CFO co-option and CEO compensation*

Shane S. Dikolli Darden School of Business

University of Virginia 100 Darden Blvd

Charlottesville, VA 22903 USA dikollis@darden.virginia.edu

John C. Heater

The Fuqua School of Business Duke University 100 Fuqua Drive

Durham, NC 27708 USA john.heater@duke.edu

William J. Mayew

The Fuqua School of Business Duke University 100 Fuqua Drive

Durham, NC 27708 USA william.mayew@duke.edu

Mani Sethuraman

Samuel Curtis Johnson Graduate School of Management Cornell University

Sage Hall Ithaca, NY 14853 USA

mani.sethuraman@cornell.edu

May 2019

* This paper was previously titled “The CEO-CFO Relationship and CEO Compensation”. We appreciate helpful

comments and suggestions from Margaret Abernethy, Dirk Black, Rob Bloomfield, Brian Cadman, Kristina Demek, Rachel Hayes, Leslie Hodder, Pat Hopkins, Khim Kelly, Scott Jackson, Nan Jiang, Bob Lipe, Luann Lynch, Patrick Martin, Robin Roberts, Thomas Steffen, Shyam Sunder, Jake Thomas, Mohan Venkatachalam, Frank Zhang and workshop participants at the AAA Management Accounting Section Midyear Meeting and at the following universities: Central Florida, Duke, Indiana, Melbourne, Monash, South Carolina, Utah, Virginia (Darden), and Yale. We are grateful to professors Jeffrey Coles, Naveen Daniel, and Lalitha Naveen for generously providing us with board co-option data for use in this study. We also thank Michael Minnis for sharing data on the proportion of CEO communication (relative to other corporate employees) on conference calls.

CFO co-option and CEO compensation

Abstract

We study whether relative power in the CEO-CFO relationship influences CEO compensation. To

operationalize relative power of a CEO over a CFO, we define CFO co-option as the appointment

of a CFO after a CEO assumes office. We find that CFO co-option is associated with a CEO pay

premium of about 10%, which is concentrated more in the early years of the co-opted CFO’s tenure

and in components of compensation that vary with the achievement of analyst-based earnings

targets. Our evidence also indicates that a primary channel through which CEO power over a newly

co-opted CFO yields the achievement of earnings targets is through the use of earnings

management to inflate earnings. We document that newly co-opted CFOs relied primarily on using

discretionary accruals to manage earnings prior to the Sarbanes-Oxley regulatory intervention and

switched to real activities manipulation afterwards. The evidence thus suggests that the form of

earnings management depends on costs imposed on the CFO to inflate earnings.

Keywords: CFO, CEO, Executive Compensation, Monitoring, Financial Reporting, Managerial

Power, Earnings Management, Discretionary Accruals, Real Earnings Management

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CFO co-option and CEO compensation

1. Introduction

Understanding executive pay levels is an important area of research for both corporate

stakeholders and scholars in multiple disciplines including finance, accounting, strategy, and

management. The hierarchical power structure in firms generally implies that superiors influence

the pay of subordinates, such as, for example, boards of directors over CEOs and CEOs over CFOs

(Finkelstein, 1992). However, recent research by Coles et al. (2014) provides evidence that power

over pay may not solely be hierarchical. Coles et al. (2014) studies variation in the power a CEO

has over the board with respect to CEO pay, based upon whether board members were hired before

or after the CEO was hired. Board members hired after the CEO are referred to as co-opted, and

CEO pay is observed to be increasing in the extent to which the board is co-opted, consistent with

co-option decreasing board monitoring and facilitating a wealth transfer to the CEO.

In this paper, we extend the study of co-option with regard to CEO pay. However, rather

than considering how CEO pay can be increased via co-option enhancing a CEO’s power up the

firm hierarchy, we examine whether co-option exacerbates a CEO’s power down the firm

hierarchy. Specifically, we study whether co-opting the CFO, an immediate subordinate of the

CEO, plays any role in influencing CEO compensation. Using the relative tenure of the CEO to

the CFO (Friedman, 2014), we define a CFO as co-opted if the CFO’s tenure is less than the CEO’s

tenure. Existing research suggests CEOs can utilize their hierarchical power to pressure CFOs into

manipulating reported earnings (Feng et al., 2011). Under the notion that CFOs can influence CEO

pay through reported earnings and stock price response to earnings, we begin by documenting that

CEOs receive higher levels of compensation when the firm’s CFO is co-opted. This effect is

incremental to the higher levels of compensation received due to board co-option, which remains

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positively associated with CEO compensation in our sample.

An alternative interpretation of the positive association between CFO co-option and CEO

compensation is that a co-opted CFO provides synergies in the decision-making process, which in

turn enhances firm prospects, resulting in higher CEO compensation. To disentangle this

competing interpretation, we partition CFO co-option into early years and later years of a co-opted

CFO’s tenure. Synergies should grow over time as the CEO and CFO repeatedly interact, implying

a stronger co-option effect on CEO compensation in later years of a co-opted CFO’s tenure. A

power-based interpretation, however, would imply stronger co-option effect in early years of the

CEO-CFO relationship, consistent with the notion in the literature that a CFO builds reputation

within the firm over time and is less susceptible to undue influence by the CEO in later years. For

example, prior work has shown that firms with a greater fraction of corporate leaders appointed

before the CEO’s start date (suggesting more independent non-CEO leadership) are associated

with reduced corporate fraud (Khanna et al., 2015) and acquisitions that better maximize firm

value (Landier et al., 2012).

Consistent with the co-option effect capturing relative power in the CEO-CFO relationship,

we observe the CFO co-option effect on CEO pay is concentrated in the early years of a co-opted

CFO’s tenure. Moreover, when we decompose the level of CEO compensation into its components

(Cadman et al., 2010), we find that this newly co-opted CFO effect is concentrated in the non-

salary (i.e., variable) portion of CEO compensation. This is consistent with CFO co-option effects

residing in the portion of CEO pay that is more likely to be tied to reported firm performance,

given financial reporting is directly under the purview of CFO.

Without access to the details of each CEO’s compensation contract, it is not possible to

assess how a co-opted CFO might be able to influence the variable component of CEO pay. We

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therefore proxy for performance targets in the compensation contract indirectly by studying the

achievement of analysts’ earnings targets for two reasons. First, meeting or beating earnings targets

is associated with higher stock returns (Bartov et al., 2002) and is often a determinant of the

variable portion of CEO pay, either via annual bonuses (Matsunaga and Park, 2001) or

performance-based equity grants (Bettis et al., 2010). Second, CFOs can, within their assigned

decision rights, influence whether a firm meets or beats earnings targets through discretionary

accrual choices.

In our empirical estimation, we focus specifically on analyst-based earnings targets

because discretionary accruals can best be attributed to earnings management for achieving

analyst-based targets rather than other reporting objectives such as loss avoidance or exceeding

prior period earnings (Ayers et al., 2006). Moreover, achieving analyst-based targets is

unambiguously rewarded relative to other targets in the capital market (Brown and Caylor 2005).

We begin by showing that firms with income increasing discretionary accruals are more likely to

meet or beat earnings by one cent. We then show that this effect is entirely driven by newly co-

opted CFOs, consistent with the achievement of analyst earnings targets via accruals occurring

precisely when CFOs are most susceptible to pressure by CEOs.

We interpret this result as evidence that CFO co-option influences a CEO-preferred

outcome of achieving analyst-based targets. If CFOs trade off the benefits of being sympathetic to

the CEO’s preferences against the cost of manipulating earnings, we should observe smaller co-

option effects when the cost to bias is higher. To examine this issue, we utilize the Sarbanes-Oxley

Act (SOX) as an exogenous shock to the cost to bias (Friedman, 2014). Consistent with the SOX

regulatory intervention curbing accruals-based earnings management to achieve compensation

outcomes (Carter et al., 2009), we find discretionary accruals lead to achieving analyst-based

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earnings targets for newly co-opted CFOs only in the pre-Sarbanes-Oxley period, but not

afterward.

