ESSAYS ON THE INTERDEPENDENCE OF EARNINGS QUALITY, FINANCING
OPTIONS, AND EXECUTIVE CONTRACTUAL CLAUSE
A Dissertation
by
NACASIUS UJAH UJAH
Submitted to Texas A&M International University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
December 2013
Major Subject:
International Business Administration
Essays on the interdependence of earnings quality, financing options, and executive
contractual clause
Copyright 2013 Nacasius U. Ujah
ESSAYS ON THE INTERDEPENDENCE OF EARNINGS QUALITY, FINANCING
OPTIONS, AND EXECUTIVE CONTRACTUAL CLAUSE
A Dissertation
by
NACASIUS UJAH UJAH
Submitted to Texas A&M International University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Approved as to style and content by:
Chair of Committee,
Committee Members,
Head of Department,
Dean Robert S. Sears
Dr. Anand Jha
Dr. William Gruben
Dr. Siddharth Shankar
Dr. Antonio Rodriguez
December 2013
Major Subject:
International Business Administration
DEDICATION
To the memory of my father: Isaac Adole Ujah
v
ABSTRACT
Essays on the Interdependence of Earnings Quality, Financing Options, and Executive
Contractual Clause (December 2013)
Nacasius Ujah Ujah, M.B.A, University of Central Arkansas, 2006;
Chair of Committee: Dr. Robert S. Sears
These essays explore the events which prompt managers to managing a firm’s
earnings. Using econometric investigative tools and US Firm data, I find that managing
earnings is exacerbated by golden parachute provision to executives – namely the Chief
Executive Officers, older executives, the firm’s prior cost of capital – specifically, the high
cost of debt, and firms located farther from stakeholders monitoring – specifically, the
regional offices of the Securities and Exchange Commission (SEC).
In the first essay, three questions are posed. These are: (i) Does the prior cost of debt
and equity impact the propensity for firms to manage earnings? (ii) What type of prior cost of
capital affects managed earnings behavior? (iii) Does the location of firms amplify the prior
cost of capital effects on managed earnings? To mitigate and aid in the restructuring of a
firm’s cost of capital, better performance and improved financial statements contributes
immensely. As such, when a firm’s prior cost of capital is high, the firm is more likely to
manage its earnings to help attain favorable placements – in the equity market and to lower
borrowing costs from the debt market.
vi
In the second essay, two questions were asked. These are: (i) Do firms with golden
parachute provisions manage their earnings more? (ii) Does the CEO’s age play a major role
on the propensity to which firms manage their earnings? Arguably, golden parachutes can
serve as a soft-landing mechanism for most CEOs or serve as an impetus for managers to
undertake riskier projects. The empirical evidence in essay two suggests that firms with a
golden parachute provision are more likely to manage their earnings. Furthermore, I find that
the age of the CEO enhances the proclivity for firms to manage earnings.
vii
ACKNOWLEDGEMENTS
I would like to thank first and foremost my committee members Dr. Sears, Dr.
Gruben, Dr. Jha, and Dr. Shankar for their valuable help and support throughout this
research. Especially, I would like to thank my advisor, Dr. Sears for his seasoned guidance,
for helping me through the tough parts, and for always motivating me to keep going. Also, to
the committee for their thorough readings and thoughtful comments over the course of this
research.
Thanks also to my friends and colleagues and the department faculty and staff for
making my time at Texas A&M University a great experience. Finally, thanks to my mother
and father for their encouragement and to my wife and son for their patience and love. Wife,
without your care, I would not have made it this far.
viii
TABLE OF CONTENTS
Page
ABSTRACT……………………………………………………………………………… v
ACKNOWLEDGEMENTS .............................................................................................. vii
TABLE OF CONTENTS ................................................................................................. viii
LIST OF TABLES ...............................................................................................................x
LIST OF FIGURES .......................................................................................................... xii
INTRODUCTION………………………………………………………………………...1
CHAPTER
I
INTRODUCTION: MANAGED EARNINGS .................................................1
Principal-Agent Conflicts and Managed Earnings ......................................1
Factors Influencing Managed Earnings .......................................................2
Methods of Managing Earnings ...................................................................4
Dissertation Focus .......................................................................................5
Summary and Implications .......................................................................11
II
ESSAY ONE: MANAGED EARNINGS, FIRM COST OF CAPITAL, AND
FIRM LOCATION ...................................................................................13
Introduction ...............................................................................................13
Literature Review and Hypotheses Development .....................................17
Sample and Methodology .........................................................................23
Empirical Results ......................................................................................36
Conclusion .................................................................................................42
III
ESSAY TWO: ON THE EFFECTS OF A GOLDEN PARACHUTE ON
MANAGED EARNINGS .........................................................................44
Introduction ...............................................................................................44
Data and Sample .......................................................................................49
Key Independent Variables .......................................................................50
Managed Earnings Metrics ........................................................................58
Empirical Results ......................................................................................65
Additional Tests .........................................................................................70
Conclusion ................................................................................................74
ix
IV
CONCLUSION AND FUTURE RESEARCH ..............................................77
REFERENCES ..................................................................................................................80
APPENDICES
A
TABLES FOR ESSAY ONE ................................................................................90
B
TABLES FOR ESSAY TWO .............................................................................109
C
FIGURES FOR ESSAY TWO ...........................................................................122
VITA………… ................................................................................................................125
x
LIST OF TABLES
Page
Table I: Description of Selected Firms ............................................................................90
Table II: Managed Earnings Proxies ................................................................................91
Table III: Correlation Matrix among the Dependent Variables ........................................92
Table IV: Numerical Conversion of Bond Rating ...........................................................93
Table V: Investable Firms vs. Speculative Firms ............................................................94
Table VI: Managed Earnings across US States ...............................................................95
Table VII: Firms Distance to SEC Regional Offices .......................................................97
Table VIII: Control Variables ............................................................................................99
Table IX: Summary Statistics ..........................................................................................100
Table X: Prior Cost of Capital Sample Correlation Matrix .............................................101
Table XI: Effect of Location and Prior Cost of Capital on Managed Earnings .............103
Table XII: Quartile Effect of Location and Prior Cost of Capital on Managed
Earnings ........................................................................................................105
Table XIII: Interactive Effect of Location and Prior Cost of Capital on Managed
Earnings ........................................................................................................106
Table XIV: Simultaneous Effect of Prior Cost of Debt and Prior Cost of Equity .........107
Table XV: Duration of Past Debts on Managed Earnings .............................................108
Table XVI: Estimates of CEOs Pay for Performance Sensitivity ..................................109
Table XVII: Summary Statistics – Independent and Control Variables .........................110
Table XVIII: Summary Statistics – Dependent Variables ...............................................111
Table XIX: Golden Parachute Sample Correlation Matrix..............................................112
Table XX: On the Effect of Golden Parachute on Managed Earnings ..........................114
xi
Table XXI: Fixed Effect Regression on the Effect of Golden Parachute on Managed
Earnings ........................................................................................................115
Table XXII: Above Industry Effect of Golden Parachute on Managed Earnings .........116
Table XXIII: Quartile Regression Effect of Golden Parachute on Managed
Earnings .......................................................................................................117
Table XXIV: CEOs Age a Factor on Managed Earnings ..............................................118
Table XXV: Incentive Effect Hypothesis of Golden Parachute on Managed
Earnings ......................................................................................................119
Table XXVI: Pre and Post SOX Effect of Golden Parachute on Managed Earnings ....120
Table XXVII: Pre and Post SOX Magnitude Effect of Golden Parachute Coefficients
on Managed Earnings ..................................................................................121
xii
LIST OF FIGURES
Page
Figure 3.1: Growth in the Use of Golden Parachute .......................................................122
Figure 3.2: Annual Sales for Sample Firms ....................................................................123
Figure 3.3: Comparison of Pay for Performance and Operating Performance
for Non-Golden Parachute adopted Years vs. Golden Parachute adopted
Years ..................................................................................................................124
1
CHAPTER I
INTRODUCTION: MANAGED EARNINGS
1.1
Principal-Agent Conflicts and Managed Earnings
Conflicts can arise between owners/principals and agents/managers of businesses as
they may have different interests. While owners desire to increase firm value, agents are
tasked with owners’ responsibilities to increase the value of the firm. However, agents may
have “other” motives beside the tasks adjudicated to them. These motives, such as, managing
earnings can obscure firm performance and lessen the abilities of owners to make informed
decisions.
Many scholars have focused on the negative effects of these motives. This
dissertation attempts to explore and contribute to the literature by considering the effects of:
(i) prior costs of accessing capital, and (ii) executive indemnity contractual rights, on
managing earnings.
These collective “motives” may be reflected by the actions of managers on firm
earnings termed “managed earnings.” The ability to manage earnings captures the actions of
managers beyond the powers given to them by the firm owners.
Granted, managerial
decisions can transfer risk to the shareholders and in some instances managers either push the
margin of their actions too thin or use the choices available to them to amass personal wealth.
Either way, managers have the ability to manage financial records within financial reporting
standards in an effort to placate their owners and other stakeholders, thus managing a firm’s
earnings.
The literature on managed earnings makes a distinction between discretionary and
non-discretionary actions of managers, where discretionary methods denote the actions
Journal Model: Journal of Finance
2
beyond the latitude given to managers. In this dissertation, “managed earnings” is defined as
the manager’s use of judgment in financial reporting and in structuring transactions to alter
financial reports to either mislead stakeholders about the underlying economic performance
of the company, or to influence contractual outcomes that depend on reported accounting
numbers (Healy and Wahlen, (1999)).
1.2
Factors Influencing Managed Earnings
The existing literature on managed earnings began from the accounting discipline,
first with the intent to offer empirical evidence2. Since then, further research has cut across
interdisciplinary business disciplines with motivations arising from capital market
expectations and valuation (Cohen and Zarowin (2010), DeAngelo (1988), Gong, Louis, and
Sun (2008), Kanagaretnam, Lim, and Lobo (2010), Perry and Williams (1994), and Teoh,
Welch, and Wong, (1998a)), contract agreement (DeAngelo, DeAngelo, and Skinner (1994),
DeFond and Jiambalvo (1994), Holthausen, Larcker, and Sloan (1995), Kothari, Leone, and
Wasley (2005), Kothari, Shu, and Wysocki (2009), and Watts and Zimmerman (1978)), and
anti-trust and other government regulations (Han, Kang, Salter, and Yoo (2008), Jones
(1991), and Shen and Chih, (2005)).
To date, the empirical evidence on the impacts of managed earning in academic
research is mixed. For example, evidence found in articles on contract agreement (
DeAngelo, DeAngelo, and Skinner (1992) and Healy and Palepu (1990)) examines whether
firms close to their dividend covenant changed accounting methods or accruals to avoid
cutting dividend payments. They conclude that there was little evidence of earnings
management among firms close to their dividend covenant. However, Guidry, Leone, and
2
McNicols and Wilson (1988) were among the first studies to model specific behavior of managed earnings.
3
Rock (1999) investigate whether managers make discretionary accrual decisions to maximize
their short-term bonuses and Holthausen et al. (1995) examine the extent to which executives
manipulate earnings to maximize the present value of bonus plan payments. Both papers
conclude that managers use accounting judgment to increase earnings-based bonus awards.
Also, Chen and Cheng (2004), and Louis (2004) find evidence that managed earnings
is not a general or widespread event, rather, it is associated with incentives related to
accruals. In a related paper Sloan (1996) finds a significant inverse relation between accruals
and long-term returns for all firms. Conversely, other studies document managed earnings
behavior prior to corporate events. For example, Gong et al. (2008) notes that postrepurchase abnormal returns and improvement in operating performance are driven by prerepurchase downward earnings management. Teoh et al. (1998a) finds that earnings are
managed upward prior to season equity offerings by firms. Coles, Hertzel, and Kalpathy
(2006) find that accruals are abnormally low in the period following announcements of
cancellations of executive stock options up to the time the options are reissued, but stock
prices are unaffected by these apparent manipulations.
A central theme emerging in the above papers is the existence of conflicts of interest
between the stakeholders and their agents. Healy and Wahlen (1999) cite four areas in which
firms manage earnings. The areas are: (i) prior to public securities offerings, (ii) increasing
managers’ compensation or job security, (iii) avoiding violating debt covenants, and (iv)
reducing regulatory costs and/or increasing benefits. Yet, within the existing literature on
managed earnings there remain some unanswered questions. Unlike prior research that
considers the impact of earnings management on explained variables, this dissertation posits
4
that past and present events do influence the propensity to manage earnings and that the
country of incorporation matters.
1.3
Methods of Managing Earnings
Earnings management can be classified into three categories: (i) fraudulent
accounting, (ii) accrual earnings management, and (iii) real activities earnings management.
The focus of this dissertation is on the latter two categories in that fraudulent accounting
involves accounting choices that violate the general accounting principles and the law.
Accruals earnings management entails masking the true economic performance (Dechow and
Skinner, 2000). On the other hand, real earnings management occurs when managers
undertake actions that stray from best practices (Gunny, 2005). While both accrual and real
earnings management hide the true state of a firm, the two are different in their methods as
accrual management revolves around the accounting methods used to dampen or modify the
fluctuation of a firm’s underlying cash flows in order to produce a result that investors can
believe. In contrast, real earnings management is accomplished by changing the firm’s
underlying day to day operating performance.
Degeorge, Patel, and Zeckhauser (1999) note that accrual earnings management
involved misreporting earnings to affect discretionary accounting decisions and outcomes
already realized. Also, Degeorge et al. (1999) note that real or direct earnings management
entails adjusting financial statements to affect the strategic timing of investment, sales,
expenditures, and financing decisions. While there are numerous articles that have
considered the effect of accrual earnings management, there are very few papers that have
considered the effect of real earnings management. Nevertheless, real earnings management
is receiving greater attention as noted by Mr. Douglas R. Conant, President and Chief
5
Executive Officer of Campbell Soup Company, until July, 2011. During the company’s third
quarterly earnings conference in 2008, Mr. Conant states, “…. We then didn’t have the right
pricing in the plan. We then managed our marketing plans to manage our earnings to ensure
that we were supporting the business but also delivering our earnings and at the same time,
competition was more competitively successful than they had been in prior years3”
(Campbell Soup Company, (2008)). Cohen, Dey, and Lys (2008) note that after the passage
of the Sarbanes Oxley Act (SOX) in 2002, many firms switched from accrual management to
the real management method.
1.4
Dissertation Focus
In this dissertation, two issues will be examined that are pertinent to the topic of
earnings management. Each of these issues is investigated in two separate essays.
1.4.1
Essay One
The first essay explores the association between firm location, prior cost of capital,
and earnings management. Here I offer two propositions. First, a firm’s location can be
defined by two constructs – (i) the degree of geographical concentration of a firm’s
headquarter within counties, and (ii) the distance between a firm’s headquarter and the
security exchange commission (SEC). I argue that groups of firms that are in close proximity
to each, otherwise known as “clustered” share similar traits to managing earnings than firms’
farther from the cluster. This disparity in managing earnings behavior among the two groups
is enhanced by the degree of effective monitoring – either by their shareholders, auditing
committee, and other stakeholders – of both groups.
The word “earning” can be clearly heard at time 33:40 in the audio version of the conference call but has been redacted from the call
transcript available at http://seekingalpha.com/article/77913-campbell-soup-f3q08-qtr-end-4-27-08-earnings-call-transcript?page=-1
3
6
This dissertation proposes that clustered firms exhibit higher monitoring from
stakeholders, compared to firms farther away. A recent report from the U.S. government
accountability office (GAO) suggests that SEC officials view travel outside their geographic
jurisdiction as a significant cost (GAO (2007)). Thus distant firms would exhibit a higher
propensity to manage earnings. To my knowledge this is the first research to consider this
effect on managed earnings. However, the locational effect literature in corporate finance
suggests that location can affect firm’s capital structure (Shleifer and Vishny (1992) and
Williamson, (1988)), firm acquisition (Almazan, De Motta, Titman, and Uysal (2010)),
corporate payout and dividend policy (John, Knyazeva, and Knyazeva (2008, 2011)), and
SEC monitoring (Kedia and Rajgopal (2011)).
Second, a firm’s past costs of capital may impact the propensity to manage earnings.
Specifically, I will investigate which costs of capital – cost of debt or cost of equity – have
the highest impact on the firm’s susceptibility to manage earnings. It is my speculation that
debt would have a higher impact as managers would prefer not to violate any debt covenants.
This is important as it will offer evidence to the reactionary behavior of managers as well as
offer empirical evidence supporting the debt covenant hypothesis.
The debt covenant hypothesis asserts that firms make accounting decisions to reduce
the likelihood that they will be in violation of their debt obligations. This could entail
adjusting financial statements by shifting earnings from future periods to current periods.
For debt, I argue that high levered firms face bigger hurdles in the renewal of debt contracts
relative to low levered firms. Thus, high levered firms’ ex-post would manage their earnings
more. Likewise, for equity I contend that information to the market is important to firms, as
such, riskier firms will reduce their managed earnings behavior.
7
This investigation differentiates itself from prior literature on costs of capital and
managed earnings in three ways. First, unlike most prior papers I consider the heterogeneous
nature of firm’s capital structure rather than treating each costs of capital separately. For
example, Liu, Ning, and Davidson (2010) examine the effect of firms managing earnings
before issuing bonds to achieve a lower cost of borrowing, Prevost, Rao, and Skousen (2008)
study the effect of earnings management on the firm’s marginal cost of debt, Gupta and
Fields (2006) explore the relation between firms maturity structures and the propensity to
manage earnings, and Francis, LaFond, Olsson, and Schipper (2004) examine the relation
between the cost of equity and seven attributes of earnings.
Second, most scholarly research documents earnings management impact on the
related dependent variable. To my knowledge, this proposed question and its empirical tests
will share similarities with few like Gupta and Fields (2006) which empirically test debt
maturity and managed earnings. Third, I employ the various method of managing earnings –
accrual and real-activity proxies, thus differentiating this essay to prior research that have
solely considered only accrual managed earnings. Furthermore, the empirical evidence of this
research will contribute to the findings of Francis, LaFond, Olsson, and Schipper (2005)
findings that discretionary accruals are associated with greater costs of equity and with
higher realized costs of debt, but dissimilar as we take into account accrual and real managed
earnings and we investigate which costs of capital greatly impacts managed earnings.
In summary, the proposed hypotheses for essay one are:
For prior cost of equity effect on managed earnings
8
H1a: Higher prior cost of equity will positively affect the propensity to manage
earnings using the real activities method.
H1b: Higher prior cost of equity will negatively affect the propensity to manage
earnings using the accrual method.
For prior costs of debt effect on managed earnings
H2a: Higher prior costs of debt will positively affect present accrual managed
earnings.
H2b: Higher prior cost of debt will negatively affect present real-activities managed
earnings.
For location effect on manage earnings
H3a: Firm location will positively impact managed earnings
H3b: Remote firms are more likely to manage their earnings than clustered firms
1.4.2
Essay Two
The second essay explores the association between golden parachutes and managed
earnings. A golden parachute can be defined as the benefit received by an executive in the
event that a company is acquired and/or the executive’s employment is terminated and/or the
executive remains with the firm after a recessionary cycle. Specifically, I investigate two
questions. First, are golden parachute firms more inclined to manage earnings? Second, does
the age of managers’ matter on how earnings are managed? For both questions, I expect a
golden parachute and its adoption to affect managed earnings. By answering these questions,
this essay will attempt to show how contracts like golden parachute motivate or discourage
top executives in improving firm value, consequently shedding light on this issue.
9
Consequently, performance is a desired goal for executives as poor performing
executives tend to be replaced more often. Although, the provision of a golden parachute to
executives could infer a sense of entrenchment, an executive performance is vital for nonindemnified and indemnified manager. Given the need to perform, executives may be
inclined to manage earnings. Empirical evidence suggests that firms adjust their financial
records during certain events to reflect better performance, for instance Kellogg and Kellogg
(1991) find that firms manage their earnings upward the quarter just preceding a season
equity offering. Teoh,Welch, and Wong (1998b) find that issuers of seasoned equity offering
that adjust their financial report for higher net income prior to the offering have lower longrun abnormal stock returns and net income. Besides these events, Falaschetti (2002) notes
that golden parachute enhance ‘efficiency’ by increasing the credibility with which owners
can commit against opportunism.
As such, performance and firm value in light of golden parachute could shed light on
two competing non-takeover hypotheses that have been examined in the golden parachute
literature. These hypotheses are: (i) the incentive effect hypothesis and (ii) the entrenchment
effect hypothesis. The incentive effect hypothesis suggests that employment contracts like
golden parachute alleviate managerial concern regarding short-term profits (Narayanan
(1985) and Stein (1988, 1989)). Therefore, a golden parachute encourages executives to
make investment decisions that will maximize shareholders’ value in the long-run, thus
eliminating the short-term concerns of executives.
I contend that competition induces
managers to perform, as such, when their performance does not meet or exceed expectations
these executives are more inclined to manage their earnings.
10
Similarly, firms without a golden parachute provision could manage their earnings
more, and/or firms with a golden parachute provision may manage their earnings more while
their performance and firm value diminishes. These conjectures lend hand to the
entrenchment effect hypothesis. The entrenchment effect hypothesis suggests that contracts
entrench poor-performing executives by insulating them from the discipline of the corporate
control market and internal governance mechanisms (Bertrand and Mullainathan (2003) and
Narayanan and Sundaram (1998)). As such, an interaction between golden parachutes and
performance will shed light on the behaviors of managers on managing earnings. Also, the
interaction will elucidate the golden parachute hypothesis holds.
In conjunction with golden parachute, I further investigate the behavior of managed
earnings as executives gets older. I align my rationale with works of (Knoeber (1986) and
Conyon and Florou (2002)). Knoeber (1986) who note that incidence of golden parachutes
increases capital expenditure as waiting for future information to assess firm performance
becomes more valuable. Conyon and Florou (2002) documents that firms cut back on capital
expenditure as the chief executive become older, suggesting that older executives may be
more risk averse.
If the presence of a golden parachute increases capital expenditure and the age of the
executive reduces capital expenditures, I predict that older executives motivated under the
incentive effect hypothesis are more likely to manage their earnings. However, if the
entrenchment hypothesis holds then, older executives will manage their earnings less.
Existing literature on golden parachute finds its effect on merger and acquisition likelihood
(Bebchuk, Cohen, and Wang (2012), Fich, Tran, and Walkling (2012)), the disciplinary role
of the market (Bebchuk, Cohen, and Ferrell (2009), Evans and Hefner (2009), and Narayanan
11
and Sundaram (1998)). To my knowledge, no one has explored the effect of golden parachute
on managed earnings.
In summary the proposed hypotheses for essay two are:
H1: Firms with golden parachute provision manage their earnings more.
H2: Firms performance of managed earnings is higher for firms with a golden
parachute provision.
H3: Older executives will manage their earnings more
1.5 Summary and Implications
The findings of these essays have three broad implications. First, for theoretical
research in corporate finance, accounting, and international business management, the results
in essay one show that the debt covenant hypothesis is an effective tool that can exert more
pressure on managers to adjust financial performance. Also, the results in essay two support
the incentive effect hypothesis in executive contract literature in explaining executive
behavior on firm earnings rather than the entrenchment hypothesis.
Second, the empirical results offer evidence suggesting that the issues explored haves
applicative effects such as the impact of manager’s behavior to managed earnings, the role of
contractual indemnity on firms’ managed earnings, etc. Finally, the results show that
managed earnings can be a double-edged sword as firms offering indemnity to the executive
manage earnings more and debt funding exacerbate the propensity to manage earnings.
Therefore, financial statements which are meant to be a better barometer in assessing the
state of the firm are highly questionable. Beyond industry, managed earnings have a severe
12
impact on firm’s earnings. The reminder of this dissertation proceeds as follows: Chapters II
and III discuss each essay and their results respectively, and Chapter IV concludes and
discusses the limitations of each essay as well as the directions for future research.
13
CHAPTER II
ESSAY ONE: MANAGED EARNINGS, FIRM COST OF CAPITAL, AND FIRM
LOCATION
2.1
Introduction
Empirical evidence of earnings management can broadly be categorized into six groups.
These groups are (i) initial public offerings (IPO), (ii) seasoned equity offerings (SEO), (iii)
corporate governance, (iv) auditor’s responsibilities, (v) cost of equity, and (vi) cost of debt.
Some scholars who have documented these effects include (Francis et al. (2004), Perry and
Williams (1994), Prevost et al. (2008), Rangan (1998), Teoh et al. (1998a, 1998b), and Xie,
Davidson, and DaDalt (2003)). However few scholars have examined whether prior events
prompt firms to manage their earnings. Noteworthy is the research by (Cornett, Marcus, and
Tehranian (2008), Gupta and Fields (2006), Leuz, Nanda, and Wysocki (2003) and
Chtourou, Bédard, and Courteau (2001)).
This paper aligns its position with the latter group as I examine the effects of firm
location and prior costs of capital on managed earnings. In so doing, I ask the following
questions:
1. Does the prior cost of debt and the prior cost of equity impact a firm’s earnings
management?
2. If both prior costs do, then, which has a higher impact on the firm’s managed
earning behavior?
3. Does a firm’s location magnify prior costs of capital effects on managed
earnings?
14
Empirical evidence on a firm’s costs of capital defined as the cost of acquiring debt
and the cost of acquiring equity ignores the presence of both debt and equity except (Francis
et al. (2005)). In so doing, prior analyses negate the effect and possibly lack thereof of other
sources of financing. Jensen and Meckling (1976) note that the existence of debt helps to
mitigate the agency costs of equity. Also, Jensen (1986) argues that reduction in equity
related agency costs arises by the elimination of free cash flow resulting from an increase in
the relative amount of debt financing. As such, both debt and equity can be utilized by firms.
Similarly, the corporate finance literature documents evidence on the effects a firm’s
location on its capital structure, acquisition, corporate payout and dividend policy (Almazan
et al. (2010), John, Knyazeva, and Knyazeva (2008, 2011), Shleifer and Vishny (1992), and
Williamson (1988)). Firm location offers a quintessential benefit. For example, when
economic gain diminishes, corporations may relocate or adjust financial records to reflect
favorable performance. I focus on the adjustment of financial records, positing that firms
manage earnings differently, depending on their proximity to stakeholders and monitoring
mechanisms.
Kedia and Rajgopal (2011) find that firms located closer to the securities and exchange
commission (SEC) and in areas with greater past SEC enforcement activity are less likely to
restate their financial statement. Following Kedia and Rajgopal (2011), I posit that firms
located in less concentrated areas, and firms farther from enforcement and monitoring
manage earnings more.
Using COMPUSTAT data for non-financial and non-utility regulated US headquartered
firms from 1980 through 2010; the investigation begins by finding the association between
firms’ prior costs of capital and managed earnings. Then, the investigation delves further to
15
examine which prior costs of capital significantly impact a firm’s propensity to manage
earnings. I expect that prior costs of capital do instigate managers to manage earnings
positively for cost of debt. Similarly, I expect an opposite movement for the firm’s cost of
equity. Also, I suspect that the cost of debt would have more influence on managed earning
behavior: as managers would prefer not to violate their debt obligation and would prefer to
keep or better their debt rating. Hence, the empirical evidence in this essay will point to the
reactionary behavior of managers and show that the debt covenant obligation exacerbates
firms to manage earnings more.
This proposition is counter to the debt covenant hypothesis. The hypothesis asserts that
firms make accounting decisions to reduce the likelihood that there will violate their debt
obligations by adjusting financial statements through the shifting of earnings from future
periods to current periods. I reason that the reactionary behavior of managers facilitates the
position of financial records in present and future periods. Thus, firms with higher bond
ratings would manage their earnings more to abate their chances of not renewing their debt
contracts relative to lower debt bond rated firms. Accordingly, lower debt rated firms, ex
post, would manage their earnings less. Likewise, for equity I contend that information to the
market is crucial to firms as firms with higher prior cost will manage earnings less.
Empirically, I use two metrics for accrual earnings management and three metrics for
real earnings management for a sample of 1,627 firms with total firm annual observations of
32,109. On balance, the evidence suggests that the reactive behavior of managers to the prior
cost of capital varies depending on the technique in question. Overall, I find positive
associations between prior cost of debt and the accrual approach to manage earnings.
Similarly, I find negative associations between prior cost of equity and managed earnings.
16
A closer look at the real-activity approaches to managing earnings reveals that
discretionary operating cash flow had similar directionality to the accrual approach in all
tests performed. Whereas, the prior cost of debt had an inverse association with discretionary
expenses and discretionary production cost. These findings are statistically and economically
significant. The remaining outcomes show that (i) the prior cost of debt effect outweighs the
prior cost of equity effect on managed earnings, and (ii) the effect of firm location on the
firm’s prior costs of capital increases the firm’s propensity to manage earnings.
This paper contributes to the extant literature in the following ways. First, prior
literature investigating the debt covenant hypothesis assumes that the debt covenant
obligation allows managers to adjust financial statements from future periods; however, this
evidence suggests that higher prior costs of capital play an important role on a firm’s
managed earnings behavior. Second, by linking a firm’s location to cost of capital and
managed earnings behavior suggests that the need for more oversight from stakeholders is
required to properly assess the “true” value of a firm.
Third, a subtle implication from the debt structure tests is that variations in a firm’s
capital structure, often attributed to the tradeoff theory and the pecking order theory may not
