Formal and Real Authority in Organizations: An Empirical Assessment ∗
Feng Li
University of Michigan
Michael Minnis
University of Michigan
Venky Nagar
University of Michigan
Madhav Rajan
Stanford University
May 2009
Analytical models such as Aghion and Tirole (1997) distinguish formal authority from real
authority: a manager could be formally responsible for a decision, but in reality may acquiesce to
her subordinate. Organizational theorists (Simon 1997) suggest that human communication
settings are best suited to uncover real authority. We use the extent to which CEOs speak in
conference calls to measure their real authority over top management. Using a large database of
firm conference call transcripts, we find that our real authority measure is distinct from the CEO’s
formal authority measures, and is significantly associated with organizational factors as
theoretically predicted. CEOs with real authority also receive higher wages. The joint tests of our
measure of real authority with real authority theories and labor market theories suggest that real
authority is a distinct organizational feature that is measurable in large samples.
∗
We thank seminar participants at the University of Michigan and the University of Southern California for their
comments. We also thank Paul Michaud for his significant assistance in programming and data management issues.
Formal and Real Authority in Organizations: An Empirical Assessment
“Scientia potentia est (knowledge is power).”
Francis Bacon, Meditationes Sacrae (1597)
Heather Bellini (UBS Analyst): Hi, good morning, everybody. I just wanted to ask a
question, Steve. Typically, revenue synergies in software deals have been elusive, at least
that's what us and the industry would at least remember. Can you talk with us a little
bit about the revenue synergies you would expect, why you would expect to get them,
and over what time frame we could expect to see them, have this play out? Thank you.
Steve Ballmer (Microsoft CEO): Yes. I am going to let Kevin take this.
Microsoft Corporation conference call on February 1, 2008. 1
1. Introduction
A key component of organizational design that has received significant research attention is
the allocation of authority to employees (Fama and Jensen 1983; Harris and Raviv 2005; Roberts
2007). To measure authority, empirical analyses have typically relied on organizational charts (orgcharts), surveys, job titles, or statements of job responsibilities. 2 Aghion and Tirole (1997) label all
these measures of delegation, decentralization, and management responsibilities as “formal
authority.” They then introduce an additional concept, “real authority”: complex management
decisions require subjective soft knowledge (Petersen and Rajan 1994; Stein 2002; Petersen 2004),
and the person with real authority is the person who possesses this knowledge. This person gains
real authority because subjective knowledge is not easily communicated and transferred through
the management chain, resulting in the individual with formal authority in many cases simply
“rubber-stamping” this person’s decision.
Aghion and Tirole (1997) derive several testable
predictions about real authority and how it differs from formal authority, and then reveal a critical
1
Source: http://www.microsoft.com/presspass/press/2008/feb08/02-01Transcript.mspx.
See, for example, Baiman, Larcker, and Rajan (1995), Nagar (2002), Aggarwal and Samwick (2003), Moers (2006),
Campbell, Datar and Sandino (2009), and Ortega (2009).
2
1
obstacle: “The key issue is, of course, the measurement of real authority” (p. 26, emphasis theirs).
This paper is an attempt to overcome that obstacle.
Aghion and Tirole’s (1997) concern is a substantive one: while org-charts, titles, and
formal job responsibilities are easily measured, it is difficult to extract via surveys or other means
the identities of those who possess subjective knowledge. The ambiguous, ineffable, soft nature of
subjective knowledge can cause survey responses to be unreliable and unverifiable (Bertrand and
Mullainathan 2001), and yet this knowledge is key to firm value (Li 2008; Petersen 2004; Tetlock,
Saar-Tsechansky and Macskassy 2008). Organizational theorists argue that human interactions
and communication channels such as meetings are the primary organizational mechanism by
which the management chain recognizes the presence of subjective knowledge and lets the owner
of this knowledge substantively influence the decision (Vancil 1978, Simon 1997). We therefore
focus on a publicly-available human communication setting, namely the setting of conference calls.
In addition to providing formal accounting and other firm performance reports, many
publicly traded firms host earnings conference calls each quarter during which management
describe the performance and strategy of the firm and field a dynamic question and answer session
with analysts (Kimbrough 2005). Conference calls are a significant source of information for
investors (Bushee, Matsumoto and Miller 2003, 2004), and analysts pay keen attention to softer
knowledge in these calls (e.g., Mayew and Venkatachalam 2008). We argue that conference calls
provide an excellent setting for measuring the extent to which real authority is spread across top
management.
Specifically, we propose that the extent to which the Chief Executive Officer (CEO) speaks
in these conference calls is a proxy for the extent to which he or she possesses subjective knowledge
2
about firm operations. Our assumption is that, unlike hard formal information, it is difficult to
prime a responder about subjective knowledge adequately in advance of a Q&A session. Further,
given the importance placed on the conference call by market participants, top management is
better off letting the person in the management team most comfortable with the relevant
subjective knowledge answer the analyst’s question (see the Microsoft quote above and the case
study in Appendix A). To the extent the speaker is indeed the possessor of subjective knowledge,
he by definition has real authority: another individual may have the formal authority, but has to
rely on the decision made by the person with the real authority. Knowledge thus confers power. 3
We obtain machine readable texts of 17,400 conference calls from 2003-2007 and measure
the amount of communication conducted by the CEO, relative to others in top management. We
hypothesize and find that our measure of the CEO’s real authority is related to its theoretically
predicted determinants. Specifically, we predict (based on prior literature) and find that the CEO
has more real authority when the non-CEO management team has weaker monetary incentives,
the urgency of decision-making is lower, the tasks considered are more important, less technical or
innovative expertise is required, and the span of the CEO’s control is smaller. Second, we find
that these results obtain after controlling for measures of the CEO’s formal authority and CEO’s
individual characteristics such as ownership, tenure, prestige, and overconfidence. Real authority
thus appears to be a distinct organizational feature.
Our predictions above are joint tests of our assertion that conference calls can measure real
authority and of theories of real authority such as Aghion and Tirole (1997) and Baker, Gibbons
and Murphy (1999). We therefore provide additional economic evidence on the validity of our
3
Even in academic settings, for example, live seminars with interactive Q&A are the primary organizational
mechanism to assess a researcher’s real contribution to a research paper.
3
measure of real authority. Under the assumption that well-functioning labor markets reward real
authority (Rosen 1982), we conduct a joint test of labor market efficiency and our measure of
authority. We show that wages are an increasing function of real authority, in that our measure of
CEO real authority is positively associated with the wages of the CEO relative to top management,
after accounting for a rich set of controls. Overall, we provide, to the best of our knowledge, the
first large sample tests of theories distinguishing real authority from formal authority.
The remainder of the paper is organized as follows. In the next section, we describe the
theory, the measure of real authority, and our hypotheses. In Section 3, we describe our data and
variable measurement, as well as provide a discussion of the descriptive statistics. In Section 4 we
discuss the results, and in Section 5 we provide the results of our robustness tests. Finally, we
conclude the paper in Section 6.
2. Hypothesis Development
2.1 Defining and Measuring Real Authority
In the course of their jobs, different employees in an organization interact with different
stakeholders, causing knowledge to be spread out across the firm (Roberts 2007). Managerial
decisions require this knowledge, leaving organizations with two design alternatives: decision rights
can be ceded to the subordinate personnel with the information, or knowledge can be transferred
from informed subordinates to those with the authority to make decisions (Jensen and Meckling
1992).
The first alternative faces control costs: a self-interested subordinate may make a
suboptimal decision from the firm’s perspective. The second alternative faces knowledge transfer
costs: it is costly — both in terms of cash costs and personnel time — to transfer information.
4
If knowledge can be codified and transmitted, the organization can make assessments of
knowledge transfer costs and delegate accordingly. And in fact, organizations do so with formal
org-charts, job design, and delegation choices. However, in addition to such formal or hard
knowledge, there is also a nebulous, shifting, dynamic, subjective (or soft) knowledge that is
equally important for decision-making and firm value (Li 2008; Tetlock, Saar-Tsechansky and
Macskassy 2008). More important, this subjective knowledge is more difficult to communicate
and distribute (Petersen 2004; Stein 2002; Petersen and Rajan 1994).
Most organizations
recognize and accommodate this subjective knowledge not by constantly shifting org-charts, but by
acquiescing to the decision suggested by the person with the knowledge. Petersen (2004) and
Petersen and Rajan (1994) provide several examples of subjective knowledge: a given employee may
have a good feel for the client’s prospects and needs, and thus may be best suited to build a
relationship with the client. Though the employee’s boss may be formally in charge of approving
loans to the client, he may acquiesce to the employee’s decision. This employee thus gains “real
authority” that is not evident in his or her formal org-chart position and job description.
The concept of real authority has been formalized both by game theorists (e.g., Aghion and
Tirole 1997; Baker, Gibbons and Murphy 1999) and organizational theorists. 4 In his classic
treatise, Simon (1997, Ch. VII) writes at length about the overlapping roles of formal authority
and informal influence, the end result of which, as Vancil (1978, p. 62) notes, is that in many cases
it is impossible to say which manager really “made” the decision. More important, however,
organizational theorists have mapped the channels through which subjective knowledge causes
4
To illustrate real authority as an equilibrium phenomenon, Baker, Gibbons and Murphy (1999) build a reputationbased repeated principal-agent model. In this model’s equilibrium, the uninformed principal never overturns the
proposal of the agent and the agent only proposes projects with high expected outcomes for the principal (i.e., does
not abuse the informal authority). By approving all proposed projects, the principal has thus ceded informal (or real)
authority to the agent.
5
ambiguity in the autonomy underlying org-charts. Both Simon and Vancil argue that the decisionmaking process in such settings is typically one of human sociality where managers talk and
communicate in formal and informal meetings and other social intercourse settings. In the course
of such communication processes, the management chain gauges and assesses the presence of
subjective knowledge, and the person with the knowledge is ultimately able to influence the
decision of the manager with the formal authority. As a result, communication processes in the
organization are intricately tied to decision-making processes (Simon 1997, Ch. VIII).
The above arguments suggest that a natural measure of real authority is to examine a
communication setting where the researcher can observe the flow of conversation and social
intercourse among management. 5 While much of the management’s social intercourse is private,
new media and new communications technology provide a clean setting for an external researcher,
namely conference calls. In particular, top management routinely participates in these calls. We
use this setting to discern how real authority is distributed among top management on the basis of
their speaking patterns.
