CEOs with a larger interest in the firm’s pensions (QDB and SERP combined) are less likely
to freeze the DB plan.
The next section develops our hypotheses regarding pension funding and freezes.
Section three describes the research design and sample. Our results are described in section four
and Section five concludes.
2. Motivation and Hypotheses Development
2.1 Background
Pension plans are a relatively common feature of employee compensation arrangements.
These plans typically allow employers to set aside some portion of pay that is tax deductible to
the employer and tax deferred to the employee. Employers that offer pension benefits either
offer a defined contribution (DC) plan or a defined benefit (DB) plan. Under a defined
contribution plan, the employer commits to a yearly cash infusion which is deposited directly
into a trust account each period. The employee controls how these funds are invested and bears
the investment risk. Once funds are contributed, DC plans cease to be an obligation of the firm.
Under a DB plan, the employer promises to deliver a future set of cash flows to the
employee, from retirement until death, typically based on the employee’s years of service and
salary earned. Traditional DB plans are generally not transportable from one organization to
another, helping to bond employees to stay with the firm. An agency theoretic view of a DB
plan would predict that these plans emerge in organizations where employee turnover is costly.
In North America, defined benefit plans were very popular in earlier decades. In the
5
1970’s more than 70% of employers who sponsored retirement plans offered their workers DB
plans, with the remainder offering DC plans. In recent times these fractions have fully reversed
(Perun and Valenti [2008]). Trends in the number of plans are casually linked to changes in
regulation (Butrica et al. [2009]). For example, the popularity of DB plans increased following
the 1978 Revenue Act which allowed employers tax deductible contributions to DB plans and
decreased following an increase insurance premiums paid to the Pension Benefit Guaranty
Corporation (PBGC). Major market movements have also affected DB plan popularity. Perun
and Valenti [2008] suggest booming stock markets during the 1980’s and 1990’s and resultant
contribution holidays helped spur DB plan adoptions, while market downturns, such as the 2008
stock market crash, decreased pension assets and funding levels, reducing their popularity.
Figures 1 and 2 show that in 2008 the percentage of underfunded DB pension plans in our
sample rose dramatically, as average funding levels sharply declined. Most recently, new
legislation in the form of the Pension Protection Act of 2006 (PPA) imposes faster restoration of
funding imbalances that occur when financial markets fall. In response to this regulatory change,
Campbell, Dahliwal, and Schwartz [2010] document negative excess returns to corporate
sponsors with underfunded plans. Butricia et al. [2009] conjecture that the PPA will further
reduce the number of DB plans.
2.2 Hypothesis Development
Our paper examines how CEO incentives are linked to QDB plan health, as measured by
funding levels, and to decisions to freeze additional pension plan benefits. We begin this section
by describing what determines funding levels and how the CEO can influence these levels. We
then discuss how CEO wealth can be linked to funding levels and freeze decisions.
2.2.1 What determines QDB plan funding levels?
6
Plan funding is the difference between the fair value of plan assets and present value of
plan obligations accrued to date. The present value of plan obligations is measured by the PBO
(projected benefit obligation) which is the discounted value of expected future payments to
retirees that have been earned to date. The forecast of future payments requires assumptions
about life expectancies, employee turnover, retirement dates and future salary levels used to
determine the pension payments.4 In addition, the discount rate used to compute the obligation is
important. When interest rates fall, as occurred in 2008, the pension obligations increase.
The other part of the funding equation, the value of plan assets, is determined both by
contributions made to the pension trust, and, by the effects of market movements on asset values.
The level of cash contributions is subject to regulatory minimums (based primarily on current
service costs5). The purpose of minimum contributions is to increase the security of the pension
obligation promise. Once contributions are invested in pension assets, market forces determine
the actual return on plan assets. Therefore despite the level of initial contributions, market
fluctuations and other pension plan uncertainties can cause temporary under or overfunding.
When underfunding is severe, pension legislation requires that firms make additional
contributions to resolve the situation over some period of years6. However, firms can choose to
contribute more cash than the statutory minimum to rectify a funding shortfall more quickly than
legislation requires, or even to overfund the plan, but there are limitations on the tax deductibility
of excess contributions.
4
Firms also report ABO (accumulated benefit obligation), the present value of their current DB pension obligations,
assuming no increase in future salaries. If a firm decides not to continue offering their workers a DB plan for their
future services (i.e., a plan freeze) then future pension payouts will be based on current, not future, salary levels.
Freezing therefore typically improves plan funding, by reducing PBO to be equal ABO.
5
Current service costs are the benefits workers accrue under the DB plan when they work for the firm for an
additional year.
6
Specific funding rules have varied over time. Following ERISA, updated legislation governing minimum funding
levels is contained in the Pension Protection Act (PPA) of 1987 and 2006, the Retirement Protection Act of 1994
and the Worker, Retiree and Employer Recovery Act of 2008 (Chen et al. (2013)).
7
Although some prior research has linked executive incentives to the selection of the
valuation assumptions that drive the pension obligation (e.g., Asthana [1999] for UK plans and
Comprix and Muller III [2011]), this is not our focus. We assume the CEO has the ability to
affect funding levels through four types of real decisions. First, the CEO can decide to
contribute more funds than regulations require. Assuming these cash flows are invested
prudently, we expect firms that contribute above the minimum will be better funded. However,
it is not straightforward to measure minimum required contributions to DB plans, therefore
research on this tool for managing pension health is relatively scarce. One exception is Rauh
[2006] who finds evidence that firms face real investing trade-offs when forced to increase their
minimum payments to DB plans.
Second, the degree to which plan assets are subject to market fluctuations is determined
by investment risk which prior research has assumed is influenced by key executives. For
example, Rauh [2009] and AL 2014 examine whether top executives take excessive risks by
overweighting investments in equities versus debt, or, if instead CEOs limit the investment risk
of their DB plan to help assure funding levels. Rauh [2009] documents that sponsoring firms
facing financial distress tend to invest DB plan assets in safer investments. Using a different
research design, AL 2014 find that CEO’s (CFO’s) equity and QDB incentives do not (do)
influence asset mix and funding decisions.
Third, CEOs can change the funding status of the pension plan by altering key
contracting parameters. The firm can introduce various types of freezes that limit the ability of
current and/or new employees to accrue additional benefits under their DB plan. We will build
on recent work by Comprix and Muller III [2011], which links freeze decisions to pension
assumptions. Their work provides a base model for freezing decisions, but does not consider
8
executive incentive-alignment through wealth.
Fourth, other strategic decisions can dramatically influence funding levels. For example
if a merger or spin off occurs the firm may acquire or get rid of under or over-funded QDB plans.
2.2.2 Agency Conflicts and QDB Funding
While QDB plan funding levels incorporate the influence of all four channels by which
CEOs can influence plan health, the following discussion and our first empirical analysis focuses
on the first type of decision, the level of voluntary contributions to the QDB plan.7 We predict
that contribution decisions will reflect the alignment of CEO interests with firm claimants:
employees, debt-holders and equity holders. Figure 3 shows, for our sample, how CEO wealth is
allocated on average among various forms of compensation: 74% is held in equity interests; 2%
in the QDB plan, 16% in the supplemental pension (SERP) and 8% in other deferred
compensation. These last three categories represent debt-like claims and have become known as
inside debt in recent studies such as Sundarem and Yermack [2007], Wei and Yermack [2011],
and Cassell, Huang, Sanchez and Stuart [2012]. Observe in Figure 4 that, within our sample, the
actual average allocation of the book value of firm assets among firm claimants closely mimics
the CEO's average wealth allocation. On average, the firm's short and long-term debt obligations
and unfunded pension obligations make up 27% of firm assets with the remaining 70% of assets
being financed with equity and operating liabilities. Research on inside debt tries to determine if
executive interests are aligned with the firm's debt and equity claimants due to the weighting of
inside debt and equity in their compensation package.
In this paper, we examine the funding of the qualified DB plan (i.e., in Figure 4), which
represents the employees who form a constituency that is distinct from equity holders and other
classes of debt-holders. Analogously, the division of CEO wealth may reflect these three distinct
7
Empirically, we will use control variables to help isolate the effect of voluntary contributions on QDB plan health.
9
categories. Returning to Figure 3, while the inside debt literature tends to view QDB, SERP and
deferred compensation as homogenous, these slices of the wealth pie represent different types of
debt to the CEO.8 In particular, and as noted by AL [2014], the QDB interest helps to align the
interests of the CEO with the employee stakeholders, not with more standard debt-holders or
equity holders. In addition, QDB wealth is partially secured by the assets held in the pension
trust, and only the executive’s share of the unfunded part is unsecured; by contrast, the CEO's
SERP is entirely unsecured.
A CEO who holds all slices of the pie in Figure 3 can be expected to internalize tradeoffs among shareholders, debt-holders and employees. Assuming that external financing is not
costless, a dollar of cash contributed to QDB plan assets can represent different trade-offs to
different claimants. Shareholders internalize the trade-off between tax benefits supplied by the
plan, versus, a reduction in resources available for positive net present value investing, or for
paying dividends. Unsecured standard debt-holders (and SERP holders) value positive net
present value investing differently than shareholders because their claims are bounded on the
upside and are senior to shareholders in the event of bankruptcy. Relative to shareholders, these
claimants find more circumstances in which the preference is to keep assets within the firm (as
opposed to in the QDB plan), and invested at lower risk. QDB holders place a higher value on
allocating assets to the pension trust relative to shareholders, but even these claimants can prefer
to keep assets within the firm if this allows for job security, i.e., if bankruptcy looms.
We are particularly attracted to the conflicts between SERP and QDB interests which are
a side-effect of tax laws that limit the maximum size of an annual pension that any one
individual can expect to receive from a QDB plan. Even if the board of directors wanted to
increase the incentive alignment of top management with that of QDB plan participants, the tax8
Exceptions to this view are Anantharaman et al. [2011], and Cadman and Vincent [2014].
10
shielded maximums are insufficient for highly-paid executives. Excess executive pension
promises spill into SERP, causing the incentives to shift from that of a QDB participant, looking
to increase the security on a shared debt claim, to that of an unsecured debt-holder. This leads us
to our first hypothesis:
H1A: Managers with a larger interest in their company's qualified DB pension plan,
relative to their interest in SERP, make decisions that leave the plan more fully funded.
While the hypothesis seems straightforward, tension is created by minimum pension
funding requirements under the Employment Retirement Security Act of 1974 (ERISA) that
mandates funding levels that may exceed the levels the CEO would otherwise consider to be
optimal. In addition, as emphasized in Figure 3, the fraction of CEO wealth that is tied up in the
QDB plan is small, raising the possibility that it is not economically significant.
We expand our hypothesis H1A to incorporate all components of the CEO's wealth in
the firm that is not in the employee QDB plan, including their equity interest, the subject of AL
2014 study.
H1B: Managers with a larger interest in their company's qualified DB pension plan,
relative to each of their other claims, including non-QDB inside debt and equity, make
decisions that keep the plan more fully funded.
Clearly the interests of equity holders are not necessarily aligned with those of unsecured
debt-holders. The limited upside to debt pay-offs make debt-holders more risk averse than
equity holders, and unsecured debt-holders can prefer to keep assets within the corporation
(rather than committing assets in the QDB plan) to help assure financial resources for debt
repayment. Counteracting this liquidity preference, an asset that grows within the QDB plan,
grows in a tax free way, whereas the same asset growth within the firm is subject to taxes. The
11
difference in shareholder and debtholder preferences for liquid assets will cause the shareholders
to value a dollar invested in the QDB plan, more than it is valued by an unsecured debtholder on
average.9 As the shareholders enjoy more of the benefits from this tax strategy than debt holders,
this leads to the following prediction:
H1C: Managers with a larger portion of their wealth in the firm's equity relative to their
debt interest in SERP will make decisions that keep the plan more fully funded.
The power of our tests of H1C will depend on the prevalence of sample firm-years in
which the interests of shareholders and debt-holders are not aligned with regard to QDB plan
funding. In the case of a relatively unconstrained firm, a CEO faces low costs of raising capital
and s/he can place cash into the QDB plan with no worries that SERP pension claims can be
satisfied. At the other extreme, when the firm is very near to bankruptcy, shareholders will
prefer to invest in risky but positive NPV projects, or to pay dividends rather than to contribute
scarce cash to a QDB plan. Debtholders and SERP-endowed CEOs will prefer to keep cash on
hand as security for debt claims. Even employees can prefer to keep assets in the firm, rather
than in the QDB plan to insure the firm avoids bankruptcy and the concomitant job losses.