We also find that in the post-SOX period where the costs to bias using accruals is higher,

the pressure on newly co-opted CFOs to inflate earnings manifests through real earnings

management. Specifically, we find income-increasing real earnings management is associated with

a higher likelihood of achieving analyst-based earnings targets for newly co-opted CFOs in the

post-SOX period, but not before. This suggests a newly co-opted CFO substitutes accruals

management with real activities manipulation when engaging in earnings management to enhance

CEO compensation after the SOX intervention (Cohen et al., 2008).

Our analysis is subject to two important limitations. First, our focus on a co-opted CFO’s

use of earnings target achievement to influence CEO pay levels enables us to speak only to one

aspect of the CFO’s relationship with the CEO. We caveat that other factors, such as whether the

board consults the CFO as part of a 360-degree performance evaluation of the CEO, may also

explain part of the pay premium.1 Second, while our study documents a strong association between

CFO co-option and CEO compensation, our inferences are limited in that we do not establish

causality. Nevertheless, given the general lack of understanding of how inter-personal

relationships among managers influence firm outcomes, our findings shed initial light on the role

the CFO plays in generating higher levels of compensation for the CEO, and the channel through

which the CFO influences the achievement of analyst-based earnings targets.

With these caveats in mind, our paper contributes to the literature in three fundamental

ways. First, we expand the implications of co-option by the CEO beyond the board of directors

1 In a survey of 20 US public company compensation committee members (17 out of the 20 served as the committee

chair), Hermanson et al. (2012) document that compensation committees get feedback or comments about CEO performance from management and those who report to the CEO.

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(Coles et al., 2014) and top-level executive team (Khanna et al., 2015; Landier et al., 2012) by

focusing on a specific senior executive with financial reporting responsibilities. Our results suggest

CEOs can have differential effects on CFOs based on whether such individuals were hired before

or after the CEO. More generally our results provide new evidence on how relative power matters

among upper management generally (Finkelstein, 1992; Van Essen et al., 2015) and between the

CEO and CFO specifically (Friedman, 2014).

Second, our study builds on an emerging literature studying the CFO’s role in the C-suite.

For example, prior studies explore the role played by CFOs as board members (Bedard et al.,

2014), the effect of CFOs on accounting practices (Feng et al., 2011), and the relative influence of

the CFO (versus the CEO) on earnings management (Jiang et al., 2010). In studying co-option, we

explore aspects of social dynamics involving the CFO that can influence accounting choices that

are distinct from individual style effects examined by prior literature (Ge et al., 2011).

Third, we provide archival evidence to supplement experimental research on the CEO-

CFO relationship. Bishop et al. (2016), in an experiment using sixty-nine public company CFOs,

documents that CEO social influence pressure on a CFO significantly increases a CFO’s

willingness to revise their initial inventory adjustments, which directly affects financial reporting

outcomes. Our results provide external validity for their conclusion that CEO pressure can

influence financial reporting outcomes under the control of the CFO.

We proceed as follows. In the next section we develop testable hypotheses about the

relation between CFO co-option and CEO compensation. We then describe our archival data

(Section 3) and tests of our two main hypotheses (Section 4). We next explore the mechanisms by

which the association between CFO co-option and CEO compensation obtains (Section 5). We

then provide additional analyses and robustness checks (Section 6) and conclude (Section 7).

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2. Hypotheses Development

Prior evidence based on CEO compensation research supports both a “competitive pay”

perspective, suggesting pay levels are driven primarily by the market for talent, and a “rent

extraction” perspective suggesting pay levels are influenced more by managerial power and

politics.2 Our investigation focuses on the “rent extraction” perspective, as we examine whether

the CEO is able to utilize power in the relationship with the CFO to achieve higher pay. Our

objective is not to rule out the competitive pay perspective in explaining CEO compensation

practices. The conceptual variable of interest in this investigation, however, is the relative power

in the CEO-CFO relationship.

To formalize a hypothesis regarding the relationship between CEO pressure over the CFO

and CEO pay, we must first conceptualize a construct that captures variation in CEO power over

a CFO beyond the standard power afforded to the CEO by organizational hierarchy. We propose

that such power originates from the CFO appointment process. DeMars (2006) suggests that CEOs

participate extensively in the vetting of potential CFO candidates and the final approval of CFO

appointments.3 As a consequence, given an opportunity to vet candidates for a firm’s CFO

position, a CEO is likely to prefer hiring a candidate that would help maximize the CEO’s utility.

CEOs can pressure newly-appointed CFOs through either threat of termination (Feng et al., 2011;

Mian, 2001) or more broadly through social influence pressure (Khanna et al., 2015). Social

influence pressure from the CEO can also be decomposed into compliance pressure, in the form

2 Several researchers find CEO pay is driven by CEO performance (Hermalin, 2005; Falato et al., 2015) and CEO’s

relative expertise in the top management team (Li et al., 2014). Proponents of the rent-extraction perspective argue that executives have power over boards (Coles et al., 2014) and the level of pay is much higher than what is dictated by arms-length contracting (Bebchuk and Fried, 2004).

3 Additionally, Khanna et al. (2016) contends that CEOs are heavily involved in recruiting and appointing top executives, suggesting such executives are more likely to share common beliefs with the CEO and may be more beholden to the CEO than an executive appointed during a previous tenure would be.

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of a request, and obedience pressure, in the form of a directive (Bishop et al., 2016).4

To develop a proxy for relative CEO-CFO power that is non-hierarchical, we follow Coles

et al. (2014) who construct a variable called “board co-option” to capture the power of the CEO

up the organization hierarchy. Board co-option is defined as the fraction of board members

appointed after the CEO took office. The intuition behind the measure is that the CEO is more

likely to approve the appointment of a candidate to the firm’s board of directors if the economic

interests of the candidate align with those of the CEO. Such board selections weaken monitoring

as CEO-appointed board members are more likely to be individuals who can be influenced by the

CEO. Coles et al. (2014) find that CEO pay is increasing in board co-option, consistent with the

notion in our study that CEO power over the CFO can facilitate rent extraction.5

Analogously, we operationalize CEO power over the CFO with a variable we label “CFO

Co-option.”6 CFO Co-option is an indicator variable that denotes whether the CFO was appointed

after the CEO took office. So long as the CEO is able to influence a CFO’s appointment, it follows

that a CFO appointed after the appointment of the current CEO will be more aligned with the

CEO’s economic interests compared to a CFO appointed before the current CEO.

Using CFO Co-option as an empirical proxy for CEO power over the CFO, we

hypothesize:

H1: CFO Co-option is positively associated with CEO compensation.

Evidence consistent with H1 would support the idea of a CEO pressuring a CFO to

4 Compliance pressure manifests in the form of a request where the CEO asks the CFO to revise an estimate, while

obedience pressure manifests in the form of a directive where the CEO tells the CFO to revise an estimate (Bishop et al., 2016).

5 It is important to note that contracts may be designed in anticipation of CEO pressure on the CFO. Friedman (2014) offers a theoretical model of inter-executive influence between the CEO and CFO that may lead to a bias in financial reporting and ultimately a CEO compensation premium. A key result from the model is that a riskier reported performance measure, arising from lower levels of reporting effort, increases the firm’s cost of compensating the CEO (Friedman, 2014 – see Proposition 3). Critical to this theory is information asymmetry at contracting. In this study, we are unable to observe the extent of contracting information asymmetry; thus, our focus is on the implications of the level of the reported performance measure.

6 In suggesting empirical proxies for his theory on CEO pressure on the CFO, Friedman (2014) also suggests tenure of the CEO relative to the CFO.

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facilitate rent extraction. However, such an association could also exist due to synergies between

the CEO and CFO. For example, CFO Co-option might be positively associated with firm

outcomes, and in turn CEO compensation, because of lower coordination costs or more efficient

(or higher-quality) strategic decision-making. Under our maintained assumption that CFO Co-

option captures notion of CEO power over the CFO, we should observe a stronger association

between CFO Co-option and CEO compensation in the early years of a co-opted CFO’s tenure.

This follows from the notion in the literature that a CFO gains independence from the CEO over

time. For example, prior work has shown firms with a greater fraction of corporate leaders

appointed before the CEO’s start date (suggesting more independent non-CEO leadership) are

associated with reduced corporate fraud (Khanna et al., 2015) and acquisitions that better

maximize firm value (Landier et al., 2012). Synergy between the CEO and CFO should arguably

grow over time through repeated interactions, or at a minimum not differ between the early and

later years of a co-opted CFO’s tenure. We therefore hypothesize:

H2: CFO Co-option is more positively associated with CEO compensation for newly co-

opted CFOs relative to long-term co-opted CFOs.