Conference calls are an essential form of communication and disclosure between
management and capital providers for many public firms. 6 During the 1990’s and 2000’s, firms
5
The Wall Street Journal reports an interesting example of how the level of communication reflects the extent of the
real authority of an individual. James Lambright was the head of the U.S. Export-Import Bank (a position that reports
directly to the President of the United States) when he was asked to take the role of chief investment officer for the
Troubled Asset Relief Program (a position that reports to an assistant secretary of the Treasury). While this job
transition surely resulted in the loss of formal authority from an organizational chart perspective, Mr. Lambright
significantly increased his real authority becoming, according to the article, “one of the most powerful men in
American finance.” The article cites the extent of communication Mr. Lambright engages in during conference calls
with executives as the measure of power, noting that “top executives regularly call him and his team for advice.” While
Mr. Lambright is multiple layers down the org-chart from the Treasury Secretary, the executives seek to talk to the
individual with real authority (Solomon 2009).
6
As another example of the link between communication and real authority is the reported need for communication
between stakeholders and American International Group’s (AIG) three newest directors. When the U.S. government
took a substantial stake in AIG, it appointed three new directors to the company’s Board. Since their appointment,
The New York Times has reported that the three have “remained invisible to the public,” with little communication.
6
increasingly utilized conference calls as a broad, inexpensive, and timely disclosure forum (Bushee,
Matsumoto and Miller 2003). 7 Further, after the Securities and Exchange Commission enacted
Regulation FD in October 2000, most companies made their conference calls openly available,
rather than limited to participants who were invited by the firm to participate (Bushee, Matsumoto
and Miller 2004).
Research examining conference calls finds that these events are not simply a regurgitation
of other corporate communications (e.g., press releases), but rather provide new and separate
information. Bowen, Davis and Matsumoto (2002) find that conference calls improve analysts’
ability to accurately forecast firms’ earnings. Frankel, Johnson and Skinner (1999) and Bushee,
Matsumoto and Miller (2003) find altered trading patterns during conference calls, suggesting that
the market receives new information during the call. Kimbrough (2005) finds that conference
calls can mitigate investor under-reaction to earnings announcements which may be partly
responsible for the well documented post-earnings announcement drift. In sum, conference calls
are a high profile form of communication upon which future capital providers or even customers
form opinions.
What kind of information is released in conference calls?
While data such as firm
financial performance is discussed, recent research indicates that it is the more subjective
information (e.g., the tone of the discussion, the inflection of the executives’ voices, and how
management “feels” about achieving targets) that investors and analysts play close attention to;
after all, analysts already have the hard knowledge from the financial reports (Mayew and
This lack of communication has fostered concerns about who “really is in charge” of AIG. Interestingly, the article
reports that the three trustees will reportedly make “their public debut” in a forum very similar to the one in which we
examine in this study—the annual shareholder’s meeting (Andrews 2009).
7
Bushee, Matsumoto and Miller (2003) note that the increase in the number of conference calls prompted several
studies to examine the impact of conference calls on trading (Frankel, Johnson and Skinner 1999) and on analysts’
forecasts (Bowen, Davis and Matsumoto 2002).
7
Venkatachalam 2008). 8 As a result, we argue that it is in the management’s best interest to let the
manager with the subjective knowledge or the real authority field the questions. A firm where the
CEO is very “hands on” and has more real authority is more likely to field analyst questions; firms
with a “hands-off” CEO are more likely to let other managers speak. As an example, we provide a
case study of Google in Appendix A.
One potential concern with our argument is that we could be observing “communication
delegation,” as opposed to real authority delegation. However, in the view of organizational
theorists, subjective knowledge, real authority, and communication are all intricately connected: it
is the difficulty of transferring subjective knowledge that endows the owner of the knowledge with
real authority.
Communication can provide assurance to others that the speaker indeed is
comfortable with the nuances of the decision and his or her opinion should be followed. It is that
very nuance that management needs to communicate to the analysts in open-ended Q&A sessions.
Of course, the manager truly in charge can prime another manager to provide canned
information, but evidence such as Mayew and Venkatachalam (2008) and others indicates that
conference call listeners have a demand for soft information other than information easily
obtained in a press release.
For example, Cisco Systems, Inc. was recently criticized for a
conference call which had “a scripted feel” (Vance 2009).
Communication patterns of course have a whole host of other underlying determinants
in addition to real authority: the business model may not require much subjective knowledge,
some managers may be self-aggrandizing and wish to “hog the mike”, etc. Our hypotheses, which
we turn to next, attempt to recognize these underlying determinants as well.
8
For example, a study by the Association for Investment Management & Research found that 95 percent of analysts
and investors view the “conference call as the most important form of technology-aided communications between
public companies and the investment community” (Stewart 2002).
8
2.2 Hypotheses
We frame our hypotheses in the context of the real authority of the CEO relative to top
management. Broadly speaking, analytical studies such as Aghion and Tirole (1997) and Baker,
Gibbons and Murphy (1999) model a manager with the formal authority as the principal (in our
case the CEO) and a subordinate with the subjective knowledge as the agent (in our case the
remaining management team). In these models, the agents propose projects which the principal
must accept or reject.
The principal cannot interact with all the company’s stakeholders
personally; as a result much subjective knowledge about the projects resides with her subordinates.
The uninformed principal must then decide to invest resources in becoming informed. This is
costly, however, in that time and expense are required to gain this information, and further, the
potential for an informed principal to overrule the subordinate may reduce the subordinate’s
initiative to search for projects ex ante. Therefore, the extent to which the principal acquiesces to
the agent’s decision and expertise is the measure of the agent’s real authority. 9
These theoretical analyses generate a rich set of equilibrium results, some of which are
tightly tied to specific modeling assumptions, and others (in our opinion) that are more broadly
valid. We cull these results to generate a set of hypotheses that are a) broadly consistent with these
models, and b) empirically testable. Specifically, following Aghion and Tirole’s (1997) equilibrium
9
Despite their broader similarities, there are significant differences across the model setups. For example, Aghion and
Tirole (1997) argue that a principal can delegate formal authority to an agent, while Baker, Gibbons and Murphy
(1999) argue that “decision rights in organizations are not contractible” and, therefore, “formal authority only resides
at the top” of the organization (abstract). However, Baker, Gibbons and Murphy (1999) do suggest that there exists
“delegated authority” and “informal authority” within organizations. According to Baker, Gibbons and Murphy
(1999), delegated authority arises when an informed principal effectively promises not to overturn the decisions of the
subordinate, whereas informal authority arises when an uninformed principal “rubber-stamps” the subordinate’s
decisions. This notion of informal authority is similar in spirit to Aghion and Tirole’s (1997) real authority and results
in similar predictions, which we discuss in Section 2.
9
results, we predict an association between the extent of real authority that the principal retains and
the incentives of the agents, the decision urgency of the projects, the importance of the task
considered, the technical competency required, and the span of control of the principal. 10
Likewise, Baker, Gibbons and Murphy (1999) suggest similar situations in which the principal
must approve or veto proposals prior to being informed, such as projects in which the subordinate
has particular expertise, the outcome is inconsequential for the principal, or a decision is needed
quickly. 11 We discuss each of these factors in turn and then state our main hypothesis.
Incentives: Aghion and Tirole (1997) note that higher monetary incentives for the agent
may have two complementary effects on the agent’s real authority: 1) it will increase the probability
that the agent will recommend a good project, and 2) it will decrease the need for monitoring by
the principal and hence the probability that the principal will overrule the project proposal. These
incentives align the agent’s interests with those of the principal, reducing control costs. If the
agent’s monetary incentives are low, the principal is likely to retain real authority.
Decision Urgency: Another factor affecting the principal’s real authority is the time it takes
for the agent to effectively communicate the information to the principal (a form of high
knowledge transfer costs). The benefits to the principal to maintain real power over the decision
may be more than offset by the crucial time lost in the communication and decision process.
Task Importance: Ceding real authority to the agent creates control costs: the agent can
make suboptimal decisions from the perspective of the principal. When the suboptimal decision
10
Aghion and Tirole (1997) also predict that a CEO’s real authority will be lower when she has a reputation for
infrequently intervening in the decision process and when the agent has multiple principals. As we had difficulty
identifying proxies for these predictions, we do not test these in this paper.
11
Baker, Gibbons and Murphy (1999) present these as potential scenarios in which informal authority may arise, not
sufficient conditions for informal authority. In these situations in their model, if the agent’s incentive to abuse the
informal authority is too great then the principal may also choose to veto all projects, which would prohibit the agent
from acquiring informal authority.
10
may result in a sufficiently large negative outcome (i.e., control costs are high), the principal may
be unwilling to cede real authority. Therefore, the principal will be relatively more willing to cede
real authority on projects with less importance to the principal.
Technical Competency: The principal simply may not have the technical competence or
experience to fully understand the nature of the projects proposed by the manager. Instead, the
principal may be selected for her ability to effectively impact the marginal productivities of the
employees throughout the organization (Rosen 1982) — i.e., manage people and processes. The
principal thus may have lower real authority in firms which require a high level of technical
expertise (e.g., scientific innovation).
Span of Control:
A principal with broad control over productive labor is likely
overburdened with project proposals from subordinates. Increasing numbers of project proposals
by subordinates increases the knowledge transfer costs from the subordinates to the principal.
With each marginal project proposal, the average time spent on each project proposal decreases
resulting in the CEO being less informed about each project, on average. Therefore, increases in
span of control magnify the information asymmetry (i.e., increase knowledge transfer costs)
between the principal and the agent, thus endowing the agent with more real authority.
Drawing on our measure of CEO speaking as the proxy for her real authority, we combine
the above predictions into our first hypothesis:
H1:
The real authority of the CEO is lower in firms with higher monetary
incentives for subordinates, higher decision-making urgency, lower
shareholder communication importance, higher technical complexity, and
greater span of control.
11
An immediate alternative explanation to H1 is that what we are observing are variations
not in real authority but in formal authority. A talented subordinate who wants to rise through
the ranks may not be content with verbal CEO promises to acquiesce to the subordinate’s
knowledge and authority. He may want to see definitive formal limits on CEO power, which
ultimately seeps into conference call patterns. Likewise, a “hands on” CEO may wish to sidestep
internal top management dissent and challenges to her decisions, and seek authority over top
management through formal means. As a result, in models such as Aghion and Tirole (1997),
organizations employ both formal and real authority mechanisms to deal with economic forces.
We therefore need to control for measures of formal authority in H1.
We measure the CEO’s formal authority through job title concentration, including the
CEO’s position on the board and founder status. 12 In addition, CEOs derive formal authority or
power not only from formal channels but also from other sources such as ownership and prestige
(Finkelstein 1992). We consider these attributes in development of our second hypothesis to
consider the possibility that other forms of CEO authority may be associated with a CEO’s real
authority. We motivate these additional measures next.