Hence, the divergence of interests between debt-holders’ (SERP) preferences to keep
assets in the firm versus shareholders’ favoring the tax-free investing opportunities of a QDB
plan will occur somewhere in between these two extremes. In our empirical section we partition
our sample by the existence of financial constraints. While we expect H1A and H1B to be easier
to detect in the financially constrained samples, we do not have predictions about which samples
will embody more powerful tests of H1C.
9
Our intuition for shareholders is consistent with Tepper [1981] and Black [1980] who argue that tax arbitrage
should lead to overfunding of qualified DB plans. Thomas [1988] notes that the arguments in Tepper and Black are
weakened under the actual practice that over-funding cannot be withdrawn. The fact that assets are held in trust is
what we are arguing drives a wedge between unsecured debtholders and shareholders.
12
With the exception of H1B, we do not have a formal hypothesis regarding direction of
tension between the CEO's deferred compensation pay and his or her other interests in the firm.
Deferred compensation comprises current compensation voluntarily earmarked by the CEO to be
received in the future. The reason for deferral is to take advantage of tax planning incentives.
Anatharaman et al. [2011] argue that deferred compensation shares aspects with both inside debt
such as SERP, and equity. With respect to equity, deferred compensation can contain equity
grants. Similar to SERP, it can be paid out on a fixed schedule in the future, and it is unsecured.
However, its maturity can be shorter than SERP because some corporations allow it to be paid
before retirement. Anatharaman et al. find that private debt contracts appear to respond to SERP
wealth, but not to deferred compensation wealth.
2.2.3 Additional factors affecting agency cost tradeoffs
We acknowledge that the intensity of agency conflicts between equity holders, debtholders and employees will shift with a number of factors that are specific to the firm (e.g., cash
flows relative to demands for cash), specific to the CEO (e.g., risk aversion and time until
retirement), and specific to employees (e.g., tenure with the firm and time to retirement). For
example, firms with low costs of raising capital and strong cash flows relative to cash needs such
as investing and debt servicing, will have fewer agency conflicts, and will find that maximising
contributions to the QDB plan is tax efficient and value maximizing.
However, as the firm
approaches bankruptcy, from a debtholder’s standpoint, the desire to assure sources of repayment
for debt servicing is more acute, and exacerbates the debt-holder’s conflict of interest with
shareholders. In addition, conflicts between secured and unsecured debt-holders increase. Our
funding models will include control variables for some of these factors. Mainly, similar to
AL2014, we explore cross-sectional variation in our results for financially-constrained and non-
13
constrained sub-groups as identified by various proxies for financial constraints.
2.2.4 Hypothesis Development for Plan Freezes
Our hypotheses above focus on funding levels as a summary of executive decision
making about cash contributions to the qualified plan. We now turn to a second kind of
executive decision that can limit the future pension benefits employees are able to earn, and
thereby affect pension obligations and funding levels. Specifically, we consider the effect of
CEO incentives on the decision to carry out a hard freeze of a qualified DB plan.
Comprix and Muller III (2011) define a “hard” freeze as the elimination of the accrual of
new benefits for all employees. A hard freeze occurs when a firm decides to stop offering both
existing and new employees a compensation package that includes a DB pension entitlement.
While the DB pension entitlement that existing employees have earned (due to past services to
the firm) must still be honored under a hard freeze, going forward they will no longer accrue
additional pension benefits from their future services. In addition, the growth in pension
entitlement due to future salary increases is also frozen. Therefore, freezing the pension plan,
without a commensurate adjustment to other parts of the compensation package, is typically
viewed negatively by employees.10 Even if a frozen DB plan is replaced by contributing an equal
dollar amount to a new DC plan, such a change is likely to benefit the firm, because the existing
QDB obligation is capped, which likely makes the frozen DB plan members worse off. Existing
members are worse off because under the new DC plan the company no longer guarantees them
a lifetime payment in retirement. Instead, the employee must bear the investment risk associated
with the returns on their retirement savings and the defined benefit retirement pension they have
10
Firms often introduce other retirement benefits, such as a defined contribution pension plan, when the existing DB
plans are frozen. Rauh, Stefanescu, and Zeldes [2013] tackle whether freezes imply increases in labor
compensation.
14
earned thus far no longer increases with future salary increases.11 In other words, the ABO and
the PBO become equal once the plan is frozen. This benefit is reflected on the sponsoring firm’s
balance sheet by an improvement in the funding status of the pension plan.
Munnell et al. (2007) consider factors they expect to influence a firm’s decision to freeze
their defined benefit plans. They hypothesize that recent freezes are likely to be influenced by
the lack of pension alignment between workers and management. Noting that CEO
compensation levels are almost 100 times larger than the maximum tax-qualified pension
permitted under ERISA, they argue that executive decisions are being driven by growth in nonqualified SERPs which cause the QDB plan to be less relevant to the CEO. The arguments in
Munnell et al. (2007) lead to the following hypothesis:
H2A: Firms with a CEO that has a larger portion of their pension entitlement in SERP
relative to their QDB pension entitlement are more likely to freeze their tax-qualified
defined benefit pension plan.
However, a key contributor to large SERP entitlements is an executive’s excess pension
entitlement that exceeds the tax-qualified limits. To the extent that the QDB and SERP
entitlements are determined by the same formula, CEO and workers’ pension interests are likely
to be aligned regarding freezing of DB pensions.12 If freezing the employees’ QDB plan results
in freezing the CEO’s excess entitlements in their SERP, then large amounts of CEO wealth in
11
Munnell et al. (2007) show that employees who switch from a DB plan to a DC plan mid way through, or late in
their careers, are more negatively impacted by the switch than employees switching early in their career.
12
Cadman and Vincent [2014] point out that there are two types of executive pension arrangements. A nonqualified plan is linked directly to the terms offered to rank and file employees and is widely available to many
executives. Special plans, on the other hand, are tailored to individual executives. We refer to these special plans as
top-hat plans. Freezing the executives’ non-qualified pension can be tied to freezing the QDB plan, whereas the tophat plans are not “frozen.” We do not separate these two types of plans, so we are assuming that non-qualified plans
are held by the majority of CEOs in our sample with SERP wealth.
15
SERP and QDB combined are likely to motivate executives not to freeze either plan.13 This
leads to the following alternative hypothesis:
H2B: Firms with a CEO that has a larger combined pension entitlement in SERP and in
the QDB plan are less likely to freeze their tax-qualified defined benefit pension plans.
In testing this hypothesis we define a hard freeze as proposed in Comprix and Muller III
[2011], as if it is a one-time event. Casual inspection of freeze patterns suggests this definition
runs the risk of over-simplifying the process by which firms phase-out defined benefit plans.
Large firms, particularly unionized firms may have more than one defined benefit plan. To hardfreeze all plans can require a more gradual process. While the definition used in Comprix and
Muller III suits their focus on negotiations with unions, a richer framework would employ a
more continuous measure of plan freeze. It is difficult to know if the particular definition of plan
freeze we use in this study weakens or strengthens the tests of our hypotheses.
3. Research Method, Measurement, and Descriptive Statistics
3.1 Research Method for H1A, H1B, and H1C
To examine the hypothesis that funding status varies cross-sectionally with the allocation
of the CEO’s four wealth components shown in Figure 3, we estimate two different regression
models and we use two different measures of funding status. Funding status is the difference
between pension plan assets and PBO of all qualified DB plans14. Funding status is standardized
by total assets to reflect its economic significance to the firm and by PBO to reflect the fraction
of the employee QDB plan that is under or overfunded. Our first model, Equation (1), tests
13
Choy et al. [2014] also make the assumption that CEOs’ non-qualified plans are frozen at the same time as the
qualified plan. We examined a random sample 15 CEO’s with SERP and found a 10 appeared to be frozen
contemporaneously with the QDB plan.
14
COMPUSTAT records PBO from the 10-K pension footnote. This is total PBO of all DB plans, including SERP
and foreign plans. To focus on decisions for U.S. tax-qualified plans only, we attempt to remove SERP and non-US
plans from our pension measures by hand-collecting voluntarily-disclosed detail on SERP and foreign plans from
10-K footnotes. In cases where SERP is not disclosed separately in the footnotes, we use executive SERPs reported
in proxy statements and available from EXECUCOMP. However, proxy statements report ABO, rather than PBO.
16
whether the CEO’s QDB plan allocation as a fraction of their total pension entitlement
(QDB_TO_PENSION) is positively related to funding (H1A), holding constant the CEO’s share
of equity (CEO_EQUITY).
FUNDING _ STATUS T OSCALER i ,t   0   1QDB _ TO _ PENSIONi ,t   2 CEO _ EQUITYi ,t
  3 L _ TAXRATEi ,t   4 L _ TOBINQi ,t   5 L _ GROWTH i ,t
  6 L _ SIZEi ,t   7 L _ LEVERAGEi ,t   8 L _ ROAi ,t
(1)
  9 L _ OCFi ,t   10 STD _ OCFi ,t   11 L _ LOGPLANi ,t
  12 ARRi ,t   13 L _ ARRi ,t   14 HYBRIDPLANi ,t
  15 INACTIVEPLAN i ,t   16 ASP _ DISCRATEi ,t
99
7
h 1
n 1
   h Industryh    n Yearn   it
We include a number of control variables for the firm’s financial position, plan performance, and
economy-wide factors. All variables are defined in Table 1.
In order to test H1B and H1C as well as H1A we estimate a second regression model,
equation (2), where each component of CEO wealth is included separately. We break CEO
wealth into four components, the fraction of CEO wealth in QDB (CEO_QDB), their fraction in
SERP(CEO_SERP), their fraction in equity (CEO_EQUITY), and in “other deferred
compensation” (CEO_DEFER).
FUNDING _ STATUS TO SCALAR i,t = a0 + a1CEO _ DEFERi,t + a2CEO _ SERPi,t + a3CEO _ EQUITYi,t
+ControlsEquation1
(2)
Note that Equation 2 omits “CEO_QDB”, allowing us to use the t-statistics on
CEO_DEFER, CEO_SERP and CEO_EQUITY to directly test H1B that the funding status is
greater for CEOs with larger QDB interests relative to their other debt and equity claims. We
predict negative coefficients on these three variables. We use an F-test to test H1C that CEOs
with more equity relative to SERP will support a better funded QDB plan, by testing whether the
difference between CEO_EQUITY and CEO_SERP is positive.
17
We intend for the coefficients on the incentive variables in equations (1) and (2) to reflect
the CEO’s taste for making more than the minimum contributions to the DB plan over time, and
therefore we control for other determinants of funding status related to the firm’s ability to
finance the qualified plan and other circumstances affecting the plan’s health. Controls include
the marginal tax rate (L_TAXRATE); two proxies for growth opportunities − Tobin’s Q
(L_TOBINQ) and sales growth (L_GROWTH); firm size (L_SIZE); leverage (L_LEVERAGE);
firm profitability (L_ROA) and cash constraints, as measured by cash flow from operations
(L_OCF) and the standard deviation of operating cash flow (STD_OCF). Overall, we expect that
higher tax rate creates greater incentives to fund pension plans. It is not clear whether growth
firms will be more or less willing to make voluntary contributions to their DB pension plans as
capital investments create an alternative demand for limited cash reserves (Rauh 2006). Larger,
less cash-constrained, more profitable firms and firms with less leverage are expected to be in
better financial health and more able to make contributions to their DB plans if they decide to do
so.
Following prior studies, we also control for the plan-level variables. Funding status will
vary with the actual investment performance of pension plan assets, thus we control for the
current and prior years’ return on plan assets (ARR and L_ARR) and we expect these variables to
be positively related to funding. Similarly, we include the discount rate used by the firm to
present value their DB plan obligation (ASP_DISC RATE) and expect when the discount rate is
higher the obligation is smaller and funding status is improved. We assume that management
does not have discretion over the choice of discount rate. Finally we include the size of the DB
plan measured by the log of the pension obligation (L_LOGPLAN). In the regression that scales
our dependent variable by PBO, this control variable serves to indicate the economic magnitude
18
of the plan relative to firm size. This is the approach taken in AL 2014. In the regression where
the scalar for our dependent variable is total assets, the dependent variable already captures
economic magnitude of the pension obligation, so it is less clear that this control is necessary.