be well suited to explain the findings of this research. The findings suggest that firms
simultaneously issue different debt types from different sources as well as simultaneously
using both debt and equity.
Theory suggests the use of short-term maturities of debt to enhance lenders’
enforcement of debt contracts. This paper finds a possible downside to having too much
current debt. Finally, the debate over managed earnings behavior concludes that overall it
affects firms’ economic benefits. In this paper, the evidence shows that a firm’s prior costs of
17
capital and location matters. The reminder of this essay proceeds as follows Section 2
provides background and describes prior research. Section 3 describes the sample selection
procedure and research methodology. Section 4 presents empirical results and robustness
tests. Section 5 contains concluding remarks.
2.2
Literature Review and Hypotheses Development
2.2.1
Managed Earnings
The literature on earnings management offers two broad methods of managing
earnings: the accrual and the real-activities approach. Accruals earnings management entails
masking or trying to obscure true economic performance (Dechow and Skinner (2000)). Real
earnings management occurs when managers undertake actions that stray from the best
practice (Gunny (2005)). The two are different in their methods as the accrual technique
revolves around the accounting methods used to dampen the fluctuation in a firm’s
underlying cash flows to generate a number that investors can believe. In contrast, the real
activity approach is accomplished by changing the firm’s underlying day to day operating
performance. Degeorge et al. (1999) summarizes the two distinct categories as: (i) accruals
earnings management involves discretionary accounting of decisions and outcomes already
realized and (ii) real-activities or direct earnings management as the timing of investments,
sales, expenditures, and financing decisions.
I utilize both methods as little work has been done on real-activities managed
earnings, thus shedding more light on the real-activity methods of managing earnings. In a
survey by Graham, Harvey, and Rajgopal (2005) find that managers prefer real-activities
over accruals managed earnings. There are two foreseeable reasons why managers may be
more inclined to manage their earnings using the real-activity methods. First, the accrual
18
method is more likely to draw auditor or regulatory scrutiny. Second, the accrual method
may be more risky in that realized shortfalls between unmanaged earnings and a desired
threshold can be conflicting. If, reported income falls below the firm’s threshold and accruals
methods are exhausted managers are left with only one option – using the real-activities
which can be adjusted at or after the end of the fiscal reporting period.
Consistent with the above reasons, researchers like Cohen and Zarowin (2010) and
Gunny (2005) compared the effect of both managed earnings methods and find that realactivities managed earnings effect is more severe on declining post seasoned equity offering
(SEO) performance than accrual managed earnings, and real activities managed earnings has
a significant negative impact on future operating performance. Roychowdhury (2006) finds
evidence that firms use multiple real earnings methods in order to meet certain financial
reporting benchmarks to avoid reporting annual losses. In particular, his evidence suggests
that managers are providing price discounts to temporarily boost sales, reducing discretionary
expenditures in order to improve reported margins, and over producing to lower the cost of
goods sold.
Given the difference and reference to why and how firms manage their earnings. This
paper tests both methods of managed earnings as the dependent variable. The expectations
are that: overall, accrual managed earnings will be most impacted by firms’ prior costs of
capital and location. However, given the rise in the adoption of the real-activities managed
earnings methods, I expect that real-activities managed earnings will be significant as well.
2.2.2
Cost of Equity and Managed Earnings
The role of managed earnings in equity markets have been the subject of much
research. However, no research to my knowledge, has considered whether the prior cost of a
19
firm’s equity influences whether firms manage their earnings. Generally, research considers
how firms’ managed earnings impact market valuation and executive compensation
(Bergstresser and Philippon (2006), Burgstahler and Dichev (1997), and Teoh et al. (1998a,
1998b)), resource allocation (Louis (2004), Jiang, Petroni and Yanyan (2010), and Xie et al.
2003)). This paper builds on Francis et al. (2005) which documents that higher discretionary
accruals are associated with greater cost of equity. This research redirects this question to
explore if a strong association exists between prior firm’s costs of equity on the proclivity for
firms to manage earnings.
Easley and O’Hara (2004) designed a multi-asset rational expectations model in which
private versus public composition of information affects required returns and the cost of
capital. In their model, relatively more private information increases uninformed investors’
risk of holding the stock, because privately informed investors are better able to shift their
portfolio weights to take advantage of new information. They note that an important role for
accounting information is to reduce the cost of capital by decreasing the systematic risk of
shares to uninformed investors. Leuz and Verrecchia (2004) examine the role of performance
reports in aligning firms and investors with respect to capital investments. Poor quality
reporting impairs the coordination between firms and their investors with respect to the
firm’s capital investment decisions, thereby creating information risks.
Both papers predict that firms with more information risk have higher costs of
capital. “Information risk” concerns the uncertainty or imprecision of information used or
desired by investors to value securities. Tinkering with financial statements affects the
quality of financial reporting and the cost of equity. But, do costs of capital impede the
propensity to manage earnings? Managers, as savers of private information, have a better
20
knowledge about the state of the business than the general public. Thus, if a firm’s present
cost of equity is high, managers may adjust financial statements to lower firms cost of capital
in future periods. As such, I contend that present high cost of equity will impact the future
cost of equity, to which managers will react by adjusting firm’s earnings.
H1a: Higher prior cost of equity will positively affect the propensity to manage
earnings using the real-activities method
Unlike real-activity managing earnings, which revolve around the timing of
investment decisions and financing decisions, accrual earnings management involves
discretionary choices by managers. As such, I speculate that the effect of prior cost of equity
will be different to both managed earnings methods. For instance, it is easier for managers to
camouflage real-activities methods as “normal” compared to the accrual method which is
guided by the generally accepted accounting principles (GAAP). Thus, managers may be
wary of the substantial cost on firms. Also, accrual choices may misrepresent the firm’s
underlying operating performance, but does not alter the operation of the firm. Therefore, I
predict that higher prior cost of equity will lead to lesser accrual adjustments.
H1b: Higher prior cost of equity will negatively affect the propensity to manage
earnings using the accrual method
2.2.3
Cost of Debt and Managed Earnings
Only recently has the link between the debt market and firms managed earnings
behavior gained attention. Perhaps this is due to the complex structure of the debt market;
however firms at different stages of their business cycle may need to utilize this market as
their source of capital. However, a limitation to the extant literature is that their analyses are
21
focused on the accrual approach, thus negating the real-activities approach to managing
earnings.
Two streams of research from the debt market have investigated managed earnings
behaviors. These are: First, considering the cost of accessing this market, Prevost et al.
(2008) find that higher accruals are associated with higher costs of debt. Second, considering
the debt structure of firms, Gupta and Fields (2006) document that firms with more current
debt are more apt to manage their earnings.
In both areas of research, covenant violations pose a potential challenge for firms. For
instance, debt recovery and renegotiation processes can affect credit renewal and the
potential for changes in future credit rating. Roberts and Sufi (2009) document that around
30% of publicly traded firms violate a debt covenant at some point in their lives. Chava and
Roberts (2008) show that, depending on the type of covenant, 15% to 20% of outstanding
loans are in violation during a typical quarter.
Other empirical evidence on a firms’ cost of debt and managed earnings has been
mixed. For instance, DeAngelo et al. (1994) and Healy and Palepu (1990) conclude that there
was little evidence of managed earnings. Rather, firms are more inclined to reduce dividend
payments and restructure their operations and contractual relations. Dichev and Skinner
(2002) note that a large number of firms avoid debt covenant violations by reporting financial
measures at or just above the covenant thresholds. DeFond and Jiambalvo (1994) find that
firms use excessive discretionary accruals in the year preceding reported debt covenant
violation.
H2a: Higher prior cost of debt will positively affect present accrual managed earnings
22
H2b: Higher prior cost of debt will negatively affect present real-activities managed
earnings
2.2.4
Effect of Firm Location on Managerial Decisions
The Finance literature documents that geographical location of a firm plays a role in a
firm’s decision making. According to agency theory, there are always conflicts between the
objectives of managers and the goals of shareholders. Geographic proximity between firms
and their stakeholders could alleviate agency cost concerns by facilitating better monitoring.
Therefore, it is reasonable to expect that firms located in more remote areas have more severe
agency concerns than firms located in urban areas. John, Knyazeva, and Knyazeva (2008)
finds evidence that firms in rural areas have higher dividend yields than those in central
cities, as managers in remote firms have more discretion in managing cash flow. In addition,
Cumming (2006) shows a positive relationship between geographic proximity and
investment portfolio size.
The distance between firms and investors can serve as a proxy for information
asymmetry between insiders and outsiders. Loughran and Schultz (2006) compare financing
decisions of rural and urban firms and find that rural firms are less likely to rely on external
equity financing. They show that this is due to the higher cost for investors to ascertain the
financial performance of rural firms, which translates to an adverse selection of urban firms
by investors and consequently higher cost premium of equity financing for rural firms.
Similarly, Francis, Waisman, and Hasan (2007) document that firms in remote rural areas
exhibit significantly higher costs of debt capital compared with those in urban areas. Cai and
Tian (2013) show firms with urban headquarters are more transparent with lower information
asymmetry, and have higher chances of takeover.
23
Another theory that could link geographic location with managerial decisions is related
to knowledge spillover. “Knowledge spillover theory” proposes that firms in big cities
perform better, as social interaction is more intensive in the financial centers, and managers
have more opportunities to network and to build valuable relationships with their peers.
There is also a learning effect that citizens are generally more educated in big cities and are
more able to learn from their peers. Christoffersen and Sarkissian (2009) find a positive
relationship between city size and mutual fund performance. Their results support the
argument that managers in larger cities are more experienced, with more knowledge, and
therefore perform better.
For location effect on Managed Earnings
H3a: Firm location positively impact managed earnings
H3b: Remote firms are more likely to manage their earnings than clustered firms.
2.3
Sample and Methodology
2.3.1
Data and Sample Description
Based on the hypotheses, data are needed for earnings management, cost of debt, cost
of equity, location, and other controlling variables. Firms’ general descriptive data, financial
data, and proxies for cost of debt were obtained from COMPUSTAT North America. Proxies
for a firm’s cost of equity were obtained from the Center for Research in Security Prices
(CRSP) Annual Stock Portfolio Database. Both costs of debt and equity data were obtained
on a monthly basis. Firms with five or more monthly observations in a particular year were
retained in the final sample. The deletion of firms with fewer than five monthly observations
is meant to reduce variations and improve reliability in the final sample. Also, by imposing
24
the aforementioned restriction, I limit the effect of survival bias as setting a full restriction of
retaining only firms with all observations would enhance such bias.
Data from COMPUSTAT and CRSP were merged after deleting firms classified
under the SIC codes 4900 through 4999 and 6000 through 6999. Also, to minimize variation
in abnormal accrual I filtered out merger activity identified by footnote ‘AB’ for net sales as
mergers can result in abnormal accruals which are not related to manage earnings, hence
biasing the results. Finally, considering the location effect firms that are not headquartered in
the United States were deleted from the final sample.
The sampling universe includes non-financial and non-utility firms in COMPUSTAT
North America from 1980 through 2010. The final sample consists of 32,109 firm annual
observations for 1,627 firms as shown in Table I. The constructions of the main variables
used in this study are described below.
[Table I]
2.3.2
Earnings Management Metrics
2.3.2.1 Accrual-based Earnings Management
The commonly applied cross-sectional approach to calculate discretionary accruals is
used in this paper. For every year I estimate the models for every firm classified by its two
digits SIC code. This approach partially controls for industry-wide changes in economic
conditions that affect total accrual, allowing the coefficient to vary across time (DeFond and
Jiambalvo (1994) and Kasznik (1996)). The primary models are the modified Jones model
(Jones (1991)) and the adjusted modified Jones model (Kothari et al. (2005)).
25
I subscribe to the modified Jones model because of its power in detecting managed
earnings. For instance, Bartov, Gul, and Tsui (2000) and Dechow, Sloan, and Sweeney
(1995) find that a cross-sectional modified Jones model is consistent in detecting earnings
management. However Kothari, et al. (2005) argue that measuring discretionary accruals
without controlling for firm performance will produce misspecification. Therefore, they
propose to include a control for firm performance in the model. The inclusion of return on
asset (ROA) as a scaling variable will help mitigate some of the problematic heteroskedastic
and misspecification issues that exist in the modified Jones model. Thus, the empirical
models for estimating discretionary accruals are based on the following cross-sectional
model:
Eq. (1)
Eq. (2)
where equation 1 is the modified Jones model and equation 2 is the adjusted modified Jones
model for fiscal year t and firm i.
TA represents the total accruals defined as TAit = EBXIit – CFOit
EBXI is the earnings before extraordinary items and discontinued operations
(item 123)
CFO is the operating cash flows from continuing operations derived from Net
Cash flow – Extraordinary Items and Discontinued Operations (item 308 –
item 124).
Assetsit-1 represents total assets lagged one year (item 6)
26
∆Salesit is the change in revenues from the preceding year (item 12)
∆Recit is the change in accounts receivable4 from the preceding year (item 2)
PPEit is the gross value of the property, plant and equipment (item 7)
ROAit is the firm’s return on assets (item 172 / item 6).
The measure of discretionary accruals will be the residual of equations 1 and 2.
2.3.2.2 Real Earnings Management
Similarly, I rely on proxies developed in prior studies for real earnings management.
Similar to Roychowdhury (2006), Cohen et al. (2008), and Cohen and Zarowin (2010), I
consider three metrics to study the level of real-activities managed earnings. These are the:
(i) abnormal levels of cash flow from operations, (ii) abnormal levels of discretionary
expenses, and (iii) abnormal levels of production costs.
Abnormal cash flow from operations can occur through discounts and offering lenient
credit terms. Discount offers and lenient credit terms can temporarily boost sales volume, but
these are likely to disappear once a firm reverts to old prices. The additional sales will
increase current period earnings assuming margins are positive. However, the effect of
discounts and lenient credit terms will lower cash flows in the current period.
Abnormal discretionary expenses revolve around advertising, research and
development, selling, general, and administrative expenses. Reducing these expenses will
enhance current period earnings and impact the current period cash flow provided that the
firm pays these expenses with cash.
4
The inclusion of change in receivables in the model eliminates the tendency to measure discretionary accruals with error when discretion
is exercised over revenues.
27
Abnormal production cost occurs through declaring a lower cost of goods sold
through increased production. To increase a firm’s earning, managers can increase
production. In so doing, managers end up spreading the fixed overhead costs over a larger
number of units, thus lowering the fixed costs per unit. Ceteris paribus, as the reduction in
fixed costs per unit is not offset by any increase in marginal cost per unit, total cost per unit
declines.
Estimating real earnings management, I run the following cross-sectional regression
for each firm industry and year. First, normal levels of CFO, Discretionary Expenses, and
Production Costs are generated using models developed by Dechow, Kothari, and Watts
(1998). Equation 3 expresses cash flow from operations (CFO) (item 308) as a linear
function of sales and change in sales. The abnormal CFO is derived from actual CFO minus
the fitted level of CFO generated using the estimated regression. The above method of
deriving abnormality is applied to all the models of real earnings management.
Eq. (3)
To insulate firms in the dataset from significantly lower residual caused by increase
in reported earnings in a certain year, discretionary expense is modeled as:
Eq. (4)
Production costs are defined as the sum of cost of goods sold (item 41) and change in
inventory (item 303) during the year. Thus, production cost is modeled as the function of
contemporaneous sales, contemporaneous change in sales, and lagged change in sales.
Eq. (5)
28
In equation 4, DISC represents the discretionary expenses defined as the sum of
advertising expenses (item 45), research and development expenses (item 46), and selling,
general, and administrative expenses (item 132). In equation 5, PROD represents the
production costs defined as the sum of cost of goods sold and the change in inventories.
Given sales levels, I assume that firms that inflate earnings are likely to have: (i)
unusually low cash from operations, (ii) and/or unusually low discretionary expenses, and
(iii) and/or unusually high production costs. Besides these three measures, Cohen and
Zarowin (2010), and
Zang (2011)
created two aggregate measures for real earnings
management. Rather than creating the aggregate for real earnings management, I generate
two proxies to capture firm deviation from the absolute values of each earnings management
proxy, and a dummy variable where 1 represents firms that have a higher managed earnings
value above the proxies mean. These two proxies will aid in shedding more light on the
reactionary behavior of managers to manage earnings for firms with mild versus firms with
higher managed earnings.
[Tables II and III]
Evidence from Table II shows that the discretionary behavior of managers proxies
indicate that firms in the sample manage earnings differently; where some manage their
earnings downward and others upward. For instance, the average values of each proxy
indicate the mean value of zero, while the dispersion of the sample range from 0.84 for DA2
to 2.23 for ABCFO suggests that some firms greatly manage their earnings. Other summary
statistics offer a better insight into the behavior of managed earnings among firms. Overall,
the quartile range indicates that firms in the 25th and median range manage their earnings
downward. Due to the mean value of zero for the discretionary behaviors of managers to
29
earnings, I generate additional indices for managed earnings – the absolute values for each
earnings management model.
Since this paper’s investigation is twofold, the raw discretionary values should be
sufficient in answering the first part – investigating the effects of past cost of capital markets
and firm location on a firm’s managed earnings. The absolute values should aid in answering
the second part – investigating which cost of capital has the highest impact on managed
earnings. Absolute discretionary accruals and real-activity earnings are about 19% to 28% of
firms’ assets and have a range of 4% to more than 200%. Table III reports the correlation
between the various proxies of earnings management.
The correlation between discretionary accrual methods and the real-activities earnings
method are negative except for abnormal cash flow from operations. While the strong
positive correlations suggest that accrual methods and the real-activity methods – abnormal
cash flow from operations practices are used to achieve similar objective. – The negative
correlation typified by the remaining proxies of the real-activity methods could suggest
various accounting practices utilized by firms.
2.3.3
Capital Market
The capital market offers a substitution opportunity for firms either to increase firm’s
debt or increase firm’s equity. However, this tradeoff has its pecuniary cost as procuring
more debt increases a firm’s riskiness, whereas selling equity entails losing ownership stake.
In this section, the proxies used for the two independent variables are discussed.
2.3.3.1 Cost of Accessing the Capital Market and Earnings Management
30
Accessing fund from the capital market may be restricted to firms with better rating
and sound calculated risk. As such, firms’ may be more inclined to managing their earnings
for favorable positioning. Nonetheless, what roles do cost functions of these markets play on
the propensity of managed earnings? This is what this paper investigates. I propose that
higher costs of capital can cause two reactive behaviors by managers either manages earnings
more or less. Both approaches to managing earnings can impact the monitoring effects by
lenders and market valuation by speculators. I assume that lenders and speculators are not
oblivious to the financial gimmicks of managers. As such, higher costs of capital will lead to
managing earnings downward.
Four measures to capture the cost of accessing capital markets used by Francis et al.,
(2005) and Minton and Schrand (1999) are applied in this paper. The cost of debt is
substituted with the Firm’s average monthly Standard and Poor’s (S&P) Credit Ratings
report. Specifically, the data for the domestic long term credit rating were obtained from
COMPUSTAT North America. The results are unchanged when the ratio of Interest expense
to average interest bearing debt outstanding (INTex) is used, however I only report the bond
rating results.
The literature suggests that firms with lower bond ratings have higher debt financing
costs (Calomiris, Himmelberg and Wachtel (1994) and Ogden (1987)). I generate two
measures using the average S&P ratings. First, following Klock, Mansi, and Maxwell (2005)
numerical values are assigned to each rating where a AAA bond is valued at 22 and a D rated
bond is valued at 1, thereafter averaging the values to generate a numerical value for each
firm dubbed “Rate.” Second, after the average value for each firm is derived I generate a
dummy variable “Grade” where one is assigned to investment grade firms, that is, firms with
31
a BBB (14) rating and higher and zero for speculative grade firms which are firms with an
average value below 14. Table IV provides the numerical conversions for S&P firm bond
ratings. The summary information for investable versus speculative rated firms are offered in
Table V. For brevity, Table V reports average financial statistics of the sample by grades.
[Table IV and V]
Highlights from Table V show a contrast between investment grade and speculative
grade firms. Rough estimation suggests that higher total accruals would lead to higher
propensity to manage earnings using accrual methods. The total accruals for investment
grade firms are more than twenty times the mean value for speculative grade firms. Likewise,
the accrual methods of earnings management are higher for investment grade firms. On the
other hand, discretionary managed earnings via the real-activities methods offers a different
insight as investment grade firms manage their earnings through abnormal cash flow from
operations and abnormal cash flow from discretionary expenses less compared to speculative
grade firms. Other variables suggest that investment grade firms are more profitable, use
more short term debts, have more long term debts, and have high returns on their assets.
The cost of capital is proxied by the firm’s systematic risk (BETA) and the total equity
price risk (σRET). In a Black, Lintner, and Sharpe world, cross sectional variations in firms’
cost of equity is a direct result of cross sectional variations in firms’ betas – a statistical
measurement of the relationship between the market and the firm. Thus, I assume that higher
beta suggests higher costs of raising equity capital, but if this assumption does not hold, then
other systematic factors not captured by beta or idiosyncratic risk can affect the cost of
equity. Therefore, the higher the total risk of a firm’s stock – systematic and unsystematic
risks – the higher is its risk adjusted cost of raising equity capital. Although Campbell,
32
Dhaliwal, and Schwartz (2012), and Pástor, Sinha, and Swaminathan (2008) suggest the use
of an implied cost of equity as a better measure to capture time variation in expected stock
returns, since the focus here is on managed earning behavior, the aforementioned method
would suffice. BETA and σRET5 are already calculated and made available via CRSP.
2.3.3.2 Financing Structure of Firms
Considering the heterogeneous structures of capital can offer a better insight into
covenant obligations. To unravel this debacle, I specifically focus on firms’ debt structure.
By debt structure, I classify firms’ debt based on duration or maturity. To this end, debt is
classified as short term, medium term, and long term following (Barclay and Smith (1995),
Gupta and Fields (2006), and Johnson (2003)). Short term debt is defined as what matures in
a year or less, medium term debt is defined as debt that matures in more than a year but less
than five years, and long term debt is debt that matures in more than five years.
Data for constructing the three debt maturity stages for firms are available via
COMPUSTAT. For short term debt I use debt in current liabilities (item 34). For medium
term debt I use the sum of items 91, 92, 93, and 94. I measure long term debt as the
difference between total liabilities (item 181) and short term-debt plus medium-term debt.
All variables are scaled by total assets (item 6). I assume that firms that have higher short
term debt are more likely to manage their earnings.
5
Find the entire derivations in Scholes and Williams (1977) paper titled Estimating Betas from NonSynchronous Data.
33
2.3.4
Location
Firm location is defined based on a firm’s headquarter by two constructs. First, I
capture the degree of geographical concentration of a firm’s managed earning in a given
county dubbed “deviation.” This I define as:
Deviation = xi – yi
Eq. 6
where:
Xi = County’s earnings management =
and
Yi = County’s share of public firms =
This construct measures the effect of clustering in a given county. The assumption
underlying the deviation measure is that managed earnings behavior should be randomly
distributed. Simply put, a county that accounts for 8% of all public firms should also account
for 8% of managed earnings on average. A positive deviation suggests that a county’s share
of managed earning firms is higher than the county’s share of all public firms. Thus, positive
deviations indicate greater geographic concentration in managed earnings. For brevity, rather
than county level, Table VI reports the distribution of managed earnings at state level.
[Table VI]
States without a single firm representation and firms headquartered in any Island
territories of the United States are not included in the sample. As such, I do not report
deviations for Maine, North Dakota, New Mexico, Puerto Rico, US Virgin Islands, and
34
Wyoming. From Table VI, states with the greatest positive deviations include Texas, Ohio,
Tennessee, Virginia, and Wisconsin. For instance, Texas accounts for 8.85% of all public
firms, but have 12.48% of all managed earning firms. Alternatively, California, New York,
Massachusetts, New Jersey, and Utah are states with the lowest deviations.
Second, I estimate the distance between firms located in counties and SEC regional
offices. This construct examines enforcement effect on a firm’s propensity to manage
earnings as SEC is demanding greater assurance about the quality of earnings. Recently, the
SEC charged both the former chief accounting officer and a former assistant controller of
Dell with involvement in improperly adjusting reserves, over a course of years, in order to
allow the company to meet financial targets. Nevertheless, reports from the SEC suggest that
SEC officials view travel outside their geographic jurisdiction as a significant cost affecting
the efficient allocation of their investigative resources (GAO 2007). Geographically,
monitoring of closer firms is likely to be efficient not only because they are cheaper but also
because the SEC is likely to know more about these firms.
Regional offices of the SEC have jurisdiction over particular states. Firms within a
particular state are monitored by one of the eleven SEC regional offices. SEC regional
offices are located in New York, NY; Boston, MA; Philadelphia, PA; Miami, FL; Atlanta,
GA; Chicago, IL; Denver, CO; Fort Worth, TX; Salt Lake City, UT; Los Angeles, CA; and
San Francisco, CA. I find the distance to the SEC regional office for each firm by measuring
a straight line distance using the zip codes of the firm location and the SEC office that have
jurisdiction over such firm.
Table VII offers a snapshot reporting the distance of randomly selected firms that are
farther from the SEC regional office as well as firms that closer to the SEC regional offices.
35
[Table VII]
2.3.5
Control Variables
To investigate the effect of both financing choices and location to firm’s managed
earnings behavior, I include in the regressions control variables that have been identified in
the earnings management literature. Table VIII details the method used to arrive at the values
for each control variables.
[Table VIII]
Firm size (Size) and Firm profitability (Profit) are included because larger firms tend be
less risky and can enjoy lower costs of capital. Firms’ default risk is measured by three ratios.
The first two ratios control for debt effect to total assets (Lev), and return on assets (ROA),
the last controls for cash flow effect (Vol) where higher (Lev), lower (ROA) and higher (Vol)
values reflect greater default risk. In addition, I include firms’ capital intensity (Cap) to
account for differences in firms’ asset structure, where firms with greater capital intensity
present lower risk to capital providers. Firms with greater capital intensity present lower risk
to debt providers, thus they are expected to have a lower costs of capital (Ashbaugh-Skaife,
Collins, and LaFond, 2006). Tobin’s Q is expressed as the market to book ratio (MB) – is
included to control for market hype and speculation effects. Finally, I include year and
industry dummies to control for time period and industry variation in earnings management.
Managed earnings measures already incorporate some industry and year information.
However, by including these dummies in the regression the results are more robust as it
should aide in capturing any remaining industry and year effects.
36
2.4
Empirical Results
This section documents the univariate and the multivariate tests performed and their
results. All through the multivariate tests, the explained variable shall be managerial
discretion proxied by the measures of firm’s managed earnings. The focal independent
variables are measures of previous cost of debt, previous cost of equity, and location.
Considering time and cross-sectional variance in the sample, I set the sample as panel data.
These initial results are reported using a multivariate regression technique. Furthermore, I
perform other tests like quartile and industry fixed-effect models to make the findings more
robust.
Table IX reports summary statistics for the independent and control variables used in
this study. The sampled firms have on average the value of 12 or BB+ debt rating. About
32% of the firms have an investment grade debt. Firm beta and total firm risk are closer to
their 75% quartile range with a value higher than 1 and value of 0.03 respectively. Overall
firm risk is high in the sample and the total firm risk is low. Firm location shows that
deviation from the total firms in states is about 8% and the average distance of firms to SEC
enforcement offices is 158 miles.
[Table IX]
Table X shows the correlation matrix between measures of earnings management, costs
of capital markets, location, and seven control variables. Most reported correlations are
significant at 95% or better. As seen from this table, managed earnings proxies interact
differently with the proxies for firms’ costs of capital. For instance, the accrual methods
denoted by DA1 and DA2 are positively correlated to costs of debt and negatively correlated
to cost of equity.
37
The real-activities methods show that abnormal cash flow from operations (Abcfo)
have a stronger impact on the costs of capital markets than abnormal discretionary expenses
and abnormal production expenses. I document a negative correlation between abnormal cash
flow and debt rating and firm risk. Consistent with other studies I find that firm size is
negatively correlated with accrual method earnings management variables. Size is positively
correlated with the propensity to manage earnings through the real-activities methods except
for abnormal cash flow from operations. Interestingly, firms’ market to book value is
negatively related to discretionary accrual earnings management and abnormal cash flow
from operations, but positively correlated to abnormal production cost.
[Table X]
The multivariate analyses reports are shown beginning in Table XI, documenting the
effects of prior costs of capital and location on managed earnings from various perspectives.
Table XI results are performed using multivariate regression techniques. This regression
technique generates similar results to the ordinary least squares regressions (OLS), but
differentiates itself from OLS as it can offer estimates of a single regression to more than one
dependent variable. Here, I consider the effect of prior cost of debt and prior cost of equity
separately.
Besides the multivariate techniques, industry fixed effect analyses were performed
and similar results were found. I do not report the findings of the fixed effects here. Panel A
and panel B report regression results for discretionary accrual and discretionary realactivities managed earnings proxies. Prior cost of debt proxies is the focal explanatory
variable in the first panel. Similarly, prior cost of equity is the focal explanatory variable in
38
the second panel. The costs of capital are lagged by a year, proposing that firm’s higher cost
burden should prompt managers to adjust their earnings.
[Table XI]
Evident from Table XI, prior cost of debt and prior cost of equity are statistically
significant regarding their impacts on managed earnings. However, the statistically
significant impact on managed earnings varies depending on the method used to calculating
discretionary managed earnings, as well as, the cost of capital being investigated. Both
accrual methods and abnormal cash flow from operations report a positive impact by prior
cost of debt and a negative impact by prior cost of equity. I explain the statistical importance
that for every percentage point increase in debt rating, investment grade, firm beta, and total
firm risk, discretionary accruals using the modified Jones (1991) model ‘DA1’
would
increase by 0.023%, 0.165%, and decrease by 0.025% and 1.906% respectively.
Translating these findings economically suggests that for a one standard deviation