3. Data

3.1 Sample Selection

Our sample consists of a matched EXECUCOMP-COMPUSTAT-CRSP-IBES dataset

spanning the period 1993-2015. We obtain manager-level data from EXECUCOMP, firm-level

accounting variables from COMPUSTAT, firm-level stock returns from CRSP, and analyst

estimates and actual EPS data from IBES.

We further restrict the sample for the following reasons. To determine CFO co-option, we

must identify both a CEO and CFO and the year in which s/he starts at the firm. First, we identify

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an executive as a CEO if “ceoann=CEO” in EXECUCOMP, which indicates that s/he was the

appointed CEO for all or most of the indicated fiscal year. Second, we require that a CFO be

identifiable in EXECUCOMP. Following Jiang et al. (2010), we identify an executive as a CFO if

“cfoann=CFO” in EXECUCOMP, or if the executive holds one of the following titles as defined

by the “titleann” variable in EXECUCOMP: CFO, chief financial officer, chief finance officer,

treasurer, controller, or vice president-finance. Third, we require that sufficient compensation data

exist for both the identified CEO and the CFO. Fourth, we require IBES data to compute analyst

median forecast, standard deviation of forecasts, and number of analysts. We calculate forecast

error as the difference between the firm’s actual annual earnings per share (EPS) and the most

recent median analyst forecast (computed at least three days prior to the earnings announcement

date). These requirements collectively reduce our sample size to 21,361 firm-year observations

corresponding to 2,360 unique firms.

Our next data restriction pertains to the inclusion of Board Co-option as a control variable,

which captures the proportion of directors on the board appointed after the CEO assumed office

(Coles et al., 2014). We obtain these data from Coles et al. (2014) and merge each firm-year board

co-option metric into our sample. Because the data is limited to firm-years through 2014, we use

2014 values to fill in missing 2015 values within-firm when available, and use the within-firm

average value of board co-option for missing firm-year data.7 These requirements collectively

reduce our sample size to 18,177 firm-year observations corresponding to 1,815 unique firms.

Finally, in tests to address the robustness of our main results, we merge in data related to

real and accruals-based earnings management. Our final sample, with these restrictions, is 17,726

firm-year observations corresponding to 1,793 unique firms.

7 As a robustness check, we later use a multiple imputation approach to fill in missing values of Board Co-option.

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3.2 Descriptive Statistics

Table 1 indicates total CEO compensation is left skewed, with a mean (median) CEO

compensation of $5.05M ($3.19M), comparable with values reported in prior research for S&P

1500 firms (Otto, 2014). The mean (median) of the natural logarithm of the CEO compensation is

8.033 (8.067), which we use in our multivariate analysis to alleviate any concerns arising from

skewness of the CEO compensation measure. About 67% of the firm-year observations in our

sample contain co-opted CFOs. On average, CEOs have mean (median) tenure of about 8 (6) years,

consistent with prior research (Coles et al., 2014).

Table 2 presents bivariate correlations. The Pearson and Spearman correlations between

Ln (CEO Compensation) and CFO Co-option are negatively and statistically significant (p<0.01).

While this implies CEOs with co-opted CFOs receive lower pay, caution should be exercised when

interpreting bivariate relationships with compensation levels, as these are not adjusted for standard

economic determinants of pay. The correlation between board co-option and CEO pay is also

negative, although in multivariate estimations the relationship is positive (Coles et al., 2014). CFO

co-option is positively correlated with board co-option, consistent with the possibility that CEO

power runs up and down the firm hierarchy at the same time. This highlights the need to control

for board co-option and numerous other factors before drawing a conclusion with respect to CFO

co-option.

4. Main Results

4.1 Test of Hypothesis 1

To estimate a multivariate specification, we follow the extant literature and use the

following pooled cross-sectional ordinary least squares (OLS) regression to estimate CEO

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compensation as a function of established economic determinants (Core et al., 2008; Jiang et al.,

2010; Cadman et al., 2010):

Ln (CEO Compensation)i,t = α0 + Economic Determinantsi,t + gi,t (1)

We report the results of this base estimation in column 1 of Table 3. The economic

determinants we include begin with those included in Core et al. (2008), including CEO tenure,

size (Ln(Rev)t-1, S&P Indicator), growth opportunities (BTMt-1), both lagged and contemporaneous

performance (Returnt, Returnt-1, ROAt, ROAt-1), and industry and year fixed effects.8 We then also

include CEO compensation determinants suggested by subsequent research (e.g., Jiang et al.,

2010) such as proxies for leverage (Leverage), risk (Std Dev Operating CF, Std Dev Revenue, Std

Dev Sales Growth), and the sensitivity of the CEO’s and CFO’s wealth to stock price (CEO Equity

Incentive and CFO Equity Incentive).

All independent variable effects that are also in Core et al. (2008), with the exception of

S&P 500 and Ln (CEO Tenure), are comparable to those presented by Core et al. (2008).

Additionally, the explanatory power in Table 3 column 1 (R2 = 55.1%) is larger than the R2 of

43% documented in Core et al. (2008), because of the inclusion of additional control variables

motivated from subsequent research. We then augment the estimation in Table 3 column 1 by

adding a proxy for board co-option (Coles et al., 2014), and present the results in column 2. The

coefficient on board co-option (see column 2) is positive and statistically significant (p<0.01)

consistent with the findings reported by Coles et al. (2014).

Our next specification adds CFO Co-option to our base compensation model as follows:

Ln (CEO Compensation)i,t = α0 + α1CFO Co-optioni,t + Economic Determinantsi,t + εi,t (2)

8 Core and Guay (2008) use annual regressions to determine a residual compensation for each firm-year, but report

pooled cross-sectional results that they report to be substantively similar to the results from the annual regressions. For parsimony, we report only the pooled cross-sectional results.

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H1 predicts that the sign of the coefficient on CFO Co-option (α1) is positive. Column 3 of

Table 3 reports an estimate of α1 equal to 0.100 (p<0.01), which is about half of the 0.207 (p<0.01)

effect of a fully co-opted board. Despite the positive correlation between CFO and board co-option,

the inclusion of CFO Co-option does not change the effect of board co-option in any meaningful

way. Economically, this implies that CEOs with newly co-opted CFOs earn a pay premium about

10% higher relative to CEOs with CFOs who are not co-opted. This evidence provides strong

support for H1.

4.2 Test of Hypothesis 2

We next report tests of H2 by examining whether the H1 findings are consistent for both

new and long-term co-opted CFOs. To do so, we first refine the CFO Co-option measure into two

variables, i.e., CFO co-option conditional on the CFO’s first three years of tenure (CFO Co-option

(0-3)) and CFO co-option conditional on the CFO’s fourth year of tenure and beyond (CFO Co-

option (4+)). We choose three years as the delineation point because we are interested in the early

years of CFO tenure and 3 years captures the beginning point of a CFO’s tenure through to an

endpoint of a compromise between median CFO tenure (2 years) and average CFO tenure (3.3

years). If our CFO co-option variable is capturing CEO-CFO synergy instead of CEO pressure on

the CFO, we would expect both decomposed CFO co-option variables to be associated with higher

levels of CEO compensation, under the assumption that CEO-CFO synergy remains constant or

improves with time. On the other hand, if co-opted CFOs become more resistant to CEO pressure

over time, then we would expect the magnitude of the coefficient on CFO Co-option (4+) to

diminish relative to CFO Co-option (0-3).

Column 4 of Table 3 reports an insignificant coefficient on CFO Co-option (4+) (p>0.10),

which is consistent with the idea that long-term co-opted CFOs become resistant to CEO pressure.

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The coefficient on CFO Co-option (0-3) is positive (0.109) and statistically significant (p<0.01),

confirming that the effect of CFO co-option on levels of compensation appears to be concentrated

in the early years of the CEO-CFO relationship. The difference between the CFO Co-option

coefficients on the two decomposed co-option variables is statistically significant (p<0.01).

Overall, the evidence provides strong support for H2.