As subsequently discussed in our result section, the tests for the equality of coefficients on equity
versus SERP (i.e., H1C) are influenced by this control variable.
We hand-collect from proxy statements and 10-K filings two indicators for changes in the
DB plan. The first pension change variable, HYBRIDPLAN, is set to one if a firm has stopped
offering its traditional DB pension plan to new employees and replaced it with a cash balance
DB pension plan. Under a cash balance plan, retirement benefits accrued by employees do not
depend on estimates of future salary increases and turnover rates, but rather are a function of
promised contributions and accrued interest. Therefore, the pension obligation (and funding
status) is likely to be less volatile and potentially better funded.
The second pension status indicator variable, INACTIVEPLAN, equals one for the firm
years when a firm has a QDB plan obligation but it no longer offers a QDB pension plan to new
or existing employee. In this case, the plan does not accrue future benefits based on additional
years of service, potentially improving the status of the plan. However, managers of inactive
plans might not take as much care of the DB plan after the freezing decision, leading to even
more underfunding.15
Along with the control variables discussed earlier, we also include year fixed effects
(∑
) and industry fixed effects (∑
). These fixed effects control for
economic events in a year that might influence all firms the same way and for cross-sectional
differences in industries. The error terms are undoubtedly correlated across the same firm
15
As mentioned in our theory section, a decision to switch to a cash balance plan, or to freeze existing DB plans are
alternative ways of managing the funding status of DB plans, and these choices can be influenced by the incentive
alignment of the CEO with firm employees.
19
through time. To control for this we cluster our standard errors by firm. Finally, although we
have directional hypotheses, we report p-values for two-tailed t-tests.
3.2 Research Method for Hypothesis 2
We analyze the determinants of hard freezes (ADOPTHARD) using a probit model,
where ADOPTHARD (an indicator variable taking the value of one for the firm year when the
DB plan is first frozen) is regressed on measures of CEO incentives and control variables.
ADOPTHARDit   0   1CEO _ INCENTIVESi ,t 1   2UNIONi ,t   3 L _ UNDERFUNDEDi ,t
  4 L _ FUNDINGi ,t   5 L _ TAXRATEi ,t   6 L _ SIZEi ,t
  7 L _ PLANSIZEi ,t   8 L _ OCFi ,t   9 L _ LOSSi ,t
(3)
5
  10 L _ GROWTH i ,t    t YEARt   it
t 1
We estimate the above regressions using all non-frozen firm years in our sample, together
with the first year when a firm froze all their pension plans.16 We measure all the independent
variables in the year prior to the freezing year to capture the information that management (and
the board) had when the freezing decision was made. As our CEO incentive variables are not
available before 2006, our freeze analysis begins in 2007.
CEO_INCENTIVES is one of three alternate ways to measure CEO wealth components:
(a) QDB_TO_PENSION and CEO_EQUITY; (b) CEO_DEFER, CEO_SERP and CEO_EQUITY
(with CEO_QDB left in the intercept); and (c) CEO_DEFER and CEO_PENSION (with
CEO_EQUITY in the intercept). Specifications (a) and (b) generally parallel the incentive
variables for the funding status regressions from equation (1) and (2) and enable us to test H2A,
that when CEOs have a larger SERP they are more likely to hard freeze the pension plan. If
H2A is true and SERP makes the CEO more willing to hard freeze their DB plan, we expect the
16
Firms with multiple plans do not necessarily freeze all of their plans at the same time. In particular, unionized
plans are sometimes the last plan to freeze. We attempt to identify the final date on which all U.S tax-qualified DB
plans become frozen.
20
coefficient on QDB_TO_PENSION in specification (a) to be negative and the coefficient on
CEO_SERP in specification (b) to be positive. However, if SERP together with QDB makes the
CEO less willing to adopt a hard freeze as H2B predicts, then QDB_TO_PENSION and
CEO_SERP will not be significant in specifications (a) and (b), while in specification (c), the
coefficient on CEO_PENSION, which sums together the CEO’s SERP and QDB, is expected to
be negative. In (c) we combine CEO_QDB and CEO_SERP because we expect them to create
similar incentives when CEOs anticipate that a hard-freeze of the QDB plan will also hard-freeze
their SERP.
Following Comprix and Muller III (2011), we first include a UNION indicator variable if
any of the firms’ DB plans are subject to a collective-bargaining agreement. In this case, the
majority of the affected employees have to agree to the freeze, therefore we predict a negative
association between UNION and ADOPTHARD. We also include variables that capture the
health of employers’ DB pension plan. The variable L_UNDERFUNDED is an indicator variable
equal to one if the firm’s DB pension plans are underfunded in year t-1 and zero otherwise.
L_FUNDING measures lagged funding status and is defined as pension plan assets divided by
PBO. We expect ADOPTHARD to be positively associated with L_UNDERFUNDED and
negatively associated with L_FUNDING if DB plans that are in poorer health are more likely to
be frozen.
The next six control variables are intended to capture the employer’s tax incentive, the
size of employer, and the employer’s financial condition. L_TAXRATE is the firm’s lagged
marginal tax rate. L_SIZE is lagged firm size, defined as the natural log of total assets and
L_PLANSIZE is lagged pension plan size, measured as PBO divided by total assets. Firms with a
higher marginal tax rate are more likely to benefit from the tax savings of keeping the DB plan,
21
they are thus less likely to freeze the plan. We posit a negative relation between ADOPTHARD
and both L_FIRMSIZE and L_PLANSIZE if larger employers and those with relatively large
defined benefit plans likely face greater resistance to freezing their plans due to a larger number
of employees being affected. In addition, those firms are more likely to face greater negative
publicity if they freeze a relatively large plan. We have two measures of firm performance:
L_OCF defined as lagged cash flow from operations divided by lagged total assets; and L_LOSS,
an indicator variable set equal to one if the firm reported a loss in the prior year and zero
otherwise. Finally, L_GROWTH is the lagged percentage change in sales. Employers which
experience declining operating cash flow, and sales, and/or have recently reported a loss are
more likely to hard freeze their DB plan because these are evidence that they can no longer
sustain a healthy DB plan. Therefore, we expect the coefficient on L_OCF and L_GROWTH to
be negative and the coefficient on L_LOSS to be positive.
Along with the control variables discussed earlier, we also include year fixed effect,
consistent with Comprix and Muller III (2011). These fixed effects control for market wide
events in a year that might influence all firms the same way such as falling interest rates.
3.3 Sample
Our sample selection approach is shown in Table 2 Panel A. We first identify 8,221
firm-years on Compustat with defined benefit pension obligations and positive pension plan
assets between 2006 and 2012.17 Second, we match these firm-years to Execucomp annual
compensation at the CEO level, requiring that the CEO’s total pension value is non-missing. We
lose 3,254 firm-years because Execucomp only covers S&P 1,500 firms. CEOs have multiple
pension plans. In the third step, we identify the CEO’s interest in the qualified DB plan from
17
Beginning in 2006, the SEC required, for the first time, the finer details on the pension plans and deferred
compensation of their CEO, CFO and three other highest paid executives.
22
Compustat Executive Compensation Pension Benefit data, which has the breakdown of the
CEO’s total pension in different pension plans. We require that our CEOs are in this database.
We lose 10 observations due to this requirement.18 In the next step, firms that only have DB
plans in foreign countries are eliminated. We also exclude observations where the CEOs are in
the last year of their tenure, and their interest in the DB plan was positive in the prior year, but
fell to zero for the year when they left the firm.19 Finally, we delete firms with missing control
variables, leaving us with a final sample of 3,745 firm year observations.
Panel B of Table 2 shows that the number of sample firms in a given year is declining
over time, presumably due to companies moving away from DB pension plans.20 Table 2 Panel
C reports the industry distribution of our sample firms compared to the Compustat population.
DB plans are more popular in traditional industries such as manufacturing, chemicals, consumer
non-durables and utilities which are 52% of our sample, compared with 18% of Compustat firms.
3.4 Descriptive Statistics
Table 3 Panel A reports descriptive statistics for the variables used in funding status
regressions. In terms of the status of DB plans, the average PBO (ABO) is $2.91 Bill ($2.73
Bill), which decreases to $2.42 Bill ($2.38 Bill) after removing foreign pensions and SERP. Half
of the sample has pension obligations of more than $0.5 Bill. Some obligations are very large,
leading to a mean ADJUSTED_PBO (ADJUSTED_ABO21) that is much larger than the median.
The average pension plan has assets with a fair value of $2.44 Bill ($2.08 Bill after adjustments).
During our sample period, our sample firms’ pension plans are generally underfunded, with an
18
To identify the qualified defined benefit plan, we manually read the description of each plan in the proxy
statements and/or 10-K filings.
19
In the final year of the CEO’s tenure, this measure falls to zero if the CEO is paid out upon retirement, even
though during the year, up until their retirement, the CEO may have had wealth in the firm’s pension plans.
20
The exception is 2006. In 2006 the sample is smaller because firms with a fiscal year end prior to December 15,
2006 were not required to follow the new reporting rules to disclose executive pension in their proxy statements.
21
The adjusted ABO sample is smaller because 10-K footnotes that disclosure SERP and foreign plans report PBO
and plan assets, but do not always report ABO. As we analyze PBO, not ABO, this is not a problem.
23
average funding status to PBO (total assets) –18%, (– 3%).
The next group of variables measure CEO wealth. In addition to the CEOs’ dollar
entitlement in QDB, SERP, EQUITY, and DEFER categories, this panel also reports the fraction
of total wealth that is held in the QDB plan (2%), in SERPs (16%), in other deferred
compensation (8%) and in equity (74%), which demonstrates how the average CEOs’ relative
incentives are divided. Our second CEO incentive variable (QDB_TO_PENSION) is based on
the size of the CEO’s qualified DB pension entitlement (QDB_VALUE) to their total pension
entitlement (PENSION_VALUE_TOT). For firms with zero denominators the ratio is set to zero
as they have no interest in the employee qualified DB plan. The CEO’s mean (median) qualified
DB pension entitlement is 18% (6%) of their total pension entitlement.22
Median CFO total
wealth is only 20% that of CEO total wealth and tends to include more QDB and less SERP,
while the fraction of their wealth in equity is very similar to what CEOs experience.
Table 3 Panel B reports mean and median funding status, discount rate, and inactive (i.e.
previously frozen) pension plans by year (See also figures 1-2). There is a marked decline in
median funding status from –5% of PBO in 2007 to –30% in 2008, at the start of the financial
crisis, and this remains low throughout the rest of our sample period. A contributing factor to
this trend is the decline in the discount rate used to present value the obligation during the same
time period. The indicator variable INACTIVE identifies firms that have previously frozen all of
their DB plans and are no longer accruing benefits. In 2006 20% of sample DB plans are
inactive, while by the end of the sample period this has risen to 38% of the sample.
The bottom of Table 3 Panel C reports the Spearman correlations between funding status
and the determinants. The correlation between QDB_TO_PENSION (CEO_QDB) and
22
These amounts are higher if we exclude cases where the CEO has no interest in the qualified DB plan (the
untabulated mean is 25% and the median is 12%). For those CEOs with a positive interest in the employee qualified
DB plan (QDB_VALUE_A) their average dollar entitlement is $580,000 and their median entitlement is $421,000.
24
(FUNDING_STATUS TO PBO) is 0.12 (0.04) while the correlation to FUNDING_STATUS TO
TA is -0.02 (-0.17), suggesting that CEO wealth in the employee QDB plan is more positively
related to the fraction of the DB plan that is underfunded, rather than underfunding as a
percentage of the firm's assets. Funding status to total assets is negatively related to the fraction
of the CEO's wealth in SERP and positively related to their wealth in equity. As the majority of
a CEO’s wealth in the firm is either in SERP or in equity the correlation between these two
fractions of CEO wealth is significantly negative at –0.80.