increase in prior debt rating, prior investment grade, prior firm beta, and prior total firm risk,
discretionary accruals (DA1) go up by 9.44% (0.0247 * 3.82), 7.60% (0.1652 * 0.46), and go
down by 1.46% (-0.024 * 0.61), and 3.81% (-1.9055 * 0.02) respectively. Similarly, a
standard deviation increase in prior debt rating, prior investment grade, preceding firm beta,
and preceding total firm risk, abnormal discretionary expenses go down by 7.83% and
6.21%, and go up by 3.58% and 3.37% respectively.
The location proxies were also found to be statistically significant with an
economical effect between 2% to 4%. Not reported are tests for multicollinearity at the end
of every regression output. Multicollinearity can inflate the standard errors and bias the
coefficient estimates, thus I perform a test known as variance inflation factor (VIF). Extant
39
literature proposes that a VIF greater than 10 merits further investigation or that a variable
with such value should be dropped from a regression model. Here, the VIFs were all below 2.
Given the robustness of the post-estimation tests and the economic contributions
derived from this empirical test, these results suggest that firms are more likely to manage
earnings positively via the accrual method and negatively via the real-activities method when
it concerns firms’ cost of debt. A reverse effect is found regarding firms’ cost of equity. Thus
affirming sets of propositions in this paper: (i) that the effect of prior cost of capital on
managed earnings varies depending on the funds to access – debt or equity; (ii) the economic
effect of firms’ prior cost of debt to managed earnings is greater than the economic effect of
firm’s prior cost of equity to managed earnings; (iii) the average value to the real-activities
earnings management method is greater than the average value of the accrual earning
management method hence, overall, firms uses the real-activities method extensively.
For brevity, I do not include the modified Jones (1991) model DA1. DA1 results are
closely similar to the Kothari et al. (2005) model DA2. Also, for brevity I report results on
only one measure for prior cost of debt, prior cost of equity, and location. Table XII reports
quartile regression results for effects of prior costs of capital and location on managed
earnings at the twenty-fifth and seventy-fifth quartile. Quartile regression would allow one to
explore the difference in behavior of managers at either ends of the spectrum.
[Table XII]
Following previous economic effect interpretations, I document on average that the
effects of prior cost of debt are significant and higher at both 25th and 75th quartile – 2.88%
and 3.61% - than prior cost of equity – 1.83% and 2.95% - respectively. Like in Table IX, the
directionality of the coefficients for the accrual and abnormal cash flow from operations are
40
similar. However, discretionary accrual and discretionary cash flow are higher at the 75 th
quartile than the 25th quartile whereas, discretionary expenses and discretionary production
cost are higher at the 25th quartile. This finding suggests that firms at the lower quartile
manage their earnings more through the real-activities approach and firms at the higher
quartile manage their earnings more using the accrual approach.
So far I have documented the overall effects of prior cost of capital as well as the
relationship between cost of capital markets and the conditional quartiles of managed
earnings. In Table XIII, I delve further into the statistical significance of prior cost of capital
markets on managed earnings. This time, I include an interactive term of location and costs
of capital to each model. By this, I investigate if location intensifies the effect of prior cost of
capital. A positive value for the effect of the interaction term would imply that the higher
costs of capital, the greater the effect of location on managed earnings. Similarly, the higher
the deviation, the greater the effect of capital markets’ cost on managed earnings.
[Table XIII]
The F-test joint hypothesis test between the interaction and the main independent
variable is reported, where the yes reported in Table XIII means I can reject the joint
hypothesis and that the model is not misspecified. From the table, almost all models are well
specified except for the interactive term and deviation in the third column. The negative
coefficient and the statistical significance of the interactive term infer that firms with lower
debt grade in states with lower deviations have a less of a drive to adjust discretionary
accrual and discretionary cash flow. Likewise, in states with higher deviation, the higher the
effect of debt grade on discretionary expense and on discretionary production. This finding
concludes that a firm’s environment influences the propensity to manage earnings.
41
So far, the regression results have considered the costs of capital markets
independently. However, one of the arguments put forward in this essay is to investigate
which cost of capital has a greater burden on managed earnings. For instance, from the
results shown in Table XI, I find that: the propensity to manage earnings positively via
discretionary accrual is higher for firms with higher prior cost of debt.
Also, I find that: the economic effect for prior cost of debt is higher than prior cost of
equity. Thus, firms are more susceptible to managing earnings when cost of debt is high.
But, the evidence from Table XI is not substantive to assume the simultaneous use of the
capital market by firms. To achieve this, I perform a seemingly unrelated regression (SUR)
test by including both prior cost of debt and prior cost of equity into the same equation. SUR
allows cross-equation restrictions to be imposed or tested and to gain efficiency by reducing
contemporaneously correlated error terms (Baum 2006). The results are shown in Table XIV.
[Table XIV]
Shown in Table XIV are the two proxies for prior cost of equity. Both proxies are
included in the regression models to examine which risk in the sample firms are more
susceptible. I find that both risk metrics are important. However, depending on the
discretionary behaviors of managers firm’s idiosyncratic risk is not as important as the firm’s
total risk. Also, prior cost of debt is statistically significant in all proxies for firm
discretionary behavior. I surmise from Table XIV that the prior cost of debt effects
outweighs the prior cost of equity effects on the propensity to manage earnings. Therefore,
debt covenant obligation is an important factor to managing earnings.
[Table XV]
42
To support these findings, I further examine debt maturity’s association with
managing earnings. So far I find that prior cost of debt positively impacts managed earnings.
That is, firms with higher short-term debt manage earnings more. Table XV reports findings
based on debt maturity. As expected, firms with higher short-term debt manage their earnings
more using the discretionary accrual and discretionary cash flow method. While, short-term
debt is adjusted less using discretionary expenses and discretionary production cost. The
result is robust after controlling for industry fixed effect.
2.5
Conclusion
I explore the association of past costs of capital markets on earnings management by
answering the following questions. First, what effect do prior cost of debt and prior cost of
equity have on managed earnings? Second, does location impact the prior cost of capital’s
effect on managed earnings? Third, which costs of capital greatly affect managed earning
behavior of firms?
This investigation focuses on the events that prompt managers’ behavior with respect
to discretionarily managing earnings. I employ discretionary accrual, discretionary cash flow,
discretionary expenses, and discretionary production cost as proxies for managed earnings.
Using a large sample of firms from 1980 to 2010, I present evidence that managing earnings
methods yield different results depending on the influence of prior costs of capital. I find that
the effect of prior cost of debt to discretionary accrual is positive, prior cost of debt effect to
discretionary production cost is negative. Also, this finding suggests that real-activities
managed earnings methods are more inversely correlated than accrual managed earnings to
the main independent variables.
43
Other findings show that the prior cost of debt effect outweighs the prior cost of
equity effect on managed earnings. This relationship is robust in all measures of managed
earnings as well as through alternative testing methods in this paper. Also, the effect of
location on prior cost of capital affects the propensity of managed earnings. Therefore debt
plays a vital role in firms’ position and the quality of reported earnings.
44
CHAPTER III
ESSAY TWO: ON THE EFFECT OF A GOLDEN PARACHUTE ON MANAGED
EARNINGS
It’s hard to understand how derivative traders at AIG warranted any bonuses, much less $165
million in extra pay. How do they justify this outrage to the taxpayers who are keeping the
company afloat?
President Barack Obama6
3.1
Introduction
In Corporate Finance and Accounting literatures the effects of golden parachutes and
managed earnings is extensively documented. However, there are limited attempts made to
bridge the gap between these two practices. As such, in this essay I explore the effect of
adopting a golden parachute on a firm’s propensity to manage earnings. I find that firms with
a golden parachute have a higher propensity to managing earnings positively through the
accrual method and negatively through the real-activity approach. Also, a firm with a golden
parachute tends to have a higher return on asset, under-take riskier investments, and their
chief executive officer (CEO) tends to be older. Finally, CEOs with golden parachute tend to
be less sensitive to their pay-for-performance clause.
From a stockholder perspective, a golden parachute can serve as an impetus for value
maximization7 or as inertia for wealth maximization8. Similarly, from an agent’s standpoint it
connotes a sense of soft landing for top executives where it could align with the
This was President Obama’s response on the planned payments which American International Group (AIG) announced they were paying
their executives as bonuses. Beside the executive payment, AIG announced that the financial unit of the company would receive $450
million and the entire company’s bonus could reach $1.2 billion - http://www.foxbusiness.com/markets/2009/03/17/americaninconscionable-group/
7
See Lambert and Larcker (1985) and Evans and Hefner (2009)
8
See Cotter and Zenner (1994) and Bange and Mazzeo (2004)
6
45
stockholders’ view as an impetus for wealth maximization9 or, it could insulate its holders
from efficiency. On the other hand, managed earnings reflect the discretionary behavior of
managers. Hence, I investigate the following two questions:
1) Do golden parachute firms manage their earnings more or less?
2) Does the age of the CEO for firms that have adopted a golden parachute matters when
it comes to managing earnings?
The term “golden parachute” refers to the benefit received by an executive in the
event that a company is acquired and/or the executive’s employment is terminated and/or the
executive remains with the firm after a recessionary cycle. Since its introduction and its
adoption in the corporate world, it has received much scrutiny. For instance, through the
recent recession, scholars Fich et al. (2012), Jenter and Lewellen (2011), Bebchuk et al.
(2012), Mansi, Nguyen, and Wald (2012), and Brusa, Lee, and Shook (2009) and
professionals have tried to shed more light on the adoption of golden parachutes even as the
general public clamor in disdain for its usage10.
A firm’s usage of managed earnings is an accepted activity which, to some,
borderlines on unethical behavior and greed. The intent is to adjust financial indicators that
may favorably suit a company and/or the managers at the time. These adjustments can be an
upward or downward movement11. Given the rise of the adoption of a golden parachute by
publicly traded firms – see Figure 1 – and the proliferation of papers on the topic of managed
9
See Knoeber (1986), Jensen (1986), and Berkovitch and Khanna (1991)
A simple search over the internet using phrases like – golden parachute news, golden parachute good or bad, or people vs. golden
parachute offers public opinions perspective on firms choices of adopting a golden parachute.
11
Upward movement can include adjusting earnings to meet or beat analyst predictions, favorable performance, issuing IPOs and SEOs.
Downward adjustments in earnings can include adjusting to hide net value of project from stakeholders and managers attempt to avoid
payment of dividend from excess cash-flow.
10
46
earnings, I attempt to bridge the gap between these two managerial tools by asserting that the
presence of a golden parachute can exacerbate the proclivity for firms to manage earnings.
In Figure 1, notice that the adoption of golden parachutes by firms took a dive in the
year 2007 and rose in 2009, perhaps, suggesting that firms responded to the outcry of public
sentiments. From 1992 to 2011, there is an increase of 520% in the number of firms that have
adopted a golden parachute. However, on the downward slope from 2006 to 2008 the
adoption of a golden parachute by firms plunged by about 51%. Between the years 2008 and
2009 the number of firms adopting a golden parachute grew by over 149%. Since the year
2009, there has been a gradual increase on the adoption of a golden parachute.
The extant literatures on managed earnings suggest two ways whereby managers can
adjust financial statements. These are through: (i) the accrual based approach and (ii) the
real-activity based approach. The accrual approach suggests that managers can manage firm
earnings through discretionary accrual choices that are allowed following the US accounting
principle. Whereas, the real-activity approach suggests that managers can either alter the
timing and scale of firm activities like productions, sales, investment, and financing
activities. The existing literature offers three real-activity approaches. These are: excessive
cash flow from operations, excessive discretionary expenses, and excessive production costs.
In this paper, I attempt to separate the effect of golden parachute on the two approaches.
To minimize the ‘cookie’ trail by not overshooting a firm’s position and making it
obvious to analysts and stakeholders, managing earnings methods may not be simultaneously
utilized as, the positive adjustment in one approach can suggest a negative or no-adjustment
to the other approach. Prior research suggests that the accrual method is used more.
However, in the post Sarbanes-Oxley (SOX) Act of 2002 the real-activity methods usage has
47
risen. Consequently, I contend that the positive association of a golden parachute on the
accrual approach will be higher in pre-SOX era. Nevertheless, in the post SOX era, the
accrual method usage will shrink compared to the real-activity approach.
Furthermore, I attempt to shed light on two theories of why firms use golden
parachutes. These theories are: (i) the incentive effect hypothesis and (ii) the entrenchment
effect hypothesis. The incentive effect hypothesis suggests that employment contracts such as
a golden parachute alleviate managerial concern regarding short-term profits (Narayanan
(1985) and Stein (1988, 1989)). The entrenchment effect hypothesis suggests that contracts
entrench poor-performing executives by insulating them from the discipline (for example,
early termination of the executive) of the corporate control market and internal governance
mechanisms (Bertrand and Mullainathan (2003) and Narayanan and Sundaram (1998)).
From the definitions of the two theories – Entrench and Incentive effect hypotheses,
firm performance and firm valuation are central to distinguishing between the two
possibilities. Given these, the interactions of firm performance and firm risk on a golden
parachute should ameliorate the relevant hypothesis. Hence, for the incentive effect
hypothesis to hold I propose the following conditions: (i) the interactive terms between
golden parachutes and firm performance on managed earnings to be positive, and (ii) the
interactive terms between golden parachutes and firm risk on managed earnings to be
positive.
Using a sample obtained from Risk Metrics, EXECUCOMP, CRSP, and
COMPUSTAT for 1,184 firms from 1992 through 2011, the empirical tests find that golden
parachute firms are more inclined to manage their earnings. This result is validated using
other regression models such as fixed industry effect and quartile regressions. The results
48
were robust even in the pre and post SOX era. Also, the aforementioned conditions
supporting the incentive effect hypothesis were met. Hence, managerial concerns regarding
short-term profits are alleviated as managers with a golden parachute are more inclined to
manage their earnings upward.
Additionally, in examining the effect of an executive’s age on the propensity to
manage earnings. Conyon and Florou (2002) note that firms cut back on capital expenditure
as the chief executive become older, suggesting that older executives may be more risk
averse. As such, I argue that older executives will be predisposed to managing earnings in
order to show their competence. Empirically, I find that younger executives (CEOs younger
than 55 years) are less likely to manage their earnings positively. The substantive
contribution of younger CEOs managing their earnings less ranges from about 1.6% to 7.6%
of their age group’s standard deviation. Whereas older executives (CEOs older than 68
years), have a substantive contribution ranging from 1.2% to 9.0% of their age group’s
standard deviation.
This paper contributes to literatures in corporate finance as well as in the accounting
discipline in the following ways. First, it offers other avenues to the empirical testing of
golden parachute hypotheses. To the best of my knowledge, recently published papers by
Fich et al. (2012), Bebchuk et al. (2012), Hartzell, Ofek, and Yermack (2004), and Bange and
Mazzeo (2004) investigate the effect of golden parachutes using samples ending in 2007,
2006, 1997, and 1990 respectively. In the recent years, governance structures have arguably
experienced major shifts – notably in the adoption of a golden parachute, as such, revisiting
and analyzing its effect is important.
49
Second, I show that short-term events are not the only behavior that affects the
propensity for firms to manage earnings. As in this paper, a golden parachute which serves as
a long-term lure for managers can also be an incentive to manage earnings. On the one hand,
it cushions the manager’s position and concerns regarding prospective jobs thus becoming
less active with her firm valuation. On the other hand, it cushions her position and offers an
opportunity to ‘prove to peers’ that she is the best for the job.
Third, existing studies focus on the impact golden parachutes have on firm
performance. I advance this literature by also examining the potential effect of a golden
parachute, given the proclivity for firms to manage earnings, and by exploring the effects of
performance and golden parachutes in the face of managing earnings. Finally, the DoddFrank Act of 2010 mandated that shareholders votes are needed on future adoption of a
golden parachute by public firms. Hence, this work offers insight on the wealth and incentive
effects of managers. This evidence is important in the ongoing debate regarding best
practices in corporate governance, firm valuation and agency problem in corporate finance.
The rest of the paper is organized as follows: Section 2 discusses the data and its
characteristics, Section 3 describes the measurement of key independent variables, Section 4
describes proxies for managed earnings, Section 5 presents empirical results, Section 6 offers
robustness results, and Section 7 concludes.
3.2
Data and Sample
The data sample for this paper is collected from four different sources: (1) data for
golden parachutes come from Risk Metrics (formerly IRRC), (2) data for executive
compensation comes from EXECUCOMP, (3) data for stock prices and returns come from
CRSP, and (4) financial data comes from COMPUSTAT. Due to data limitations mainly
50
from Risk Metrics and EXECUCOMP, my sample contains a total of 20,800 yearly
observations for 1,184 firms from 1992 through to 2011. Because I use more than a single
source for firm level data, the data were merged from all four sources using firms CUSIP and
fiscal year.
Prior to 2007, Risk Metrics published eight volumes of governance measures dating:
1990, 1993, 1995, 1998, 1999, 2002, 2004, and 2006. Each volume tracked governance
characteristics for about 1,400 to 2,000 firms. Because not every year is covered, I follow
Gompers, Ishii, Metrick (2003) and Bebchuk et al. (2012) who suggested the forward filling
technique. The forward filling technique assumes that for a firm that is present in two
consecutive Risk Metrics volumes that the governance measure (Golden Parachute) remains
the same from the first publication to the next.
Risk-taking variables are calculated using CRSP monthly and COMPUSTAT annual
databases. From the compensation data in EXECUCOMP, pay- sensitivity (vega) is derived.
However, in this paper I subscribe to a different measure of pay-sensitivity because vega
only reflects the sensitivity of managers’ stock options to the increase in stock volatility.
Here, I follow Jensen and Murphy (1990) and Schaefer’s (1998) measure of estimating the
pay-performance sensitivity of CEOs.
I delete firms with dual-class stocks from the sample due to the special voting
structure of these firms. The voting structure of these firms may suggest a very different role
for governance mechanisms. Following the convention in corporate finance literature, I
delete finance (one-digit SIC code equals 6) and utility (two-digit SIC code equals 49) firms
due to the highly regulated nature of those industries.
3.3
Key Independent Variables
51
I describe the main variables used in the empirical analysis in this subsection. The
focus of this paper is on three concepts – golden parachutes, firm performance, and managed
earnings. As such, I explain the variables used at every stage of the empirical tests. In
addition to golden parachutes and firm performance, I explore the relationship between risktaking behaviors of the firm and CEO pay-to-performance sensitivity. For firm performance,
I use both market and accounting measures of performance, and for managed earnings, I use
the accrual and real earnings methods.
3.3.1
Risk Taking
Riskier corporate operations can trigger volatile returns; as such, the riskiness of
firms’ projects can allow firms to manage their earnings. Therefore, I employ two proxies to
measure the degree of risk-taking in firm operations based on the market and earning
behavior. The first measure is a market proxy using the stock return volatility which is
defined as the standard deviation of daily stock returns over the fiscal year (SD), following
the convention in the literature (e.g., Core and Guay (1999), Coles, Daniel, and Naveen
(2006), Brick, Palmon, and Wald (2012)). It is derived using the following equation below:
Eq. (1)
√
∑(
where σ is the firm standard deviation,
̅)
is firm i’s return values, ̅ is the mean of
the firm return values, and N is the number of observations for each firm.
Unlike equation 1, the second measure is derived as the standard deviation of an
industry average of a firm’s earnings, and then scaled by the average total assets. The
standard deviation of earnings is derived following the equation below:
52
Eq. (2)
√
)
∑(
where σ is the firm’s standard deviation,
is firm i earnings values, μ is the mean of
the firm industry earnings (earnings before interest, taxes, and depreciation) values, and N is
the number of observation for each firm. The standard deviation (σ) of each firm within an
industry is aggregated then divided by the average asset which is derived following the
equation below:
Eq. (3)
(
)
where σ is the firm standard deviation, AT is the Firm Total Asset last year (t-1) and
the present (t).
By industry, I use the forty-eight industry classification derived by Fama and French
(1997). Fama and French’s (1997) groupings is widely adopted in academic papers. For
instance, it has been applied in asset pricing (Chan, Lakonishok, and Swaminathan (2007),
Hong, Torous, and Valkanov (2007), Daniel and Titman (2006), Purnanandam and
Swaminathan (2006), Brennan, Wang, and Xia (2004), Ferson and Harvey (1999), and Pástor
and Stambaugh (1999)), corporate finance (Graham and Kumar (2006) and Flannery and
Rangan (2006)), accounting (Chan, Frankel, and Kothari (2004), and Francis et al. (2005)),
and economics (Bebchuk and Grunstein (2005), and Wulf (2002)).
3.3.2
Pay-for-Performance Sensitivity
53
Pay for performance is grounded on the notion that CEOs are rewarded for their
actions which should yield a higher return to the firm’s expected cost. However, the impetus
for managers is that their private gain outweighs their private cost. Therefore, the presence of
golden parachute should ameliorate concerns for private benefits.
The impact of a change in firm value on the manager’s wealth is defined as pay for
performance. From the traditional agency literatures, cash salaries are argued to create an
incentive for managers to avoid risk, thereby inducing managers to act in bondholders’
interests12. However, given that pay for performance is disbursed at a set time, I argue that
pay-for-performance measures will have a positive association with firm’s managed
earnings.
To examine pay for performance sensitivity I estimate three coefficient levels to show
the linkage between firm size and the extent to which managers’ compensation depends on
the wealth of the firm’s shareholders. The coefficients are extracted from the modified
variation of the Jensen and Murphy (1990) linear model. The modification was first initiated
by (Schaefer (1998)). Schaefer (1998) suggested controlling for industry effect in equation 4
below.
Unlike Jensen and Murphy (1990) and Schaefer (1998) which consider two
definitions of CEO pay, I consider three definitions of CEO pay. These are salary, salary plus
bonus, and entire compensation which is a broader definition to include all other forms of
compensation or benefit accrued by the CEO.
Eq. 4
(
)
(
)
(
)
where wage is CEO salary and firm value is total market value.
12
See Jensen and Meckling (1976), Myers (1977), Haugen and Senbet (1981), and Brick, Palmon, and Wald (2012)
54
Eq.5
(
)
(
)
(
)
where wage is CEO salary plus bonus and firm value is total market value.
Eq. 6
(
)
(
)
(
)
where wage is CEO total compensation and firm value is total market value.
I estimate equations (4 to 6) using ordinary least squares and control for industry
fixed effect13.
[Table XVI]
Results are presented in Table XVI. Jensen and Murphy (1990) and Schaefer (1998),
did not test for change in CEOs salary. I compare their results with the change in salary and
bonus, and change in total wealth of CEO measured as the inclusion of salary, bonus, and
other compensation14. Compared to Jensen and Murphy (1990) and Schaefer (1998), I find
some similarities. Using change in salary plus bonus as a dependent variable, I obtain a pay
for performance sensitivity of 0.0101774, which suggests that CEO salary plus bonus
changes by $10.18 for each $1,000 change in shareholder wealth. This result is different from
Jensen and Murphy’s (1990) estimate of 0.0000135, implying that CEOs get an extra 1.35
cents for each $1,000 increase in shareholders’ wealth. Schaefer’s estimate was 0.0000241,
implying that CEOs get an extra 2.41cents for each $1,000 increase in shareholders wealth.
The values reported here indicate that pay for performance sensitivity have risen sharply over
13
The industry fixed effects are computed using Fama French 48 industry classification.
All other compensation is defined in EXECUCOMP data definition to include personal benefits, termination or change-in-control
payments, contributions to defined contribution plans, life insurance premiums, gross-ups and other tax reimbursements, discounted share
purchases etc.
14
55
time151617 suggesting that the combination of the CEO’s salary and bonus has become an
important incentive mechanism, compared to prior decades.
Employing change in CEO wealth as a dependent variable, I obtain a pay for
performance sensitivity of 0.0066795, which translates to a change in CEO wealth of about
$6.68 cents for a change in shareholders’ wealth compared to Jensen and Murphy’s (1990)
estimate of $2.00 and Schaefer’s (1998) estimate of $12.50. Like the estimate in Table XVI,
the Jensen and Murphy (1990) estimate was statistically significant, but Schaefer’s (1998)
was not. Overall, the estimate for CEOs’ total compensation shows an upward trend and high
sensitivity of pay for performance. For inclusion in this essay, I use the second regression
model – that is equation 5 has the highest pay for performance sensitivity, I extract the
coefficient (α1) at industry levels to represent a consensus pay for performance (PoP)
sensitivity for each firm within a particular industry using the Fama-French forty-eight (48)
industry classification as a measure of pay for performance.
3.3.3
Operating Performance Measures
Performance is a desired goal for executives, as poor performing executives tend to
be replaced more often. Given the need to perform, executives may be inclined to manage
earnings. Empirical evidence suggests that firms adjust their financial records during certain
events to reflect better performance. For instance, Kellogg and Kellogg (1991) find that firms
manage their earnings upward in the quarter just preceding a seasoned equity offering. Teoh
et al. (1998b) find that issuers of seasoned equity offerings who adjust financial reports for
higher net income prior to the offering have lower long-run abnormal stock returns and net
15
My regression result are comparable to Schaefer (1998), however it is not directly comparable to Jensen and Murphy (1990) because they
did not use fixed effects.
16
Jensen and Murphy (1990) data was from 1974 to 1986. Schaefer (1998) data was from 1991 to 1995.
17
When the data range is compressed to the same timeline of past scholars, the pay to performance sensitivity of change in salary and bonus
are qualitatively similar in sign and magnitude.
56
income. Beside these events, Falaschetti (2002) notes that golden parachutes enhance
‘efficiency’ by increasing the credibility with which owners can commit against
opportunism.
If a golden parachute induces a CEO to ‘work’ for her company, then their
performance should be higher than firms not granting golden parachutes to their CEOs.
Following Barber and Lyon (1996) and Lie (2001), I use operating income before
depreciation (EBITDA, COMPUSTAT item 13) scaled by the average of the beginning and
ending periods’ book value of assets (i.e.,
(
)
) as my primary measure of
operating performance. An advantage to using the operating income before depreciation is
that this measure is not affected by changes in capital structure (Grullon and Michaely
(2004)). Unlike income before extraordinary items, which are sensitive to changes in interest
payments, EBITDA is not. Another advantage is that operating income before depreciation
is not affected by factors such as special items and income taxes that usually affect other
measures of earnings.
To test the robustness of my results, I also examine other accounting and market
based performance measures. The performance measures include the return on cash-adjusted
assets (R_cAT), the return on sales (ROS), and the market to book ratio (MTB). I use these
measures because they overcome some of the potential problems associated with ROA.
Barber and Lyon (1996) note three drawbacks associated with ROA. First, the total assets on
a firm’s balance sheet are recorded at historic cost, while operating income is recorded in
current dollar value. Second, total assets measure reflects all of the assets of the firm, not just
operating assets. Finally, operating income is an accrual based measure that managers could
over- or understate by increasing or decreasing discretionary accruals.
57
The caAT is equal to the operating income before depreciation scaled by the average
of beginning and ending period book value of cash-adjusted assets. The cash-adjusted assets
are equal to the book value of total assets minus cash and marketable securities (CHE,
COMPUSTAT item 1). The ROS is equal to the operating income before depreciation scaled
by the average of beginning and ending period sales (SALE, COMPUSTAT item 12).
The MTB is equal to market value scaled by the average of beginning and ending
period book value of total assets. The market value is defined as book value liabilities (LT,
COMPUSTAT item 181) minus balance sheet deferred taxes and investment tax credit
(TXDITC, COMPUSTAT item 35) plus preferred stock – where preferred stock is equal to
liquidating value (PSTKL, COMPUSTAT Item 10) if available, or redemption value
(PSTKRV, COMPUSTAT Item 56) – plus market equity – where market equity is equal to
common shares outstanding (CSHO, COMPUSTAT item 25) times the stock price
(PRCC_C, COMPUSTAT item 199).
3.3.4
Other Variables
This section details the control variables used. Besides the year and industry panel
fixed effects, I control for the effect of a CEO’s tenure (Duration), the size of the firm
(LogSize), and a firm’s leverage position. Two methods are applied for the calculation of
leverage: (i) book leverage (BLev), where the total book debt is divided by the average total
assets of the firm. (ii) market leverage (MLev), where the book debt is divided by the market
value of the firm.
[Table XVII]
58
Using several measures for firm performance, riskiness of the business, and pay-forperformance sensitivity allows one to access the nature of a business from several
perspectives. In Table XVII, the summary statistic reflects the variations in my sample. For
instance, since performance proxies have different denominators – average total asset,
average sales, and average cash adjusted total assets, their weights and their means vary
greatly. Although the mean value of the ROA and R_cAT are similar, the Market to book
value or Tobin’s Q suggest that on average, for the sample of firms included in this research
the market value is greater than the firm’s total assets.
3.4
Managed Earnings Metrics
Methods for managing earnings can be categorized into two groups: (i) the accrual
based managed earnings approach or (ii) the real managed earnings approach. The
implication of these two forms of managed earnings is that through the accrual approach,
firms’ earnings could be lowered and investors could be misinformed. However, the real
managed earnings approach can have a deteriorating effect on the future value of the firm as
it often cuts funding for research and development, increases price discounts, and reduces
capital investments. The challenge however is which, if either of these approaches is being
used by the firm.
Unlike the accrual based approach, which is easier to detect, the real based approach
is somewhat difficult to identify, as the real approach is included in the three facets of the
statement of cash flow. As such, managers may choose either the accrual approach or the real
approach. Recent empirical evidence cites the use of the real-activities approach. For
instance, in the aftermath of recent regulation like SOX, Cohen and Zarowin (2010) find that
59
firms’ usage of the real-activities approach is on the rise. In the competitor pricing arena,
Chapman and Steenburgh (2011) finds that firms that just beat their prior year’s quarterly
earnings are by an average of 10-15% more likely to reduce their price in the final month of
the fiscal quarter. Chapman and Steenburgh (2011) concluded that the results are consistent
with firms using the real-activity managed earnings approach to discount prices. In this essay
I use both approaches.
3.4.1
Accrual-based Earnings Management
The commonly applied cross-sectional approach to calculate discretionary accruals is
used in this essay. For every year I estimate the models of every firm classified by its twodigits SIC code. This approach partially controls for industry-wide changes in economic
conditions that affect total accruals, allowing the coefficient to vary across time (DeFond and
Jiambalvo (1994) and Kasznik (1996)). For this essay, the main models are the modified
Jones model Jones (1991) and the adjusted modified Jones model Kothari et al. (2005).
I use the modified Jones (1991) model because of its power in detecting managed
earnings. For instance, Bartov, Gul, and Tsui (2000) and Dechow, Sloan, and Sweeney
(1995) find that the cross-sectional modified Jones (1991) model were consistent in detecting
earnings management. However, Kothari, et al. (2005) argue that measuring discretionary
accruals without controlling for firm performance will produce misspecification. Therefore,
they propose to include a control for firm performance in the model. The inclusion of return
on asset (ROA) as a scaling variable will help mitigate some of the heteroskedastic and
misspecification issues that exist in the modified Jones model.
Thus, the empirical models for estimating discretionary accruals are based on the
following cross-sectional model:
60
Eq. (7)
Eq. (8)
where equation 7 is the modified Jones (1991) model and equation 8 is the Kothari et
al. (2005) model for fiscal year t and firm i.