5. How Do CFOs Influence CEO Compensation?

Thus far, we have not articulated a specific mechanism by which a newly co-opted CFO’s

actions might impact CEO compensation. To the extent that favorable or less conservative

judgments in the financial statements can generate higher CEO compensation outcomes, a CEO’s

compensation can be improved by pressuring an appointed CFO to favorably bias financial

statements. Empirical evidence suggests that CFOs have the ability to bias financial statements

(Indjejikian and Matejka, 2009).

5.1 Is CFO Co-option Associated with Performance-Varying Compensation?

Our first step in exploring a possible mechanism for our main results is to examine whether

CFO Co-option (0-3) is associated with CEO compensation that is designed to vary with

performance.9 To do so, we decompose our dependent variable, CEO Compensation, into the

following component parts: CEO salary (Ln(CEO Salary)), CEO total compensation unrelated to

salary (Ln(CEO Variable Comp.)), and CEO equity awards (Ln(CEO Equity Awards)). To the

extent that the CEO Salary is typically unrelated to firm performance biased by a co-opted CFO,

we expect no association between CFO Co-option (0-3) and Ln(CEO Salary). To the extent that

CEO compensation is linked to reported performance, which the CFO can bias, we expect a

9 De Angelis and Grinstein (2015) find that accounting performance measures are critical to CEO contracting in S&P

500 firms, determining 79% of the value within their performance award packages. Prior research finds CEO equity incentives are positively related to meeting earnings target as well as to managing earnings through accruals (Bergstresser and Philippon, 2006).

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positive relation between CFO Co-option (0-3) and variable components of pay.

Table 4 reports the results. We first note in all three columns that the coefficient on CFO

Co-option (0-3) is significantly greater than the respective coefficient on CFO Co-option (4+),

consistent with the earlier support for H2 (p<0.05 in each case). In column 1, there is no significant

association between CFO Co-option (0-3) and Ln(CEO Salary), as expected (p>0.10). In Columns

2 and 3, the coefficients on CFO Co-option (0-3) in the compensation component regressions are

positive and statistically significant (p<0.01). Taken together, this suggests that newly co-opted

CFOs are associated with higher levels of specific CEO compensation components that are tied to

reported firm performance. We therefore turn to an examination of how a CFO might influence

the variable component of CEO pay.

5.2 Is Earnings Management to Meet Targets Stronger for Newly Co-opted CFOs?

Our exploration of the mechanism for our main results investigates whether newly co-opted

CFOs can inflate reported performance linked to CEO compensation. Given that a newly co-opted

CFO has decision authority over financial reporting, we expect the level of reported financial

performance is both (1) influenced by the CEO through pressure on a newly co-opted CFO, and

(2) beneficial to the CEO.

An outcome that meets both conditions is the achievement of analyst-based earnings

targets. Meeting analyst-based earnings targets can significantly affect CEO compensation,

particularly as target-beating performance outcomes can increase both share prices and variable

compensation such as bonus grants or equity awards (Matsunaga and Park, 2001; Bartov et al.,

2002; Graham et al., 2005; Bettis et al., 2010). To the extent that newly co-opted CFOs manage

earnings in a way to achieve earnings targets, we expect the relation between earnings management

and the firm meeting or beating forecasts by a narrow margin will be stronger for firms with newly

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co-opted CFOs.

Empirically, we assess whether the interaction of CFO Co-option (0-3) with proxies for

earnings management is a determinant of narrowly achieving earnings targets, after controlling for

previously established determinants of a firm’s propensity to narrowly meet or beat analyst targets.

We estimate the following linear probability model:

Meet/Beat0.01i,t = λ0 + λ1 CFO Co-option (0-3)i,t + λ2 Pos.Disc.Acc.i,t + λ3 Pos.REM i,t +

λ4 CFO Co-option (0-3)i,t x Pos.Disc.Acc.i,t + λ5 CFO Co-option (0-3)i,t x Pos.REM i,t +

∑ λn Controlsi,t + di,t (3)

where Meet/Beat0.01 is an indicator variable set to one if the firm met or exceeded the median

analyst consensus earnings targets by 1 cent or less, and zero otherwise; Pos.Disc.Acc. is equal to

1 if the value of discretionary accruals (Disc.Accruals) is greater than zero, and 0 otherwise

(Dechow et al.,1995); Pos.REM is equal to 1 if the value of both abnormal discretionary expense

(Abn.Disc.Exp) and abnormal production expense (Abn.Prod.Exp) are greater than zero, and 0

otherwise (Roychowdhury, 2006). Controls is a vector of control variables that includes CEO/CFO

equity incentives (Core and Guay, 1999; Bergstresser and Philippon, 2006; Jiang et al., 2010) and

other firm characteristics such as balance sheet constraints (Barton and Simko, 2002). Year and

industry fixed effects based on 2-digit SIC code are also included.

We report the results from estimating equation (3) in Table 5. In column 1, we estimate the

model without including CFO Co-option or interaction variables. We find that the coefficient on

Pos.Disc.Acc. is positive (0.020), consistent with prior research, and statistically significant

(p<0.01). We find that the coefficient on Pos.REM is statistically insignificant (p>0.10). Taken

together, an average effect in our sample is that firms use accruals-based earnings management

rather than real earnings management to narrowly meet or beat analyst earning targets.

16

Table 5 Column 2 reports estimates of the effect of earnings management on meeting or

narrowly beating earnings benchmarks after including CFO co-option main effects. Both the

coefficients on CFO Co-option (0-3) and CFO Co-option (4+) are statistically insignificant

(p>0.10). That is, being co-opted alone is not sufficient to beat earnings benchmarks.

Table 5 Column 3 interacts our measures of CFO co-option with indicator variables for

positive values of earnings management (Pos.Disc.Acc. and Pos.REM). We expect that λ4 (λ5) will

be positive, if accruals-based (real) earnings management is a channel through which a co-opted

CFO meets or narrowly beats an analyst-based target. The coefficient on the interaction of CFO

Co-option (0-3) and Pos.Disc.Acc is positive (λ4 = 0.028) and statistically significant (p<0.05).

Moreover, the main effect of positive discretionary accruals is no longer statistically significant,

which implies it is the combination of both positive discretionary accruals and a co-opted CFO

that explains benchmark beating behavior. Turning to real earnings management, the coefficient

on the interaction of CFO Co-option (0-3) and Pos.REM is statistically insignificant (p>0.10), and

both main effects for CFO co-option and positive real earnings management remain statistically

insignificant. Taken together, our results from columns 2 and 3 suggest that across the entire

sample, co-opted CFOs use accruals-based earnings management to inflate earnings, which

facilitates meeting or narrowly beating analyst-based earnings targets.

5.3 Does Earnings Management Vary in Response to SOX?

Our findings thus far that co-opted CFOs enhance CEO compensation through the use of

accrual management to achieve analyst-based targets are based solely on statistical associations.

We have no exogenous variation that help establish that co-opted CFOs are causing CEO pay to

increase, although finding effects solely in the variable component of CEO pay and only for newly

co-opted CFOs when discretionary accruals are income increasing is difficult to reconcile with

17

alternative interpretations. To move closer towards a causal link, we investigate whether the results

vary in predictable ways in subsamples defined by an exogenous event. We use the SOX regulatory

intervention as the exogenous event because prior research implies the use of accruals-based

earnings management diminished after the regulation imposed requirements for the CEO and CFO

to certify financial reporting disclosures (Cohen et al., 2010). Prior work also suggests that

earnings management shifted from accruals-based to real earnings management in the post-SOX

period (Cohen et al., 2010).

Table 5 Column 4 restricts estimation of equation (3) to the sub-sample years 1993-2001

(pre-SOX period) and column 5 reports the results of estimation for the sub-sample years 2002-

2015 (post-SOX period). We test whether the coefficients of CFO co-option interacted with

earnings management measures differ across these subsamples. For the interaction of co-opted

CFOs and accruals-based earnings management, we find a positive and statistically significant

coefficient pre-SOX (λ4 = 0.073), but not post-SOX. Conversely, for the interaction of co-opted

CFOs and real earnings management, we find a positive and statistically significant coefficient

post-SOX (λ5 = 0.037) but not pre-SOX.