4. Results
4.1 Results for Hypothesis 1: Funding Status
Table 4 contains our main results examining the relation between the funding status of
employee DB pension plans and the CEO's incentive alignment with various constituencies.
Columns 1 and 3 test H1A that there is a tension between CEO claims in QDB versus SERP.
We find the coefficients on QDB_TO_PENSION are positive, consistent with the hypothesis that
the more the CEO has in the QDB plan and the less they have in SERP, the better funded is the
overall plan. Columns 2 and 4 test H1B that all components of CEO wealth imply poorer
funding relative to QDB interests. Here, again, we confirm hypothesis H1A, that funding status
is lower when CEOs have a larger interest in SERP, relative to QDB (t-statistic = -2.36, -2.12).
In fact, all types of CEO wealth, not in the QDB plan, yield point estimates that are consistent
with lower funding levels, but all three are statistically significant only in column 2 when the
dependent variable is scaled by assets.
Tests of H1C, that SERP interests lead to lower funding on average than equity interests,
can be found in the last two rows of this table where an F-test is reported. We accept H1C in
column 4 where our dependent variable is scaled by PBO, but not in column 2 when we scale our
25
dependent variable by assets. In unreported tests that omit L_LOGPLAN from column 2, the Ftest suggests that SERP interests imply more underfunding than do equity interests. This issue
about whether to include or exclude L_LOGPLAN from column 2 was mentioned in Section 3.
In summary, Table 4 provides consistent support for H1A regardless of specification.
The importance of equity incentives and deferred compensation depends on the measure of the
dependent variable and whether or not L_LOGPLAN is included as a control variable. In general,
underfunding is related to SERP relative to QDB.
With respect to control variables, firms facing a higher tax rate (L_TAXRATE), those with
stronger pension plan asset performance (ARR and L_ARR) and firms using a higher discount
rate to present value their plan obligations (ASP_DISC RATE) tend to have better funded plans
using both measures of funding status, as expected. Larger plans (L_LOGPLAN) tend to be better
funded as a fraction of PBO, but they are less well funded with respect to total assets. Funding
status to total assets is stronger in larger firms (L_SIZE), but worse for firms that have inactive
plans (INACTIVEPLAN), however both variables are unrelated to funding status as a fraction of
PBO. On the other hand, funding status to PBO is more positive amongst firms with recent
profitability (L_ROA) and it is lower when firms are more highly levered, as expected. The
negative relation with leverage is likely to reflect competition for scarce resources in financially
constrained firms.
4.2 Division of Sample by financial constraints
In this section, we explore the conditions under which the influence of CEO’s incentives
on funding status is expected to be more evident. We focus on moderating factors that would
cause cross-sectional variations in this relation: financial constraints. Rauh (2006) argues that the
existence of financing constraints are likely to intensify the tension that exists between funding
26
capital projects versus contributing cash to the corporate DB pension plan. There are a number of
plausible approaches to sorting firms into financially constrained and unconstrained categories in
the extant accounting and finance literature. Since we do not have strong priors about which
approach is best, we use four alternative schemes to partition our sample.
Under the first scheme, we rank firms based on Ohlson (1980)’s OSCORE or the MKMV
estimated default probability and assign those firm-years in the top third of each distribution to
the financially constrained group. The rest of the observations are assigned to the financially
unconstrained group. The second way we partition the sample uses bond ratings assigned by
Standard & Poor’s. Firm-years with no bond rating are categorized as financially constrained,
while financially unconstrained firms are those whose bonds are rated. The third scheme,
partitions the sample based on dividend payout. Firm-years when no annual dividend is paid are
assigned to the financially constrained group, while those paying dividends are unconstrained.
The idea is that firms facing financial constraints have lower payout ratios. Finally, under the
fourth scheme, we construct an index of firm financial constraints (“HP index”) based on results
in Hadlock and Pierce (2010) by applying the following equation:
HP index = -0.737 × Ln(Assets) + 0.043 × Ln(Assets)2+ 0.04 × Firm Age
We then assign these firm-years with greater (less) than median HP index value to
financially constrained (unconstrained) group.
In Table 5, we estimate the funding status regression for the four pairs of subsamples in
each panel, respectively. For each of the dependent variables – funding status to total assets and
funding status to PBO, the first (second) column presents the results for the financially
unconstrained (constrained) sample. Across all four panels and two alternative dependent
variables, we notice large, negative coefficients on CEO_SERP, CEO_EQUITY in the financial
27
constrained sample, while the same coefficients are mostly insignificant among the
unconstrained firms. Although the coefficients on CEO_DEFER are negative, they are not
always significant. Together these results suggest that CEOs with more wealth in QDB, relative
to SERP and equity wealth are associated with better funded pension plans and this significant
overall relationship predicted by H1A and H1B is largely driven by firms with greater degree of
financing friction.
The bottom of each panel in Table 5 reports the results of the F-test of H1C. The
coefficient on CEO_SERP is almost always more negative than that on CEO_ EQUITY across
different panels. However, the difference is only significant in five cases. As seen in Table 4,
when the difference is significant, generally it is when funding status is measured as a percentage
of PBO. Evidence supporting H1C is also evenly distributed among financial-constrained and
uncontained samples.23 As mentioned in section 2, ex ante, we would not expect the prediction of
H1C to necessarily be more evident in a sample of financial constrained firms; partitioning is
likely to reduce the power of our test of H1C.
Overall, the results in Table 5 suggest that the association between funding status and
CEO incentives is most clearly present in financially constrained firms.
4.3 Results for CFO incentives
AL 2014 investigate the effect of both CEO and CFO equity incentives on underfunding
of the employee pension plans. Their measures of executive incentives differ from ours as they
don't consider the tensions between each component of the executive's wealth, but rather they
consider only the delta and vega of their stock and option holding. However, similar to us they
23
In addition, failing to find evidence consistent with H1C when funding status is measured relative to total assets
could be due to controlling for plan size. In untabulated analysis, we find that when we take plan size out of the
regressions using funding status to total assets, the F-tests become significant at the 10% level or better across all 8
subsamples.
28
do investigate the size of the manager's uninsured tax-qualified pension. They find evidence that
CFO equity and tax-qualified pension incentives are significantly related to pension
underfunding but in general they do not find this to be true for CEOs.
We investigate the impact of CFO incentives using our research design which focuses on
SERP incentives. In Table 6 we repeat the CEO incentive analysis reported in Table 4 replacing
the relative components of CEO wealth incentives with the incentives of the CFO. In contrast to
the results reported for the CEO, we do not find any significant relation between the components
of the CFO's pension wealth and funding status. In unreported analysis, this lack of result also
holds among the data partitions we show in Table 5.
4.4 Endogeneity
It is possible that compensation contracts and the employer’s DB pension funding
decisions are endogenously determined by unobservable firm characteristics, rendering spurious
the relationship between CEO incentives and QDB pension plan funding status that we document
so far. For example, firms at the end of their life-cycle may choose to assign executive
compensation that is less influenced by equity incentives than a firm early in the life-cycle.
Often QDB plan firms are in traditional industries that are late in their life-cycle.
To address the endogeneity of compensation incentives, we perform a two-stage least
squares (2SLS) estimation of funding status models, with second-stage results tabulated in Panel
A of Table 7. We follow Chava and Purnanandam [2007], who employ industry-medians of the
endogenous variables. In addition, we hand-collect the number of years the CEO has been
employed by the firm. We separately regress each CEO incentive variables
(QDB_TO_PENSION, CEO_EQUITY, CEO_DEFER, CEO_SERP) on all of the variables used
in Table 4 along with the instruments their respective industry median and the number of years
29
the CEO has worked with the firm.24
As shown in panel A of Table 7, when funding status is measured relative to total assets,
the Hausman test rejects the consistency of ordinary least squares (OLS), indicating that
endogeneity should be accounted for. To check whether the instruments are weak, we conduct
the Basmann over-identification test. Failing to reject that the instruments are uncorrelated with
the error term from the second-stage adds some confidence about the instrument validity. We
report in the first two columns the second-stage regressions in which CEO incentive variables are
replaced by their predicted values from their respective first-stage regression. Most notably, the
fitted value of QDB_TO_PENSION (CEO_DEFER, CEO_SERP, CEO_EQUITY) is highly
positively (negatively) significant, consistent with the results reported in Table 4.
These results contrast with the last two columns of Table 7 panel A, where funding
status is relative to PBO. While the results of Model 3 are similar to those in Table 4, the last
column reports that none of the incentive measures has regression weights differing from zero.
However, the Hausman test fails to reject the consistency of OLS in the first model 3, and the pvalue is only significant for the second model at the 5% level. In addition, the Basmann tests
suggest that the instrument variables jointly do not pass the exogeneity requirement. One
explanation is that there is no endogeneity problem when the dependent variable is measured
relative to PBO. Alternatively, although we have gathered the best IVs we can find, our IVs may
not be valid as indicated by the Basmann test.
In their study of CEO compensation incentives and firm cash holdings, Liu and Mauer
24
We report results for a smaller set of instrumental variables for brevity. We looked to other studies such as AL
2014 and Liu and Mauer [2011] for additional IVs. We have checked the robustness of our two-stage least squares
(2SLS) results by using a variety of additional instruments e.g., CEO age, CEO tenure, firm age, idiosyncratic risk
of the firm. In all cases, the results are similar to those reported in Panel A of Table 7 when the instrument variable
passes the over-identification tests. The instrumental variables we report, behave the best in over-identification
tests.
30
[2011] lag their executive compensation variables and argue that lagging helps control for
potential endogeneity of compensation practices. Accordingly in Panel B, we lag the CEO
wealth component variables to represent the historical value. This panel confirms the negative
and statistically significant effect of CEO wealth incentives on funding status, as in Table 4. In
fact, there is an overall increase of the adjusted R2 for all four models relative to those reported in
Table 4.
4.5 Results for Hypothesis 2: Determinants of a plan freeze
Table 8 presents the summary statistics for our analysis of QDB plan freezes. In Panel A
we report the number of new freezes during our sample period. Hard freezes occur in all years,
but drop off slightly in the later years, when more than a third of plans are already frozen (see
Table 3, Panel B). Of our full sample of 705 firms, 133 (19% of sample firms) institute a hard
freeze between 2006 and 201225. However, because the freeze regressions use lagged
independent variables our freeze analysis begins in 2007, reducing the number of freezes we
examine to 109. Panel B reports descriptive statistics for the firms in the freeze regression and
Panel C reports these separately for the frozen and non-frozen subsamples. Consistent with
H2B, we see that the median pension wealth of CEOs that adopt a hard freeze is approximately
half what it is for those that did not freeze (8% versus 15%).
Table 9 reports the factors related to adoption of a hard freeze. Counter to the prediction
in Munnell et al. (2007) we find no support for H2A that managers with a large interest in SERP
relative to their interest in the QDB plan are more likely to introduce a hard freeze. In fact
QDB_TO_PENSION is positive and significant in column 2, while in column 3 SERP is not
significantly different from QDB (-0.817).26 In column 4 we directly test H2B by leaving
25
26
An additional 159 firms enter our sample already frozen.
This result is somewhat puzzling and we don’t have an immediate explanation for the finding.
31
CEO_EQUITY in the intercept and combining the CEO’s QDB and SERP into a single variable
(CEO_PENSION). This variable is significantly negative supporting our prediction that CEO's
with a large combined interest in QDB and SERP are less likely to introduce a hard freeze.
Consistent with the result for total pension, the F-test in column 3 indicates that CEOs with more
SERP relative to equity are less likely to hard freeze their employee pensions, suggesting interest
alignment. The signs of the control variables in the hard freeze regression are consistent with
our expectations. Like Comprix and Muller III (2011) we find firms are more likely to hard
freeze their DB pension plans is the firm is smaller and experiencing losses and when the plan is
larger. We also find that hard freezes occur more often if the firm has a lower tax rate and if
underfunding is more severe.
5. Conclusion
On average, a CEO in our sample holds 74% of his or her wealth claims against the firm
in equity instruments with the remaining 26% held in pensions and other deferred compensation.
These latter non-equity fixed claims make the CEO a creditor of the firm, leading economists to
label this 26% inside debt. Prior research on executive inside debt has latched onto the idea that
the ratio of inside debt to equity held by the CEO can mitigate or exacerbate conflicts of interest
between external debt and equity-holders.