TA represents the total accruals defined as TAit = IBCit – CFOit

IBC is the earnings before extraordinary items and discontinued operations
(COMPUSTAT item 123) and CFO is the operating cash flows from
continuing operations derived from Net Cash flow – Extraordinary Items and
Discontinued Operations – taken from the statement of cash flows
(COMPUSTAT item 308 – COMPUSTAT item 124)

Assetsit-1 represents total assets lagged one year (COMPUSTAT item 6)

∆Salesit is the change in revenues from the preceding year (COMPUSTAT item 12)

∆Recit is the change in accounts receivable18 from the preceding year (COMPUSTAT
item 2)

PPEit is the gross value of the property, plant and equipment (COMPUSTAT item 7)

ROAit is the firm’s return on assets (COMPUSTAT item 172 / COMPUSTAT item
6).
The measure of discretionary accruals will be the residual of equations 7 and 8, however the
result shown in subsequent Tables relating to managed earnings will mostly be using the
residuals from equation 8.
3.4.2
18
Real Earnings Management
The inclusion of change in receivables in the model eliminates the tendency to measure discretionary accruals with error when discretion
is exercised over revenues.
61
Like accrual based earnings management, I rely on proxies developed in prior studies
for real earnings management. Similar to Roychowdhury (2006); Cohen et al. (2008); and
Cohen and Zarowin (2010), I consider three metrics to study the level of real activities
managed earnings. These are the: (i) abnormal levels of cash flow from operations, (ii)
abnormal levels of discretionary expenses, and (iii) abnormal levels of production costs.
Abnormal cash flow from operations can occur through discounts and offering of
lenient credit terms. Discount offers and lenient credit terms can temporarily boost sales
volume, but these are likely to disappear once a firm reverts to old prices. The additional
sales will increase current period earnings assuming margins are positive. However, the
effect of discounts and lenient credit terms will lower cash flows in the current period.
Abnormal discretionary expenses revolve around advertising, research and
development, selling, general, and administrative expenses. Reducing these expenses will
enhance current period earnings and impact the current period cash flow provided that the
firm pays these expenses with cash.
Abnormal production costs occur through declaring lower costs of goods sold to
increase production. To increase a firm’s earning, managers can increase production. In so
doing, managers end up spreading the fixed overhead costs over a larger number of units,
thus lowering the fixed costs per unit. Ceteris paribus, as the reduction in fixed costs per unit
is not offset by any increase in marginal cost per unit, total cost per unit declines.
In the estimation of real earnings management, I run the following cross-sectional
regression for each firm, industry and year. First, normal levels of CFO, discretionary
expenses, and production costs are generated using models developed by Dechow, Kothari,
and Watts (1998).
Equation 9 below expresses cash flow from operations (CFO)
62
(COMPUSTAT item 308) as a linear function of sales and change in sales. The abnormal
CFO is derived from actual CFO minus the fitted level of CFO generated using the estimated
regression. The above method of deriving abnormality is applied to all the models of real
earnings management.
Eq. (9)
To insulate firms in the dataset from significantly lower residual-effect caused by
increase in reported earnings in a certain year, discretionary expense is estimated as:
Eq. (10)
Production costs are defined as the sum of costs of goods sold (COMPUSTAT item
41) and change in inventory (COMPUSTAT item 303) during the year. Thus, production cost
is modeled as the function of contemporaneous sales, contemporaneous change in sales, and
lagged change in sales.
Eq. (11)
In equation 10, DISC represents the discretionary expenses defined as the sum of
advertising expenses (COMPUSTAT item 45), research and development expenses
(COMPUSTAT item 46), and selling, general, and administrative expenses (COMPUSTAT
item 132). In equation 11, PROD represents the production costs defined as the sum of cost
of goods sold and the change in inventories.
Given the rise in sales for my sample firm – see Figure 2, following Gunny (2005),
Roychowdhury (2006), and Cohen and Zarowin (2010), I assume that firms that inflate
earnings upward are likely to have unusual low cash from operations, and/or unusual low
63
discretionary expenses, and/or unusual production costs. Besides these three measures,
Cohen and Zarowin (2010) and Zang (2011) created two aggregate measures for real
earnings management. Rather than creating the same aggregate measures alone, in this paper,
I create three aggregate measures: the first two are similar to Cohen and Zarowin (2010) and
Zang (2011) models, and the last aggregate measure is a combination of all three measures of
real managed earnings.
In the first measure RM1, I multiply abnormal discretionary expenses by negative one
and add it to abnormal production costs and then aggregate both. In the second measure
RM2, I multiply both abnormal cash flows from operations and abnormal discretionary
expenses by negative one then aggregate the two. In the third measure RM3, I multiply both
abnormal discretionary expenses and abnormal cash flow by negative one and then add them
to abnormal production costs. The equations for RM1, RM2, and RM3 are given below as
equation 12, 13, and 14. The intuition behind the multiplication by negative one for abnormal
cash flow and abnormal discretionary expenses is that it converts both models to align with
abnormal production costs. Hence, all three aggregate models suggest that positive values
indicate higher propensity of managed earnings.
Eq. (12)
(-1 * r_DISXit) + r_PRODit
Eq. (13)
(-1 * r_CFOit) + (-1 * r_DISXit)
Eq. (14)
(-1 * r_CFOit) + (-1 * r_DISXit) + r_PRODit
Where r_DISX is abnormal discretionary expense, r_CFO is abnormal cash from
operations, and r_PROD is abnormal production cost.
[Table XVIII and XIX]
64
Evidence from Table XVIII shows the statistic for the discretionary behavior of
managers. Proxies of managed earning using the accrual and real-activity approach indicate
that firms in our sample manage their earnings. Because the managed earning models are
estimated using the COMPUSTAT universe and large array of industry classification, the
means of the accrual and real-activity proxies are not zero, yet, the means were statistically
tested and none were significantly different from zero. However, the statistics are similar to
those reported in prior research such as Cohen et al. (2008), Gunny (2005), and Jones (1991).
Considering the nature of managed earnings, the regression model uses either: (i) the
signed form – current state – or (ii) the transformed state which considers firms’
discretionary allowance of their managers above their peers within the same industry. The
second method is applied because among other things, the intent of this paper is to show that
firms’ that offer higher discretionary actions to their managers suffers greatly in the presence
of a golden parachute. Jackson and Pitman (2001) noted three definitions of managed
earnings. The first and second definitions suggest that managed earnings are intended to
obtain private gain or to influence contractual outcomes. The third definition suggests that
managing earnings entails the restructuring of reporting or production/investment decisions
around the bottom line impact. Thus, generating a proxy of managed earnings above peers in
the same industry validates a test for the first two definitions, and the third is validated by
looking at other avenues used to manage earnings. Here, I consider various approaches to
manage earnings using the real-activities method.
On the movement of managed earnings either higher or lower, this relationship is
reflected in Table XIX which offers the pairwise correlation between variables. As expected,
accrual and real-activity approach proxies for managed earning behavior are highly
65
correlated, except for abnormal production costs, with its highest correlation to abnormal
cash flow at -0.0757. The low correlation of abnormal production cost to other managed
earning proxies may suggest that not all managed earnings methods are utilized by firms at
the same time. Also, the correlation among the managed earning proxies reflects the concerns
in prior literature (Gunny, 2005; Gunny, 2010; Cohen and Zarowin, 2010) suggesting that
managed earnings using both methods do not occur simultaneously. Hence, as the accrual
approach is utilized, the real-activities approach decreases and vice-versa.
By aggregating the real-activity managed earning proxies, high correlation to the
accrual managed earning proxies is also seen between RM1 and EM1 at 0.9409, and lowest
between RM3 and EM1 at -0.8056. Overall, the independent variables and the control
variables were at best minimally correlated among each other with the highest correlations
shown to be between return on asset (ROA) and return on cash adjusted asset (R_cAT) at
0.7346, Book Leverage (BLev) and Market Leverage (MLev) 0.675, the lengths of the CEO
stay in office (duration) and the size of the firm (LogSize) at (0.2314). The highest inverse
correlation is between market to book ratio (MTB) and the Market Leverage (MLev) at 0.345219.
3.5
Empirical Results
This section documents the univariate and multivariate tests and their results. All
through the multivariate tests, the explained variable will be managed earning proxies
through the accrual approach and the real-activity approach. Given that the modified Jones
(1991) model (EM1) and the Kothari et al. (2005) adjusted for performance model (EM2) are
positive and highly collinear, I only offer results using the EM2 model for the accrual
19
All correlation matrix values reported were statistically significant at 10% or less.
66
approach20. For validity, besides the ordinary least square (OLS) method, quartile, logistic,
and panel fixed effect regression methods are used as a test for the robustness of the findings.
3.5.1
On the Adoption of Golden Parachute by Firms
I perform a univariate test comparing the group sample means using pay for
performance (PoP) measure and all measures of operating performance (ROA, R_cAT, ROS,
Tobin). These results are shown in Figure 321. The mean pay-for- performance value is
higher for firms that have a golden parachute (Panel A). Unlike pay-for-performance tests
which shows a wider margin for firms’ with golden parachute provisions, the mean operating
performance values are higher for firms that have not adopted a golden parachute (Panel B).
Thus, managers with a golden parachute are more concerned about pay-for-performance than
the operating and fiscal position of their firms.
Although the univariate test reflects the behaviors of managers with a golden
parachute in regards to performance, I perform other multivariate tests to assess these
managers position with respect to the degree of managing earnings. As stated, this paper
suggests that the propensity to manage earnings varies among firms’, specifically
distinguishing between the adoption of a golden parachute and non-golden parachute in firm
years. Based on the hypotheses, I argue that the adoption of a golden parachute by a firm
should lead to an increase in managed earnings. Given this, I run the following regression 22:
Eq. (15)
20
By performing the multivariate tests using the modified Jones (1991) model, the results are similar, thus to ignore repetition I do not
publish their results. These results are available should you want to view them.
21
I empirically test the two group mean sample. Only the mean test for return on sales (ROS) was not statistically different for the two
groups. This is expected, as the mean values in Panel B of Chart 3 reveals quantitatively similar values.
22
A post-estimation test known as the Variance Inflation Factor (VIF) was performed on all OLS models. The VIF assess the severity of
multicollinearity among the explanatory variables in an ordinary least squares regression analysis. Generally, a VIF mean score equal to or
higher than 10 suggests collinearity problem, here, the highest VIF was 1.69 suggesting that the explanatory variables do not suffer from
multicollinearity.
67
Where: i and t represent firms and year respectively