The difference in interaction coefficients in the subsamples is statistically significant for

both accruals-based earnings management (i.e., a significant reduction in the use of accruals-based

earnings management by co-opted CFOs post-SOX) and real earnings management (i.e., a

significant increase in real earnings management by co-opted CFOs post-SOX). Taken together,

these results provide strong support for the notion that the CFO’s use of earnings management

increases CEO compensation. Additionally, it suggests a substitution effect between accruals-

based earnings management and real activities management after regulatory intervention imposed

higher costs to engage in the former.

18

6. Additional Analyses and Robustness Checks

In this section, we investigate possible alternative explanations for our main findings. First,

to ensure our findings with respect to achieving earnings targets are not due to spurious firm

performance and benchmark beating (Ayers et al., 2006), we replicate our analysis using pseudo

benchmarks that beat earnings by large amounts and are, therefore, unlikely to reflect managed

earnings. Table 6 reports these results, where we examine the likelihood of firms beating analyst-

based earnings targets by greater than one cent. We find no evidence that newly co-opted CFOs

utilize discretionary accruals or real earnings management for inflating earnings to exceed targets

by more than one cent either pre-SOX or post-SOX. This helps alleviate the concern that CEOs of

high performing firms that generally exhibit higher levels of earnings and discretionary accruals

also tend to co-opt their CFO.10

Second, it is possible that our CFO co-option measure is a proxy for the Li et al. (2014)

measure of CEO expertise relative to the CFO. If CEOs systematically approve the appointment

of CFOs that possess relatively lesser expertise than they do, then our inferences on the effects of

co-option are confounded by relative CEO expertise. In other words, relative CEO expertise is

potentially a correlated omitted variable. To rule out this possibility, we check the correlation

between CFO co-option and (1) the proportion of text spoken during a conference call by the CEO

relative to other company executives, and (2) the proportion of the number of comments spoken

by the CEO relative to other executives. In both cases, the magnitude of the correlation is small

and statistically insignificant (untabulated). We interpret this evidence as inconsistent with the

10 More specifically, this helps alleviate the concern that CEOs who know firm performance will be favorable tend to

co-opt their CFO, and the latent favorable firm performance increases discretionary accruals, in turn facilitating the achievement of earnings targets. If this general phenomenon were occurring, the interaction of CFO co-option and discretionary accruals would predict a relatively higher location in the earnings surprise distribution as captured by our pseudo target

19

explanation that CFO co-option is a proxy for CEO relative expertise.

Third, we use the multiple imputations ([MI]) package in STATA to estimate missing

values of board co-option. We provide a detailed discussion of the execution of this approach in

Appendix B. Results are presented in Table B.1 of Appendix B. Our estimated coefficients,

statistical inferences, and qualitative inferences are consistent with those presented above, which

use single imputation. We conclude that the use of single imputation does not meaningfully bias

our inferences.

Finally, in this study, we included leverage and performance volatility as explanatory

variables in our estimations for CEO compensation that were not included in the original Core et

al. (2008) study. To ensure our results are not driven by this design choice, we estimate

compensation regressions after excluding leverage and performance volatility controls. Our

inferences remain unaltered.

7. Conclusion

We argue that CEO influence over a CFO’s appointment exacerbates the CEO’s power

over the CFO, particularly early in the CFO’s tenure. We then examine whether a newly co-opted

CFO – a CFO appointed after a CEO assumes office – influences the CEO’s compensation. We

find that CEOs with a newly co-opted CFO earn more than 10% higher compensation after

controlling for standard compensation determinants. This effect is partly driven by newly co-opted

CFOs managing earnings to meet externally-focused earnings targets, and additional analysis

reveals that newly co-opted CFOs are able to achieve analyst targets by engaging in accruals-based

earnings management prior to SOX and real earnings management post-SOX.

Our findings shed light on how the formation of the relationship between two of the top

decision-makers in a firm (i.e., the CEO and CFO) shape payouts to a firm’s CEO. Our findings

20

also further our understanding of how the intersection of financial reporting and performance

evaluation processes impact corporate outcomes in general, and executive compensation in

particular. The findings provide further evidence of a link between power structure at the top levels

of a corporation and financial reporting decisions, which can generate a CEO compensation

premium. Whether the involvement of the CEO in the CFO appointment process generates indirect

benefits that could exceed the compensation premium documented in this study is a topic worthy

of future research.

It is also important to note that the informal interactions between executives and board

members in a corporate setting are unobservable to the researcher (especially on matters relating

to executive compensation). The possibility remains that CFOs could influence CEO

compensation through discussions with the board of directors. While this is not a channel we can

empirically investigate, compensation consultants claim that such interactions between CFOs and

board members do exist. To date, the impact of such interactions on CEO compensation outcomes

remains unknown. While it is conceivable that a CFO has more knowledge about both CEO effort

and CEO performance compared to outsiders, it is difficult to decipher the magnitude of this

information and the potential bias that CFOs may induce before sharing this private information

with other stakeholders such as board members and shareholders. Board members may still be

interested in obtaining more inside information about the CEO performance due to the inherent

information asymmetry that exists between outsiders and managers, but may appropriately weight

or discount the information that the CFO provides before taking a strategic decision. Board

members may also choose not to discount CFOs’ opinions (knowing well they are biased) if it is

optimal to do so in equilibrium. For example, boards may be dependent on the CFO in other critical

matters and the benefits from forging better ties with the CFO by giving weight to CFOs’ biased

21

opinions may outweigh the costs. These aspects are also worthy of future research and would

deepen our understanding of the CEO-CFO relationship.

22

Appendix A Description of Regression Variables

Variable Description

CFO Co-option Indicator variable set to 1 if the CFO was hired into the current position by the current CEO, and 0 otherwise. Variable is measured by observing the time-series of CFO and CEO names in EXECUCOMP for a given firm.

CFO Co-option (0-3)

Indicator variable set to 1 if the CFO was hired into the current position by the current CEO within the last 3 years, and 0 otherwise. Variable is measured by observing the time-series of CFO and CEO names in EXECUCOMP for a given firm.

CFO Co-option (4+)

Indicator variable set to 1 if the CFO was hired into the current position by the current CEO 4 years ago or more, and 0 otherwise. Variable is measured by observing the time-series of CFO and CEO names in EXECUCOMP for a given firm.

Board Co-option Proportion of the board that consists of directors appointed after the CEO joined (Coles et al., 2014)

Ln(CEO Comp.)

Natural log of total CEO compensation - salary, bonus, other annual, total value of restricted stock granted, total value of stock options granted (Black-Scholes), long term incentive payouts, and all other total. [EXECUCOMP: tdc1].

Ln(CEO Salary) Natural log of CEO salary. [EXECUCOMP: salary].

Ln(CEO Variable Comp) Natural log of total CEO compensation less salary. [EXECUCOMP: tdc1-salary].

Ln(CEO Equity Awards) Natural log of the value of CEO equity (stock + option) awards for the year. In Execucomp, this data is only available for fiscal years from 2006 onward. [EXECUCOMP: stock_awards-option_awards].

Meet/Beat 0.01 Indicator variable set to 1 if the firm beats median consensus analyst EPS estimates for the fiscal year by $0.01 or less, and 0 otherwise. [IBES]

Beat >0.01 Indicator variable set to 1 if the firm beats median consensus analyst EPS estimates for the fiscal year by more than $0.01, and 0 otherwise. [IBES]

Disc. Accruals Discretionary accruals calculated using the modified-Jones model (Dechow et al., 1995).

Abn. Prod. Exp. Abnormal production expense calculated following Roychowdhury (2006).

Abn. Disc. Exp Abnormal discretionary expense calculated following Roychowdhury (2006). Values are multiplied by -1 to represent higher values of abnormal discretionary expense imply higher earnings management.

Ln (CEO Tenure) Natural logarithm of the number of years the CEO has been in the current position with the company.

Ln(Rev.) Natural logarithm of revenue [COMPUSTAT: revt].