While much of the prior research views inside debt being homogenous, our paper pulls
apart the pension component of inside debt. We recognize that incentives created by the CEO's
partially secured, tax-qualified DB pension differ from those of their unsecured, tax
disadvantaged, DB pension (SERP). We build on the inside debt literature, noticing that any
interest the CEO holds in the QDB plan, can serve to balance a third set of firm constituents,
employees, against the interests of bondholders and shareholders. We directly examine how this
32
segmentation of wealth, leads to decisions about the QDB plan.
We argue that CEO equity interests tend to favor the tax benefits of funding the QDB
plan, while large SERP interests imply trading-off the value created by the tax arbitrage against
their risk-averse tendency to hold assets in the firm as security for paying off SERP. We find
that CEOs with a larger QDB claim relative to their SERP (and equity) have better funded QDB
plans, and, that across specifications SERP interests are more consistently associated with
underfunding than are equity interests. Our results suggest that the companion tax policies that
intend to create an incentive to fund DB plans, but also to prevent the shielding of pensions at
higher salaries, creates internal turmoil for decision makers that hold both SERP and QDB.
Noting this potential dissonance between the actions that maximize the value of SERP
versus QDB, prior research by Munnell et al. [2007] predicts that SERP interests make CEOs
more inclined to reduce the obligation associated with QDB plans through hard freezes. While
this conjecture has some appeal, it fails to recognize that from the CEO’s point of view, changes
to QDB plan parameters are likely to spill onto SERP plans (since SERP was created by tax
policies that place limits on maximum deductions in QDB). If freezing the QDB plan leads to a
freeze of the SERP, the CEO may find a hard freeze to be harsh medicine for containing pension
costs. This paper is among the first to analyze the effects of CEO incentives on QDB plan
freezes. We find that, indeed, CEOs with larger pensions (meaning QDB plus SERP) relative to
equity are less likely to implement full freezes.
Our paper finds thought-provoking evidence that CEO wealth allocations impact both the
funding of qualified DB plans, and on how these allocations affect hard freezes. Whereas prior
research has had difficulty linking CEO equity incentives to QDB plan decisions, our paper
provides new evidence that the SERP claim of the CEO is associated with more underfunding of
33
QDB plans, and that combined pension interests are associated with fewer hard freezes of QDB
plans.
34
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37
Table 1
Variable definitions
Variable
Definition
Pension plan funding status
ABO
ADJUSTED_ABO
PBO
accumulated benefit obligation (ABO) for employee DB pension plans.
(Compustat: PBACO)
ABO, excluding pension obligations related to SERP and foreign DB pension plans.
This variable is missing when 10-k footnote reports SERP PBO but not SERP ABO.
projected benefit obligation (PBO) for employee DB pension plans (Compustat:
PBPRO)
ADJUSTED_PBO
PLAN ASSETS
PBO, excluding pension obligations related to SERP and foreign DB pension plans.
fair value of pension plan assets for the employee DB pension plans. (Compustat:
PPLAO)
ADJUSTED_ PLAN ASSETS
FUNDING_STATUS
TO PBO
FUNDING_STATUS
TO TA
adjusted pension plan assets, excluding pension plan assets of foreign DB plans.
(ADJUSTED_PLAN ASSETS - ADJUSTED_PBO) / ADJUSTED_PBO
(ADJUSTED_PLAN ASSETS - ADJUSTED_PBO) / Total Assets
Pension plan freezes
ADOPTHARD
indicator variable for adopting a hard freeze of the pension plan, equals one for the
firms when they first freeze all of their non-foreign qualified DB pension plans, and
zero otherwise
CEO Incentive variables
QDB_VALUE
QDB_VALUE_A
SERP_VALUE
DEFER_VALUE
EQUITY_VALUE
WEALTH_VALUE_TOT
PENSION_VALUE_TOT
CEO_QDB
CEO_SERP
CEO_DEFER
CEO_EQUITY
CEO_PENSION
QDB_TO_PENSION
the present value of the CEO’s accumulated benefit under the qualified DB plan
the present value of the CEO’s accumulated benefit under the qualified DB plan,
excluding all firms with QDB_VALUE = 0
the present value of the CEO’s accumulated benefit under the supplemental
executive retirement plan (SERP)
the CEO’s year-end balance in non-pension deferred compensation plans
the estimated present value of the CEO’s equity ownership and stock options
CEO total wealth in the firm at fiscal year end, it is equal to the sum of the present
value of the CEO’s qualified DB pension, SERP, non-pension deferred
compensation, and the present value of the CEO’s equity claims
the CEO’s present value of accumulated pension benefits from all pension plans in
the firm (i.e., QDB_VALUE + SERP_VALUE)
the present value of the CEO’s accumulated benefit under the qualified DB plan,
deflated by the CEO’s total wealth (QDB_VALUE/WEALTH_VALUE)
the present value of the CEO’s accumulated benefit under the supplemental
executive retirement plan, deflated by the CEO’s total wealth:
(SERP_VALUE/WEALTH_VALUE)
the CEO’s non-pension deferred compensation, deflated by the CEO’s total wealth:
(DEFER_VALUE/WEALTH_VALUE)
the CEO’s total equity claims deflated by the CEO’s total wealth
CEO_QDB+ CEO_SERP
the present value of the CEO’s accumulated benefit under the qualified DB plan,
38
deflated by the CEO’s accumulated pension benefits from all pension plans in the
firm, calculated as: QDB_VALUE/PENSION_VALUE_TOT) when
PENSION_VALUE_TOT is greater than zero, and zero otherwise
CFO Incentive variables
WEALTH_VALUE_TOT_CFO
CFO_QDB
CFO_SERP
CFO_DEFER
CFO_EQUITY
QDB_TO_PENSION_CFO
CFO total wealth in the firm at fiscal year end, it is equal to the present value of the
CFO’s qualified DB pension, SERP, non-pension deferred compensation, and the
present value of the CFO’s equity claims
the present value of the CFO’s accumulated benefit under the qualified DB plan,
deflated by the CFO’s total wealth (QDB_VALUE/WEALTH_VALUE)
the present value of the CFO’s accumulated benefit under the supplemental
executive retirement plan, deflated by the CFO’s total wealth:
(SERP_VALUE/WEALTH_VALUE)
the CFO’s non-pension deferred compensation, deflated by the CFO’s total wealth:
(DEFER_VALUE/WEALTH_VALUE)
the CFO’s total equity claims deflated by the CFO’s total wealth
the present value of the CFO’s accumulated benefit under the qualified DB plan,
deflated by the CFO’s accumulated pension benefits from all pension plans in the
firm, calculated as: QDB_VALUE/PENSION_VALUE_TOT) when
PENSION_VALUE_TOT is greater than zero, and zero otherwise
Control variables
L_TAXRATE
lagged marginal tax rate after interest deductions (Compustat: bcg_mtrint)
L_LOGPLAN
lagged pension plan size, where pension plan size is calculated as log of
ADJUSTED_PBO
standard deviation of operating cash flow for the current and past four years
STD_OCF
L_OCF
L_TOBINQ
L_GROWTH
L_SIZE
L_LEVERAGE
lagged operating cash flow, where operating cash flow is measured as cash flow
from operations divided by total assets(Compustat: OANCF/AT)
lagged Tobin’s q, where Tobin’s q is the ratio of the market value of assets to book
value of total assets. The market value of assets is obtained as total assets –
common equity – deferred taxes + market value of equity:
(Compustat: PRCC_F×CSHO+AT-CEQ-TXDB)/AT).
lagged percentage sales growth (Compustat: (SALE-LagSALE)/LagSALE)
lagged logarithm of total assets at the end of the fiscal year (Compustat: log(AT))
lagged leverage ratio, where leverage ratio is measured as total debt (the sum of debt
in current liabilities and long-term debt) to total assets (Compustat:
(DLC+DLTT)/AT)
L_ROA
ARR
L_ARR
HYBRIDPLAN
INACTIVEPLAN
ASP_DISC RATE
UNION
lagged return on assets, where return on assets is defined as the ratio of income
before extraordinary items to beginning total assets: (Compustat: IB/LAG(AT))
actual return on plan assets (Compustat: (PBARAT/PBBAT)*100)
lagged actual return on plan assets
an indicator variable which equals one for the firm-years where new employees can
only participate in the cash balance DB pension plan instead of the traditional DB
pension plan, and zero otherwise
indicator variable that equals one for firm years with all the domestic DB plans
previously frozen and are no longer accruing benefits, zero otherwise.
pension benefits discount rate (%) (Compustat: PBARR)
an indicator variable equal to one if any of the firm’s defined benefit pension plan is
subject to a collective-bargaining agreement, and zero otherwise.
39
L_UNDERFUNDED
L_FUNDING
L_PLANSIZE
L_LOSS
lagged indicator variable for underfunding, where underfunding is equal to one if the
firm’s DB pension plans are underfunded and zero otherwise
lagged funding status, where funding status is measured as ADJUSTED_PENSION
ASSETS divided by ADJUSTED_PBO
lagged pension plan size, where pension plan size is calculated as ADJUSTED_PBO
divided by total assets
lagged loss dummy, equal to one if the firm reported a loss in year t-1and zero
otherwise (Compustat: based on sign of IB)
Moderating variables
DISTRESS
indicator variable for financial distress, equals one if either Ohlson (1980) OSCORE
or MKMV estimated default probability (which is based on the Vasicek-Kealhofer
model [Kealhofer 2003a,b] and Black-Scholes [1973]) is in the top third of the
sample, zero otherwise.
40
Table 2
Sample selection and sample description
Panel A. Sample of firms with defined benefit pension plans
Sample
Number of firm-years from Compustat Pension Annual data
with positive PLAN ASSETS and ABO from 2006 to 2012
8,221
Require Execucomp CEO total pension to be non-missing
(3,254)
Require CEO level defined benefit pension value from
Compustat Executive Compensation Pension Benefits data
from 2006 to 201227
(10)
Delete firm-years with DB plan in foreign country only
(292)
Delete firm-years where the CEO’s DB pension plan was
previously positive, but it fell to zero in their last year of
tenure
(71)
Delete firm-years with missing control variables
(849)
Final CEO_QDB Sample (705 unique firms)
3,745
27
From Compustat Executive Compensation Pension Benefit data, we first identify the CEO for each firm year by
merging with Execucomp annual data (CEOann=“CEO”). CEOs have multiple pension plans. If a CEO-firm-year
has total pension value non-missing (i.e., it is covered by execucomp) but defined benefit pension value is missing,
we coded the CEO's defined benefit pension value as zero. In order to identify the qualified defined benefit plan, we
manually read the description of each plan in the proxy statements and/or 10-K.