ME is the dependent variable managed earning proxy by the accrual and real-activity
approach models,

GP is a dummy variable for firms that have adopted a golden parachute in a particular
year: GP = 1, No GP = 0,

Performance proxy by four different measurements,

Risk proxy by two measurements,

PoP is pay for performance sensitivity at industry level,

Duration is CEO tenure,

Size is Log of Firm’s total assets, and

Leverage is proxied by two measurements.
From the pairwise correlation Table, that is Table XIX, I expect that the behavior of
managed earnings will not be similar across all firms as the increase in the accrual approach,
suggest a decrease in one or more methods of the real-activity approach. Also, note that the
behavior of the accrual approach is similar to the behavior of the abnormal cash flow method
thus, suggesting that the expectation for the accrual method may be similar at least to the
abnormal cash flow method. These I find in the regressions test performed on the sample.
The results shown in Table XX present the pooled linear regression models.
[Table XX]
68
As shown in Table XX, the adoption of golden parachutes by firms in a particular
year are positively correlated and statistically significant23 with the propensity for managers
to manage earnings under the accrual approach as well as for abnormal cash-flow from
operations. Because the accrual approach is most commonly used, this finding was expected.
The adoption of a golden parachute with the remaining un-aggregated models of the realactivity approach were not statistically significant, as such I focus on the aggregated models.
Similarly, the anticipated negative correlation behavior on the adoption of a golden
parachute by firms in a year is exhibited on the three aggregated models of real-activity
approach (RM1, RM2, and RM3). However, only RM2 and RM3 were statistically
significant. Other pertinent findings revealed in Table XX include: almost across all
regression models except RM2, firms that perform less manage their earnings more (ROA),
pay-for-performance sensitivity (PoP) is positively and statistically significant with almost all
managed earning models except abnormal discretionary expenses (r_DISX), where it was
inversely significant. Also, the pay-for-performance sensitivity measure was not significant
with abnormal cash-flow from operations.
[Table XXI]
A confirmatory test is performed using three different regression models. In the first
test, I perform a fixed effect panel regression test. By this, the initial pooled regression model
explanatory variables are forced into non-random effects for year and industry classifications
of the firms. The results are tabulated in Table XXI. The fixed effect results are similar to the
pooled regression models. However due to high collinear effect of the pay for performance
23
For this paper, the statistically significant tests reflect a value greater than or equal to 1.96 t-test value. This value is synonymous to 95%
or more certainty of the coefficient significance to the dependent variables.
69
sensitivity (PoP) and the year and industry interactions, pay for performance sensitivity (PoP)
was eliminated from the Table24.
If CEOs are given leeway by stakeholders to manage a firm, it is possible that
managed earning25 proxies may reflect two components: (i) the “allowed” discretion to
manage firm earnings, and (ii) the “not so allowed” discretion to manage firm earnings. In
the second test, I attempt to desensitize both by subtracting each firms year manage earning
value from their industry median. Here, I assume that the industry median is a representation
of the allowed discretionary leeway given to managers to manage earnings. As such, in the
second test I investigate the impact of a golden parachute adoption on firms managed
earnings values above their industry median. The results are shown in Table XXII26.
[Table XXII]
Consistent with findings in prior regression models, I find that firms that adopt a
golden parachute in a particular year tend to manage their earnings more than firms that do
not adopt a golden parachute. This evidence is statistically significant under the accrual
approach, abnormal cash flow from operations method, and the aggregated model RM2.
Given that the un-aggregated models – abnormal production costs (r_PROD) and abnormal
discretionary expenses (r_DISX) – are not statistically significant in the regression tests,
managed earnings behavior using these two real-activity methods is not as rampant as
anticipated nor as suggested in prior literatures (see Cohen et al. (2008), Cohen and Zarowin
(2010), Gunny (2010)).
24
Post estimation test, show that the omitted variable in all model (pay sensitivity per industry) is collinear with GP, Duration, Size, and
Book Leverage as such STATA eliminated pay for performance sensitivity (PoP).
25
Anecdotal and scholarly evidence suggest that managers are given the leeway to manage earnings, however it is possible that managers
may exert on their discretion beyond the scope given to them as such disseminating the proxies of managed earnings will be useful to shedlight on firm behavior.
26
I also perform the analysis using industry mean and the result were similar
70
[Table XXIII]
In the third confirmatory test, I perform quartile regressions. By performing quartile
regressions, I highlight the distributive behaviors of managers across the sample. As shown
in Table XXIII, golden parachute firms respond differently to managing earnings. Golden
parachute firms tend to reduce their managed earnings behavior if they consider their
managed earnings values to be high, whereas, golden parachute firms in the lower quartile
manage their earnings more. Also, operating performance and pay for performance retained
their directionality however their values were a bit higher for firms at the lower quartile.
Hence, pay for performance sensitivity, firm performance, and firm’s risk position do impact
the propensity for firms to manage earnings.
3.6
Additional Tests
3.6.1
On the effect of the CEO’s Age on Managed Earnings
In conjunction with the empirical analysis, I further investigate the behavior of
managed earnings by the age of the executives. I align my rationale with the works of
(Knoeber (1986) and Conyon and Florou (2002)). Knoeber (1986) who note that incidence of
golden parachutes increases capital expenditure as waiting for future information to assess
firm performance becomes more valuable. Conyon and Florou (2002) documents that firms
cut back on capital expenditure as the chief executive become older, suggesting that older
executives may be more risk averse.
If the presence of a golden parachute increases capital expenditure and if the age of
the executive reduces capital expenditures, I predict that older executives motivated under
the incentive effect hypothesis are more likely to manage earnings. However, if the
entrenchment hypothesis holds, then, older executives will manage their earnings less.
71
Existing literature on golden parachutes finds golden parachute effect on merger and
acquisition likelihood (Bebchuk et al. (2012) and Fich et al. (2012)), the disciplinary role of
the market (Bebchuk et al. (2009), Evans and Hefner (2009) and Narayanan and Sundaram,
1998)). To my knowledge, no one has explored the effect of golden parachutes on managed
earnings.
To examine the age of CEOs effect on the propensity to manage earnings, I use two
estimates for age. The first estimate, pools the raw age of the CEOs into linear regression
models. The second, the raw age of CEOs are broken into quartiles to access and show a
clear behavior on the progressive CEO age effect on manage earnings. The results are shown
in Table XXIV.
[Table XXIV]
In Table XXIV, ten regression models are itemized. The first two consider the raw
age of CEOs, the next four considers the first quartile for CEO ages, and the last four
considers the third quartile for CEO ages. CEO ages in the first quartile are from fifty-five
years and descending, whereas the CEO ages in the third quartile are from sixty-eight years
and ascending. Clearly from the first two columns, CEO ages (PAGE) are statistically
significant, but their economic contributions are not evident as compared to the quartile
breakdown of their ages. As shown in the next eight columns, the difference is heightened.
From the last four columns, the regression results show that CEOs older than or equal to
sixty-eight years of age tend to manage their earnings more compared to their younger
counterparts. Hence, the insurance-effect or incentive-effect hypothesis for golden parachute
is valid. These results are even stronger when the raw age data for CEOs are interacted with
the dummy variables of one depicting the adoption of a golden parachute in a particular year.
72
3.6.2
Interaction Between the Explanatory Variables
So far, the regression models have been successful in showing that golden parachute,
pay-for-performance sensitivity, firm performance, firm risk-taking measures, and chief
executive age affect firm manage earnings. However, the results so far have been unable to
elucidate on the relationship between pay-for-performance, firm performance, risk taking,
and the executive age effect on managing earnings in light of golden parachute provisions.
An interactive term between golden parachute provisions and other explanatory
variables could offer insight on two non-takeover hypothesis of golden parachute – the
incentive vs. the entrenchment effect hypothesis. Also, by testing the hypothesis, it should
offer a better insight on the need for firms to offer a golden parachute to managers. The
incentive-effect hypothesis asserts that managerial concerns regarding short-term benefits is
alleviated, thereby, encouraging managers to undertake projects which create maximum
shareholder wealth in the long-run. Billett and Xue (2007) and Bebchuk et al. (2012)
conclude that firms which have adopted a golden parachute have higher market value, hence
less likely to become a target.
The entrenchment hypothesis asserts that a golden parachute insulates CEOs that are
not performing and protects them from the corporate control market and internal governance
mechanisms discipline. Bertrand and Mullainathan (2003) and Atanassov (2005) suggest that
entrenched managers prefer an easy road and are reluctant to invest in innovative or risky
projects. Berger, Ofek, and Yermack (1997) and John, Litov, and Yeung (2008) using a
variety of proxies for entrenchment find a negative association with risk-taking. As such,
given that performance – operating performance and pay-for-performance, and risk-taking
are central to both hypotheses prediction, an interactive term between golden parachute and
73
these variables should bolster and reaffirm either hypothesis. The regression results are
reported in Table XXV.
[Table XXV]
From Table XXV, the variables of interests are the interaction terms between golden
parachutes (GP) and firm performance (ROA), pay-for-performance sensitivity (PoP), risktaking (SD), and the executive’s age (AGE). Although the un-aggregated measures for the
real-activity manage earnings are reported, the focus here should be on the accrual approach
(EM2) and the aggregated models (RM1, RM2, RM3) for the real-activity approach. The
interaction between a golden parachute and firm performance (GP*ROA) across Table XXV
were positive and statistically significant. This suggests that golden parachute firms have
higher performance and manage earnings positively.
The interaction term between a golden parachute and risk-taking (GP*SD) suggests a
similar result. Hence, golden parachute firms undertake riskier projects and manage their
earnings more. These two interactive terms support the proposition of the incentive effect
hypothesis.
3.6.3
Pre vs. Post SOX Era
Did acts like the Sarbanes-Oxley Act (SOX) of 2002 exert stress on managers to
modify techniques of adjusting earnings? Prior research says ‘yes’. The Graham et al. (2005)
survey finds that managers prefer real-activity managed earnings to accrual based managed
earnings. Cohen et al. (2008) note an increase in the use of real-activity earnings
management after the passage of SOX. To investigate the SOX effect, I run two regression
tests: (i) prior to 2002 (that is, for samples equal to or less than the year 2001) and (ii) after
74
2003. By default, the use of the accrual based approach should decrease in firms that have
adopted a golden parachute after the SOX passage. The result is reported in Table XXVI.
[Table XXVI]
From Table XXVI, the interactive term between firm years on the adoption of a
golden parachute and post-SOX (GP_SOX) reveals that in the post-SOX era firms that have a
golden parachute used less of the accrual approach. Likewise, increase in the usage of the
real-activity approaches were statistically significant for all three techniques, however, in
only two (r_CFO and r_DISX) do I find an upward movement.
So far, the regressions in the pre and post SOX era suggest a difference in their
behavior. However, the difference in their magnitude cannot be inferred from Table XXVI.
Therefore, I perform a seemingly unrelated estimation test to assess the impact of golden
parachute managers in pre and post SOX era to managed earnings. The result is shown as
Table XXVII.
[Table XXVII]
By generating a seemingly unrelated estimation I assume a null hypothesis that preSOX and post-SOX golden parachute coefficient are equal. As reflected in Table XXVII,
golden parachute impact is not the same as proxies for real-activity approach were
statistically significant suggesting that r_CFO and r_DISX are higher in post-SOX era.
3.7
Conclusion
In this essay I explore the association between golden parachutes and managed
earnings. A golden parachute can be defined as the benefit received by an executive in the
75
event that a company is acquired and/or the executive’s employment is terminated and/or the
executive remains with the firm after a recessionary cycle. I argue that the presence of a
golden parachute increases the propensity of a firm to manage its earnings as golden
parachute firms are more inclined to show favorable performance.
Furthermore, I examine the interactive terms between the adoption of golden
parachutes, firm risk, firm performance, and CEOs age. Lys, Rusticus, and Sletten (2007)
argue that when managers are risk averse, the use of large severance agreements provides
managers with downside protection in addition to rewards for exceptional stock performance.
This downside protection induces managers to undertake risky projects which in turn will
increase the cost of capital. Alternatively, Yermack (2006) provides evidence that firms are
motivated to adopt golden “handshakes” to mitigate managerial problems including
inadequate risk-taking, shirking, entrenchment in office, and incomplete disclosure.
Similarly, Rau and Xu (2009) find that contingent severance pay is promised in advance for
managers to provide insurance for their human capital value and compensate them for the
risks they undertake.
To elucidate on the behavior of manage earnings, the empirical work in this paper
examines the two managed earning approaches: (i) the accrual approach and (ii) the realactivity approach. Cohen et al. (2008) find that the real-activity approach is on the rise since
the enforcement of the Sarbanes-Oxley Act of 2002. As such, considering this ‘other’ method
used by managers to adjust their earnings, should reveal and reflect the true positions of
managed earnings in the sample.
The empirical tests performed include: linear, quartile, and fixed-effect regression
models and in all, I find evidence in support on the adoption of a golden parachute. The
76
regression results suggest that managers with a golden parachute are more likely to manage
earnings positively. Furthermore, I find strong evidence that older chief executive officers
(CEOs) manage their earnings more.
The findings in this essay contribute to literatures in corporate finance as well as
accounting disciplines in three main ways. First, offering other avenues to empirically test
golden parachute hypotheses. Second, I show that the extent to which managed earnings
impacts the value of the firm goes beyond quarterly events. Third, the empirical results in
this essay bridge the gap between firm performance, firm risk-taking measures, chief
executive pay sensitivity, and golden parachute to the degree of managed earnings.
Finally, the findings in this paper shows that while the adoption of a golden parachute
maybe alleviate concerns for a manager’s loss of job and reporting of profits (Narayanan
(1985) and Stein (1988, 1989)) adjustment of earnings still occur in firms with golden
parachute provisions. The evidence shown here suggests that firms that have adopted a
golden parachute report higher performance, undertake higher risk, and have a higher
propensity to manage earnings. Thus, the insulating effect of a golden parachute impedes the
proper valuation of a firm. Given that firm valuation is of uttermost importance to managers,
this paper shows that the adoption of a golden parachute protects managers but disguises the
true value of the firm to stakeholders.
77
CHAPTER IV
CONCLUSION AND FUTURE RESEARCH
Managers that always promise to make the numbers’ will at some point be tempted to make
up the numbers
Warren Buffet27
The above citation embodies the empirical analyses of this dissertation. Given the
plethora of papers, managing financial numbers seems to have become a norm among
businesses. The question is why? The answer is tantamount to meeting earnings expectations
of analysts and investors. As such, firm’s values are “punished” if they do not meet market
expectations. From this dissertation, the analyses show that an executive contractual clause
such as golden parachutes and firm prior costs of capital do aggrandize the need to manage
earnings. While earnings management per se may not be contradictory to the generally
accepted accounting principle (GAAP), its effect hides the true value of firms. Also, market
information are very opaque and the costs for market participants are to rely on other
methods – perhaps questionable means – to obtain ‘accurate’ measures and values for firms.
In the first essay, three questions were posed. These are: (i) does the prior cost of debt
and equity impact the propensity for firms to manage earnings? (ii) if there is evidence of
such, then which prior costs affects managed earnings behavior? (iii) does a firm location
amplify prior costs of capital effects on managed earnings? To mitigate in the restructuring of
firms cost of capital, better performance and improved financial statements contributes
27
Note obtained from Warren Buffet 2002 letter to Berkshire Hathaway Shareholders. In this letter he offered advice to managers on
financial reporting – www.berkshirehathaway.com/letters/2002pdf.pdf
78
immensely. As such, when firms prior cost of capital are high, firms are more likely to
manage their earnings to help attain favorable placements – in the equity market and lower
borrowing cost from the debt market.
In the second essay, two questions were examined. These are: (i) do firms that have
adopted a golden parachute manage their earnings more? (ii) does the CEO’s age in firm’s
that have a golden parachute play a major role on the propensity to which firms manage their
earnings? Arguably, golden parachutes serve as a soft-landing mechanism for most CEOs.
But, if the intent of the golden parachute is to motivate managers to undertake riskier
projects, then is it possible that the CEO’s age could mitigate their proclivity to manage
earnings?
The age of an executive can serve as a good measure on how many riskier projects
she would like to take. Older executive may be hesitant to take risks. Conyon and Florou
(2002) note that firms cut back on capital expenditure as the chief executive become older.
Given this, I assume that older executives are more risk averse, as such managing earnings