A significant stream of literature has argued that management gains power through
ownership (e.g., Cheng, Nagar and Rajan 2005). In addition, Finkelstein (1992) suggests that
CEOs also gain power through prestige.
Prestige is the “reputation in the institutional
environment and among stakeholders” (pg. 510) and bestows power upon the holder via perceived
and real reductions in uncertainty. Other managers and stakeholders view a CEO with prestige as
having power through perceived powerful connections; while prestige itself allows a CEO to make
12
We typically do not have access to specific CEO employment contracts wherein the scope of formal authority is
delineated.
12
connections with outside stakeholders and gain valuable information from outside the
organization. Thus, with perceived abilities to gain information and connections to actually
acquire information, stakeholders and other managers cede power to a more prestigious CEO.
Finally, a CEO can also acquire power through psychological and sociological means.
People tend to defer to psychological traits such as confidence, and recent literature has paid much
attention to CEO overconfidence and its impact on corporate decisions (e.g., Malmendier and
Tate 2005, Ben-David, Graham and Harvey 2007, and Goel and Thakor 2008). Likewise, a CEO
with a long tenure may know the sociological innards of the organization well enough to get what
she wants. We therefore account for CEO individual characteristics such as tenure and
overconfidence. 13 We now formally propose our second hypothesis:
H1a:
Hypothesis H1 holds after controlling for the CEO’s formal authority
metrics, CEO ownership, and CEO individual characteristics such as
prestige, tenure, and overconfidence.
Hypotheses H1 and H1a are joint tests of the analytical theories of real authority and our
assertion that the communication patterns of conference calls can capture real authority. To
further establish the validity of our measure of real authority, we turn to joint tests involving the
CEO labor markets and our measure of the CEO’s real authority.
Efficient labor markets should reward authority within an organization (Rosen 1982). This
equilibrium is an outcome of a matching process of talent and responsibility. Those with more
responsibility (or authority) have an impact on productive resources below them in the hierarchical
chain and, therefore, require more talent given the multiplicative impact of their decision and
13
In a perfect labor market, CEO characteristics should match perfectly with firm characteristics and there would be
little reason to expect any variation in the former after controlling for the latter. However, a significant body of
research (e.g., Bertrand and Schoar 2003, Bennedsen, Perez-Gonzalez and Wolfenzon 2007, and Kaplan, Klebanov
and Sorensen 2008) has concluded that CEO characteristics and abilities matter to firm performance and decisions.
13
actions. Therefore, we suggest that the CEO’s compensation relative to top management will
increase with her real authority. 14 Formally stated:
H2:
The compensation of the CEO is increasing in the CEO’s level of real
authority.
Because hypotheses H1 and H1a rely on a principal-agent paradigm, they apply naturally to
the CEO (the principal) and top management (the subordinates). By contrast, the labor market
joint tests can be applied to multiple managerial positions. In our conference call setting, the
manager who speaks the most other than the CEO is the Chief Financial Officer (CFO). Testing
hypothesis H2 with the CFO would further establish the validity of our conference call measure.
We formally state this as:
H2a: The compensation of the CFO is increasing in the CFO’s proportion of
communication on earnings conference calls.
3. Data and Variable Definitions
This section describes our data sources, variable definitions and descriptive statistics. Our
study uses data from multiple sources. We collect conference call data from transcripts compiled
by ThomsonReuters, firm financial and compensation data from Compustat’s Xpressfeed and
ExecuComp databases, respectively, stock return data from CRSP, and board of directors and
governance data from RiskMetrics (formerly IRRC). 15 We discuss each of the variables below,
focusing most of our attention on our conference call data as this data is the unique contribution
of our study. Appendix B provides descriptions and definitions for all variables used in our
analysis.
14
Note that our tests measure relative compensation (though we examine the level of CEO compensation as well). We
cannot test if the management team as whole is overpaid or underpaid relative to their marginal product.
15
We use company founding year data from Jovanovic and Rousseau (2001) as downloaded from Boyan Jovanovic’s
website: http://www.nyu.edu/econ/user/jovanovi/.
14
3.1
CEO’s Real Authority Measure
Our measure of the CEO’s real authority is the extent to which the CEO participates in
earnings conference calls. Because the variable has not been used in previous studies, we describe
our process for measuring real authority in detail. We obtained over 129,000 conference call
transcripts compiled from January 2001 to September 2008 by ThomsonReuters. Table 1 reports
our conference call selection process. From the original sample of conference calls, we ultimately
use the data from 17,400 transcripts. As Table 1 indicates, we discard conference calls that have
foreign text or are not related to earnings. 16 Earnings conference calls have the advantage in our
setting in that they generally follow a consistent format across firms: an opening dialogue by
company executives followed by a question and answer session between analysts and company
executives. Non-earnings related conference calls which we eliminate are generally presentations
made at conferences or are conference calls for special events, such as mergers and acquisitions.
We eliminate these transcripts to control for special one-time items and to ensure a consistent
format for the transcript. Further, because we require compensation data for our analysis, the most
significant single reason for eliminating conference calls from the sample is the lack of
ExecuComp data.
For each conference call, we use FORTRAN code to parse the text and identify the date of
the call, the name and ticker symbol of the firm, and whether the call related to an earnings
report. 17 In addition, each time a person spoke during a conference call, the transcript reports the
16
Our FORTRAN code is not able to process transcripts which include foreign characters. These firms would likely
have been removed from the sample because of missing compensation data anyway.
17
We received the transcripts in XML (Extensible Markup Language) formatting. XML provides “tags” to identify
elements of the transcript. In particular, we used XML tags to identify the company name, date of the conference call,
15
name and title of the individual who spoke. 18 We then determine the amount of speech (both the
number of times that a person spoke as well as the number of characters spoken) for each
individual on the conference call by title. Our measure of real authority is the amount of text
spoken by the CEO as a percentage of text spoken by all company personnel on the conference
call. 19
Our coding procedure eliminated any transcripts from our sample for which we could not
identify at least one speaker during the conference call. We could not identify speakers in the
transcripts for at least three reasons:
1) the transcript explicitly stated that a speaker was
unidentified; 2) our code was unable to properly parse the name and title for at least one speaker
(or the title that was parsed was insufficient to identify the individual’s position); or, 3) analysts
were not labeled with an “Analyst” title and, therefore, we could not identify them as such. 20 As
these reasons for omitting conference call observations should not be systematically related to our
analysis, our results should be unbiased, but may have reduced power. 21
type of conference call (e.g., earnings, conference presentation, etc.) and company ticker symbol. The main body of
the conference call text did not contain XML tags and was effectively a plain text document.
18
While the transcript reports the title of the individual speaking, there is by no means a consistent set of titles across
all firms. We identified over 16,000 unique titles for individuals who spoke at least once on the conference calls.
Because it is important to our study that the executive positions are identified correctly, we manually examined each of
the titles and grouped the more than 16,000 unique titles into one of nine major title groupings: CEO, CFO,
Operations, Functions, Investor Relations, Other, Analyst, Operator, and Unknown. As the CEO and CFO conduct
a majority of the conference call speaking, we focus our reported statistics on these two roles.
19
As we describe below, in addition to the amount of text spoken, we also considered the number of times that the
CEO spoke as well as a dummy variable indicating the presence of the CEO on the conference call. See Sections 4
and 5 for a discussion of the results using these measures.
20
In the conference calls which were parsed properly, analysts were identified in the transcript by their name, firm
name and the title of “Analyst.” However, conference call transcripts also identified analysts by their name and firm
name only, omitting the title of “Analyst.” Therefore, when the transcript was parsed, the title variable was missing
and we were unable to identify the position of the individual without examining each transcript manually.
21
To provide some assurance that the conference calls that we delete in this step are not biasing our results in some
manner, we compared the eliminated conference calls to the remaining conference calls two ways: 1) We compared
the average total length of the deleted and retained conference calls (total number of characters spoken by anybody on
the conference call). The deleted conference calls on average were about 460 characters longer than the conference
calls remaining in our sample; however this is only about a 1% difference which is likely immaterial. 2) We hand
collected a random sample of 20 conference calls that we deleted and processed these conference calls manually. The
16
The conference calls are typically quarterly events; however, all other variables that we use
in this study are measured on an annual basis. We therefore convert the conference call data to
annual observations by averaging across all conference calls for a firm within a fiscal year. 22 After
this procedure, there are 6,854 firm-year conference call observations. We further eliminate firmyear observations with insufficient data from all other data sources. 23
Finally, the unit of
observation in this study is a firm-level basis (rather than firm-year); therefore we average the
annual observations for all variables within a firm to derive 1,372 firm observations. 24 Ultimately,
we reduce the sample further to 1,142 firm observations after we require each firm to have a
minimum of 2 years of data.
3.1.1 Conference Call Descriptive Statistics
Table 2 reports the descriptive statistics for the 17,400 conference calls that we parsed
without issues. We present the data for all conference calls in Panel A, by year in Panel B, and by
distribution of the percentage of times that the CEO and CFO spoke during these conference calls is similar to the
distribution of conference calls that remain in our sample. The one noticeable difference between the deleted and
retained conference calls is that the average year that the conference calls occurred in the deleted sample is about one
and a half years earlier than the average year of the retained conference calls. Through conversation with
ThomsonReuters, this likely relates to a higher incidence of inconsistent transcript formatting issues in the earlier
years, which would cause parsing difficulties for our FORTRAN code. Therefore, our data may be somewhat skewed
toward recent years.
22
To be precise, we calculate the percentage of talking conducted by the CEO for each conference call within a year
and average this value across all valid conference calls in our sample for that year (there may be from one to four
usable conference calls in a given year). We assign any conference call occurring after the third month of the fiscal
year through the third month of the following year to that fiscal year—i.e., we assume that the conference calls occur
with up to a three month lag following the quarter close. Bowen, Davis, and Matsumoto (2002) find that 75% of the
conference calls in their large sample occur in the 9 day window surrounding the quarterly earnings announcement.
23
We matched each conference call to an ExecuComp firm based on the ticker symbol as reported in the conference
call transcript. This approach matched approximately 1,500 firms. We were able to match approximately 200
additional firms by hand. After the conference calls were matched to ExecuComp firms, the firms were matched to
Compustat (gvkey), CRSP (CRSP-Compustat link) and RiskMetrics (Cusip) datasets.