41
Table 2
Sample selection and sample description (continued)
Panel B. Frequency of sample firms by year
Year
Number of firms
Percentage of Sample
2006
2007
2008
2009
2010
2011
2012
Total
484
576
573
561
543
520
488
3,745
12.92%
15.38%
15.30%
14.98%
14.50%
13.89%
13.03%
100%
Panel C. Frequency of the sample firms by Fama-French 12 industries
Industry
Consumer NonDurables
Consumer Durables
Manufacturing
Energy
Chemicals
Business Equipment
Telecom
Utilities
Wholesale, Retail,
and Some Services
Health
Finance
Other
Total
Number of firmyear observation
358
138
844
172
272
322
113
474
Percentage of
total sample
9.56%
3.68%
22.54%
4.59%
7.26%
8.60%
3.02%
12.66%
Compustat
Population
4.42%
2.09%
8.38%
4.88%
1.90%
15.73%
3.41%
2.97%
298
7.96%
8.09%
175
206
373
3,745
4.67%
5.50%
9.96%
100%
8.54%
24.82%
14.77%
100%
42
Table 3
Summary statistics for funding status regressions
Panel A. Variables used in the funding status regressions
N
Min
Q1
Mean
Median
Q3
Max
Std dev
ABO($Mill)
3,745
1.05
171.90
2,730
480
1,773
133,599
8,532
ADJUSTED_ABO($Mill)
3,367
1.05
147.75
2,378
392
1,572
108,181
7,100
PBO($Mill)
3,745
1.05
184.62
2,911
517
1,916
134,327
8,889
ADJUSTED_PBO($Mill)
3,745
1.05
153.97
2,425
424
1,638
108,548
7,109
PLAN_ASSETS($Mill)
3,745
0.81
135.00
2,440
390
1,546
117,378
7,835
ADJUSTED_PLAN_ASSETS($Mill) 3,745
0.81
115.61
2,079
340
1,385
104,070
6,512
FUNDING_STATUS TO PBO
3,745 -0.83
-0.31
-0.18
-0.20
-0.09
1.40
0.19
FUNDING_ STATUS TO TA
3,745 -0.98
-0.04
-0.03
-0.02
0.00
0.19
0.06
QDB_VALUE($000)
3,745
0.00
9.44
437
223
695
4,579
533
QDB_VALUE_A($000)
2,822
1.51
148.70
580
421
854
4,579
543
SERP_VALUE($000)
3,745
0.00
0.00
5,755
2,091
7,663
101,890
9,327
DEFER_VALUE($000)
3,745
0.00
0.00
3,857
814
3,171
245,493
12,242
EQUITY_VALUE($000)
3,745
0.00
6903.20 232,091
17,142
39,936 412,673,000 7,739,849
3,745
5.80
10,653 242,140
24,728
53,698 412,673,000 7,739,683
WEALTH_VALUE_TOT_CEO ($000)
PENSION_VALUE_TOT ($000)
3,745
0.00
148
6,192
2,584
8,336
101,891
9,554
CEO_QDB
3,745
0.00
0.00
0.02
0.01
0.03
1.00
0.06
CEO_SERP
3,745
0.00
0.00
0.16
0.09
0.25
0.99
0.18
CEO_DEFER
3,745
0.00
0.00
0.08
0.03
0.11
1.00
0.13
CEO_EQUITY
3,745
0.00
0.60
0.74
0.79
0.93
1.00
0.23
QDB_TO_PENSION
3,745
0.00
0.00
0.18
0.06
0.19
1.00
0.29
3,693
1.38
2,021
9,610
4,849
10,884
692,510
21,201
WEALTH_VALUE_TOT_CFO ($000)
CFO_QDB
3,693
0.00
0.00
0.05
0.02
0.06
1.00
0.10
CFO_SERP
3,693
0.00
0.00
0.12
0.04
0.18
0.97
0.16
CFO_DEFER
3,693
0.00
0.00
0.09
0.04
0.12
1.00
0.14
CFO_EQUITY
3,693
0.00
0.60
0.74
0.80
0.94
1.00
0.24
QDB_TO_PENSION_CFO
3,693
0.00
0.00
0.30
0.18
0.45
1.00
0.33
L_TAXRATE
3,745
0.00
0.31
0.31
0.34
0.35
0.38
0.08
L_TOBINQ
3,745
0.76
1.09
1.57
1.35
1.82
4.69
0.71
L_GROWTH
3,745 -0.43
-0.02
0.07
0.06
0.14
0.76
0.17
L_SIZE
3,745
5.10
7.42
8.51
8.35
9.56
13.59
1.59
L_TOTAL ASSETS($Mill)
3,745
165
1,665
23,638
4,215
14,234
795,337
83,977
L_LEVERAGE
3,745
0.00
0.15
0.27
0.25
0.36
0.83
0.16
L_ROA
3,745 -0.20
0.02
0.05
0.05
0.09
0.27
0.07
L_OCF
3,745 -0.69
0.06
0.09
0.09
0.13
0.83
0.07
STD_OCF
3,745
0.00
0.02
0.03
0.03
0.04
0.38
0.03
L_LOGPLAN
3,745 -0.02
5.16
6.33
6.19
7.51
11.81
1.75
ARR
3,745 -33.65
2.49
6.04
10.19
13.63
32.64
13.47
HYBRIDPLAN
3,745
0
0
0.22
0
0
1
0.41
INACTIVEPLAN
3,745
0
0
0.29
0
1
1
0.45
ASP_DISC RATE (%)
3,745
2.40
5.00
5.53
5.75
6.11
8.50
0.89
DISTRESS
3,539
0.00
0.00
0.42
0.00
1.00
1.00
0.49
ADJUSTED_PBO/TA
3,745
0.00
0.05
0.17
0.11
0.23
2.33
0.20
All the variables are defined in Table. All continuous control variables are winsorized at 1 percent and 99 percent to mitigate
outliers.
43
Table 3
Summary statistics for funding status regressions (continued)
Panel B. Descriptive statistics by fiscal year
Variables
2006
2007
2008
2009
2010
2011
2012
N
484
576
573
561
543
520
488
FUNDING_STATUS
FUNDING_STATUS
TO PBO
TO TA
Mean
-0.09
-0.02
-0.26
-0.23
-0.20
-0.25
-0.24
Median
-0.11
-0.05
-0.30
-0.24
-0.20
-0.26
-0.25
Mean
-0.01
0
-0.04
-0.04
-0.03
-0.05
-0.05
Median
-0.01
0
-0.02
-0.02
-0.02
-0.03
-0.03
ASP_DISC RATE
Mean
5.82
6.24
6.48
5.88
5.40
4.74
4.00
Median
5.85
6.25
6.30
5.86
5.40
4.71
4.00
All the variables are defined in Table 1. All continuous variables are winsorized at 1 percent and 99 percent to mitigate outliers.
44
HYBRIDPLAN
INACTIVEPLAN
Mean
0.18
0.19
0.20
0.22
0.24
0.24
0.24
Mean
0.20
0.23
0.26
0.30
0.33
0.35
0.38
Table 3
Summary statistics for funding status regressions (continued)
Panel C. Pearson (above) / Spearman (below) Correlations
Variable
FUNDING_ STATUS TO TA
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
1
0.46
0.06
-0.04
0.03
-0.12
0.09
-0.02
-0.14
-0.19
0.17
0.21
-0.18
-0.08
0.04
0.02
0.09
0.12
-0.01
0.10
0.09
FUNDING_STATUS TO PBO
0.63
1
0.08
0.00
0.04
-0.06
0.03
0.05
-0.10
-0.14
0.21
0.13
0.15
-0.05
0.02
0.09
0.01
0.11
-0.05
0.08
0.23
QDB_TO_PENSION
-0.02
0.12
1
0.33
0.09
-0.27
0.08
0.05
0.14
-0.03
0.02
0.03
-0.17
0.03
0.01
0.03
0.02
-0.17
-0.07
0.02
-0.01
CEO_QDB
-0.17
0.04
0.74
1
0.00
0.20
-0.40
0.02
0.01
0.12
0.00
-0.10
-0.02
0.03
-0.13
-0.12
-0.02
-0.11
0.01
-0.15
-0.04
CEO_DEFER
0.04
0.07
0.09
0.10
1
-0.06
-0.50
0.08
0.08
0.06
-0.02
0.05
0.10
-0.06
-0.04
-0.06
-0.01
0.13
0.06
-0.01
-0.02
CEO_SERP
-0.16
0.00
0.07
0.55
0.06
1
-0.80
0.05
-0.18
0.13
0.02
-0.03
0.26
-0.02
-0.14
-0.16
-0.05
0.15
0.09
-0.11
-0.07
CEO_EQUITY
0.14
0.00
-0.16
-0.59
-0.50
-0.78
1
-0.09
0.10
-0.17
0.00
0.02
-0.25
0.04
0.17
0.19
0.05
-0.16
-0.11
0.13
0.07
HYBRIDPLAN
-0.01
0.05
0.11
0.07
0.09
0.07
-0.08
1
0.06
0.00
-0.06
-0.07
0.17
-0.02
-0.04
-0.05
-0.02
0.17
0.04
-0.06
0.01
INACTIVEPLAN
-0.09
-0.10
-0.01
-0.13
0.06
-0.25
0.13
0.06
1
0.09
-0.12
-0.16
-0.20
0.09
-0.07
-0.09
-0.03
-0.22
-0.01
-0.12
0.02
DISTRESS
-0.16
-0.15
-0.03
0.07
0.01
0.07
-0.13
0.00
0.09
1
0.16
-0.23
-0.08
0.09
-0.19
-0.13
-0.06
-0.15
0.40
-0.24
-0.24
ASP_DISC RATE (%)
0.21
0.23
0.06
0.05
-0.02
0.04
-0.02
-0.06
-0.12
0.19
1
0.10
-0.04
0.00
0.02
0.07
0.00
-0.04
0.02
0.01
-0.32
L_TAXRATE
0.11
0.11
0.03
-0.01
0.09
0.06
-0.02
-0.05
-0.14
-0.17
0.13
1
0.07
-0.15
0.24
0.22
0.22
0.10
-0.28
0.43
-0.07
L_LOGPLAN
-0.21
0.15
0.01
0.17
0.18
0.35
-0.30
0.17
-0.21
-0.07
-0.04
0.13
1
-0.20
-0.08
-0.05
-0.06
0.75
0.09
-0.01
0.03
STD_OCF
-0.09
-0.08
-0.03
-0.07
-0.10
-0.12
0.10
-0.04
0.15
0.08
-0.03
-0.09
-0.27
1
0.07
0.12
0.03
-0.26
-0.06
0.03
0.01
L_OCF
0.02
0.05
0.00
-0.09
0.00
-0.09
0.14
-0.04
-0.08
-0.22
0.01
0.22
-0.05
0.06
1
0.59
0.03
-0.12
-0.17
0.55
-0.01
L_TOBINQ
0.02
0.13
-0.02
-0.13
-0.02
-0.13
0.19
-0.10
-0.10
-0.17
0.09
0.29
-0.01
0.11
0.56
1
0.11
-0.16
-0.11
0.61
-0.12
L_GROWTH
0.11
0.00
0.03
-0.03
0.00
-0.05
0.06
-0.02
-0.04
-0.08
0.05
0.24
-0.07
0.04
0.03
0.18
1
0.04
-0.03
0.29
-0.08
L_SIZE
0.15
0.10
-0.06
0.00
0.20
0.22
-0.19
0.16
-0.24
-0.13
-0.05
0.14
0.76
-0.36
-0.10
-0.15
0.01
1
0.13
-0.04
0.02
L_LEVERAGE
-0.08
-0.06
-0.04
0.03
0.06
0.10
-0.13
0.03
-0.02
0.39
0.01
-0.18
0.13
-0.11
-0.20
-0.16
-0.07
0.14
1
-0.22
0.01
L_ROA
0.07
0.09
0.02
-0.07
0.03
-0.06
0.11
-0.08
-0.13
-0.27
0.04
0.40
-0.01
0.07
0.59
0.66
0.33
-0.07
-0.26
1
-0.09
ARR
0.07
0.19
0.01
0.00
-0.01
0.00
0.03
0.01
-0.01
-0.18
-0.24
-0.07
0.05
0.01
0.01
-0.16
-0.12
0.04
0.03
-0.09
1
Panel C of Table 3 reports pair-wise correlations between the variables; bolded correlations are significant at the 10% level based on two-tailed critical values. All the variables
are defined in Table 1. All continuous variables are winsorized at 1 percent and 99 percent to mitigate outliers.