may serve as an ego booster and a reflector of ‘enhancing’ competency.
In both essays, I find that managing earnings is exacerbated by older executives, prior
cost of capital – specifically high cost of debt, and greater distance from the regional offices
of the Securities and Exchange Commission (SEC). For future research, three possible works
can emerge from this dissertation. For instance, by extending this research to other countries
– an example would be to examine managed earnings in firms with golden parachutes from a
pool of developed countries. Other research subjects include: examining the location effect
79
of staggered (classified) boards on firms’ managed earnings behavior and examining the
location effects of staggered boards on firm value.
80
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90
APPENDIX A
TABLES FOR ESSAY ONE
Table I: Description of Selected Firms
Sample Universe
15,929
Elimination
SIC Financials and Utility -
6760
Firms with no recorded Bond Rating -
5138
4031
Merging CRSP with COMPUSTAT -
2137
1894
Firms not headquartered in US AB Firms -
207
60
Total Firms
1,627
Table II: Managed Earnings Proxies
The final sample contains 32,109 firm year observations. DA1 is the residual from the Jones (1991) modified model in equation 1
; DA2 is the residual from the Kothari et al. (2005) model in equation 2
; Abcfo is the abnormal cash flow from operations derived from equation 3
; Abdisc is the abnormal discretionary expenses obtained from equation 4
; Abprod
is the abnormal production cost from equation 5
. AbsDA1, AbsDA2, AbsCFO, AbsDISC, and AbsPROD are the absolute values for earnings
management proxies
Table 2
Variable
Mean
S.D.
Min
Quartile Range
0.25
Mdn
0.75
Max
DA1
0
0.85
-103.17
-0.14
-0.07
0.06
34.83
DA2
0
0.84
-103.15
-0.14
-0.07
0.05
34.99
Abcfo
0
2.23
-241.79
-0.11
-0.03
0.04
170.06
Abdisc
0
1.02
-75.9
-0.12
-0.03
0.12
78.76
Abprod
0
2.1
-168.82
-0.1
0.07
0.19
191.4
AbsDA1
0.2
0.83
0
0.07
0.12
0.2
103.17
AbsDA2
0.19
0.82
0
0.07
0.12
0.2
103.15
AbsCFO
0.21
2.22
0
0.04
0.08
0.16
241.79
AbsDISC
0.22
0.99
0
0.06
0.12
0.22
78.76
AbsPROD
0.28
2.08
0
0.08
0.16
0.27
191.4
91
Table III: Correlation Matrix among the Dependent Variables
The table shows the correlation between all dependent variables. The correlation of 32,109 firm year observations across the firm’s respective
industry categories based on their SIC Codes. DA1 is the residual from the Jones (1991) modified model in equation 1
; DA2 is the residual from the Kothari et al. (2005) model in equation 2
; Abcfo is the abnormal cash flow from operations derived from equation
3
; Abdisc is the abnormal discretionary expenses obtained from equation 4
; Abprod is the abnormal production cost from equation 5
. ***, **, * are significant at 99.999%, 99%, and 95% respectively.
DA1
DA2
Abcfo
DA2
0.9903***
Abcfo
0.0358***
0.0336***
Abdisc
-0.4889***
-0.4874***
-0.3797***
Abprod
-0.1479***
-0.1465***
-0.9478***
Abdisc
0.2488***
92
Table IV: Numerical Conversion of Bond Rating
Following Klock et al. (2005) techniques to convert bond ratings to numerical value, AAA bonds are assigned the value of 22 and D rated bond is assigned the value of 1. Firms
are rated every month, as such the numerical value for a firm’s monthly rating is averaged to yield a firm’s annual rating
Conversion Values
S&P Ratings
22
AAA
21
AA+
20
AA
19
AA-
18
A+
17
A
16
A-
15
BBB+
14
BBB
13
BBB-
12
BB+
11
BB
10
BB-
9
B+
8
B
7
B-
6
CCC+
5
CCC
4
CCC-
3
CC
2
C
1
D
93
Table V: Investable Firms vs. Speculative Firms
Our final sample contains 32,109 firm year observations. The values of each variable shown below are their mean values. Profitability (Profit), Book Leverage (Lev), Total Firm
Assets (Assets), Market to Book Ratio (MB), Total Long-term debt (DLLT), Short term debt (DLC), Total Firm Revenue (Sale), Return on Asset (ROA), Capital Intensity (Cap),
and Firm volatility (Vola) are some general firm characteristics. Earnings management models are Abcfo is the abnormal cash flow from operations derived from equation 3;
Abdisc is the abnormal discretionary expenses obtained from equation 4; Abprod is the abnormal production cost from equation 5; DA1 is the residual from the Jones (1991)
modified model in equation 1; DA2 is the residual from the Kothari et al. (2005) model in equation 2. Total Revenue, Total Assets, and Total Accrual are actual values obtained or
calculated from COMPUSTAT data.
Grades
0
1
Profit
0.2039
0.9557
Lev
0.6515
0.5679
Assets
1884.4391
10831.6770
MB
12.7672
22.0331
DLLT
619.5140
2178.7242
DLC
88.6999
870.4069
Sale
1732.0705
9466.0790
ROA
0.0032
0.0667
Cap
0.0698
0.0703
Volatility
7.2603
9.4061
TA
-28.0237
824.6454
DA1
-0.0262
0.0567
DA2
-0.0195
0.0407
Abcfo
0.0201
-0.0418
Abdisc
0.0013
-0.0026
Abprod
-0.0091
0.0188
94
Table VI: Managed Earnings across US States
This table reports the number of firms and managed earnings for U.S. states. Counties refers to the number of counties in a particular state that had at least one corporate
headquarter. # of firms refers to all observed COMPUSTAT firms (active and inactive) over the period 1980 to 2010 that are headquartered in U.S. Share of US COMPUSTAT
represents the yi and it refers to percentage of firm observation in a state to the total firm observation in COMPUSTAT North America. # of Propensity refers to the observation of
firms in our sample that represents a particular state. Share of Sample represents the xi is the ratio of managed earning firms that are located in the state to the overall sample.
Deviation refers to the difference between a state’s share of managed earnings and its share of COMPUSTAT firms.
Share of US COMPUSTAT
AK
# of
Observation
120
# of Propensity
Share of Sample
Deviation
AL
1,546
0.04
13
0.04
0
0.56
179
0.56
0
AR
926
0.34
318
0.99
0.65
AZ
3,463
1.26
393
1.22
-0.04
CA
40,915
14.87
3,787
11.79
-3.08
CO
7,928
2.88
864
2.69
-0.19
CT
6,775
2.46
966
3.01
0.55
DC
857
0.31
115
0.36
0.05
DE
2,294
0.83
80
0.25
-0.58
FL
13,009
4.73
1,401
4.36
-0.37
GA
6,773
2.46
1,000
3.11
0.65
HI
587
0.21
48
0.15
-0.06
IA
1,595
0.58
145
0.45
-0.13
ID
589
0.21
124
0.39
0.18
IL
14,327
5.21
1,690
5.26
0.05
IN
3,729
1.36
346
1.08
-0.28
KS
1,771
0.64
148
0.46
-0.18
KY
1,637
0.6
222
0.69
0.09
LA
1,589
0.58
331
1.03
0.45
MA
14,165
5.15
828
2.58
-2.57
MD
5,309
1.93
491
1.53
-0.4
MI
5,138
1.87
633
1.97
0.1
State
95
Table VI: Managed Earnings across US States ‘continued’
Share of US COMPUSTAT
# of Propensity
Share of Sample
Deviation
MN
# of
Observation
7,058
2.57
800
2.49
-0.08
MO
3,967
1.44
695
2.16
0.72
MS
887
0.32
55
0.17
-0.15
MT
343
0.12
19
0.06
-0.06
NC
4,975
1.81
779
2.43
0.62
NE
924
0.34
149
0.46
0.12
NH
1,073
0.39
42
0.13
-0.26
NJ
13,583
4.94
1,256
3.91
-1.03
NV
2,854
1.04
332
1.03
-0.01
State
NY
29,422
10.7
2,473
7.7
-3
OH
9,189
3.34
1,839
5.73
2.39
OK
2,487
0.9
266
0.83
-0.07
OR
2,164
0.79
256
0.8
0.01
PA
11,757
4.27
1,547
4.82
0.55
RI
830
0.3
139
0.43
0.13
SC
1,678
0.61
152
0.47
-0.14
SD
276
0.1
41
0.13
0.03
TN
3,296
1.2
708
2.2
1
TX
24,337
8.85
4,008
12.48
3.63
UT
2,435
0.89
84
0.26
-0.63
VA
6,201
2.25
1,004
3.13
0.88
VT
429
0.16
20
0.06
-0.1
WA
4,070
1.48
617
1.92
0.44
WI
3,392
1.23
677
2.11
0.88
WV
549
0.2
29
0.09
-0.11
96
Table VII: Firms Distance to SEC Regional Offices
This table report firms distance to the SEC regional offices. Firms were selected randomly from the lower and the upper range based on their distance to the SEC regional office
which has jurisdiction over such firms. Firm Name identifies the firm. The SIC categorize firms based on their industrial classification. State reference the where a firm is
headquartered. Firm Zip Code is the zip code in which firms are headquartered. SEC Regional Office reports the SEC Office that has jurisdiction over each firm. SEC Zip Code
shows the zip code which SEC Office is located. Distance measures the straight length from each firm to the SEC regional office that have jurisdiction over the firm
Firms
ALEXANDER & BALDWIN INC
SIC
4400
State
HI
Firm Zip Code
96813
SEC Regional Office
Los Angeles, CA
SEC Zip Code
90036
Distance
2551.79
HAWAIIAN TELCOM HOLDCO INC
4813
HI
96813
Los Angeles, CA
90036
2551.79
ALASKA COMMUNICATIONS SYS GP
4813
AK
99503
San Francisco, CA
94104
2002.35
STILLWATER MINING CO
1090
MT
59012
San Francisco, CA
94104
970.92
CENTURYLINK INC
4813
LA
71203
Miami, FL
33131
855.4
QWEST COMMUNICATION INTL INC
4813
LA
71203
Miami, FL
33131
855.4
CALLON PETROLEUM CO/DE
1311
MS
39120
Miami, FL
33131
783.91
PHI INC
4522
LA
70508
Miami, FL
33131
783.07
PETROQUEST ENERGY INC
1311
LA
70508
Miami, FL
33131
783.07
STONE ENERGY CORP
1311
LA
70508
Miami, FL
33131
783.07
SKYTEL COMMUNICATIONS INC
4812
MS
70508
Miami, FL
33131
764.16
ALBEMARLE CORP
2890
LA
70801
Miami, FL
33131
743.85
HECLA MINING CO
1040
ID
83815
San Francisco, CA
94104
742.6
LAMAR ADVERTISING CO -CL A
7310
LA
70808
Miami, FL
33131
740.15
COEUR D'ALENE MINES CORP
1044
ID
83816
San Francisco, CA
94104
739.31
SHAW GROUP INC
8711
LA
70809
Miami, FL
33131
735.4
H&E EQUIPMENT SERVICES INC
5084
LA
70816
Miami, FL
33131
734.07
ITRON INC
3825
WA
99201
San Francisco, CA
94104
730.95
MERGE HEALTHCARE INC
7373
IL
60601
Chicago, IL
60604
0.56
DONNELLEY (R R) & SONS CO
2750
IL
60606
Chicago, IL
60604
0.56
97
Table VII: Firms distance to SEC Regional Offices ‘continued’
Firms
SIC
State
Firm Zip Code
SEC Regional Office
SEC Zip Code
Distance
UNITED CONTINENTAL HLDGS INC
4512
IL
60601
Chicago, IL
60604
0.56
BOWNE & CO INC
2750
NY
10041
New York, NY
10281
0.49
BARE ESCENTUALS INC
2844
CA
94105
San Francisco, CA
94104
0.47
GAP INC
5651
CA
94105
San Francisco, CA
94104
0.47
GYMBOREE CORP
2300
CA
94105
San Francisco, CA
94104
0.47
TELEPHONE & DATA SYSTEMS INC
4812
IL
60602
Chicago, IL
60604
0.44
QEP RESOURCES INC
1311
CO
80265
Denver, CO
80202
0.41
CONTINENTAL INFORMATN SYS CP
7377
NY
80265
New York, NY
80202
0.28
KODIAK OIL & GAS CORP
1311
CO
80202
Denver, CO
80202
0
MCKESSON CORP
5122
CA
94104
San Francisco, CA
94104
0
MARKWEST ENERGY PARTNERS LP
1311
CO
80202
Denver, CO
80202
0
ROHM AND HAAS CO
2821
PA
19106
Philadelphia, PA
19106
0
MOLSON COORS BREWING CO
2082
CO
80202
Denver, CO
80202
0
TRANSMONTAIGNE INC
4610
CO
80202
Denver, CO
80202
0
VECTOR GROUP LTD
2111
FL
33131
Miami, FL
33131
0
VENOCO INC
1311
CO
80202
Denver, CO
80202
0
98
99
Table VIII: Control Variables
Control variable data obtained from COMPUSTAT.
Variables
Size
Profit
Lev
ROA
Cap
MB
Definition and Data Source
Natural log of total assets + (item 6)
EBITDA (item 13) divided by total assets (item 6)
Total debt (item 9 plus item 34) divided by total assets+ (item 6)
Net income before extraordinary items (item 18) divided by total
assets+ (item 6)
Capital Expenditure (item 128) divided by total assets+ (item 6)
Market value – liabilities (item 181) minus balance sheet deferred
taxes and investment tax credit (item 35) plus preferred stock –
either liquidating value (item 10), redemption value (item 56), or
carrying value (item 130) – plus market equity (item 25 by item
199) – over total assets (item 6)
Table IX: Summary Statistics
The sample covers the period 1980 to 2010. The statistics reported below are for the independent and control variables used in this paper. The dependent variable summary
statistics is given in table 1. Our overall sample has 32,109 firm year observations
Variable
n
Mean
S.D.
Min
0.25
Mdn
0.75
Max
Rate
32099
11.79
3.82
1
9
11
14
70.75
Grade
32109
0.32
0.46
0
0
0
1
1
Beta
27987
1.03
0.61
-6.51
0.62
0.98
1.37
6.91
TotRisk
27623
0.03
0.02
0
0.02
0.02
0.03
0.56
Deviation
32109
0.08
2.07
-3.08
-1.03
0.05
0.88
3.63
Distance
32109
158.28
192.07
0
22.64
101.75
243.32
2551.79
Size
31404
6.8
1.87
-6.91
5.63
6.83
8
13.59
Vol
32109
7.94
239.15
-3078.25
0
0.18
0.54
21612
Cap
32109
0.07
0.08
-0.15
0.02
0.05
0.09
1.47
ROA
32109
0.02
0.37
-25
0
0.04
0.08
42.48
MB
32109
15.69
30.36
-0.42
0.86
6.07
19.59
1362.55
Lev
32109
0.63
2.4
-0.04
0.4
0.56
0.74
395.09
Profit
32109
0.44
48.84
-4175.74
0.06
0.17
0.35
3144.49
100
Table X: Prior Cost of Capital Sample Correlation Matrix
This table reports Pearson correlation matrix among earnings management, cost of debt, cost of equity, firm location, and control variables. The sample covers the period 1980 to
2010. All variables except Beta and TotRisk are constructed using COMPUSTAT data. Beta and TotRisk are computed from CRSP data. ***, **, * are significant at 99.999%,
99%, and 95% respectively.
DA1
DA2
Abcfo
Abdisc
Abprod
Rate
Grade
Beta
TotRisk
DA2
0.9903***
Abcfo
0.0358***
0.0336*
Abdisc
-0.4889***
-0.4874***
-0.3797***
Abprod
-0.1479***
-0.1465***
-0.9478***
0.2488***
Rate
0.0429***
0.0292***
-0.0209***
0.0078
0.0113
Grade
0.0446***
0.0335***
-0.0130*
-0.0018
0.0062
0.8224***
Beta
-0.0712***
-0.0684***
-0.0190**
0.0785***
0.0096
-0.0728***
-0.0821***
TotRisk
-0.0449***
-0.0239***
0.0066
0.0387***
-0.0034
-0.3907***
-0.3196***
0.2037***
Deviation
0.0324***
0.0313***
0.0008
-0.0578***
0.0213***
0.0045
0.0046
-0.0344***
-0.0333***
Distance
0.0142*
0.0135*
-0.0068
-0.0171**
0.0197***
-0.0014
0.0153***
-0.0326***
-0.0168*
Size
-0.0716***
-0.0799***
-0.0805***
0.0967***
0.1010***
0.5193***
0.4388***
0.1205***
-0.2521***
Lev
0.1276***
0.0493***
0.1344***
-0.2234***
-0.1224***
-0.0352***
-0.0162*
-0.0417***
0.0640***
Vol
-0.0076
-0.0095
0.0027
0.0075
-0.0035
0.0089
0.0042
0.0028
0.0035
Profit
0.0018
0.0014
0.0251***
0.0123*
-0.0264***
0.0152**
0.0072
-0.0074
-0.0072
Cap
0.0189**
0.0208***
0.0200***
-0.0024
-0.0176**
-0.0102
0.0031
0.0264***
0.0093
ROA
0.1389***
0.0000
0.0157**
-0.0446***
-0.0184**
0.0973***
0.0790***
-0.0152*
-0.1972***
MB
-0.0474***
-0.0618***
-0.0179**
0.0086
0.0245*
0.1882***
0.1419***
0.0270***
-0.1287***
Deviation
Distance
Size
Lev
Vol
Profit
Cap
ROA
Deviation
Distance
0.3036***
Size
0.0150**
-0.0059
Lev
0.01
0.0037
-0.0608***
Vol
-0.0029
0.0025
0.0072
-0.003
101
Table X: Prior Cost of Capital Sample Correlation Matrix ‘continued’
Deviation
Distance
Size
Lev
Vol
0.0037
0.0098
-0.0015
-0.0023
-0.0004
Cap
0.1124***
0.0839***
-0.1069***
-0.0027
-0.0056
0.0062
ROA
0.0143*
0.0076
0.0675***
0.4934***
0.0126***
0.0033
-0.0078
MB
0.0447***
0.0229***
0.1534***
0.0654***
0.0023
0.0155**
-0.0258***
Profit
Profit
Cap
ROA
0.0912***
102
Table XI: Effect of Location and Prior Cost of Capital on Managed Earnings
The dependent variables (Managed Earnings proxies) in Panel A and Panel B are the same. Both panel reports OLS regression result. In panel A, the examined variables effect on
managed earnings are the measures of cost of debt and location while in panel B, the examined variables are the measures for cost of equity and location. The sample covers the
period 1980 to 2010. The reported t-statistics are adjusted for heteroskedasticity. ***, **, * are significant at 99.999%, 99%, and 95% respectively.
DA1
DA2
Abcfo
Abdisc
Abprod
Panel A
Rate
0.0247***
Distance
0.0001*
Deviation
0.0248***
0.0247***
0.0248***
0.0001*
0.0138***
0.0242***
0.0242***
-0.0001
0.0138***
-0.0205***
-0.0207***
-0.0001**
-0.0009
-0.0381***
-0.0380***
0.0002***
-0.0290***
0.0223***
Size
-0.0572***
-0.0576***
-0.0572***
-0.0576***
-0.1035***
-0.1035***
0.0637***
0.0644***
0.1404***
0.1397***
Lev
0.0247***
0.0246***
0.0247***
0.0246***
0.1615***
0.1615***
-0.1178***
-0.1176***
-0.1346***
-0.1348***
Vol
0
0
0
0
0
0
0
0
0
0
Profit
0
0
0
0
0.0012***
0.0012***
0.0003*
0.0003*
-0.0012***
-0.0012***
ROA
0.2394***
0.2394***
-0.0936***
-0.0936***
-0.5303***
-0.5306***
0.3417***
0.3418***
0.4153***
0.4157***
MB
-0.0018***
-0.0018***
-0.0018***
-0.0018***
-0.0012**
-0.0012**
0.0004*
0.0005**
0.0015***
0.0015***
Cap
0.0937
0.0593
0.0935
0.059
0.4185*
0.3977*
0.0927
0.1725*
-0.2723
-0.2951
_cons
0.0931***
0.1059***
0.1013***
0.1142***
0.3413***
0.3262***
-0.1284***
-0.1506***
-0.4826***
-0.4415***
r
0.0401
0.041
0.0212
0.0221
0.0301
0.03
0.07
0.0731
0.0315
0.0315
Grade
0.1652***
0.1663***
0.1652***
0.1663***
0.1577***
0.1568***
-0.1351***
-0.1371***
-0.2373***
-0.2344***
Distance
0.0001*
2
Deviation
0.0001*
0.0137***
-0.0001
0.0137***
-0.0001**
-0.001
0.0002***
-0.0289***
0.0225***
Size
-0.0491***
-0.0495***
-0.0491***
-0.0495***
-0.0951***
-0.0949***
0.0567***
0.0575***
0.1260***
0.1251***
Lev
0.0231***
0.0229***
0.0231***
0.0229***
0.1599***
0.1599***
-0.1165***
-0.1162***
-0.1320***
-0.1321***
Vol
0
0
0
0
0
0
0
0
0
0
Profit
0
0
0
0
0.0012***
0.0012***
0.0003*
0.0003*
-0.0012***
-0.0012***
ROA
0.2513***
0.2513***
-0.0816***
-0.0816***
-0.5181***
-0.5183***
0.3316***
0.3317***
0.3951***
0.3953***
MB
-0.0016***
-0.0017***
-0.0016***
-0.0017***
-0.0010*
-0.0010*
0.0003
0.0004*
0.0013**
0.0012**
Cap
0.0946
0.0587
0.0944
0.0584
0.4203*
0.3985*
0.0914
0.1726*
-0.2773
-0.2989
_cons
0.2765***
0.2891***
0.2847***
0.2974***
0.5190***
0.5026***
-0.2797***
-0.3027***
-0.7568***
-0.7124***
0.0379
0.0389
0.019
0.02
0.0297
0.0297
0.0689
0.072
0.0304
0.0303
2
r
103
Table XI: Effect of Location and Prior Cost of Capital on Managed Earnings ‘continued’
DA1
Panel B
Beta
-0.0240***
Distance
0.0000**
Deviation
DA2
-0.0236***
-0.0240***
Abcfo
-0.0235***
0.0000**
0.0059***
-0.0304*
Abdisc
-0.0293*
-0.0001
0.0059***
0.0587***
Abprod
0.0568***
-0.0001***
0.0026
0.0036
0.0039
0.0002***
-0.0211***
0.0202***
Size
-0.0172***
-0.0174***
-0.0172***
-0.0174***
-0.0388***
-0.0387***
0.0131***
0.0136***
0.0652***
0.0645***
Lev
0.2028***
0.1996***
0.2027***
0.1995***
-0.0198
-0.0233
-0.1296***
-0.1179***
0.0833*
0.0757*
Vol
-0.0000***
-0.0000***
-0.0000***
-0.0000***
0
0
0.0000**
0.0000*
0
0
Profit
0
0
0
0
0.0016***
0.0016***
0.0003***
0.0003***
-0.0016***
-0.0016***
ROA
0.5446***
0.5423***
0.2116***
0.2094***
0.0823
0.0796
-0.1691***
-0.1605***
-0.3254***
-0.3306***
MB
-0.0016***
-0.0016***
-0.0016***
-0.0016***
-0.0012***
-0.0012***
0.0004***
0.0005***
0.0016***
0.0015***
Cap
0.1897***
0.1726***
0.1894***
0.1723***
0.6068***
0.5773***
-0.0799
-0.014
-0.4165**
-0.4419**
_cons
0.017
0.0248**
0.0253**
0.0331***
0.2777***
0.2693***
-0.0416**
-0.0667***
-0.5091***
-0.4647***
r
0.0992
0.1001
0.0691
0.0702
0.0075
0.0074
0.0158
0.0214
0.0099
0.0099
TotRisk
-1.9055***
-1.8843***
-1.9053***
-1.8841***
-1.0627***
-1.0508***
1.6830***
1.6274***
1.3058***
1.3290***
Distance
0
2
Deviation
0
0.0092***
0
0.0092***
-0.0001***
0.0021
0.0002***
-0.0220***
0.0178***
Size
-0.0215***
-0.0216***
-0.0215***
-0.0216***
-0.0466***
-0.0466***
0.0214***
0.0216***
0.0715***
0.0711***
Lev
0.2407***
0.2355***
0.2406***
0.2354***
-0.0122
-0.0142
-0.1520***
-0.1391***
0.0615***
0.0537***
Vol
0
0
0
0
0
0
0.0000**
0.0000**
0
0
Profit
0
0
0
0
0
0
0
0
0
0
ROA
0.4395***
0.4366***
0.1065***
0.1037***
0.0517
0.0506
-0.0578**
-0.0506**
-0.2909***
-0.2952***
MB
-0.0016***
-0.0016***
-0.0016***
-0.0016***
-0.0013***
-0.0013***
0.0004***
0.0005***
0.0016***
0.0016***
Cap
0.2394***
0.2116***
0.2392***
0.2114***
0.1914**
0.1759**
-0.1477***
-0.0748
-0.0406
-0.0714
_cons
0.0473*
0.0587*
0.0555*
0.0670**
0.3538***
0.3515***
-0.0757***
-0.0996***
-0.5946***
-0.5604***
0.0247
0.0253
0.0188
0.0193
0.0149
0.0149
0.0194
0.028
0.0546
0.0554
2
r
104
Table XII: Quartile Effect of Location and Prior Cost of Capital on Managed Earnings
The dependent variables in Panel A and Panel B are the same. Both panel reports quartile regression result. In panel A, Grade is used as our measure for cost of debt. In panel B,
TotRisk is used as our measure for cost of equity. The sample size is 32,109 and covers the period 1980 to 2010. We do not include Jones (1991) Modified Model (DA1) in this
section as the results are similar to Kothari et al. (2005) model. ***, **, * are significant at 99.999%, 99%, and 95% respectively.
25%
Panel A
DA2
Abcfo
Abdisc
Abprod
DA2
Abcfo
Abdisc
Abprod
Grade
0.0509***
0.0068*
-0.0667***
-0.1295***
0.1793***
0.0250***
-0.0409***
-0.0682***
Size
-0.0129***
0.0050***
0.0240***
0.0736***
-0.0376***
-0.0264***
-0.0128***
0.0278***
Lev
-0.0040***
0.0283***
-0.0975***
-0.0461***
0.2468***
0.0351***
-0.0728***
0.0193***
Vol
-0.0000***
0.0000*
0
-0.0000***
-0.0000*
0.0000***
0.0001***
-0.0000*
Profit
0
0
0
0
0
0
0
0
Cap
0.0135
0.1398***
0.0721***
-0.0822***
0.1302***
0.3513***
-0.0807***
-0.0855***
ROA
0.0737***
0.1347***
-0.0672***
-0.3119***
0.1837***
0.0882***
-0.0202***
-0.3496***
MB
-0.0008***