23

SP500 Indicator Indicator variable set to 1 if the firm belongs to S&P 500, and 0 otherwise. [EXECUCOMP: SPCODE]

BTM Book value of assets divided by the sum of the book value of liabilities and market value of equity (BVA/[BVL+MVE]). [COMPUSTAT: BVA=at; BVL=lt; MVE=csho*prcc_f]

Return Annual Stock Returns [CRSP]

ROA Income before extraordinary items divided by the average total assets for the fiscal year. [COMPUSTAT: ibc, at]

Leverage The leverage ratio of the firm. [COMPUSTAT: lt/at]

CEO/CFO Equity Incentive

Equity incentive ratio calculated following Bergstresser and Philippon (2006). The ratio proxies for the sensitivity of a manager’s wealth/compensation to a one percent change in in the value of firm equity.

Size Natural log of one-year lagged total assets. Assets are measured in $millions. [COMPUSTAT: at]

Sales Growth The percentage change in revenues compared to prior fiscal year. [COMPUSTAT: revt]

Lit. Ind. An indicator equal to 1 if a firm falls into the following SIC categories: 2826-2833, 3570-3577, 3600-3674, 5200-5961, 7370-7374, 8731-8734; and 0 otherwise.

NOA Net operating assets of the firm. [COMPUSTAT: (seq-che+lt+dlc)/revt]

Ln(Shares) Natural log of the number of outstanding shares of the firm. [COMPUSTAT: csho]

Implicit Ratio of gross PPE to total assets. [COMPUSTAT: ppegt/at]

Num. Analysts The number of analysts reporting a forecast for the firm. [IBES: numest]

Analyst Dispersion Analyst EPS forecast dispersion, measured using standard deviation of analyst estimates scaled by firm share price. [IBES: stdev; COMPUSTAT: prcc_f]

24

Appendix B Multiple Imputation of Board Co-option Variable

As described in Section 3.1, we utilized “single imputation” to generate estimates of missing values

of board co-option. Specifically, we used average board co-option values for missing data in years 1993-

2014, and rolled forward 2014 values for each firm missing 2015 data. Because using single imputation can

introduce a potential selection bias into our data, we also employ a ‘multiple imputation’ approach as a

robustness check.

Our multiple imputation procedure utilizes a simulation-based technique that runs n iterative model

estimations using simulated data for missing values. The simulated data reflects available information about

the joint distribution of missing values and other available data in the panel (i.e., mean, variance, and

covariance). Results from the n individual models are then combined to reflect the missing data uncertainty

through variation in estimates (Rubin, 1987). The purpose of multiple imputation is not to estimate a

specific value for missing data (e.g., Van Buuren, 2007; Johnson and Young, 2011; White et al., 2010), but

instead generate regression estimates that account for the uncertainty caused by missing data. This approach

alleviates any selection bias that may be introduced by our single imputation technique.

Multiple imputation requires three basic steps: 1) imputation of n generated data sets, 2) estimation,

and 3) combining/pooling the n generated analyses. We select n=17 as our number of imputations, following

the extant statistics literature (Rubin, 1987; Schafer, 1999; Van Buuren et al., 1999). Conceptually, if a

dataset was missing 50% of the observations for empirical testing, the efficiency of an n=10 imputation

procedure would produce estimates that are within about 2.5% of the standard deviation generated by a

procedure using n=∞.11 Our sample size prior to employing our single imputation procedure in our main

analyses is 20,220. Multiple imputation increases our sample size to 20,220. Without imputation (single or

multiple), 6,775 observations are omitted because of missing data on board co-option (i.e., 34%). Therefore,

11 Schafer (1999) describes that the efficiency of an estimate utilizing n=∞ imputation is √1+λ/n

-1, where # is the proportion

of data missing and n is the number of imputations. In our example above, √1+.5/10 -1

=0.976, or less than 2.5% of the procedure with n=∞.

25

selecting n=17 will produce a conservative estimate that is within less than 1% of the standard deviation of

the same procedure with n=∞. Our estimation stage uses multivariate normal (MVN) regression-based

imputation model (Dyreng et al., 2017). After combining estimates, we compute an estimated adjusted R-

squared for each of our multiple imputation estimates following Harel (2009). This procedure 1) estimates

adjusted R-squareds from each of the n imputations, 2) takes the square root (adjusted R) for each model,

3) uses a Fisher r-to-z transformation for each adjusted R to a z value, 4) averages the z values, and finally

5) converts this average z back to an adjusted R-squared.

26

Table B.1 Robustness Check:

Multiple Imputation for Missing Values of Board Co-option & CEO Equity Awards

This table reports test results of OLS-LPM regression estimates using multiple imputation for missing values of Board Co-option and Log(CEO Equity Award). This expands the complete sample to 20,220, previously restricted to 17,726 because of missing values of Board Co-option. Column (1) replicates results from Table 3, Column (4); Columns (2) to (4) replicate Table 4, Columns (1) to (3), respectively; and Columns (5) to (8) replicate Table 5 Columns (1) to (4), respectively. Year and Industry Fixed effects based on 2-digit SIC code are included where indicated. The t-statistics calculated using clustered standard errors by firm are included in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10. Adjusted R-squared are calculated for each of our multiple imputation estimates using a Fisher r-to-z transformation following Harel (2009). For further details see Appendix B. See Appendix A for variable definitions.

Pre-SOX Post-SOX (1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES Log(CEO Comp.)

Log(CEO Salary)

Log(Var Comp.)

Log(CEO Equity Award)

Meet/Beat 0.01

Meet/Beat 0.01

Meet/Beat 0.01

Meet/Beat 0.01

CFO Co-option (0-3) 0.108*** 0.003 0.207*** 0.301***

-0.009 -0.017 -0.005

(5.39) (0.27) (5.61) (3.88)

(-0.77) (-0.64) (-0.38) CFO Co-option (0-3) x Pos.Disc.Acc

0.026** 0.074*** 0.007

(2.01) (2.65) (0.50)

CFO Co-option (0-3) x Pos.REM

0.021 -0.029 0.035**

(1.46) (-0.91) (2.15)

CFO Co-option (4+) 0.045 -0.033** 0.115** 0.123

-0.009 -0.023 -0.013 (1.59) (-1.99) (2.35) (1.12)

(-0.59) (-0.52) (-0.75)

CFO Co-option (4+) x Pos.Disc.Acc

0.023 0.060 0.016

(1.32) (1.21) (0.86)

CFO Co-option (4+) x Pos.REM

0.027 0.062 0.031

(1.35) (1.15) (1.47)

Pos.Disc.Acc 0.019*** 0.002 -0.017 0.007 (3.09) (0.21) (-0.73) (0.67)

Pos.REM -0.003 -0.018 0.026 -0.027** (-0.33) (-1.45) (1.00) (-2.11)

Board Co-option 0.217*** -0.043*** 0.320*** 0.419*** -0.038*** -0.038*** -0.030 -0.039*** (7.48) (-2.58) (5.86) (3.43) (-2.93) (-2.95) (-1.17) (-2.65)

Observations 20,220 20,220 20,220 11,874 20,220 20,220 4,853 15,367 Adjusted R-squared 0.550 0.567 0.407 0.581 0.066 0.066 0.066 0.067 Previous Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes CFO Co-option (0-3) - (4+) p-value

0.063*** (0.005)

0.036*** (0.003)

0.092** (0.016)

0.178** (0.026)

CFO Co-option (0-3) x Pos Disc. Acc. Pre- vs. Post-SOX

-0.067** (0.032)

CFO Co-option (0-3) x Pos REM Pre- vs. Post-SOX

0.064* (0.068)

27

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31

Table 1 Descriptive Statistics

Descriptive statistics for the regression variables. All continuous variables (except for board co-option) are winsorized at 1% and 99%. Unlogged variables are presented for informational purposes. See Appendix A for definitions of all variables.