45
Table 4
Regressions of funding status on CEO incentives
Variables
QDB_TO_PENSION
(H1A)
CEO_DEFER
(H1B)
CEO_SERP
(H1A & H1B)
CEO_EQUITY
(H1B)
L_TAXRATE
L_TOBINQ
L_GROWTH
L_SIZE
L_LEVERAGE
L_ROA
L_OCF
STD_OCF
L_LOGPLAN
ARR
L_ARR
HYBRIDPLAN
INACTIVEPLAN
ASP_DISC RATE
Industry FE
Year FE
# of Observation
Adjusted R-square
Dependent Variable
FUNDING_STATUS TO TA
FUNDING_STATUS TO PBO
Model 1
Model 2
Model 3
Model 4
0.012**
0.058***
(2.507)
(2.925)
-0.045*
-0.083
(-1.835)
(-0.897)
-0.062**
-0.193**
(-2.361)
(-2.123)
-0.002
0.013
-0.052**
-0.122
(-0.320)
(0.703)
(-2.146)
(-1.486)
0.113***
0.117***
0.110*
0.119**
(3.278)
(3.331)
(1.856)
(2.043)
0.000
0.000
0.007
0.008
(0.014)
(0.038)
(1.037)
(1.043)
-0.002
-0.002
-0.012
-0.011
(-0.339)
(-0.373)
(-0.787)
(-0.719)
0.026***
0.026***
-0.002
-0.005
(7.713)
(7.799)
(-0.342)
(-0.712)
0.007
0.007
-0.067*
-0.068*
(0.544)
(0.566)
(-1.833)
(-1.834)
0.018
0.021
0.095*
0.103*
(0.626)
(0.745)
(1.801)
(1.936)
-0.020
-0.022
-0.041
-0.051
(-1.183)
(-1.282)
(-0.590)
(-0.720)
-0.024
-0.021
-0.013
0.001
(-0.574)
(-0.493)
(-0.097)
(0.004)
-0.024***
-0.024***
0.019***
0.020***
(-8.125)
(-8.109)
(3.227)
(3.499)
0.001***
0.001***
0.007***
0.006***
(8.729)
(8.686)
(18.813)
(18.550)
0.001***
0.001***
0.004***
0.004***
(7.655)
(7.586)
(12.405)
(11.995)
0.004
0.004
0.013
0.015
(0.959)
(1.059)
(1.006)
(1.172)
-0.010***
-0.009***
-0.011
-0.011
(-2.700)
(-2.704)
(-0.986)
(-1.000)
0.008***
0.009***
0.051***
0.054***
(2.653)
(2.753)
(4.764)
(4.964)
YES
YES
3,745
37.86%
YES
YES
3,745
37.81%
YES
YES
3,745
36.77%
YES
YES
3,745
36.56%
F tests (H1C): CEO_SERP=CEO_EQUITY
F-stats
p-Value
1.36
0.244
46
7.53
0.006
Table 4 reports the results of regressing funding status, measured relative to total assets and relative to PBO, on its
determinants using the full sample. Coefficient estimates and t-statistics are reported. *, **, and *** denote twotailed statistical significance at 10%, 5%, and 1%, respectively. Standard errors are clustered by firm. The bottom of
this table reports the F-test results. For each F-test we report the value of the F-statistic and, in the cell below, the
associated p-value. All the variables are defined in Table 1.
47
Table 5
Regression of funding status on CEO incentives − samples divided by financial constraints
Panel A: Non-distress vs. distress
Dependent Variable:
Variables
CEO_DEFER
(H1B)
CEO_SERP
(H1A & H1B)
CEO_EQUITY
(H1B)
FUNDING_STATUS TO TA
FUNDING_STATUS TO PBO
DISTRESS=0
DISTRESS=1
DISTRESS=0
DISTRESS=1
-0.005
(-0.266)
-0.006
(-0.375)
-0.014
(-0.849)
-0.068**
(-2.189)
-0.087**
(-2.490)
-0.083**
(-2.554)
0.043
(0.389)
-0.037
(-0.447)
-0.006
(-0.071)
-0.165
(-1.627)
-0.274***
(-2.660)
-0.200**
(-2.234)
YES
YES
1,480
46.85%
YES
YES
2,059
37.78%
YES
YES
1,480
42.20%
0.07
0.797
0.59
0.441
6.84
0.009
Industry FE
YES
Year FE
YES
# of Observation
2,059
Adjusted R-square
31.72%
F tests (H1C): CEO_SERP=CEO_EQUITY
F-stats
0.75
p-Value
0.386
Panel B: With credit rating vs. no credit rating
Dependent Variable:
Variables
CEO_DEFER
(H1B)
CEO_SERP
(H1A & H1B)
CEO_EQUITY
(H1B)
FUNDING_STATUS TO TA
WITH CREDIT
NO CREDIT
RATING
RATING
-0.016
-0.044
(-0.615)
(-1.141)
-0.027
-0.126***
(-1.005)
(-2.842)
-0.014
-0.113***
(-0.601)
(-2.685)
Industry FE
YES
Year FE
YES
# of Observation
2,878
Adjusted R-square
39.32%
F tests (H1C): CEO_SERP=CEO_EQUITY
F-stats
1.56
p-Value
0.212
FUNDING_STATUS TO PBO
WITH CREDIT
NO CREDIT
RATING
RATING
0.029
-0.180
(0.276)
(-1.424)
-0.069
-0.293**
(-0.688)
(-2.569)
0.004
-0.264***
(0.043)
(-2.795)
YES
YES
867
42.10%
YES
YES
2,878
37.78%
YES
YES
867
42.20%
0.80
0.372
7.29
0.007
0.31
0.578
48
Table 5
Regression of funding status on CEO incentives − samples divided by financial constraints
(Continued)
Panel C: Pay dividends vs. don’t pay dividends
Dependent Variable:
Variables
FUNDING_STATUS TO TA
FUNDING_STATUS TO PBO
PAY DIVIDENDS
NO DIVIDENDS
PAY DIVIDENDS
NO DIVIDENDS
CEO_EQUITY
(H1B)
-0.038
(-1.467)
-0.051*
(-1.666)
-0.034
(-1.276)
-0.026
(-0.717)
-0.077**
(-2.282)
-0.073**
(-2.587)
-0.047
(-0.425)
-0.132
(-1.264)
-0.060
(-0.601)
-0.093
(-0.845)
-0.226**
(-2.026)
-0.187**
(-2.006)
Industry FE
Year FE
# of Observation
Adjusted R-square
YES
YES
2,820
35.00%
YES
YES
925
45.65%
YES
YES
2,820
36.11%
YES
YES
925
44.52%
0.08
0.773
4.61
0.032
1.31
0.254
CEO_DEFER
(H1B)
CEO_SERP
(H1A & H1B)
F tests (H1C): CEO_SERP=CEO_EQUITY
F-stats
2.89
p-Value
0.090
Panel D: Low HP index vs. high HP index
Dependent Variable:
Variables
CEO_DEFER
(H1B)
CEO_SERP
(H1A & H1B)
CEO_EQUITY
(H1B)
FUNDING_STATUS TO TA
LOW HP index
HIGH HP index
-0.005
-0.049*
(-0.142)
(-1.765)
0.010
-0.103***
(0.325)
(-3.045)
0.006
-0.082***
(0.184)
(-2.829)
Industry FE
YES
Year FE
YES
# of Observation
1,872
Adjusted R-square
42.77%
F tests (H1C): CEO_SERP=CEO_EQUITY
F-stats
0.16
p-Value
0.694
FUNDING_STATUS TO PBO
LOW HP index
HIGH HP index
0.237**
-0.221**
(1.980)
(-2.521)
0.157
-0.332***
(1.480)
(-3.863)
0.209**
-0.268***
(2.022)
(-3.529)
YES
YES
1,873
38.24%
YES
YES
1,872
37.49%
YES
YES
1,873
40.55%
2.36
0.125
2.09
0.148
4.48
0.035
49
Table 5 reports the results of funding status regressions for three pairs of subsamples, respectively: (1)
“DISTRESS=0” sub-sample and the “DISTRESS=1” sub-sample ; (2) “firms with credit rating” vs. “firms with no
credit rating”; (3) “dividend paying group” vs. “no dividends group”; (4) “low HP index” sub-sample vs. “high HP
index” sub-sample; Reported are the coefficient estimates and t-statistics for the CEO incentive variables. *, **,
and *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively. Standard errors are clustered by
firm. All the variables are defined in Table 1.
50
Table 6
Regressions of funding status on CFO incentives
Variables
QDB_TO_PENSION
(H1A)
CEO_DEFER
(H1B)
CEO_SERP
(H1A & H1B)
CEO_EQUITY
(H1B)
L_TAXRATE
L_TOBINQ
L_GROWTH
L_SIZE
L_LEVERAGE
L_ROA
L_OCF
STD_OCF
L_LOGPLAN
ARR
L_ARR
HYBRIDPLAN
INACTIVEPLAN
ASP_DISC RATE
Dependent Variable
FUNDING_STATUS TO TA
FUNDING_STATUS TO PBO
Model 1
Model 2
Model 3
Model 4
0.004
0.021
(0.964)
(1.463)
0.016
0.041
(0.962)
(0.693)
-0.008
-0.058
(-0.393)
(-0.992)
-0.001
0.001
-0.006
-0.019
(-0.153)
(0.076)
(-0.276)
(-0.415)
0.120***
0.118***
0.126**
0.120**
(3.308)
(3.267)
(2.131)
(2.092)
0.000
0.000
0.009
0.010
(0.031)
(0.103)
(1.211)
(1.341)
-0.001
-0.000
-0.007
-0.006
(-0.109)
(-0.053)
(-0.458)
(-0.392)
0.026***
0.025***
-0.001
-0.004
(7.880)
(8.026)
(-0.181)
(-0.593)
0.008
0.007
-0.075*
-0.076**
(0.592)
(0.557)
(-1.934)
(-1.976)
0.017
0.015
0.088*
0.083
(0.592)
(0.542)
(1.681)
(1.587)
-0.017
-0.019
-0.032
-0.039
(-1.001)
(-1.110)
(-0.442)
(-0.537)
-0.023
-0.023
-0.021
-0.026
(-0.536)
(-0.523)
(-0.148)
(-0.182)
-0.025***
-0.024***
0.017***
0.019***
(-8.443)
(-8.491)
(2.977)
(3.311)
0.001***
0.001***
0.007***
0.006***
(8.730)
(8.623)
(18.394)
(18.109)
0.001***
0.001***
0.004***
0.004***
(7.840)
(7.766)
(12.413)
(11.999)
0.004
0.004
0.011
0.014
(0.988)
(1.126)
(0.851)
(1.060)
-0.008**
-0.009***
-0.005
-0.009
(-2.428)
(-2.657)
(-0.434)
(-0.786)
0.009***
0.009***
0.055***
0.056***
(2.826)
(2.842)
(5.086)
(5.058)
Industry FE
YES
Year FE
YES
# of Observation
3,693
Adjusted R-square
37.96%
F tests(H1C): CFO_SERP=CFO_EQUITY
F-stats
p-Value
YES
YES
3,693
38.08%
0.69
0.406
51
YES
YES
3,693
36.52%
YES
YES
3,693
36.69%
1.44
0.230
Table 6 reports the results of regressing funding status, measured relative to total assets and relative to PBO, on its
determinants using the CFO sample. Coefficient estimates and t-statistics are reported. *, **, and *** denote twotailed statistical significance at 10%, 5%, and 1%, respectively. Standard errors are clustered by firm. The bottom of
this table reports the F-test results. For each F-test we report the value of the F-statistic and, in the cell below, the
associated p-value. All the variables are defined in Table 1.