-0.0020***
0.0002***
0.0007***
-0.0023***
-0.0007***
0.0025***
0.0027***
Deviation
0.0047***
0.0008
-0.0057***
0.0238***
0.0031***
-0.0019***
-0.0289***
0.0114***
_cons
-0.0448***
-0.1453***
-0.2128***
-0.5407***
0.1562***
0.1916***
0.2383***
-0.0374***
pseudo r
0.0277
0.0278
0.0457
0.0986
0.1236
0.0483
0.0578
0.0606
TotRisk
-1.1659***
-0.0977
0.6652***
0.0124
-2.7749***
0.9333***
1.8883***
0.3050***
Size
-0.0076***
0.0084***
0.0117***
0.0504***
-0.0203***
-0.0162***
-0.0138***
0.0174***
Lev
0.0339***
-0.0156***
-0.0843***
0.0593***
0.3729***
-0.0156***
-0.1388***
0.0698***
Vol
-0.0000***
0.0000*
0
-0.0000***
0
0.0000***
0.0001***
-0.0000*
Profit
0
0
-0.0000**
0
0
0
0
0.0000*
Cap
0.0378**
0.1845***
0.0536***
-0.0678**
0.2700***
0.3421***
-0.0875***
-0.1123***
ROA
0.0884***
0.1441***
-0.0984***
-0.4657***
0.1597***
0.1409***
-0.0376**
-0.5231***
2
Panel B
75%
MB
-0.0008***
-0.0020***
0.0003***
0.0006***
-0.0025***
-0.0007***
0.0025***
0.0028***
Deviation
0.0048***
0.0005
-0.0045***
0.0227***
0.0011
-0.0014***
-0.0275***
0.0112***
_cons
-0.0576***
-0.1387***
-0.1727***
-0.4763***
0.1222***
0.1281***
0.2200***
-0.0235***
0.0292
0.0396
0.024
0.101
0.1049
0.0542
0.0754
0.0806
2
pseudo r
105
Table XIII: Interactive Effect of Location and Prior Cost of Capital on Managed Earnings
The dependent variables are proxies for firm’s managed earnings. Grade is used as our measure for cost of debt. TotRisk is used as our measure for cost of equity. Deviation is
used as measure accessing cluster effect of location. Gdev is the interactive term between Grade and Deviation. Tdev is the interactive term between TotRisk and Deviation. Joint
hypothesis tests are performed as Test. 1 tests the specification between prior cost of capital markets and the interaction terms. 2 test the specification of location and the interactive
term. Yes denotes that the joint hypothesis is rejected. The sample size is 32,109 and covers the period 1980 to 2010. We do not include Jones (1991) Modified Model (DA1) in
this section as the results are similar to Kothari et al. (2005) model. ***, **, * are significant at 99.999%, 99%, and 95% respectively.
DA2
Grade
Abcfo
0.1679***
TotRisk
Abdisc
0.1592***
-1.8734***
Abprod
-0.1407***
-1.0922***
-0.2375***
1.6600***
1.3544***
Size
-0.0499***
-0.0215***
-0.0955***
-0.0471***
0.0583***
0.0220***
0.1258***
0.0714***
Lev
0.0230***
0.2365***
0.1599***
-0.0184
-0.1163***
-0.1358***
-0.1322***
0.0563***
Vol
0
0
0
0
0
0.0000**
0
0
Profit
0
0
0.0012***
0
0.0002*
0
-0.0012***
0
ROA
-0.0823***
0.2137***
-0.5193***
0.1669*
0.3332***
-0.0678
0.3966***
-0.0659
MB
-0.0016***
0.1049***
-0.0010*
0.0457
0.0004
-0.0467*
0.0012**
-0.2922***
Cap
0.0539
-0.0016***
0.3920*
-0.0012***
0.1823*
0.0005***
-0.2904
0.0015***
Deviation
0.0176***
0.0142***
0.0046
-0.0170***
-0.0373***
-0.0070**
0.0151*
0.0296***
Gdev
-0.0130*
Tdev
_cons
2
r
-0.019
-0.166
0.0285***
0.6456***
0.0249
-0.5081***
-0.3966***
0.2996***
0.0649**
0.5058***
0.3597***
-0.3075***
-0.1061***
-0.7166***
-0.5654***
0.0202
0.0194
0.0297
0.0159
0.0727
0.0298
0.0305
0.0559
Test
1
2
Yes***
Yes***
Yes***
Yes***
No
Yes***
Yes***
Yes***
106
107
Table XIV: Simultaneous Effect of Prior Cost of Debt and Prior Cost of Equity
The dependent variables are proxies for firm’s managed earnings above their mean value. Grade is used as our measure for
cost of debt. Beta is firm’s inherent risk. TotRisk is used as our measure for cost of equity. Deviation is used as measure
accessing cluster effect of location. Test represents the joint hypothesis analysis performed on Beta and TotRisk. The sample
size is 26,516 and covers the period 1980 to 2010. We do not include Jones (1991) Modified Model (DA1) in this section as
the results are similar to Kothari et al. (2005) model. ***, **, * are significant at 99.999%, 99%, and 95% respectively.
Grade
DA2
Abcfo
Abdisc
Abprod
0.1145***
0.0402***
-0.0490***
-0.1183***
Beta
-0.0016
-0.0255***
0.0442***
-0.0057
TotRisk
-1.7284***
0.2302
0.8146***
0.2908
Deviation
0.0067***
0.0033*
-0.0204***
0.0169***
Size
-0.0331***
-0.0395***
0.0188***
0.0759***
Lev
0.2189***
-0.0221*
-0.1216***
0.0669***
Vol
-0.0000***
0.00001
0.0000**
-0.0000*
Profit
0.0002
0.00003
0.0003
0.0003
Cap
0.1056***
0.1601***
-0.0272
0.0085
ROA
0.0768***
0.0930***
-0.0635***
-0.3090***
MB
-0.0017***
-0.0012***
0.0005***
0.0016***
_cons
0.1289***
0.2744***
-0.0968***
-0.5270***
r
0.12
0.0281
0.0316
0.0706
Test
Yes***
Yes***
Yes***
No
2
Table XV: Duration of Past Debts and Managed Earnings
The dependent variables are proxies for firm’s managed earnings. Shd denotes firm short term debt – debts below one year of maturity. Md represents medium term debts – debts
maturing between two to five years. Ld indicates long term debts – debts maturing six or more years. Deviation is used as measure accessing cluster effect of location. The sample
size is 29,720 and covers the period 1980 to 2010. We do not include Jones (1991) Modified Model (DA1) in this section as the results are similar to Kothari et al. (2005) model.
***, **, * are significant at 99.999%, 99%, and 95% respectively.
DA2
Abcfo
Abdisc
Abprod
shd
0.7435***
0.7519***
1.4995***
1.5032***
-1.2211***
-1.2265***
md
-0.1576***
-0.0720*
0.1675*
0.1730*
-0.2387***
-0.2681***
ld
0.2918***
0.3189***
0.8529***
0.8547***
-0.7104***
-0.7202***
-0.7229***
-0.7339***
Deviation
0.0136***
0.0125***
-0.0266***
-0.0205***
0.0244***
0.0221***
Size
-0.0326***
-0.0475***
-0.0825***
-0.0841***
0.0462***
0.0572***
0.1031***
0.1047***
Lev
-0.1276***
-0.1524***
-0.3313***
-0.3340***
0.3051***
0.3155***
0.2807***
0.2903***
-0.0034
Vol
0
0
Profit
0
0
-0.0055
0
0
0.0012***
0.0012***
0
0.0003*
-1.3691***
0
0.0003*
0
0
-0.0012***
-0.0012***
Cap
0.2070**
0.3527***
0.7078***
0.5832***
-0.5922***
-0.6997***
0.2989***
0.2804***
0.2935***
0.2963***
-0.3239***
-0.3195***
-0.3381***
-0.3325***
MB
-0.0018***
-0.0015***
-0.0010*
0.0008***
-0.0006**
0.0016***
0.0013**
_cons
0.1603***
0.2596***
0.4113*
-0.0935***
-0.2779***
-0.4565***
-0.4687**
0.06
0.1626
0.1788
0.0552
0.0559
yes
no
yes
no
yes
2
0.1507*
r
0.0601
0.0948
Industry Fixed
Effect
no
yes
0.059
no
0.0626
-0.111
ROA
-0.0003
-0.084
-1.3736***
-0.0772
108
APPENDIX B
TABLES FOR ESSAY TWO
Table XVI: Estimates of CEOs Pay for Performance Sensitivity
CEO salary, bonus, and total compensation data were obtained from COMPUSTAT EXECUCOMP database. The data are from 1992 to 2011 after eliminating utility
and financial firms. The dependent variables are defined as: Δ in Salary which is the change of the CEO’s salary from t-1 to t. Δ in Salary + Bonus reflect the change in CEO’s
salary and bonus from t-1 to t. Δ in CEO Wealth is the change CEO’s total compensation which include personal benefits, contributions to defined contribution plans, life
insurance premiums, tax reimbursements, discounted share purchases from t-1 to t. Δ in Firm Value is the change in firm’s total market value from t-1 to t. This equation (
)
(
)
(
)
is used for the three models below. The dependent for each model are: CEO Salary, CEO Salary +
Bonus, and Total CEO Compensation respectively. N is the number of firm-annual observation. The number of annual firm observation is higher because for some firms,
EXECUCOMP did not report past CEOs salary and bonus, but their total compensation was reported. The statistically significant tests reflect a value greater than or equal to 1.96
t-test value. The t value is synonymous to 95% or more certainty of the coefficient significance to the dependent variables. ***, **, * are significant at 99.999%, 99%, and 95%
respectively.
Δ in Salary
Δ in Salary + Bonus
Δ in CEO Wealth
Independent Variable
Δ in Firm Value
0.0039087***
0.0101774***
0.0066795***
(53.11)
(27.45)
(14.28)
r2
0.2307
0.0885
0.0136
N
11,654
11,654
16,236
Note: Fama-French 48 Industry classification intercepts are not reported. The t statistics are reported in parentheses.
109
Table XVII: Summary Statistics – Independent and Control Variables
The independent and the control variables are defined as follows for the years 1992 to 2011: GP is a dummy variable were one suggest that at the given year, the firm has
awarded a golden parachute to its top executive. The performance proxies are: ROA is EBITDA scaled by the average beginning and ending period book value of total assets. ROS
is EBITDA scaled by the average beginning and ending period sales. R_cAT is EBITDA scaled by the average beginning and ending period book value of cash adjusted assets.
MTB is the market to book ratio which is calculated as the market value of the firm scaled by the average beginning and ending period book value of total assets. Firms’ risk is
measured as the standard deviation of stock return (SD) and the standard deviation of firms’ earnings scaled by the average total asset (Vol_Earning). CEO Pay to Performance
sensitivity is an extract of alpha at industry level using the 48 Fama-French Industry Classification by running regression model similar to Schaefer (1998) linear model:
(
)
(
)
(
)
From the regression, PoP considers the change in Salary as a function of change in Firm value,
while PoP1 considers change in Salary plus Bonus as a function of change in Firm Value. Duration is the CEO time spent as the head of the company. PAGE is the CEOs age.
SIZE is the book value of the firms’ total asset. The leverage measures are: BLev is the book leverage of the firms calculated as book debt scaled by the average beginning and
ending period book value of total assets. MLev is the market leverage of the firms calculated as the book debt scaled by market value.
Variable
Obs
Mean
Std. Dev.
Min
Max
GP
13,119
0.6135
0.4870
0.0000
1.0000
ROA
20,149
0.1547
0.1433
-4.1572
1.1916
ROS
20,134
0.0042
9.8651
-1334.2930
1.6645
R_cAT
20,141
0.1750
0.4348
-17.1774
7.5664
MTB
18,021
2.4219
3.4131
0.0064
198.1422
SD
19,431
0.1222
0.0387
0.0662
0.2956
Vol_Earning
20,800
0.3090
0.5378
0.0000
2.6377
PoP
11,654
0.0068
0.0058
-0.0081
0.0460
PoP1
11,654
0.0188
0.0204
-0.0131
0.3447
Duration
20,799
18.6743
18.4859
0.0000
71.0000
PAGE
16,461
61.8370
9.5293
31.0000
95.0000
Size
20,322
5248.4980
24027.0800
0.0010
797769.0000
BLev
20,209
0.4980
0.2831
-0.0802
5.4854
MLev
18,031
0.2999
0.2027
0.0006
0.9999
Performance
Risk
Pay to Performance
110
Table XVIII: Summary Statistics – Dependent Variables
Proxies for Managed Earnings highlight the propensity for firms to adjust their financial statement over the period of 1992 to 2011. EM1 and EM2 reflect the propensity
to manage earnings through the change in accrual process. I derive both accrual process proxies as the residual from the following regression model respectively
and
. The remaining models reflect
the propensity to manage earnings through the deviation from normal business activity. r_CFO, r_PROD, r_DISX are individual measures of real approach managed earnings
which are derived as the difference between the cash flow from operations, production costs, and discretionary expenses from their fitted values from these regression models
,
, and
respectively. RM1, RM2, and RM3 are the aggregate of: cash flow from operations multiply by -1 and production cost, cash flow from operations multiply by -1
and discretionary expenses multiply by -1, and cash flow from operations multiply by -1, discretionary expense multiply by -1, and production cost respectively. The means of the
accrual and real-activity proxies are not zero, yet, the means were statistically tested and there were found not to be statistically different from zero.
Variable
Obs
Mean
Std. Dev.
Min
Max
EM1
19446
0.0024
3.5062
-44.5341
458.6989
EM2
19446
0.0024
3.5066
-44.5396
458.7533
r_CFO
20148
0.0061
6.6679
-162.8215
847.1968
r_PROD
19832
0.0025
0.7008
-44.6491
37.4237
r_DISX
20194
-0.0024
4.0325
-531.0188
75.4863
RM1
19832
0.0070
4.1569
-85.1583
533.4713
RM2
20148
-0.0008
3.0761
-316.1780
187.9464
RM3
19804
0.0031
3.3720
-313.7255
225.3701
111
Table XIX: Golden Parachute Sample Correlation Matrix
Proxies for Managed Earnings highlight the propensity for firms to adjust their financial statement over the period of 1992 to 2011. EM1 and EM2 reflect the propensity to manage earnings
through the change in accrual process. I derive both accrual process proxies as the residual from the following regression model respectively
and
. The remaining models reflect the propensity to manage earnings through the deviation
from normal business activity. r_CFO, r_PROD, r_DISX are individual measures of real approach managed earnings which are derived as the difference between the cash flow from operations,
production costs, and discretionary expenses from their fitted values from these regression models
,
, and
respectively. RM1, RM2, and RM3 are the aggregate of: cash flow from operations multiply by -1
and production cost, cash flow from operations multiply by -1 and discretionary expenses multiply by -1, and cash flow from operations multiply by -1, discretionary expense multiply by -1, and
production cost respectively. The performance proxies are: ROA is EBITDA scaled by the average beginning and ending period book value of total assets. ROS is EBITDA scaled by the average
beginning and ending period sales. R_cAT is EBITDA scaled by the average beginning and ending period book value of cash adjusted assets. MTB is the market to book ratio which is calculated as
the market value of the firm scaled by the average beginning and ending period book value of total assets. Firms’ risk is measured as the standard deviation of stock return (SD) and the standard
deviation of firms’ earnings scaled by the average total asset (Vol_Earning). CEO Pay to Performance sensitivity is an extract of alpha at industry level using the 48 Fama-French Industry
Classification by running regression model similar to Schaefer (1998) linear model: (
)
(
)
(
)
. From the regression, PoP
considers change in Salary as a function of change in Firm value, while PoP1 considers change in Salary plus Bonus as a function of change in Firm Value. Duration is the CEO time spent as the head
of the company. E-AGE is the CEOs age. SIZE is the book value of the firms’ total asset. The leverage measures are: BLev is the book leverage of the firms calculated as book debt scaled by the
average beginning and ending period book value of total assets. MLev is the market leverage of the firms calculated as the book debt scaled by market value.
112
Table XIX: Golden Parachute Sample Correlation Matrix ‘continued’
EM1
EM2
r_CFO r_PROD r_DISX RM1
RM2
RM3
ROA
R_cAT ROS
Torbin SD
Vol_EarningBeta
Beta1 Duration PAGE logSize BLev
EM2
0.9999
1
r_CFO
0.9857 0.9859
1
r_PROD
-0.0176 -0.0179 -0.0757
1
r_DISX
-0.9734 -0.9734 -0.956 -0.0986
1
RM1
0.9409 0.9408 0.9142 0.2642 -0.9858
1
RM2
-0.8713 -0.8717 -0.9244 0.2926 0.7719 -0.7008
1
RM3
-0.8056 -0.806 -0.8661 0.4769 0.6909 -0.5892
0.98
1
ROA
-0.0223 -0.022 -0.0179 -0.1771 -0.0299 -0.0274 0.0497 0.0029
1
R_cAT
-0.0347 -0.0351 -0.0266 -0.1169 -0.0289 -0.0292 0.0495 0.0174 0.7346
1
ROS
-0.018 -0.0181 -0.0105 -0.0155 0.0062 -0.0086 0.0148 0.0102 0.1531 0.1877
1
Torbin
-0.0015 -0.0016 0.0065 -0.3286 0.0282 -0.0657 -0.048 -0.0968 0.1148 0.0332 -0.0208
1
SD
-0.0031 -0.0032 -0.0034 -0.0062 0.0053 -0.0059 0.0011 0.0005 -0.0401 -0.0311 -0.0089 0.0332
1
Vol_Earning 0.0269 0.0294 0.0403 -0.0433 -0.0152 0.0086 -0.0667 -0.0702 -0.1104 -0.159 -0.0494 0.1242 0.0129
1
TrialBeta
-0.0045 -0.0021 -0.0036 0.035 -0.0057 0.0104 0.0143 0.0199 0.0053 0.0078 0.0046 -0.0654 -0.0105 -0.1664
1
TrialBeta1
-0.0038 -0.0022 -0.004 0.0535 -0.0058 0.0142 0.0157 0.0252 -0.0124 0.0054 0.0041 -0.0634 -0.0041 -0.1355 0.7434
1
duration
-0.0101 -0.0102 -0.008 0.0522 0.0038 0.0046 0.0116 0.0208 0.0738 0.0686 0.0175 -0.0532 -0.0774 -0.0253 0.0236 0.0218
1
PAGE
0.0007 0.003 -0.0105 0.0493 -0.0128 0.0312 0.0254 0.0323 0.0416 -0.0032 0.0139 -0.0238 0.0756 -0.0415 0.0226 0.0165 -0.1991
1
logSize
-0.0275 -0.0273 -0.0194 0.1532 0.0095 0.0158 0.0285 0.0575 0.0927 0.0764 0.0309 -0.1435 -0.0314 -0.0862 -0.0336 -0.0313 0.2314 0.0454
1
BLev
-0.0118 -0.0115 -0.0187 0.0123 0.018 -0.0087 0.0247 0.0281 -0.0624 -0.0644 0.0037 -0.0953 0.0327 -0.0749 0.0049 0.0311 -0.0515 0.0539 0.2203
1
MLev
-0.0072 -0.0074 -0.0185 0.2642 -0.0112 0.0445 0.0553 0.0951 -0.2624 -0.1209 0.0145 -0.3452 0.0671 -0.1937 0.0712 0.112 -0.0042 0.0521 0.3179 0.675
113
Table XX: On the Effect of Golden Parachute on Managed Earnings
Pooled Linear regression models of which managed earning proxies are the dependent variables. EM2 is a proxy for accrual approach of managed earnings. r_CFO, r_PROD,
r_DISX are proxies for real activities managed earnings from the cash flow of operation aspect, production cost aspect, and discretionary expenses aspect respectively. RM1, RM2,
and RM3 are aggregate proxies for the real activities approach to managed earning. The independent variables are: GP a dummy variable, ROA is performance measure, SD is
stock volatility measure, PoP is pay for performance measure at industry level. Control variables include Duration which is the CEOs length of stay in the office, LogSize is Total
Asset logged, BLev is Book Leverage for a sample of 1,184 firms from the years 1992 to 2011. I run the following regression model:
. MEit is the dependent variable, serving as a proxy for either managed earnings approach – accrual or realactivity. The t values are reported in parenthesis. A t value equal to or higher than 1.96 is synonymous to 95% or more certainty of the regression coefficients significance to the
dependent variables. ***, **,* denotes significance at 99.99, 99, and 95 percent respectively.
EM2
r_CFO
r_PROD
r_DISX
RM1
RM2
RM3
N
0.0068***
(3.43)
-0.0877***
(-9.21)
0.0746**
(3.05)
0.9258***
(5.54)
0.0002***
(3.57)
0.0007
(1.08)
-0.0074
(-1.6)
-0.0487***
(-7.54)
12488
0.0247**
(3.38)
-0.2169***
(-6.23)
-0.0041
(-0.05)
0.4626
(0.75)
0.0003
(1.7)
0.0485***
(19.4)
-0.4684***
(-27.73)
-0.1770***
(-7.45)
12986
-0.0008
(-0.23)
-1.3041***
(-73.89)
0.1220**
(2.66)
3.2927***
(10.63)
0.0006***
(5.64)
0.0352***
(27.8)
0.0661***
(7.73)
-0.0834***
(-6.93)
12889
0.0057
(1.34)
0.1720***
(8.51)
-0.0546
(-1.04)
-4.1307***
(-11.6)
-0.0008***
(-6.77)
-0.0272***
(-18.76)
-0.0811***
(-8.27)
0.2922***
(21.17)
12995
-0.0060
(-0.76)
-1.4811***
(-39.86)
0.1835
(1.9)
7.3523***
(11.28)
0.0013***
(6.25)
0.0621***
(23.33)
0.1493***
(8.29)
-0.3745***
(-14.8)
12889
-0.0306**
(-3.32)
0.0449
(1.02)
0.0572
(0.5)
3.6687***
(4.75)
0.0004
(1.77)
-0.0213***
(-6.75)
0.5496***
(25.82)
-0.1147***
(-3.83)
12986
-0.0259**
(-2.18)
-1.2772***
(-22.58)
0.1921
(1.31)
6.7867***
(6.84)
0.0009**
(2.76)
0.0144***
(3.55)
0.6218***
(22.68)
-0.2036***
(-5.29)
12880
r2
0.0124
0.0673
0.346
0.0597
0.1689
0.0514
0.0896
GP
ROA
SD
PoP
Duration
logSize
BLev
_cons
114
Table XXI: Fixed Effect Regression on the Effect of Golden Parachute on Managed Earnings
Panel fixed-effect regression models of which managed earning proxies are the dependent variables. EM2 is a proxy for accrual approach of managed earnings. r_CFO, r_PROD,
r_DISX are proxies for real activities managed earnings from the cash flow of operation aspect, production cost aspect, and discretionary expenses aspect respectively. RM1, RM2,
and RM3 are aggregate proxies for the real activities approach to managed earning. The independent variables are: GP a dummy variable, ROA is performance measure, and SD is
stock volatility measure. Control variables include Duration which is the CEOs length of stay in the office, LogSize is Total Asset logged, BLev is Book Leverage for a sample of
1,184 firms from the years 1992 to 2011. I run the following regression:
. MEit is the dependent variable, serving as a proxy for either managed earnings approach – accrual or real-activity. vi is fixed effect estimate for industry
and time. The t values are reported in parenthesis. A t value equal to or higher than 1.96 is synonymous to 95% or more certainty of the regression coefficients significance to the
dependent variables. ***, **,* denotes significance at 99.99, 99, and 95 percent respectively. Pay for Performance industry measure is omitted due to its collinearity with the fixed
effect model.
EM2
r_CFO
r_PROD
r_DISX
RM1
RM2