N Mean Median SD P25 P75 CEO Comp. 17,726 5,045.731 3,185.220 5,498.724 1,504.924 6,356.250 CEO Salary 17,726 725.441 674.611 351.823 472.500 934.616 CEO Variable Comp. 17,726 4,309.088 2,495.049 5,251.700 971.372 5,501.428 CEO Equity Awards 10,295 3,357.566 2,044.019 3,839.889 783.900 4,544.072 Meet/Beat 0.01 17,726 0.218 0.000 0.413 0.000 0.000 Beat >0.01 17,726 0.491 0.000 0.500 0.000 1.000 CFO Co-option 17,726 0.673 1.000 0.469 0.000 1.000 CFO Co-option (0-3) 17,726 0.500 0.000 0.500 0.000 1.000 CFO Co-option (4+) 17,726 0.173 0.000 0.379 0.000 0.000 Board Co-option 17,726 0.482 0.444 0.310 0.222 0.727 Disc. Accruals 17,726 0.008 0.006 0.116 -0.034 0.051 Abn. Prod. Exp. 17,726 -0.056 -0.054 0.196 -0.170 0.045 Abn. Disc. Exp 17,726 0.007 0.011 0.229 -0.113 0.123 CEO Tenure 17,726 7.387 5.000 7.255 2.000 10.000 Rev.t-1 17,726 5,113.795 1,197.978 12,038.822 442.894 3,701.256 SP 500 Indicator 17,726 0.239 0.000 0.427 0.000 0.000 BTM 17,726 0.628 0.611 0.266 0.429 0.807 Return 17,726 0.160 0.100 0.503 -0.142 0.362 ROA 17,726 0.049 0.057 0.097 0.021 0.096 Leverage 17,726 0.500 0.502 0.216 0.347 0.636 CEO Equity Incentive 17,726 0.239 0.176 0.209 0.091 0.313 CFO Equity Incentive 17,726 0.114 0.085 0.102 0.043 0.154 Size 17,726 7.238 7.062 1.561 6.105 8.240 Sales Growth 17,726 0.100 0.074 0.227 -0.007 0.175 Lit. Ind. 17,726 0.348 0.000 0.476 0.000 1.000 NOA 17,726 1.060 0.866 0.741 0.590 1.253 Ln(Shares) 17,726 4.227 3.997 1.209 3.355 4.903 Implicit 17,726 0.484 0.577 0.362 0.265 0.774 Num. Analysts 17,726 10.229 8.000 7.360 5.000 14.000 Analyst Disp. 17,726 0.002 0.001 0.005 0.000 0.002

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Table 2 Correlation Table

Pearson and Spearman correlation matrix for regression variables. Spearman correlations are shown above diagonal and Pearson correlations below. Two-tailed p-values below 0.05 are bolded. All variables are as described in Appendix A. (N=17,726).

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (1)

Ln(CEO Comp.) 1.000 0.013 -0.057 -0.040 0.020 -0.021 0.058 0.089 0.128 0.693

(2)

Meet/Beat 0.01 0.013 1.000 0.025 -0.007 0.019 -0.074 -0.028 0.014 0.086 -0.015

(3)

CFO Co-option -0.055 0.025 1.000 0.361 -0.020 -0.060 -0.083 0.018 0.006 -0.110

(4)

Board Co-option -0.044 -0.008 0.355 1.000 -0.044 -0.064 -0.096 0.001 -0.017 -0.115

(5)

Disc. Accruals 0.024 0.015 -0.015 -0.034 1.000 0.135 0.215 0.042 0.222 0.039

(6) Abn. Prod. Exp. -0.007 -0.060 -0.057 -0.063 0.087 1.000 0.708 -0.057 -0.291 0.110 (7) Abn. Disc. Exp 0.061 -0.022 -0.086 -0.096 0.192 0.728 1.000 -0.013 0.017 0.186 (8)

Return 0.048 0.001 0.023 0.012 0.078 -0.062 -0.044 1.000 0.242 -0.008

(9)

ROA 0.133 0.070 0.010 -0.021 0.374 -0.248 0.095 0.215 1.000 -0.024

(10)

Size 0.679 -0.013 -0.100 -0.117 0.032 0.113 0.188 -0.069 0.041 1.000

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Table 3 Estimation of OLS Regressions for the Total CEO Compensation Model

This table reports estimation of OLS regressions for our CEO compensation model using a sample of S&P 1500 firms over the period 1993-2015. Following, Core, et al. (2008), all models report the pooled cross-sectional regression estimates for economic determinants of total compensation, augmented to include controls for firm risk and equity incentives. Column (1) reports the baseline determinant model for total compensation. Column (2) includes board co-option as a control variable based on data from Coles, et al. (2014). Column (3) includes CFO co-option as an explanatory variable. Column (4) splits our CFO co-option measure into newly co-opted CFOs (within 3 years of co-option) and long-term co-opted CFOs (4 plus years after co-option). Year and Industry Fixed effects based on 2-digit SIC code are included where indicated. The t-statistics calculated using clustered standard errors by firm are included in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10. See Appendix A for definitions of all variables. (1) (2) (3) (4)

VARIABLES Ln(CEO Comp.)

Ln(CEO Comp.)

Ln(CEO Comp.)

Ln(CEO Comp.)

CFO Co-option

0.100***

(4.77)

CFO Co-option (0-3)

0.109***

(5.18)

CFO Co-option (4+)

0.040

(1.37)

Board Co-option

0.209*** 0.207*** 0.211***

(6.11) (6.03) (6.14)

Ln(CEO Tenure) 0.037*** -0.002 -0.034*** -0.025** (3.29) (-0.21) (-2.64) (-1.98)

Ln(Rev.)t-1 0.430*** 0.431*** 0.431*** 0.430*** (36.50) (36.75) (36.76) (36.73)

SP 500 Indicator 0.052 0.056 0.054 0.056 (1.47) (1.61) (1.54) (1.59)

BTMt-1 -0.608*** -0.607*** -0.595*** -0.591*** (-11.47) (-11.51) (-11.30) (-11.20)

Return 0.193*** 0.193*** 0.190*** 0.188*** (13.15) (13.18) (13.01) (12.80)

Returnt-1 0.126*** 0.126*** 0.125*** 0.123*** (9.50) (9.51) (9.46) (9.35)

ROA -0.071 -0.051 -0.044 -0.046 (-0.66) (-0.48) (-0.41) (-0.43)

ROAt-1 -0.467*** -0.452*** -0.439*** -0.444*** (-4.54) (-4.41) (-4.28) (-4.33)

Leverage -0.033 -0.028 -0.030 -0.033 (-0.57) (-0.48) (-0.51) (-0.58)

Std. Dev. Operating CF 0.248 0.200 0.186 0.170 (0.79) (0.64) (0.60) (0.55)

Std. Dev. Revenue -0.453*** -0.461*** -0.466*** -0.467*** (-5.74) (-5.88) (-5.92) (-5.95)

Std. Dev. Sales Growth 0.351*** 0.336*** 0.325*** 0.320*** (7.21) (6.95) (6.74) (6.66)

CEO Equity Incentive -0.878*** -0.902*** -0.901*** -0.907*** (-8.77) (-9.04) (-9.06) (-9.15)

CFO Equity Incentive 1.708*** 1.718*** 1.773*** 1.812*** (10.02) (10.14) (10.38) (10.50)

34

[Table 3 Continued] Observations 17,726 17,726 17,726 17,726 Adjusted R-squared 0.547 0.550 0.551 0.552 Year FE Yes Yes Yes Yes Industry FE Yes Yes Yes Yes CFO Co-option (0-3) - (4+) 0.069*** p-value (0.003)

35

Table 4

Estimation of OLS Regressions for Components of CEO Compensation Model This table reports estimation of OLS regressions for testing the components of CEO compensation using a sample of S&P 1500 firms over the period 1993-2015. Column (1) reports estimates of the effect of CFO co-option on CEO salary using the full compensation model from Table 3, Column (4). Column (2) reports estimates of the effect of CFO co-option on CEO variable compensation. Column (3) reports estimates of the effect of CFO co-option on CEO Equity Awards for the sub-sample of firm-years with available data from 2006 to 2015. Fixed effects are included for year and industry (using 2-digit SIC code) where indicated. The t-statistics calculated using clustered standard errors by firm are included in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10. See Appendix A for definitions of all variables. (1) (2) (3) VARIABLES Ln(CEO Salary) Ln(CEO Var Comp.) Ln(CEO Equity Awards) CFO Co-option (0-3) 0.006 0.207*** 0.259***