24
By averaging all variables across time for a firm, we are effectively using the “between” estimator. We take this
approach primarily because all variables have a high degree of persistence for a firm across years—i.e., these are firm
characteristics that persist over time and the main variation of interest is the heterogeneity between firms (therefore,
firm fixed effects regressions are not appropriate). Using annual observations and estimating clustered standard errors
at the firm level is an alternative to our approach. See Section 5 for our robustness tests.
17
Fama-French 12-industry grouping in Panel C. Several statistics are worth noting. First, the
variables appear to be well-centered as the mean and median are similar in magnitude across the
variables.
Figure 1 presents the distribution of the amount of text spoken by all company
personnel during the conference calls. The distribution is similar to a normal distribution with
the exception of a few outliers to the extreme right. Figure 2 presents the distribution of the
amount of speaking by the CEO as a percent of total company personnel speech.
This
distribution (which by definition is distributed between 0 and 1, inclusive) has fatter tails than the
normal distribution and also has a mass point at zero. CEOs are not present (i.e., do not speak)
for approximately 9% of the conference calls in our sample. A second item worth noting is the
significant role that the CFO plays in the conference calls. In fact, while the CEO is not present
on 9% of the conference calls, the CFO is not present on only 7% of the conference calls.
In Panel B we present the conference call statistics by year. The first item to note is the
distribution of our sample across years. Our sample is skewed toward more recent years for three
potential reasons: 1) the number of conference calls has increased over this time period; 2)
ThomsonReuters collected more conference calls in more recent years; and 3) the formatting of
the transcripts was more consistent in later years, allowing our code to more effectively parse the
text without error. A second observation to note is that the length of the conference call has
increased monotonically over the time period, while the number of analyst questions has remained
fairly constant. A final item to note in Panel B is that the role of the CEO has also increased over
the years. The percentage of CEO text has increased from 46% in 2003 to 50% in 2007. Most of
this increase in the mean likely comes from a higher rate of presence by the CEOs on the
18
conference calls. In untabulated results, we find that the percentage of conference calls attended
by the CEO increased from 86% in 2003 to 93% in 2007.
Finally, Panel C reports the conference call statistics by industry. Two items are worth
noting from this table. First, there is significant variation across industry type for not only the
length of the conference call, but also the extent to which the CEO participates on average. For
example, the Utilities industries have both the least amount of text spoken by company personnel
and the least amount of participation by the CEOs. In contrast, the Health industries have the
longest conference calls on average while CEOs play the most dominant role in the Manufacturing
industries. Second, the distribution of conference calls in our sample is concentrated in five
industry classes, which is consistent with the distribution of firms in the ExecuComp dataset.
3.2
CEO’s Formal Authority Measures
Formal authority resides with the individual who has the express right to make a decision
(Milgrom and Roberts 1992, Aghion and Tirole 1997). Ideally, this measure would be constructed
from the details of the compensation contract where contingencies are expressly spelled out.
Outside researchers are typically not privy to contract details beyond financial report disclosures.
Consequently, prior studies on CEO’s formal authority (e.g., Core, Holthausen and Larcker 1999;
Adams, Almeida and Ferreira 2005; Bebchuk, Cremers and Peyer 2008) have developed their own
proxies, and we borrow three of these measures. Our first measure of formal authority is an
indicator variable for a company founder CEO. We find that just over 12% of the CEOs in our
sample also founded the company.
The second measure is an indicator variable for title
concentration. A CEO who is the Chair of the Board of Directors as well as the President of the
19
company likely has more formal authority to make decisions. 25 49% of the CEOs in our sample
have concentrated titles. 26 Our third measure is an indicator variable for the CEO as the only
insider on the Board of Directors. Similar to the title concentration argument, if a CEO is the
only executive with formal ties to the Board, she likely has higher levels of authority over the other
executives. Table 3 reports that approximately 59% of the CEOs are the only insiders on the
Board. 27
3.3
CEO Characteristic Measures
As we discussed in Section 2.2, CEOs may derive power from various sources other than
formal channels. These include ownership of the firm, tenure, prestige and overconfidence. We
discuss our proxies for each of these constructs in this section. We calculate CEO ownership as
the total number of shares owned by the CEO divided by the total number of shares outstanding
for the firm (and because prior literature has found non-linear relations, we also include the square
of CEO ownership). CEOs in our sample own approximately 1.7% of their firms on average,
while the median is only 0.3%. As to tenure, we find that the average (median) CEO tenure is
approximately 8 (6) years. 28
25
In addition, we count a CEO who is not President as having title concentration if there is no other person with the
title President or Chief Operating Officer reporting to her (see Adams, Almeida and Ferreira 2005).
26
We also considered a variable for CEO duality; however, this variable is highly correlated with the CEO title
concentration variable (which is a subset of CEO duality), and so we selected only one of these variables.
27
We compare our CEO formal authority summary statistics to Adams, Almeida and Ferreira (2005), Table 1 and find
that our CEO Founder, CEO Title Concentration, and CEO Only Insider variables are all somewhat larger in
magnitude than their study. We suggest that much of this difference is a result of differences in our samples. The
Adams, Almeida and Ferreira (2005) sample is exclusively from the 1990’s and is restricted to a sub-sample of Fortune
500 companies. It is reasonable to expect that our sample includes more company founders (our sample is much
broader than the Fortune 500), higher title concentration (Rajan and Wulf 2006 find the title of COO diminishing in
recent years), and fewer insider directors other than the CEO (our sample is taken after the passage of the SarbanesOxley Act). There may also be the possibility that our measures are slightly inflated as described in Appendix B.
28
Again, we compare our CEO characteristic summary statistics to Adams, Almeida and Ferreira (2005), Table 1 and
find that our CEO Tenure and CEO Ownership variables are similar to their study.
20
Prestige is a more difficult measure to quantify. We proxy this construct using Fortune
magazine’s annual survey of the America’s Most Admired Companies. Fortune conducts this
survey on an annual basis by surveying management of Fortune 1000 companies to rank their peer
firms on eight dimensions, including quality of management. 29 While the ranking is particular to
a firm, we suggest that the CEOs of the companies that are ranked highly gain prestige. We
operationalize this measure by creating a dummy variable indicating that the firm is one of the top
5 firms in its industry in the survey. 30
Finally, we consider CEO overconfidence. Malmendier and Tate (2005) consider CEOs
whose wealth is overexposed to the idiosyncratic risk of their firms as being overconfident. Using
proprietary data, they create measures of overconfidence based on the extent to which CEOs wait
to exercise their options or excessively accumulate stock. Because this proprietary data does not
apply to our sample, we must proxy for the extent to which CEOs wait to exercise their options
using end of year holdings of exercisable options which remain unexercised scaled by the sum of
the value of unexercised exercisable options, unexercised unexercisable options and shares of
stock. This results in a percentage of value of the CEO’s holdings that are held in unexercised
exercisable options.
On average, 29% of the total value of the CEO’s holdings is held in
unexercised exercisable options.
3.4
Determinants of the CEO’s Real Authority
29
The other dimensions include innovation, people management, use of corporate assets, social responsibility,
financial soundness, long-term investment, and quality of products or services.
30
One potential issue with this measure is that Fortune only considers the largest 1,000 companies by sales and,
within this group of firms, only considers firms that are among the ten largest within their industry. While our sample
includes these firms, it also includes other firms that were not eligible for the survey. Therefore, this measure is biased
towards larger firms.
21
As discussed in previous sections, theoretical literature (e.g., Aghion and Tirole 1997)
suggests that the level of the principal’s real authority is affected by the incentive levels of the
agents, the degree of decision-making urgency, the extent of the agent’s technical competency or
expertise, the CEO’s span of control, and the importance of the task. We discuss our proxies for
each of these factors in this section.
Agents may be provided monetary incentives via various methods. Potential proxies for
examining agents’ incentives include bonus schemes (Holthausen, Larcker and Sloan 1995),
promotion opportunities (e.g., corporate tournaments as in Bognanno 2001), and stock-based
incentives. Bonuses are an ex-post measure of incentives and typically represent a small portion of
overall incentive compensation, while the incentive effects of promotion opportunities are difficult
to measure. Therefore, we choose the third of these alternatives and measure the extent to which
the wealth of the four highest paid executives other than the CEO is sensitive to a change in the
firm’s stock price. 31 The mean reported in Table 3 of 0.95 is similar in magnitude to prior studies
using this measure (e.g., Erickson, Hanlon and Maydew 2006, Table 4 32 ).
In constructing our proxy for decision-making urgency, we use the fact that managers in
highly competitive industries need to respond quickly to competitor actions. We use the degree of
product market competition as our proxy for urgent decision-making. To do so, we calculate the
31
More precisely, we measure the extent to which the executives’ firm-based wealth as reported in proxy statements is
sensitive to the change in stock price, as we cannot measure other outside wealth of the executives. We calculate these
sensitivities for stock and options separately. For stock, the sensitivity of the executive’s wealth to price changes is
simply dependent upon the number of shares held by the executive. The sensitivity of the value of the option
portfolio to a change in firm stock price is dependent upon the number of shares underlying the options and the
“delta” of the options (Core and Guay 2002). See Appendix B for the method we used to estimate this variable.
32
In comparing our value of this measure to Erickson, Hanlon and Maydew (2006) we note that the value of our
variable excludes the CEO, whereas the value in Erickson, Hanlon and Maydew (2006) includes the CEO. However,
their sample ends in 2002 whereas ours begins in 2003. We find that the value of this measure has increased during
our sample time period. Our calculation for the 2003 sample year only (and including the CEO) is similar to (and
more directly comparable to) Erickson, Hanlon and Maydew (2006).
22
Herfindahl index based on firm sales for each 4-digit SIC industry within the entire Compustat
Xpressfeed universe. Because this measure may be a noisy proxy for our underlying construct and
because we only want to capture those industries that are highly competitive, we create an
indicator variable that is set to 1 only if a firm’s industry is in the top decile of the Herfindahl
index across all industries. Table 3 reveals that approximately 39% of our sample firms are in a
highly competitive industry. 33
We use firm research and development (R&D) intensity to capture the notion of technical
competency. Theory suggests that a CEO has less real authority if she is less informed than the
agent (Fama and Jensen 1983, Dessein 2002, Harris and Raviv 2005). If a CEO is generally
selected based on her ability to manage the organization (Rosen 1982), then firms with a high
degree of innovation and technical complexity likely have technically-knowledgeable agents who
are more informed than the CEO regarding project selection (and possess soft information
regarding project selection criteria). We therefore proxy for the degree of technical expertise with
the level of R&D expenditure by the firm, scaled by total sales. Table 3 reports that our sample
firms spend 4% of sales on R&D activities on average. 34
Our next determinant is the span of control and the overload of the principal. Span of
control may be viewed specifically as a measure of the number of executives reporting directly to
the CEO (e.g., Rajan and Wulf 2006) or more generally as control over production labor (Rosen
1982). In either perspective, as the CEO’s span of control increases, she necessarily cedes more
33
Considering that only firms within the top decile of 4-digit SIC industries received an indicator value of “1”, 39%
may seem to be high number. However, by construct of the Herfindahl index variable, those industries in the top
decile generally include a higher number of firms; whereas those industries in the lower deciles include relatively few
firms.