52
Table 7
Regressions of funding status on CEO incentives — addressing endogeneity
Panel A. Instrumental variables (IV) estimation
Variables
QDB_TO_PENSION
(H1A)
CEO_DEFER
(H1B)
CEO_SERP
(H1A & H1B)
CEO_EQUITY
(H1B)
L_TAXRATE
L_TOBINQ
L_GROWTH
L_SIZE
L_LEVERAGE
L_ROA
L_OCF
STD_OCF
L_LOGPLAN
ARR
L_ARR
HYBRIDPLAN
INACTIVEPLAN
ASP_DISC RATE
IndustryFE, Year FE
# of Observation
Adjusted R-square
p-Value for endogeneity-Hausman test
p-Value for overidentifying restriction
-Basmann test
F tests (H1C): CEO_SERP=CEO_EQUITY
F-stats (p-Value)
Dependent Variable
FUNDING_STATUS TO
FUNDING_STATUS TO
TA
PBO
Model 1
Model 2
Model 3
Model 4
0.082**
0.168*
(2.429)
(1.745)
-0.595**
-0.922
(-2.255)
(-1.297)
-0.655**
-0.984
(-2.217)
(-1.360)
-0.013
0.007
-0.569**
-0.860
(-0.616)
(0.096)
(-2.317)
(-1.380)
0.101***
0.146***
0.096
0.172**
(2.944)
(3.192)
(1.523)
(2.151)
-0.001
-0.000
0.006
0.008
(-0.272)
(-0.121)
(0.678)
(0.887)
-0.007
-0.011
-0.022
-0.026
(-1.119)
(-1.452)
(-1.236)
(-1.300)
0.030***
0.031***
0.003
0.004
(7.239)
(7.097)
(0.353)
(0.371)
0.011
0.020
-0.062
-0.052
(0.782)
(1.218)
(-1.557)
(-1.243)
0.016
0.061
0.095*
0.168**
(0.525)
(1.351)
(1.656)
(1.974)
-0.010
-0.018
-0.039
-0.052
(-0.484)
(-0.763)
(-0.526)
(-0.671)
-0.042
-0.014
-0.037
-0.001
(-0.919)
(-0.245)
(-0.243)
(-0.007)
-0.026***
-0.025***
0.018**
0.018***
(-7.414)
(-7.087)
(2.366)
(2.599)
0.001***
0.001***
0.007***
0.007***
(8.761)
(7.762)
(17.150)
(16.304)
0.001***
0.001***
0.004***
0.004***
(7.142)
(5.910)
(11.856)
(11.351)
0.001
0.004
0.010
0.015
(0.247)
(0.875)
(0.671)
(1.101)
-0.017***
-0.013**
-0.020
-0.009
(-3.060)
(-2.028)
(-1.361)
(-0.669)
0.007*
0.010**
0.047***
0.052***
(1.875)
(2.457)
(3.852)
(4.453)
YES, YES
YES, YES
YES, YES
YES, YES
3,518
3,518
3,518
3,518
29.27%
13.46%
34.00%
31.26%
0.0008
0.0001
0.167
0.033
0.182
0.680
1.66 (0.197)
53
0.101
0.031
3.20 (0.074)
Table 7
Regressions of funding status on CEO incentives -- addressing endogeneity (continued)
Panel B. Lagged incentives regression
Variables
LAG_QDB_TO_PENSION
(H1A)
LAG_CEO_DEFER
(H1B)
LAG_CEO_SERP
(H1A & H1B)
LAG_CEO_EQUITY
(H1B)
L_TAXRATE
L_TOBINQ
L_GROWTH
L_SIZE
L_LEVERAGE
L_ROA
L_OCF
STD_OCF
L_LOGPLAN
ARR
L_ARR
HYBRIDPLAN
INACTIVEPLAN
ASP_DISC RATE
Year FE
# of Observation
Adjusted R-square
Dependent Variable
FUNDING_STATUS TO TA FUNDING_STATUS TO PBO
Model 1
Model 2
Model 3
Model 4
0.013**
0.048**
(2.577)
(2.502)
-0.048**
-0.070
(-2.130)
(-0.760)
-0.064***
-0.178**
(-2.641)
(-2.008)
-0.002
0.018
-0.054**
-0.105
(-0.300)
(0.921)
(-2.408)
(-1.305)
0.123***
0.127***
0.106*
0.112*
(3.104)
(3.147)
(1.833)
(1.956)
-0.000
-0.000
0.009
0.008
(-0.014)
(-0.003)
(1.206)
(1.161)
0.001
0.001
-0.007
-0.007
(0.163)
(0.168)
(-0.425)
(-0.381)
0.030***
0.030***
-0.001
-0.003
(7.900)
(7.985)
(-0.155)
(-0.457)
0.012
0.012
-0.048
-0.050
(0.817)
(0.833)
(-1.276)
(-1.311)
0.020
0.025
0.067
0.084
(0.672)
(0.816)
(1.185)
(1.475)
-0.017
-0.020
0.002
-0.011
(-0.903)
(-1.051)
(0.036)
(-0.162)
0.000
0.005
0.038
0.065
(0.006)
(0.117)
(0.272)
(0.453)
-0.028***
-0.028***
0.018***
0.019***
(-8.190)
(-8.196)
(2.930)
(3.170)
0.001***
0.001***
0.007***
0.007***
(8.290)
(8.320)
(17.072)
(16.963)
0.001***
0.001***
0.004***
0.004***
(7.173)
(7.182)
(11.827)
(11.550)
0.003
0.004
0.015
0.016
(0.802)
(0.898)
(1.124)
(1.271)
-0.010**
-0.009**
-0.008
-0.009
(-2.442)
(-2.390)
(-0.716)
(-0.756)
0.009***
0.010***
0.048***
0.051***
(2.634)
(2.746)
(4.500)
(4.743)
YES
YES
YES
YES
3,027
3,027
3,027
3,027
40.77%
40.68%
37.68%
37.60%
F tests (H1C): CEO_SERP=CEO_EQUITY
F-stats (p-Value)
1.44 (0.231)
54
7.54 (0.006)
Panel A of Table 7 reports the second-stage results from a two-stage least squares estimation of funding status tests, with CEO
incentives QDB_TO_PENSION, CEO_DEFER, CEO_SERP, CEO_EQUITY instrumented in the first stage. Instruments are
industry medians (based on two-digit Standard Industrial Classification codes for each year) of these incentives as well as the number of
years the CEO has been with the firm, manually coded from CaptialIQ. Coefficient estimates and t-statistics are reported. *, **, and
*** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively. Standard errors are clustered by firm. The
bottom of this table reports the F-test results. For each F-test we report the value of the F-statistic and, in the cell below, the
associated p-value. All the variables are defined in Table 1. 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 overidentifying restrictions test is that the instruments are jointly valid, i.e., uncorrelated with the error term from the second-stage
regression.
Table B of Table 7 reports the results of regressing funding status, measured relative to total assets and relative to PBO, on its
determinants using lagged incentive variables. Coefficient estimates and t-statistics are reported. *, **, and *** denote twotailed statistical significance at 10%, 5%, and 1%, respectively. Standard errors are clustered by firm. The bottom of this table
reports the F-test results. For each F-test we report the value of the F-statistic and, in the cell below, the associated p-value. All
the variables are defined in Table 1.
55
Table 8
Summary statistics for hard freeze
Panel A. Distribution of newly adopted hard freezes
Year
2006
2007
2008
2009
2010
2011
2012
Total
Hard Freezes
24
22
12
26
20
15
14
133
Panel B. Variables used in hard freeze regressions
Variables
ADOPTHARD
QDB_TO_PENSION
CEO_PENSION
CEO_DEFER
CEO_SERP
CEO_EQUITY
CEO_QDB
UNION
L_UNDERFUNDED
L_FUNDING
L_TAXRATE
L_SIZE
L_PLANSIZE
L_OCF
L_LOSS
L_GROWTH
N
Min
Q1
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
2,205
0
0
0
0
0
0
0
0
0
0.17
0
4.29
0.0004
-0.69
0
-0.78
0
0.02
0.02
0
0.01
0.58
0.00
0
1
0.71
0.32
7.68
0.05
0.06
0
-0.02
Mean
0.05
0.16
0.20
0.08
0.17
0.73
0.02
0.50
0.84
0.84
0.31
8.78
0.16
0.10
0.11
0.06
Median
Q3
Max
Std dev
0
0.07
0.14
0.03
0.12
0.77
0.01
1
1
0.82
0.34
8.66
0.11
0.09
0
0.06
0
0.17
0.31
0.10
0.28
0.92
0.03
1
1
0.94
0.35
9.86
0.21
0.13
0
0.13
1
1
1
1
0.98
1
1
1
1
2.40
0.38
14.99
1.63
0.57
1
3.81
0.22
0.25
0.20
0.11
0.18
0.22
0.05
0.50
0.36
0.20
0.07
1.56
0.17
0.07
0.31
0.20
All the variables are defined in Table 1. All continuous control variables are winsorized at 1 percent and 99 percent to mitigate
outliers.
56
Table 8
Summary statistics for hard freeze (continued)
Panel C. Variables used in hard freeze regressions (Freeze and Non-freeze subsamples)
Variables
Freeze sample
QDB_TO_PENSION
CEO_PENSION
CEO_DEFER
CEO_SERP
CEO_EQUITY
CEO_QDB
Non-freeze sample
QDB_TO_PENSION
CEO_PENSION
CEO_DEFER
CEO_SERP
CEO_EQUITY
CEO_QDB
N
Min
Q1
Mean
Median
Q3
Max
Std dev
109
109
109
109
109
109
0
0
0
0
0.12
0
0.02
0.01
0
0.002
0.56
0.002
0.22
0.18
0.08
0.15
0.74
0.03
0.10
0.08
0.02
0.06
0.83
0.01
0.23
0.25
0.09
0.22
0.96
0.03
1
0.84
0.84
0.82
1
0.56
0.30
0.22
0.14
0.19
0.25
0.07
2,096
2,096
2,096
2,096
2,096
2,096
0
0
0
0
0
0
0.02
0.03
0
0.01
0.58
0.002
0.16
0.20
0.08
0.17
0.73
0.02
0.07
0.15
0.03
0.13
0.77
0.01
0.16
0.32
0.10
0.28
0.91
0.03
1
1
1
0.98
1
1
0.25
0.19
0.11
0.18
0.22
0.05
All the variables are defined in Table 1. All continuous control variables are winsorized at 1 percent and 99 percent to mitigate
outliers.
57
Table 9
Probit models of hard freeze decisions on CEO incentives
Variables
QDB_TO_PENSION
(H2A)
CEO_PENSION
(H2B)
CEO_DEFER
CEO_SERP
CEO_EQUITY
UNION
L_UNDERFUNDED
L_FUNDING
L_TAXRATE
L_SIZE
L_PLANSIZE
L_OCF
L_LOSS
L_GROWTH
Year FE
# of Observation
Adjusted R-square
F- tests:
CEO_SERP=CEO_EQUITY
p-Value
Dependent Variable: ADOPTHARD
Model 1
Model 2
Model 3
Model 4
0.009**
(2.209)
-0.012**
(-2.366)
0.003
0.003
(0.269)
(0.751)
-0.013
(-0.817)
0.008*
-0.000
(1.699)
(-0.018)
-0.004
-0.003
-0.003
-0.004
(-0.475)
(-0.367)
(-0.390)
(-0.428)
-0.023
-0.022
-0.023
-0.023
(-1.224)
(-1.188)
(-1.225)
(-1.226)
-0.018**
-0.020***
-0.019***
-0.018**
(-2.466)
(-2.616)
(-2.576)
(-2.511)
-0.008*
-0.008*
-0.008*
-0.008*
(-1.799)
(-1.758)
(-1.754)
(-1.850)
-0.013**
-0.010*
-0.012**
-0.013**
(-2.432)
(-1.830)
(-2.232)
(-2.413)
0.012***
0.014***
0.014***
0.014***
(3.286)
(3.821)
(3.832)
(3.812)
-0.005
-0.006
-0.006
-0.007
(-1.122)
(-1.266)
(-1.406)
(-1.431)
0.026*
0.028**
0.027*
0.028*
(1.812)
(1.999)
(1.946)
(1.959)
-0.008
-0.009
-0.009
-0.008
(-1.130)
(-1.274)
(-1.233)
(-1.214)
YES
2,205
6.97%
YES
2,205
7.90%
YES
2,205
7.92%
YES
2,205
7.85%
4.82
0.028
Table 9 reports the Probit regression results for the decision to hard freeze the DB plan based on 2,096 non-freezing
firm-years and 109 initial hard-freezes. The dependent variable (ADOPTHARD) is an indicator variable that equals
one if the firm adopted a hard-freeze during the fiscal year and zero otherwise. The reported coefficient is the
elasticity, which represents the change in probability of adopting a hard-freeze for a one-standard-deviation change
in the independent variable. Coefficient z-statistics are in parenthesis and are clustered at the firm level. *, **, and
*** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively. Each variable is defined in Table 1.
58
Figure 1: Funding status by year (from 2006 to 2012)
0.00
-0.05
-0.10
-0.15
-0.20
-0.25
-0.30
2006
2007
2008
2009
underfunded_PBO (Mean)
2010
2011
2012
underfunded_PBO (Median)
Figure 2: Percentage of underfunded firms by year (from 2006 to 2012)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2006
2007
2008
2009
2010
% underfunded_PBO
59
2011
2012
Figure 3: Allocation of average CEO wealth for our sample
QDB
2%
SERP
16%
Deffered
Other
8%
Equity
74%
Figure 4: Allocation of average corporate assets for our sample
Unfunded
QDB/Assets
3%
(STD+LTD)/
Assets
27%
(Equity+other
liabilities)
/Assets
70%