RM3
N
0.0056**
(2.92)
-0.1064***
(-11.41)
0.0537*
(2.31)
0.0001*
(2.2)
-0.0031***
(-4.47)
-0.0158***
(-3.43)
12488
0.0150**
(2.57)
-0.2150***
(-7.65)
-0.0686
(-0.97)
0.0002
(1.31)
0.0367***
(17.93)
-0.3751***
(-27.1)
12986
0.0050
(1.5)
-1.2426***
(-77.58)
0.0991*
(2.47)
0.0003**
(3.09)
0.0322***
(27.75)
0.0249***
(3.16)
12889
-0.0016
(-0.43)
0.1131***
(6.32)
-0.0299
(-0.66)
-0.0004***
(-3.59)
-0.0222***
(-17.07)
-0.0240**
(-2.72)
12995
0.0068
(0.99)
-1.3585***
(-41.02)
0.1339
(1.61)
0.0006**
(3.27)
0.0544***
(22.65)
0.0511**
(3.14)
12889
-0.0135
(-1.76)
0.1021**
(2.77)
0.0970
(1.05)
0.0002
(0.74)
-0.0145***
(-5.39)
0.3992***
(21.98)
12986
-0.0040
(-0.4)
-1.1611***
(-23.76)
0.2063
(1.68)
0.0003
(1.16)
0.0182***
(5.14)
0.4296***
(17.86)
12880
r2
0.1142
0.435
0.4995
0.3153
0.387
0.3773
0.3692
GP
ROA
SD
Duration
logSize
BLev
115
Table XXII: Above Industry Effect of Golden Parachute on Managed Earnings
Pooled linear regression models for firms with managed earning values above their industry median. I assume that the industry median is a representation of the allowed
discretionary leeway given to managers to manage earnings. Firm are grouped into industries based on Fama-French 48 Industry Classification. The industry median is subtracted
from all firms managed earnings value to determine the above industry managed earnings. Above Industry managed earning proxies are the dependent variables. EM2 is a proxy
for accrual approach of managed earnings. r_CFO, r_PROD, r_DISX are proxies for real activities managed earnings from the cash flow of operation aspect, production cost
aspect, and discretionary expenses aspect respectively. RM1, RM2, and RM3 are aggregate proxies for the real activities approach to managed earning. The independent variables
are: GP a dummy variable, ROA a performance measure, SD is stock volatility measure, PoP is pay for performance measure at industry level. Control variables include Duration
which is the CEOs length of stay in the office, LogSize is Total Asset logged, BLev is Book Leverage for a sample of 1,184 firms from the years 1992 to 2011. I run the following
regression:
. MEit is the dependent variable, serving as a proxy
for either managed earnings approach – accrual or real-activity. The t values are reported in parenthesis. A t value equal to or higher than 1.96 is synonymous to 95% or higher
certainty of the regression coefficients significance to the dependent variables. ***, **,* denotes significance at 99.99, 99, and 95 percent respectively.
ovIndEM2
ovIndrCFO
ovIndrPROD
ovIndrDISX
ovIndrRM1
ovIndrRM2
ovIndrRM3
N
0.0056**
(2.96)
-0.0939***
(-10.38)
0.0553*
(2.38)
-0.0510
(-0.38)
0.0001**
(2.71)
-0.0024***
(-3.66)
-0.0099*
(-2.26)
0.0189**
(3.08)
12488
0.0167**
(2.87)
-0.1922***
(-6.96)
-0.0763
(-1.06)
1.6195**
(3.33)
0.0002
(1.4)
0.0329***
(16.6)
-0.3365***
(-25.1)
-0.1315***
(-6.97)
12986
0.0059
(1.8)
-1.1902***
(-75.43)
0.1019*
(2.49)
0.5031
(1.82)
0.0002**
(2.77)
0.0308***
(27.23)
0.0097
(1.27)
-0.0707***
(-6.58)
12889
0.0020
(0.56)
0.1272***
(7.29)
-0.0349
(-0.77)
-0.6386*
(-2.08)
-0.0004***
(-3.87)
-0.0234***
(-18.68)
-0.0140
(-1.65)
0.2023***
(16.98)
12995
0.0035
(0.52)
-1.3113***
(-40.58)
0.1362
(1.62)
0.1225
(0.22)
0.0006**
(3.21)
0.0534***
(23.08)
0.0218
(1.39)
-0.2535***
(-11.52)
12889
-0.0171*
(-2.25)
0.0709*
(1.96)
0.1076
(1.14)
-1.4577*
(-2.29)
0.0001
(0.43)
-0.0111***
(-4.28)
0.3526***
(20.08)
-0.0288
(-1.16)
12986
-0.0082
(-0.82)
-1.1439***
(-23.83)
0.2176
(1.75)
-1.3434
(-1.6)
0.0002
(0.72)
0.0195***
(5.66)
0.3718***
(15.98)
-0.0942**
(-2.88)
12880
r2
0.0124
0.0561
0.3405
0.0363
0.1521
0.0315
0.0725
GP
Op_Form
SD
PoP
Duration
logSize
BLev
_cons
116
Table XXIII: Quartile Regression Effect of Golden Parachute on Managed Earnings
Two quartile regression models show the behavior of managed earning as a function of the independent and the control variables. The first quartile regression model
looks at managed earnings values at the first (lower) quarter. The second regression model looks at managed earning values at the third (upper) quarter. The managed earning
proxies are EM2 for accrual approach of managed earnings and RM1, RM2, and RM3 which are aggregate proxies for the real activities approach to managed earning. The
independent variables are: GP a dummy variable, ROA is performance measure, SD is stock volatility measure, PoP is pay for performance measure at industry level. Control
variables include Duration which is the CEOs length of stay in the office, LogSize is Total Asset logged, BLev is Book Leverage for a sample of 1,184 firms from the years 1992
to 2011. I run the following regression:
. MEit is the dependent
variable, serving as a proxy for either managed earnings approach – accrual or real-activity. The t values are reported in parenthesis. A t value equal to or higher than 1.96 is
synonymous to 95% or more certainty of the regression coefficients significance to the dependent variables. ***, **,* denotes significance at 99.99, 99, and 95 percent
respectively. The t values are reported in parenthesis below the coefficients.
First Quartile (0.25)
GP
Op_Form
SD
PoP
Duration
logSize
BLev
_cons
N
Pseudo r
2
Upper Quartile (0.75)
EM2
RM1
RM2
RM3
EM2
RM1
RM2
RM3
0.0034
(1.74)
-0.0746***
(-7.9)
-0.0030
(-0.12)
1.3321***
(8.02)
0.0001*
(2.21)
0.0029***
(4.3)
-0.0122**
(-2.66)
-0.0997***
(-15.55)
0.0338***
(3.24)
-1.7517***
(-35.2)
0.2333
(1.81)
9.1536***
(10.48)
0.0017***
(6.19)
0.0672***
(18.86)
0.1632***
(6.76)
-0.6493***
(-19.16)
0.0080
(1.04)
-0.0665
(-1.82)
0.1249
(1.31)
4.7557***
(7.39)
0.0004
(1.91)
-0.0070***
(-2.67)
0.3745***
(21.13)
-0.4628***
(-18.55)
0.0302**
(2.78)
-1.3875***
(-26.86)
0.1842
(1.37)
7.3507***
(8.11)
0.0008**
(2.66)
0.0286***
(7.74)
0.4052***
(16.18)
-0.5925***
(-16.84)
0.0037
(1.94)
-0.0743***
(-8.06)
0.0987***
(4.17)
0.9133***
(5.64)
0.0001
(1.33)
-0.0022***
(-3.25)
0.0284***
(6.35)
0.0063
(1.01)
-0.0346***
(-4.67)
-0.9813***
(-27.82)
0.1665
(1.82)
6.7502***
(10.91)
0.0010***
(5.01)
0.0228***
(9.02)
0.2198***
(12.85)
0.0900***
(3.75)
-0.0612***
(-5.01)
0.6824***
(11.75)
-0.0391
(-0.26)
1.6150
(1.58)
0.0011***
(3.39)
-0.0519***
(-12.44)
0.7476***
(26.53)
0.1243**
(3.13)
-0.0751***
(-5.15)
-0.5343***
(-7.69)
0.1116
(0.62)
6.0432***
(4.96)
0.0014***
(3.56)
-0.0327***
(-6.57)
0.9003***
(26.75)
0.1821***
(3.85)
12488
12889
12986
12880
12488
12889
12986
12880
0.0094
0.1251
0.0359
0.0811
0.0109
0.0633
0.0363
0.0379
117
Table XXIV: CEOs Age a Factor on Manage Earning
Linear regression models for firms CEO age effect to the proclivity to manage earning. The first two regression models offer an insert to the accrual approach (EM2) and
real activities approach (RM1). Although age of CEO (PAGE) is statistically significant, the coefficient values are highly minuscule to infer their impact, as such the age of the
CEOs are grouped into quartiles. The first quartile (1qAGE) represents CEOs younger or equal to age 55. The third quartile (3qAGE) represents CEOs older than or equal to age
68. The managed earning proxies are EM2 for accrual approach of managed earnings and RM1, RM2, and RM3 which are aggregate proxies for the real activities approach to
managed earning. The independent variables are: GP a dummy variable, ROA is performance measure, SD is stock volatility measure, PoP is pay for performance measure at
industry level. Control variables include Duration which is the CEOs length of stay in the office, LogSize is Total Asset logged, BLev is Book Leverage for a sample of 1,184
firms from the years 1992 to 2011. I run the following regression:
. MEit is the dependent variable, serving as a proxy for either managed earnings approach – accrual or real-activity. AGEit is a proxy for CEO age (PAGE) or CEOs
age at lower (1qAGE) quarter or upper (3qAGE) quarter. The t values are reported in parenthesis. A t value equal to or higher than 1.96 is synonymous to 95% or more certainty of
the regression coefficients significance to the dependent variables. ***, **,* denotes significance at 99.99, 99, and 95 percent respectively. The t values are reported in parenthesis
below the coefficients.
EM2
RM1
EM2
RM1
RM2
RM3
EM2
RM1
RM2
RM3
GP
Op_Form
SD
PoP
Duration
logSize
BLev
PAGE
0.0085***
(4.58)
-0.0933***
(-10.58)
0.0617**
(2.74)
0.7910***
(5.03)
0.0003***
(5.33)
0.0005
(0.8)
-0.0029
(-0.66)
0.0009***
(9.27)
0.0051
(0.64)
-1.5002***
(-39.75)
0.1114
(1.14)
7.3201***
(10.9)
0.0019***
(8.63)
0.0619***
(22.79)
0.1283***
(6.85)
0.0040***
(9.59)
1qAGE
0.0078***
(3.9)
-0.0890***
(-9.37)
0.0638**
(2.61)
0.9058***
(5.43)
0.0003***
(5.05)
0.0005
(0.67)
-0.0081
(-1.75)
-0.0021
(-0.26)
-1.4871***
(-40.1)
0.1418
(1.47)
7.2589***
(11.16)
0.0017***
(7.78)
0.0611***
(22.96)
0.1463***
(8.14)
-0.0282**
(-3.05)
0.0416
(0.95)
0.0292
(0.26)
3.6067***
(4.67)
0.0007**
(2.66)
-0.0219***
(-6.96)
0.5476***
(25.73)
-0.0214
(-1.8)
-1.2843***
(-22.73)
0.1437
(0.98)
6.6781***
(6.74)
0.0013***
(3.97)
0.0132**
(3.26)
0.6184***
(22.58)
-0.0159***
(-7.19)
-0.0652***
(-7.47)
-0.0429***
(-4.16)
-0.0758***
(-5.7)
3qAGE
0.0083***
(4.12)
-0.0894***
(-9.4)
0.0687**
(2.81)
0.9074***
(5.43)
0.0003***
(4.64)
0.0009
(1.28)
-0.0091*
(-1.97)
0.0001
(0.01)
-1.4885***
(-40.08)
0.1618
(1.68)
7.2873***
(11.19)
0.0015***
(7.28)
0.0626***
(23.53)
0.1422***
(7.88)
-0.0221**
(-2.38)
0.0347
(0.79)
0.0250
(0.22)
3.5772***
(4.64)
0.0008**
(3.16)
-0.0205***
(-6.52)
0.5391***
(25.29)
-0.0146
(-1.21)
-1.2911***
(-22.85)
0.1513
(1.03)
6.6654***
(6.73)
0.0013***
(4.14)
0.0153***
(3.79)
0.6084***
(22.17)
0.0480***
(5.38)
-0.3925***
(-15.39)
0.0689***
(6.54)
-0.1406***
(-4.66)
0.0896***
(6.6)
-0.2374***
(-6.12)
_cons
-0.1037***
(-12.32)
-0.6211***
(-17.08)
-0.0428***
(-6.58)
-0.3516***
(-13.82)
-0.0996**
(-3.3)
-0.1770***
(-4.57)
0.0121***
(5.33)
-0.0533***
(-8.19)
N
12029
12415
12488
12889
12986
12880
12488
12889
12989
12880
2
0.0213
0.1763
0.0164
0.1725
0.0527
0.0919
0.0146
0.1708
0.0546
0.0927
r
118
Table XXV: Incentive Effect Hypothesis of Golden Parachute on Managed Earnings
Pooled Linear regression models with interaction terms between golden parachute (GP) and other explanatory variables. The dependent variables are
still proxies for manage earnings, where EM2 is a proxy for accrual approach of managed earnings. r_CFO, r_PROD, r_DISX are proxies for real activities
managed earnings from the cash flow of operation aspect, production cost aspect, and discretionary expenses aspect respectively. RM1, RM2, and RM3 are
aggregate proxies for the real activities approach to managed earning. The independent variables are: GP a dummy variable, ROA is performance measure, SD is
stock volatility measure, PoP is pay for performance measure at industry level. Control variables include Duration which is the CEOs length of stay in the office,
LogSize is Total Asset logged, BLev is Book Leverage for a sample of 1,184 firms from the years 1992 to 2011. I run the following regression:
. MEit is the dependent variable,
serving as a proxy for either managed earnings approach – accrual or real-activity. Kit is the interactive terms between GP and four independent variables. The t
values are reported in parenthesis. A t value equal to or higher than 1.96 is synonymous to 95% or more certainty of the regression coefficients significance to the
dependent variables. ***, **,* denotes significance at 99.99, 99, and 95 percent respectively.
EM2
r_CFO
r_PROD
r_DISX
RM1
RM2
RM3
N
-0.0327***
(-3.77)
-0.1050***
(-9.78)
-0.0083
(-0.21)
1.0600***
(3.74)
0.0002***
(3.56)
0.0004
(0.55)
-0.0064
(-1.39)
0.0313**
(2.8)
-0.1956
(-0.56)
0.1272**
(2.52)
0.0004***
(4.35)
-0.0340***
(-4.34)
12488
0.1165***
(3.62)
-0.1771***
(-4.51)
0.2600
(1.76)
-0.6054
(-0.57)
0.0003
(1.7)
0.0493***
(19.6)
-0.4709***
(-27.85)
-0.0763
(-1.82)
1.5796
(1.22)
-0.4138*
(-2.22)
-0.0008**
(-2.37)
-0.2142***
(-7.4)
12986
-0.0613***
(-3.77)
-1.3482***
(-67.93)
-0.1273
(-1.7)
5.7840***
(10.87)
0.0006***
(5.61)
0.0346***
(27.3)
0.0684***
(8.01)
0.0915***
(4.32)
-3.7233***
(-5.71)
0.3942***
(4.18)
0.0005**
(3.12)
-0.0579***
(-3.97)
12889
0.0561**
(3.01)
0.2170***
(9.55)
0.1625
(1.9)
-7.1466***
(-11.68)
-0.0008***
(-6.71)
-0.0267***
(-18.35)
-0.0836***
(-8.54)
-0.0914***
(-3.88)
4.5178***
(6.02)
-0.3431**
(-3.17)
-0.0006**
(-2.85)
0.2741***
(16.36)
12995
-0.1192***
(-3.48)
-1.5713***
(-37.6)
-0.2887
(-1.84)
12.7508***
(11.39)
0.0013***
(6.21)
0.0610***
(22.86)
0.1539***
(8.56)
0.1877***
(4.21)
-8.0731***
(-5.88)
0.7460***
(3.76)
0.0011**
(3.02)
-0.3292***
(-10.71)
12889
-0.1725***
(-4.26)
-0.0398
(-0.81)
-0.4231*
(-2.27)
7.7542***
(5.84)
0.0004
(1.74)
-0.0225***
(-7.13)
0.5546***
(26.06)
0.1704**
(3.23)
-6.0995***
(-3.74)
0.7556**
(3.21)
0.0014**
(3.19)
-0.0597
(-1.64)
12986
-0.2221***
(-4.25)
-1.4075***
(-22.11)
-0.5420**
(-2.26)
13.0626***
(7.66)
0.0009**
(2.73)
0.0127**
(3.12)
0.6285***
(22.95)
0.2672***
(3.94)
-9.3649***
(-4.48)
1.1591***
(3.83)
0.0017**
(3.1)
-0.1208**
(-2.58)
12880
r2
0.0151
0.0684
0.3495
0.0648
0.1739
0.0548
0.0939
GP
ROA
SD
PoP
Duration
logSize
BLev
GP*ROA
GP*PoP
GP*SD
GP*AGE
_cons
119
Table XXVI: Pre and Post SOX Effects of Golden Parachute on Managed Earnings
Pooled Linear regression models with interaction terms between golden parachute (GP) and SOX variable. The dependent variables are still proxies for manage
earnings, where EM2 is a proxy for accrual approach of managed earnings. r_CFO, r_PROD, r_DISX are proxies for real activities managed earnings from the
cash flow of operation aspect, production cost aspect, and discretionary expenses aspect respectively. The independent variables are: GP a dummy variable, ROA
is performance measure, SD is stock volatility measure, PoP is pay for performance measure at industry level, SOX is a dummy variable where 1 identifies the
years regarded as ‘post-SOX’ – that is from 2003 to 2011. Control variables include Duration which is the CEOs length of stay in the office, LogSize is Total
Asset logged, BLev is Book Leverage for a sample of 1,184 firms from the years 1992 to 2011. I run the following regression:
. MEit is the dependent variable, serving
as a proxy for either managed earnings approach – accrual or real-activity. The t values are reported in parenthesis. A t value equal to or higher than 1.96 is
synonymous to 95% or more certainty of the regression coefficients significance to the dependent variables. ***, **,* denotes significance at 99.99, 99, and 95
percent respectively. The t values are reported in parenthesis below the coefficients.
Pre-SOX
Post-SOX
EM2
r_CFO
r_DISX
r_PROD
EM2
r_CFO
r_DISX
r_PROD
0.0140***
(3.36)
-0.0711***
(-3.51)
-0.1752***
(-3.33)
0.7149
(1.95)
0.0002
(1.08)
-0.0000
(-0.02)
-0.0348***
(-3.49)
0.0051
(0.35)
0.0016
(0.13)
-0.0629
(-1.02)
0.4635**
(2.9)
2.4882*
(2.21)
-0.0011*
(-2.42)
0.0541***
(12.1)
-0.4513***
(-15.34)
-0.3276***
(-7.57)
-0.0255***
(-3.45)
0.3843***
(10.6)
-0.0113
(-0.12)
-3.6523***
(-5.55)
-0.0013***
(-5.05)
-0.0119***
(-4.54)
-0.1243***
(-7.22)
0.1690***
(6.67)
0.0257***
(3.93)
-1.4861***
(-46.4)
-0.0468
(-0.56)
2.7971***
(4.81)
0.0011***
(5.02)
0.0239***
(10.31)
0.0978***
(6.41)
0.0304
(1.36)
0.0057*
(2.34)
-0.1104***
(-9.57)
0.2050***
(6.73)
0.9487***
(4.86)
0.0003***
(4.43)
0.0015
(1.83)
-0.0003
(-0.06)
-0.0710***
(-9.6)
0.0358***
(3.54)
-0.1965***
(-4.16)
0.0189
(0.15)
-0.5901
(-0.73)
0.0005
(1.82)
0.0452***
(13.54)
-0.4773***
(-20.41)
-0.1451***
(-4.68)
0.0203***
(3.48)
0.0793**
(2.91)
0.0821
(1.11)
-4.4351***
(-9.58)
-0.0008***
(-5.88)
-0.0365***
(-18.98)
-0.0460***
(-3.42)
0.3456***
(19.35)
-0.0120*
(-2.37)
-1.2162***
(-51.38)
0.0849
(1.33)
3.5410***
(8.86)
0.0006***
(4.86)
0.0418***
(25.13)
0.0382***
(3.28)
-0.1314***
(-8.51)
N
4003
4250
4255
4223
7103
7306
7309
7243
r2
0.012
0.068
0.0677
0.3807
0.0271
0.0618
0.0758
0.3267
GP
Perform
SD
PoP
Duration
LogSize
Leverage
_cons
120
Table XXVII: Pre and Post SOX Magnitude Effect of Golden Parachute Coefficients on Managed Earnings
To compare the main independent variable – golden parachute effects on managed earnings, I perform a seemingly unrelated estimation test which
compares the coefficient of golden parachute from the two regression models for each managed earnings proxy. Then I test the coefficients assuming a null
hypothesis that pre-SOX and post-SOX golden parachute coefficient are equal. The result shows that EM2 (the accrual managed earnings proxy – discretionary
accrual using Kothari et al. (2005) model) are not statistically different. However, r_CFO, r_DISX, and r_PROD (real-activity managed earnings proxy –
abnormal cash flow, abnormal discretionary expenses, and abnormal production cost) are statistically different. I report the chi-square value and their
probabilities at 99.99, 99, and 95 percent represented by ***, **, * respectively.
GP
EM2
r_CFO
r_DISX
r_PROD
2.50
4.46*
23.62***
20.59***
121
APPENDIX C
FIGURES FOR ESSAY TWO
Figure 3.1: Growth in the Use of Golden Parachutes
Data for Golden Parachute was obtained from Risk Metrics for years 1992 through to 2011. The number of golden parachute is reflected for a total of 1,184 firms. Prior
to 2007, Risk Metrics published eight volumes of governance measures dating: 1990, 1993, 1995, 1998, 1999, 2002, 2004, and 2006. Each volume tracked governance
characteristics for about 1,400 to 2,000 firms. Because not every year’s data is covered, I follow Gompers et al. (2003) and Bebchuk et al. (2012) suggested forward filling
technique. The forward filling technique assumes that for a firm that is present in two consecutive Risk Metrics volumes that the governance measure (Golden Parachute) remains
the same from the first publication to the next volume.
Golden Parachute
900
800
700
600
500
GP
400
300
200
100
0
1990
1995
2000
2005
2010
2015
122
Figure 3.2: Annual Sales for Sample Firms
Data for Firm Annual Sales obtained from COMPUSTAT for years 1992 through to 2011. Sale is reflected at mean and median for a total of 1,184 firms.
Annual Sales Growth
8000
7000
6000
,000,000
5000
4000
Average
3000
Median
2000
1000
0
1992 1993
1994 1995
1996 1997
1998 1999
2000 2001
2002 2003
2004 2005
2006 2007
2008 2009
2010 2011
123
Figure 3.3: Comparison of Pay for Performance and Operating Performance for Non-Golden Parachute adopted Years vs. Golden Parachute adopted Years
Panel A depicts how CEO’s are sensitive to their wealth when they have (1) or do not have (0) a golden parachute. Panel B depicts the operating performance of firm when they have (1) or
do not have (0) a golden parachute. Pay for Performance is obtained as the coefficient of regressing change in firm market value on the change in salary plus bonus while controlling for industry
effects. Data where obtained from EXECUCOMP for years 1992 to 2011. The main operating performance measure is EBITDA scaled by the average Total Asset of firm (ROA). However, in the
robustness section of this essay, I assess the behavior of other operating performance measure on firm managed earnings. The robustness measures for operating performance are: cash adjusted return
on asset (R_cAT), return on sales (ROS), and market to book ratio (Tobin). Data where obtained from COMPUSTAT for years 1992 through to 2011. Sale is reflected at mean and median for a total
of 1,184 firms.
Panel A
Panel B
Operating Performance
Pay for Performance
2.5
0.0071
0.007
2
0.0069
0.0068
1.5
0.0067
0
0.0066
PoP
1
1
0.0065
0.0064
0.5
0.0063
0.0062
0.0061
0
0
1
ROA
R_cAT
ROS
Tobin
124
125
VITA
Nacasius Ujah Ujah received his Bachelor of Business Administration degree in
Economics with International Trade concentration from the University of Central Arkansas at
Arkansas in 2003. He entered the International Business program at the University of Central
Arkansas in May 2005 and received his Master of Business Administration degree in May
2006. His PhD is from Texas A&M International University where he majored in
International Business with a concentration in Finance. His research interests include
executive compensation, board structure and governance, risk management, and bank
infrastructures in emerging markets.
Mr. Ujah may be reached at School of Business, Henderson State University, 1100
Henderson Street, Arkadelphia, AR 71999. His email is [email protected].