34
If the R&D variable in Compustat has a missing value, we re-code the value to zero. Approximately 43% of our
sample report missing values for R&D and, including the missing values, approximately 55% report zero R&D
expenditures. These results are similar to the findings of Bebchuk, Cremers and Peyer (2008) and Coles, Daniel and
Naveen (2008).
23
real authority because she has a finite amount of time to monitor and review projects. Empirically
we take the latter view of span of control for data availability reasons. 35 We proxy for the span of
control and CEO overload using the number of employees in the firm—as the CEO’s breadth over
production labor increases, the CEO has less real authority as a result of overload. The average
firm has approximately 22,000 employees, while the median has approximately 6,000. Given the
skewness of this variable, we use the natural log.
Finally, we examine task importance. The conference call itself is an important task for top
management, for it is in important source of information for capital providers. However, the
conference call setting may be less relevant for highly regulated firms. Industries such as utilities
and telecommunications are scrutinized by federal, state and local regulators, whose substitute
monitoring and information collection role could diminish the benefit of the conference calls to
capital providers. In such settings, the CEO may leave the conference call activities to lower-level
management. Therefore, we use an indicator variable if the firm is in a Fama-French “Utilities” or
“Telecom” industry as our proxy for low task importance.
3.5
Firm Characteristics and Controls
We include a full battery of control variables that prior literature has associated with CEO
authority, compensation and firm performance.
Studies such as Yermack (1996), Core,
Holthausen and Larcker (1999), and Chhaochharia and Grinstein (2009) have shown that the
structure of the Board of Directors is related to firm performance and CEO compensation.
Following this literature, we use the Board size (number of directors), percentage of insider
directors (employees, former employees or relatives of employees), and percentage of outsider
35
We also examined the ratio of employees-to-sales and our results are unchanged using this measure.
24
directors over the age of 69 as Board structure variables. We find that the average Board size is 9.3
directors, which is smaller than the finding of Core, Holthausen and Larcker (1999), Table 1, and
Bushman et al. (2004), Table 2, but consistent with Chhaochharia and Grinstein (2009) who use a
more recent sample period similar to ours. 36 The percentage of insider directors is 28%, which is
also smaller than Core, Holthausen and Larcker (1999), Table 1, but is similar to Ryan and
Wiggins (2004), Table 2.
Adams, Almeida and Ferreira (2005) provide evidence that the concentration of power in
the CEO is associated with performance variability.
As such, we also include the standard
deviation of return on assets and stock returns for the five year window beginning four years
before the year of interest. To control for firm governance characteristics, we use the G Index from
Gompers, Ishii and Metrick (2003). Finally, we include control variables for firm size, growth, and
profitability.
3.6
Compensation Measures
Our first measure of compensation is the total compensation of the CEO divided by the
total compensation of the CEO and four highest paid executives other than the CEO. By scaling
CEO compensation by the top five executives’ compensation, we are attempting to control for
unobservable factors of the wage function that are firm specific. 37 We find that the CEO receives
38% of the compensation that is paid to the top five executives. This is similar to the amount
reported in Bebchuk, Cremers and Peyer (2008). Our second measure of compensation is the
36
The Core, Holthausen, and Larcker (1999) sample is from the years 1982-1984; the Bushman et al. (2004) sample is
from 1994; and the Chhaochharia and Grinstein (2009) sample is from 2000-2005.
37
Bebchuk, Cremers, and Peyer (2008) use this compensation variable as a proxy for “CEO Centrality,” or the
“relative importance of the CEO within the top executive team in terms of ability, contribution, or power” (abstract).
In contrast, we simply view this variable as a scaled version of CEO compensation which controls for unobservable
wage factors and can partially be explained by the extent of CEO delegation.
25
natural log of total CEO compensation. This wage-level specification assumes that we are able to
capture all factors associated with the wage function with control variables.
4. Results
4.1
Correlations
Table 4 provides the Pearson correlations for all variables. Because we have an extensive
set of variables, we present the correlations in two panels. Panel A reports the correlations
between CEO percentage text, our main measure of CEO real authority, and two other proxies:
the percentage of questions fielded by the CEO, and the presence of the CEO. The three
measures are highly correlated — in particular, there is little difference between the number of
times that the CEO speaks and the amount of text spoken.
Panel A also examines our
hypothesized determinants of the CEO’s real authority. Consistent with hypothesis H1, our real
authority measure is negatively correlated with equity incentives for other management, and with
product market competition, regulation, and levels of R&D. The correlations are statistically
significant for all variables except R&D/Sales. This indicates, at least at the univariate level, that
the variation in the CEO’s real authority is consistent with the predictions of analytical models of
real authority.
Panel B of Table 4 presents the correlations between our measure of CEO’s real authority
and all other variables. The most interesting finding is that that our CEO real authority measure
is not strongly related to formal measures of CEO authority; the correlation with CEO Only Insider
is the only statistically significant association. This provides some initial evidence that we are
measuring a construct distinct from that of formal authority.
26
4.2 Multivariate Results on CEO’s Real Authority
Table 5 provides multivariate tests of hypotheses H1 and H1a. In models (1) – (5) we test
each determinant variable separately and generally find evidence consistent with our prediction
H1. The CEO’s proportion of communication is negatively associated with non-CEO incentives,
decision urgency, task irrelevance, and technical knowledge. In model (5) we find a negative
coefficient on LN(Employees), but this is not statistically significant.
We then include all
determinant variables jointly in model (6) and find that all coefficients have signs in the predicted
direction and are all statistically significant except for decision urgency. In model (7) we test H1a
by also including our variables for CEO formal authority, CEO ownership, CEO tenure, prestige,
and overconfidence. Our results continue to hold. This provides significant evidence in favor of
the predictions of real or informal authority models such as Aghion and Tirole (1997) and Baker,
Gibbons and Murphy (1999).
We assess the robustness of the results in Table 5 to our CEO text measure. We find that
our results (untabulated) are not sensitive to using the number of times that the CEO speaks
during the conference call rather than the amount of text. We also confirm that using firm-years,
rather than firms, as the unit of observation does not change our inferences. 38 Therefore, Table 5
provides support of our joint hypotheses H1 and H1a that we are measuring real authority and
that the real authority of the CEO is lower in environments with high knowledge transfer costs or
low control costs.
Table 5 also highlights the relation between our measure of real authority and the
measures of a CEO’s formal authority and characteristics.
38
The coefficients on CEO Title
When using firm-years as the unit of observation, we estimate the standard errors by clustering at the firm level.
27
Concentration and CEO Only Insider are positive (though CEO Only Insider is not significant),
indicating that a CEO with more formal authority has more real authority. The coefficient on
CEO Founder is negative, but insignificant. This suggests that real authority may be more closely
linked to title or position based formal authority rather than a historical notion of authority. We
also note that both ownership and tenure have non-linear relations with the CEO’s real authority.
At lower levels, ownership has a negative relation with the CEO’s real authority, which would
appear to be the opposite direction as expected because presumably the task of communicating
with the capital markets would be more important for a CEO with higher levels of share
ownership. On the other hand, the CEO’s real authority is increasing (at a decreasing rate) with
the length of the CEO’s tenure. This seems to be a reasonable result assuming that the CEO
continues to build knowledge in the organization over time and, therefore, is less reliant on the
soft information of her subordinates. 39 Finally, we find that prestige is negatively associated with
the CEO’s real authority. This is an interesting result which may suggest that CEOs seeking
prestige do not do so through conference calls. 40 We conclude that our measure of real authority
is associated with formal measures of authority and CEO ownership, tenure, and prestige.
Because analytical papers such as Aghion and Tirole (1997) make a distinction between
real and formal authority, we further consider the nature of formal authority. We could not find
any prior empirical studies on formal authority that examined its determinants in a manner similar
to Table 5; therefore, we repeat the regressions from Table 5 using the same regressors but with
39
Interestingly, the relation between the CEO’s real authority and tenure reaches a maximum around 10 years and
then has a negative relation. At that length of tenure, the CEO may be more concerned about grooming the next
CEO and therefore begins speaking less, i.e., relinquishing real authority to her replacement.
40
This result may also be the outcome of our proxy choice for prestige. As we mentioned in Section 3, Fortune
magazine only covers the largest 1,000 firms. Therefore, our result may also be proxying for a dimension of firm size
not already modeled with the number of employees or assets.
28
formal authority proxies as dependent variables. Table 6 presents our results. Unlike our results
in Table 5 which indicate a consistently negative relation between CEO real authority and
predicted determinants, Table 6 reveals that the same does not obtain for measures of the CEO’s
formal authority. Only one coefficient is significantly negative in each specification and CEO Title
Concentration and CEO Only Insider have a determinant variable which is significantly positive.
These results suggest that our CEO real authority measure is separate from CEO formal authority,
and lend further support to our joint tests of the validity of our CEO text variable as a measure of
real authority and theories of real authority.
4.3
CEO’s Real Authority and CEO Compensation
Hypothesis H2 predicts a positive relation between CEO wages and real authority. We
regress CEO wages on our proxy of CEO real authority and various controls for the wage function.
We consider both scaled (by the total wages of the top 5 executives) and unscaled (natural log of
wages) CEO wages. We also examine total wages (including cash, stock, and option components)
and cash wages only (salary and bonus).
Table 7 presents the results of our analysis. As an initial assessment, in the first regression,
we only include our real authority measure and firm characteristic controls and find that the
coefficient on CEO real authority is strongly significantly positive; that is, compensation is an
increasing function of real authority. In model (2), as a baseline, we regress wages on measures of
the CEO’s formal authority, the CEO’s characteristics and firm controls and find results
consistent with prior literature (e.g., Core, Holthausen and Larcker 1999, Bebchuk, Cremers and
Peyer 2008, and Chhaochharia and Grinstein 2009).