(0.42) (5.25) (3.29) CFO Co-option (4+) -0.037** 0.106** 0.051

(-2.07) (2.07) (0.49) Board Co-option -0.023 0.345*** 0.277**

(-1.17) (5.77) (2.21) Ln(CEO Tenure) 0.142*** -0.084*** -0.136**

(16.27) (-3.75) (-2.52) Ln(Rev.)t-1 0.220*** 0.543*** 0.574***

(28.86) (27.40) (12.20) SP 500 Indicator -0.023 0.068 0.157

(-1.00) (1.26) (1.37) BTMt-1 -0.095*** -0.854*** -1.073***

(-3.05) (-8.51) (-5.34) Return 0.030*** 0.350*** 0.214***

(4.43) (13.03) (3.66) Returnt-1 0.013** 0.202*** 0.220***

(2.30) (8.69) (4.03) ROA 0.044 0.283 -1.106***

(0.79) (1.41) (-2.84) ROAt-1 -0.085 -0.582*** -0.566

(-1.60) (-3.03) (-1.54) Leverage 0.080** -0.031 0.232

(2.23) (-0.31) (1.13) Std. Dev. Oper. CF -0.046 -0.425 -0.760

(-0.25) (-0.76) (-0.62) Std. Dev. Revenue -0.290*** -0.631*** -1.614***

(-6.76) (-4.72) (-4.69) Std. Dev. Sales.Gro. 0.038 0.407*** 0.487*

(1.34) (4.73) (1.96) CEO Eq. Incentive -0.970*** -1.930*** -2.934***

(-11.73) (-8.78) (-7.33) CFO Eq. Incentive 0.692*** 2.968*** 4.918***

(6.59) (8.66) (7.26)

36

[Table 4 Continued] Observations 17,726 17,726 10,295 Sample Years 1993-2015 1993-2015 2006-2015 Adjusted R-squared 0.567 0.412 0.248 Year FE Yes Yes Yes Industry FE Yes Yes Yes CFO Co-option (0-3) - (4+) 0.043*** 0.101** 0.208**

p-value (0.001) (0.011) (0.016)

37

Table 5 Mechanisms: Co-option and Earnings Management to Meet/Beat Benchmarks and Regulatory Effects

This table reports test results of OLS-LPM regression estimates on whether a firm with a co-opted CFO beats analysts’ median consensus EPS forecast by $0.01 using earnings management. All regressions are estimated using a sample of S&P 1500 firms. Pos.Disc.Acc is equal to 1 if the value of Disc. Accruals>0, and 0 otherwise. Pos REM is equal to 1 if the value of both Abn. Disc. Exp>0 and Abn. Prod.>0, and 0 otherwise. Column (1) reports estimates of the effect of Pos.Disc.Acc and Pos.REM on just meeting or beating earnings benchmarks. Column (2) reports estimates of the effect of Pos.Disc.Acc and Pos.REM on just meeting or beating earnings benchmarks, after including measures of CFO co-option. Column (3) interacts our measures of CFO co-option with Pos.Disc.Acc and Pos.REM. Column (4) restricts Column (3) to the sub-sample years 1993-2001 (pre-SOX). Column (5) restricts Column (3) to the sub-sample years 2002-2015 (post-SOX). Coefficients of CFO co-option interacted with earnings management measures are tested across subsample and results reported at the bottom of the table. Year and Industry Fixed effects based on 2-digit SIC code are included where indicated. The t-statistics calculated using clustered standard errors by firm are included in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10. See Appendix A for remaining variable definitions. Pre-SOX Post-SOX (1) (2) (3) (4) (5)

VARIABLES Meet/Beat

0.01 Meet/Beat

0.01 Meet/Beat

0.01 Meet/Beat

0.01 Meet/Beat

0.01 CFO Co-option (0-3)

0.012 -0.011 -0.028 -0.005

(1.26) (-0.83) (-0.96) (-0.36) CFO Co-option (0-3) x Pos.Disc.Acc

0.028** 0.073** 0.012

(2.07) (2.34) (0.79) CFO Co-option (0-3) x Pos.REM

0.022 -0.035 0.037**

(1.39) (-1.02) (2.14) CFO Co-option (4+)

0.011 -0.005 -0.022 -0.006

(0.88) (-0.28) (-0.45) (-0.35) CFO Co-option (4+) x Pos.Disc.Acc

0.015 0.042 0.010

(0.80) (0.78) (0.49) CFO Co-option (4+) x Pos.REM

0.022 0.037 0.027

(1.07) (0.64) (1.20) Pos.Disc.Acc. 0.020*** 0.020*** 0.003 -0.018 0.008

(2.98) (3.00) (0.27) (-0.69) (0.70) Pos.REM -0.008 -0.008 -0.022* 0.029 -0.033**

(-0.88) (-0.87) (-1.70) (1.01) (-2.41) Board Co-option -0.042*** -0.043*** -0.043*** -0.038 -0.043***

(-3.07) (-3.09) (-3.08) (-1.45) (-2.82)

Observations 17,726 17,726 17,726 4,085 13,641 Adjusted R-squared 0.067 0.067 0.067 0.069 0.067 All Controls Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes CFO Co-option (0-3) x Pos Disc. Acc. Pre- vs. Post-SOX p-value

-0.061* (0.076)

CFO Co-option (0-3) x Pos REM Pre- vs. Post-SOX p-value

0.072* (0.057)

38

Table 6 Benchmark Falsification Tests: Beating Analysts' EPS Forecast by >1 Cent

This table reports results of OLS-LPM regression estimates on whether a firm with a co-opted CFO beats analysts’ median consensus EPS forecast by >$0.01 using earnings management. All regressions are estimated using a sample of S&P 1500 firms. Pos.Disc.Acc is equal to 1 if the value of Disc. Accruals>0, and 0 otherwise. Pos REM is equal to 1 if the value of both Abn. Disc. Exp>0 and Abn. Prod.>0, and 0 otherwise. Column (1) reports estimates of the effect of positive values of earnings management on just meeting or beating earnings benchmarks. Column (2) reports estimates of the effect of positive values of earnings management on just meeting or beating earnings benchmarks after including measures of CFO co-option. Column (3) interacts our measures of CFO co-option with positive values of earnings management. Column (4) restricts Column (3) to the sub-sample years 1993-2001 (pre-SOX). Column (5) restricts Column (3) to the sub-sample years 2002-2015 (post-SOX). Coefficients of CFO co-option interacted with earnings management measures are tested across subsample, with results reported at the bottom of the table. Year and Industry Fixed effects based on 2-digit SIC code are included where indicated. The t-statistics calculated using clustered standard errors by firm are included in brackets. Two-tailed p-values are reported: *** p<0.01, ** p<0.05, * p<0.10. See Appendix A for remaining variable definitions.

Pre-SOX Post-SOX (1) (2) (3) (4) (5)

VARIABLES Beat

>0.01 Beat

>0.01 Beat

>0.01 Beat

>0.01 Beat

>0.01 CFO Co-option (0-3)

0.001 0.011 0.035 0.004

(0.12) (0.69) (0.99) (0.23) CFO Co-option (0-3) x Pos.Disc.Acc

-0.026 -0.044 -0.020

(-1.50) (-1.22) (-0.98) CFO Co-option (0-3) x Pos.REM

0.013 0.056 0.001

(0.68) (1.36) (0.05) CFO Co-option (4+)

0.006 0.012 0.004 0.014

(0.34) (0.53) (0.08) (0.60) CFO Co-option (4+) x Pos.Disc.Acc

-0.010 0.001 -0.010

(-0.41) (0.01) (-0.38) CFO Co-option (4+) x Pos.REM

-0.003 -0.011 -0.005

(-0.11) (-0.14) (-0.17) Pos.Disc.Acc -0.012 -0.012 0.002 0.007 0.003

(-1.49) (-1.49) (0.19) (0.22) (0.21) Pos.REM 0.001 0.001 -0.005 -0.020 -0.006

(0.09) (0.10) (-0.31) (-0.58) (-0.36) Board Co-option 0.032** 0.031** 0.031* 0.014 0.035*

(2.00) (1.97) (1.96) (0.44) (1.95) Observations 17,726 17,726 17,726 4,085 13,641 Adjusted R-squared 0.034 0.034 0.034 0.039 0.036 All Controls Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes CFO Co-option (0-3) x Pos Disc. Acc. Pre- vs. Post-SOX p-value

0.024 (0.560)

CFO Co-option (0-3) x Pos REM Pre- vs. Post-SOX p-value

-0.055 (0.226)