29
Specifically, we find a strong positive
relation between CEO compensation and the title concentration of the CEO, the CEO as the only
insider, the CEO’s tenure length, firm size, profitability, and governance characteristics. CEO
compensation is negatively associated with the percentage of ownership the CEO has in the firm,
also consistent with prior findings. Growth is the only variable that is sensitive to the specification
of CEO compensation in that it is negatively associated with the scaled version of CEO
compensation, but positively associated with unscaled CEO compensation. Based on the results of
models (1) and (2), it appears that our measure of real authority is related to wages as predicted
and that our models are consistent with prior findings as well.
Models (3) through (6) combine our measure of real authority with the formal authority
measures, CEO characteristics and the full gamut of controls. In models (3) and (4) we use total
compensation (scaled and unscaled, respectively) and in models (5) and (6) we use cash
components of compensation only. Again, we find that our measure is positively associated with
CEO compensation, as predicted, and the coefficients on the remaining variables are consistent
with prior research. With these results, we confirm our H2 hypothesis and prior theoretical and
empirical literature that wages are positively associated with authority.
Table 7 also allows us to compare the magnitude of the impact of real versus formal
authority and other firm characteristics on wages. Using model (3) we calculate the impact on the
portion of compensation paid to the CEO of a one standard deviation change in various
independent variables. A standard deviation increase in the CEO’s real authority is associated
with an increase in the relative portion of compensation paid to the CEO of 1.5 percentage points
(or about a 4 percent increase relative to the mean value of 38%). This compares very similarly to
a one standard deviation increase in firm size which has an approximate impact of 1.7 percentage
30
points. By comparison, an increase in CEO’s formal authority (which is measured using indicator
variables, so the increase here is simply the value of the coefficient) is associated with an increase in
the CEO’s compensation portion of 2.0 and 3.0 percentage points for CEO Title Concentration and
CEO Only Insider, respectively. Thus, both real and formal authority appear to have significant
wage implications.
Table 8 provides a test of hypothesis H2a that CFO compensation should increase in CFO
real authority. 41 Table 8 shows that CFO compensation is consistently significantly positively
associated with the CFO’s real authority, though the economic magnitude is substantially reduced.
A one standard deviation increase in CFO real authority is associated with the CFO’s
compensation as a portion of the top five executives increasing by 0.3 percentage points.
In sum, Tables 5 through 8 provide evidence supporting the joint tests of real authority
models and our measure of real authority as well as joint tests of labor market models and our
measure of real authority. Real authority thus appears be an organizational feature distinct from
formal authority.
5. Additional Analyses and Robustness Checks
The predicted organizational determinants of CEO’s real authority in Section 2 are driven
by some aspect of information asymmetry among the firm’s employees. While we cannot measure
directly the information asymmetry inside a firm, we can do so for the firm’s shareholders.
Recognizing that external measures of information asymmetry are a poor proxy for internal
41
Note that we include variables for the CFO’s ownership and presence on the Board of Directors, but do not include
other variables that are included for the CEO either because of a substantial reduction of data (e.g., Tenure because
date of employment is frequently missing) or because the variable is not relevant for the CFO (e.g., Title Concentration
and Only Insider).
31
measures of information asymmetry, we conjecture that in firm environments in which
information asymmetry is higher, the association between CEO real authority and its hypothesized
the determinants should be stronger.
To test this comparative static of hypothesis H1, we
segregate our sample into low and high external information asymmetry groups. We create this
partition using two proxies for external information asymmetry: 1) the amount of price protection
by market makers proxied by the spread between the bid price and ask price of the firm’s stock,
and 2) the overall information environment proxied by stock return volatility. 42 In untabulated
results, we find that the coefficients are generally larger and more frequently statistically significant
in the sample of firms with high information asymmetry, reinforcing our main results.
We then reconsider the effect of the CFO on our CEO text variable.
In earnings
conference calls, the CFO’s portion of communication is substantial because financial results are
typically reviewed and discussed. As a result, the variation in the CEO communication patterns
driven by the CFO may not indicate differences in authority, but rather may reflect the importance
of financial communication. We therefore consider an alternative calculation of our measure of
the percentage of CEO communication in which we remove the CFO from the denominator. 43
Using this measure, the CEO speaks about 70% of the time. In Table 9, column (1) we re-execute
model (7) from Table 5, and find that our inferences remain virtually unchanged — only the
coefficient on LN(Employees) loses significance relative to the results in Table 5.
Next, we examine the potential influence of outliers in the determinants of authority
delegation. The unit of observation for our study is the firm-level. Because we average the
42
See, for example, Glosten and Milgrom (1985) and Kyle (1985) for the theoretical linkage between information
asymmetry and the bid-ask spread.
43
Specifically, the alternative measure is the amount of text spoken by the CEO divided by the total amount of text
spoken by company personnel other than the CFO.
32
variables across all available years for a particular firm and require at least 2 years of observations,
we did not winsorize the data, allowing, instead, the averaging process to ameliorate extreme
observations. However, we identified an extreme observation which may have undue influence on
the results. Microsoft Corporation’s Non-CEO Equity Sensitivity value is 223, which is about 8
times larger than the next largest observation. 44 Table 9, column (2) presents the results of Table
5, model (7) after removing the Microsoft observation.
The coefficient on Non-CEO Equity
Sensitivity remains positive, but is no longer statistically significant.
Our inferences of the
remaining determinant variables are unaffected.
In columns (3) and (4) of Table 9, we relax our requirement that each firm have at least 2
annual observations. 45 Column (3) indicates that our inferences are unaffected. Finally, instead of
using firm assets to proxy for firm size, we use firm sales. Column (4) presents the results using the
natural log of firm sales rather than firm assets. In this regression, Non-CEO Equity Sensitivity and
Product Market Competition are now significant, while the LN(Employees) is no longer significant at
traditional levels. Our hypotheses H1 and H1a on the theoretically motivated determinants of real
authority thus appear to be empirically robust.
Finally, we examine the number of business segments reported by the firm as an additional
construct of organizational design. Generally Accepted Accounting Principles (GAAP) “requires
that disaggregated information be presented based on how management internally evaluates the
44
This result is, of course, due to the fact that Bill Gates—who retains substantial shareholdings—is no longer the CEO,
but is one of the top 4 non-CEO executives.
45
When we relax the 2 year restriction, the sample includes an extreme observation for the variable R&D/Sales.
Arqule, Inc. conducted significantly more R&D activities than sales activities. As a result, the firm’s R&D/Sales value
is about 7 times larger than the next highest observation. In regressions (3) and (4) of Table 9, we removed this
observation.
33
operating performance of its business units” (Berger and Hann 2003, pg. 164). 46 We count the
number of segments each of the firms reports and use this as a proxy for the breadth of the
organization. 47 We consider that the number of segments that a firm operates (controlling for
firm size) may have counteracting forces on the CEO’s real authority. First, as the number of
operating units increases, the CEO’s span of control may be increased, thus reducing his real
authority over any specific unit as discussed in Section 2. Alternatively, the CEO may split the orgchart into many formal units to disperse formal authority across more and smaller units (recall that
the number of reported segments reflects the internal org-chart under SFAS 131). In this regard,
as the number of segments increases, each of the individual managers has less authority, while
concentrating the authority in the centralized CEO position.
To investigate this relation, we re-examine our results from Table 5, model (7) after
including a variable for the number of segments that the firm reports. Table 10 presents our
results. Columns (1) and (2) indicate that there is a significantly positive relation between the
CEO’s real authority and the number of segments, indicating that perhaps the CEO gains
authority as the firm becomes more dispersed. In column (3) we also include the squared version
of this variable. The coefficient on the number of segments remains significantly positive, while
the coefficient on the squared term is significantly negative. Our inference is that the CEO may
initially gain real authority by subdividing operating units but that eventually the effects of the
46
This statement from Berger and Hann (2003) specifically relates to the requirements of SFAS 131. Prior to SFAS
131, which was passed in 1997, SFAS 14 was the ruling standard. SFAS 14 had a more strict definition of segments,
requiring firms to segment their information based on the industry level rather than internal org-charts.
47
We calculate this variable using the number of segments that the firm reports as tabulated in the Compustat
Segments file. For firms that report segments in more than one category (e.g., geography and business), we use the
largest number reported in any one category. In untabulated results, we find that firms report a mean (median) of 3.1
(3.0) reporting segments.
34
increased span of control dominate. 48 These results further support the notion of a connection
between organizational design and the CEO’s real authority.
6. Conclusion
This study uses the setting of firm earnings conference calls to investigate differences in
formal and real authority in organizations. We use the percentage of text spoken by the CEO
during these conference calls as a proxy for the real authority of the CEO over top management.
We find that our measure of real authority is separate from previously used measures of CEO
formal authority. Guided by prior theoretical literature, we predict that the CEO’s real authority
over top management will be affected by the incentive levels of the top management, the degree of
decision-making urgency, the importance of the task, the extent of the top management’s technical
competency or expertise, and the CEO’s span of control. Using our measure of the CEO’s real
authority, we find results consistent with these hypotheses. We then examine whether managers
with more real authority also receive higher wages. Such tests are also joint tests of our measure of
real authority and theories of efficient labor markets. Our results, again, are consistent with our
predictions: wages are positively associated with the amount of real authority maintained by the
executive.
Aghion and Tirole (1997) note that measurement difficulties are the reason why theory is
ahead of empirics in concepts such as real authority.
Soft concepts such as real authority,
influence, subjective knowledge, etc., manifest themselves in human interpersonal interactions
(Simon 1997), an activity that has historically been nonconforming to large-scale empirical analyses
48
Our results indicate that this switch occurs between 7 and 8 reporting units. As a specification check on the validity
of the nonlinear relation with the squared term, we added a cubed term. This term was insignificant.
35
by external researchers. The traditional research methodology therefore is laboratory experiments
where the subjects and their interpersonal interactions can be clearly observed. Recent advances in
media and computing technology, however, have put vast quantities of visual, audio, and textual
information on the Internet, opening up new methodologies and measurement techniques for
large-scale capture of soft information of human interpersonal interaction. This is precisely what
we do in our study to test analytical organizational theories.
36
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Appendix A: Google, Inc. Example
We provide a brief example to highlight the manner in which our measure may be effective in capturing the notion
of real authority. ∗ On August 1, 2008, Google, Inc. hired a new Chief Financial Officer from outside the company, Patrick
Pichette. Prior to arriving at Google, Mr. Pichette was the top operations executive at Canada’s largest phone company. He
was brought into Google to specifically ensure that the company maintained efficient operations and, according to Google’s
CEO Eric Schmidt, to review internal business plans “systematically, business after business.” Lashinsky 2009 documents
the following changes at Google since Mr. Pichette joined the company:
Since he started as CFO on Aug. 1, Google has shut down numerous projects, facilities, and perks, from the
seemingly trivial - an unneeded gourmet cafe at its headquarters, the annual companywide ski trip - to the
significant. The latter includes the termination of a major effort called Lively, a virtual-environment product
that mimicked Second Life, and the shuttering of a failed acquisition, dMarc Broadcasting, through which
Google had attempted to broker radio advertising. In January, Google publicized its first layoffs, the
termination of 100 recruiters made redundant because the company has dramatically reined in its hiring.
While Lashinsky (2009) also indicates that the aforementioned actions at Google may have occurred with an alternative
CFO, given management’s comments and Mr. Pichette’s background, it appears that he has begun to have a significant
impact on Google’s investment decisions.
Mr. Pichette has certainly made an impact on the conference calls. The chart below presents the percentage of
speaking by Google’s CFO. Δ The 2008 4th Quarter conference call was the first earnings conference call in which Mr.
Pichette spoke. During this conference call he spoke more text (35%) and more times (66%) than any other executive. In
reading the transcript of the conference call, we noted that Mr. Pichette had significant control over the dialogue, even at
one point “delegating” a response to the CEO, Eric Schmidt. While this example is only anecdotal, it provides an
illustration of why we suggest that there is a link between speaking on the conference calls and real decisions made within
the company.
Google
Percentage of Text Spoken by the CFO
40%
35%
30%
25%
20%
15%
10%
5%
0%
2004
2005
2006
∗
2007
2008
The source of information for this illustration is from Google, Inc.’s corporate website (www.google.com/corporate) and Lashinsky
(2009), an article posted to the CNN Money website http://money.cnn.com/2009/03/17/technology/lashinsky_google.fortune
/index.htm.
Δ
Mr. Pichette started at Google on August 1, 2008. Upon review of the 3rd Quarter 2008 conference call, Mr. Pichette did not speak.
Because our dataset is missing a few non-4th quarter conference calls for Google during these years and to mitigate any seasonality
concerns, we present 4th quarter conference calls only (which typically occur in January the following year). However, the conference
calls which are not included in the graph have the same downward trend prior to Mr. Pichette’s arrival at Google.
42
Appendix B: Variable Definitions
This appendix describes the data source and measurement of each variable used in our study. All data is for the years 2003 – 2007. We collect conference call data
from earnings conference call transcripts compiled by ThomsonReuters. The data for all remaining variables is sourced from Compustat, ExecuComp, CRSP, and
RiskMetrics (formerly IRRC) datasets. We average the annual observations of each variable for a given firm across all available years within our timeframe.
Therefore, the unit of observation for each variable is the firm level, rather than the firm-year level.
(t = year, m = month, i = firm, j = executive)
Variable
CEO’s Real Authority
Percentage of CEO
Text
CEO’s Formal Authority
CEO Founder
Description
Formula
The ratio of the number of characters spoken by the CEO during the
conference call to the number of characters spoken by all company executives
during the conference call. This variable was created by parsing the text of
earnings conference call transcripts acquired from ThomsonReuters. See
Table 1 for a description of the conference call selection process. We create
an annualized version of this variable by averaging the data across all
conference calls within a fiscal year (we assume that conference calls that
occur up to 3 months after the fiscal year-end are associated with earnings for
that year). We multiply this variable by negative 1 such that larger values
indicate higher real authority at the subordinate level.
This is a dummy variable = 1 if the CEO was a company founder; and 0
otherwise. To determine if the CEO was a company founder, we first
established the date in which the CEO joined the company by using the
earlier of the becameceo and joined_co variables from ExecuComp. We then
established the year in which the company was founded by using the firm age
data used in Jovanovic and Rousseau (2001) as downloaded from Boyan
Jovanovic’s website: http://www.nyu.edu/econ/user/jovanovi/. This data
provides the founding year, incorporation year and exchange listing year for
approximately 7,700 firms. Because the founding year data is frequently
missing, to establish the first year for the firm, we first use the founding year,
then the incorporation year and finally the listing year. We then compare this
founding year to the year that the CEO joined the company. If the difference
between these years is less than 2, we then set the CEO Founder variable = 1.
As the founding year (instead the incorporation or listing year) is not used for
all firms, the CEO Founder variable may be overstated. On the other hand, if a
founding year was missing, then we assumed that the CEO was not a founder
to avoid dropping observations. This may potentially understate this variable.
43
⎛
⎜
CharactersSpokenCEO ,i ,t
∑t ⎜⎜ J
⎜⎜ ∑ CharactersSpoken j ,i ,t
⎝ j =1
=
T
=
∑ CEOFounder
i ,t
t
T
⎞
⎟
⎟
⎟
⎟⎟
⎠
CEO Title
Concentration
This is a dummy variable = 1 if the CEO is the Chair of the Board and the
President (or the CEO is the Chair of the Board and no other executive is the
President or COO); and 0 otherwise. To determine the titles of the
executives, we use the RiskMetrics database. This database contains all
company directors and identifies their position within the company if the
director is also an employee. Specifically, RiskMetrics has dummy variables
identifying the chairman, president, and coo. If the CEO is also identified as the
Chairman and President, or is identified as the Chairman and no other
director is identified as the President, then CEO Title Concentration = 1; and
is 0 otherwise. Using RiskMetrics may overstate this variable somewhat in the
case in which a company has a President, but the President is not also a
director.
CEO Only Insider
This is a dummy variable = 1 if the CEO is the only company employee on the
Board of Directors; 0 otherwise. The RiskMetrics database classifies all
directors as employee (E), linked (L) or independent (I). This variable is coded
as 1 if the CEO is the only employee (E) listed on the Board according to
RiskMetrics.
Determinants of CEO’s Real Authority
Non-CEO Equity
This variable measures the sensitivity of the wealth of the top 4 non-CEO
Sensitivity
executives to a 1% change in stock price. We measure this variable as the sum
of total shares held (shrown_excl_opts), total unexercised unexercisable options
(opt_unex_unexer_num) and total unexercised exercisable options
(opt_unex_exer_num) for all 4 top executives—excluding the CEO—times the
fiscal year-end stock price divided by 100. Before summing the three
variables, we multiply the options holdings by the estimated delta of the
options. This is necessary because options portfolio values are less sensitive to
changes in stock price than stock portfolios. We calculate the delta using the
Black-Scholes model. This model requires the following inputs (our
assumptions in parentheses): dividend yield (3-year average yield prior to year
of observation), expected volatility (standard deviation of stock returns during
prior 60 months), number of years until option maturity (assumed 5 years for
all options), current stock price (prcc_f from Compustat), strike price (assumed
to be the price needed to derive the intrinsic value reported in ExecuComp),
and risk-free rate (assumed the risk-free rate reported in ExecuComp). Note
that some prior studies have calculated separate deltas for newly granted
options and all other options; however, starting with fiscal year 2006,
ExecuComp no longer reports the expiration date of the newly granted
options, requiring us to make a similar set of assumptions for new and old
options so we aggregated all options. We recoded any missing ExecuComp
44
=
=
∑ CEOTitleConcentration
i ,t
t
T
∑ CEOOnlyInsider
i ,t
t
T
⎛⎛ 4
⎞
⎞
⎜ ⎜ ∑ ( shares j ,i ,t + (delta i ,t * options j ,i ,t )⎟ * Pr ice ⎟
⎜
⎟
⎜ j =1
⎟
⎠
⎟
∑t ⎜ ⎝
100
⎜
⎟
⎜
⎟
⎝
⎠
=
T
Product Market
Competition
Regulated Industry
variables to zero if missing.
This is a measure of the competitiveness within an industry, as proxied by
firm concentration. For each year and 4 digit SIC in Compustat, we calculate
the Herfindahl index based on the sales for each firm in the industry. The
Herfindahl index is calculated as the sum of the squared market shares of each
firm within the 4 digit SIC. For each year, we then create deciles of the
industry Herfindahl indices. The Product Market Competition variable is a
dummy variable = 1 if the industry is in the lowest (most competitive) decile;
and zero otherwise.
This is a dummy variable = 1 if the firm is in a Utilities industry (as defined by
the Fama-French 12 industry segmentation).
R&D/Sales
Research and development expenses (Compustat data item xrd) divided by
sales (Compustat data item sale). We re-code missing values of R&D to zero
to avoid significant observation losses.
LN(Employees)
The natural log of the total number of employees of the firm (Compustat data
item emp).
CEO Characteristics
CEO (CFO)
Ownership
CEO Tenure
Prestige
This is the percentage of outstanding company shares owned by the CEO
(CFO). We use the shrown_excl_opts variable in ExecuComp to identify the
number of shares held by the CEO (CFO) and the variable shrsout to identify
the total number of shares outstanding for the firm. This variable may
contain noise because the measurement of shrsout is at the end of the fiscal
year, whereas shrown_excl_opts may be measured at a date after the fiscal year
and before the proxy statement date. To incorporate the potential for nonlinear relations, we also use the squared version of this variable, denoted CEO
Ownership2 (CFO Ownership2).
This is the number of years that the CEO has been in office. We subtract the
becameceo variable in ExecuComp from the year variable and add 1 (to count
the first year in office as 1). To incorporate the potential for non-linear
relations, we also use the squared version of this variable, denoted CEO
Tenure2.
This is a dummy variable = 1 if the firm is listed as one of the top 5 companies
in its industry of Fortune Magazine’s annual America’s Most Admired
Companies survey.
45
=
=
∑ Herfindahl _ decile _ dummy
T
∑Utilities _ industry _ dummy
i ,t
t
T
⎛ R & Di ,t ⎞
⎟
⎟
t ⎝
i ,t ⎠
=
T
∑t LN (Total _ Employees) i,t
=
T
∑ ⎜⎜ Sales
=
=
=
i ,t
t
⎛ shrown _ excl _ opts CEO ,i ,t ⎞
⎟
⎟
shrsout
,
i
t
⎝
⎠
T
∑ ⎜⎜
t
∑ ( year
i ,t
− becameceoi ,t + 1)
t
T
∑ Top _ 5 _ Company
t
T
i ,t
Overconfidence
This measures the extent to which the CEO holds unexercised exercisable
options. Its measured as the intrinsic value of the unexercised exercisable
options divided by the value of the total holdings of the CEO, which is the
sum of the value of unexercised exercisable options, unexercised unexercisable
options, and shares of stock.
Firm Characteristics and Controls
Board Size
This is the number of directors on the Board of Directors. We count the
number of directors listed in the RiskMetrics database for the measurement of
this variable.
Percentage Insiders This is the percentage of insiders on the Board of Directors. The RiskMetrics
database classifies all directors as employee (E), linked (L) and independent (I).
The linked directors include former employees and family members. We
count any director that is an employee (E) or linked (L) as an insider.
Percentage of
This is the number of directors on the Board of Directors who are over the
Outsiders over Age age of 69 divided by the total number of directors. Director ages and the total
69
number of directors are taken from the RiskMetrics database.
LN(Assets)
The natural log of total company assets (Compustat Fundamentals Annual
dataset item at).
Growth
The year-over-year sales growth as defined by (salest – salest-1)/salest-1
ROA
Return on Assets as measured by Income before Extraordinary Items
(Compustat data item ib) divided by year-end assets (Compustat data item at).
Returns
Annual returns for the fiscal year calculated by compounding the monthly
returns from the CRSP monthly data file (variable ret in the CRSP file).
ROA Volatility
The standard deviation of annual ROA for the 5 year window from t-4 to t.
46
=
=
⎛ opt _ unex _ exer _ est _ valCEO ,i ,t ⎞
⎟
⎟
(
value
_
of
_
holdings
)
CEO ,i ,t
⎝
⎠
T
∑ ⎜⎜
t
∑ Number _ of _ Directors
⎛
=
i ,t
t
T
# _ of _ Insidersi ,t
∑ ⎜⎜ Total _# _ of _ directors
t
⎝
i ,t
⎞
⎟
⎟
⎠
T
⎛ # _ of _ Directors > Age69 ⎞
∑t ⎜⎜ Total _# _ of _ directors i,t ⎟⎟
i ,t
⎝
⎠
=
T
(
_
) i ,t
LN
Total
Assets
∑t
=
T
⎛ Salesi ,t − Salesi ,t −1 ⎞
⎟
∑t ⎜⎜ Sales
⎟
,
−
1
i
t
⎝
⎠
=
T
⎛ Income _ before _ Extraordinary _ Itemsi ,t ⎞
⎟
∑t ⎜⎜
⎟
Total _ Assetsi ,t
⎝
⎠
=
T
12
⎡⎛
⎞ ⎤
(ret i ,m ,t + 1) ⎟⎟ − 1⎥
∑t ⎢⎜⎜⎝ ∏
⎠ ⎦
⎣ m =1
=
T
⎛ Income _ before _ Extraordinary _ Itemsi ,t ⎞
⎟
∑t δ ⎜⎜
⎟
_
Total
Assets
i ,t
⎝
⎠
=
T
Return Volatility
The standard deviation of annual returns for the 5 year window from t-4 to t.
⎡⎛
=
Capex/Assets
Capital expenditures (Compustat data item capx) divided by total assets
(Compustat data item at).
G Index
The Gompers, Ishii and Metrick (2003) Governance Index provided by the
RiskMetrics database. This is a composite measure of 24 charter provisions.
Because this measure is not updated annually, for any year of missing data, we
use the previous year’s value.
This is a dummy variable = 1 if the CFO is on the Board of Directors; 0
otherwise according to the RiskMetrics dataset.
CFO on Board of
Directors
Number of
Segments
Compensation Measures
CEO-to-Top 5
Compensation
LN(CEO Total
Compensation)
We count the number of segments reported by the firm in the Compustat
Segments dataset. For firms that report by more than one segment type (e.g.,
business, geography, product, etc.), we use the segment type with the highest
number of reported segments.
The ratio of total CEO compensation to the top 4 highest paid executives plus
the CEO. Total compensation for the variable is the same as defined for the
LN(CEO Total Compensation) variable, using tdc1 from ExecuComp.
Natural log of CEO total compensation. Total compensation includes salary,
bonus, other annual compensation, restricted stock grants, long-term
incentive program payouts, and value of option grants (as determined by the
Black-Scholes formula) as reported by ExecuComp’s tdc1 variable. 1 is added
to the tdc1 value prior to taking the natural log of the variable.
47
12
⎞
⎤
⎠
⎦
∑ δ ⎢⎜⎜ ∏ (reti ,m,t + 1) ⎟⎟ − 1⎥
t
⎣⎝ m =1
T
⎛ Capital _ Expenditures
∑t ⎜⎜ Total _ Assets i,t
i ,t
⎝
=
T
=
=
=
∑ GIndex
i ,t
t
T
∑ CFO _ on _ Board _ dummy
t
T
∑ Number _ of _ Segments
t
T
⎛
⎜
tdc1
∑t ⎜⎜ 5 CEO,i,t
⎜⎜ ∑ tdc1 j ,i ,t
⎝ j =1
=
T
=
⎞
⎟
⎟
⎠
∑ LN (tdc1
⎞
⎟
⎟
⎟
⎟⎟
⎠
CEO ,i ,t
t
T
+ 1)
i ,t
i ,t
0
500
Frequency
1000
1500
2000
Figure 1: Distribution of Conference Call Length
0
20000
40000
60000
80000
Amount of Text Spoken by Company Personnel
100000
This figure shows the distribution of the number of characters spoken by company
personnel during the 17,400 earnings conference calls in our sample. Table 2 reports the
descriptive statistics.
0
500
Frequency
1000
1500
2000
Figure 2: Distribution of the Percentage of CEO Text
0
.2
.4
.6
Percentage of Text Spoken by CEO
.8
1
This figure shows the distribution of the amount of text spoken by the CEO as a percent
of total company personnel speech during the 17,400 earnings conference calls in our
sample. During approximately 9% of the conference calls the CEO was not present (or at
least did not make any comments). Table 2 reports the descriptive statistics.
48
TABLE 1: Conference Call Selection
Number of transcripts received from ThomsonReuters
(January 2001 - September 2008)
129,924
Transcripts which contain foreign characters
(23,981)
Not "Earnings" related
(24,840)
Do not have both a presentation section and a Q&A section
(4,185)
Not an ExecuComp firm
(44,235)
Unable to identify at least one speaker on the conference call
(10,705)
More than 1 CEO or CFO in the conference call identified
(642)
More than 1 conference call in a month
(493)
No data in Compustat
(26)
Keep only years 2003 - 2007
(3,417)
Eligible conference calls
17,400
Data for conference calls occurring the same fiscal year are averaged
within the year resulting in the following number of firm-years
6,854
Insufficient data in Compustat, ExecuComp or RiskMetrics
(2,446)
Eligible firm-years
4,408
All variables are averaged across time within each firm to derive
the following number of firm observations
1,372
Require a minimum of 2 annual observations per firm
1,142
This table reports the selection of conference calls. We received 129,924 individual conference call
transcript files from ThomsonReuters. We then used FORTRAN to parse these text files to determine
the amount of text spoken by each individual. We only kept conference calls which indicated that
they were related to earnings (as indicated in the header of the text file). Additional conference calls
were eliminated because of formatting issues with the text file or lack of data availability as described in
this table. The conference calls are generally held on a quarterly basis; however, because all other data
used in our study is on an annual basis, we average the conference call data within a year for a given
firm. We then create a firm level observation by averaging all annual values for a firm across all years
of data available. Finally, we require a minimum of 2 annual observations to be included in the
sample, reducing the number of firm observations to 1,142.
49
TABLE 2: Conference Call Descriptive Statistics
Panel A: All conference calls
Variable
Total Length
Total Comments
Analyst Questions
Percentage CEO Text
Percentage CFO Text
Percentage CEO Comments
Percentage CFO Comments
Mean
32,484
49
42
48%
33%
49%
32%
Median
32,412
46
40
51%
32%
52%
30%
Standard
Deviation
9,865
23
21
24%
20%
25%
22%
Total
Length
30,512
31,571
32,734
32,977
33,232
Total
Comments
47
49
50
49
49
Analyst
Questions
42
42
43
41
42
Text
CEO
46%
47%
47%
49%
50%
CFO
33%
33%
33%
32%
32%
Comments
CEO
CFO
46%
32%
46%
33%
48%
33%
50%
32%
51%
31%
Total
Length
33,669
32,752
31,002
30,970
33,576
32,923
33,799
27,454
33,415
34,326
31,889
33,306
Total
Comments
52
55
53
52
54
47
35
44
50
48
47
51
Analyst
Questions
45
47
46
47
48
40
28
38
42
40
40
43
Text
CEO
47%
50%
53%
50%
51%
52%
40%
36%
46%
44%
43%
50%
CFO
32%
32%
32%
19%
30%
33%
35%
42%
34%
32%
34%
33%
Comments
CEO
CFO
47%
32%
50%
32%
54%
31%
51%
17%
54%
29%
53%
32%
40%
33%
36%
41%
47%
34%
45%
31%
43%
34%
50%
33%
Minimum
1,998
1
0
0%
0%
0%
0%
Maximum
100,986
223
203
100%
100%
100%
100%
N
17,400
17,400
17,400
17,400
17,400
17,400
17,400
Panel B: By year
Year
2003
2004
2005
2006
2007
N
1,832
3,092
3,714
4,102
4,660
%
10.5%
17.8%
21.3%
23.6%
26.8%
N
%
5.3%
2.4%
13.1%
3.9%
2.8%
20.8%
2.2%
4.4%
12.1%
6.9%
13.9%
12.1%
Panel C: By industry
Industry
Non Durable Consumer Goods
Durable Consumer Goods
Manufacturing
Energy
Chemicals
Business Equipment
Telecom
Utilities
Shops
Health
Money
Other
922
415
2,284
683
480
3,620
387
771
2,102
1,203
2,419
2,114
This table provides descriptive statistics for the 17,400 earnings conference calls that were selected as described in Table 1. Total Length = the total number of characters (letters)
spoken by a company employee during the conference call; Total Comments = the total number of times that a company employee spoke during the conference call; Analyst Questions =
the number of times that an analyst spoke; Percentage CEO (CFO) Text = the total number of characters spoken by the CEO (CFO) divided by the Total Length; Percentage CEO (CFO)
Comments = the total number of times that the CEO (CFO) spoke divided by Total Comments.
50
TABLE 3: Descriptive Statistics
Variable
CEO's Real Authority
Percentage CEO Text
CEO's Formal Authority
CEO Founder
CEO Title Concentration
CEO Only Insider
CEO Characteristics
CEO Ownership
CEO Tenure
Prestige
Overconfidence
Determinants of CEO's Real Authority
Non-CEO Equity Sensitivity
Product Market Competition
Regulated Industry
R&D/Sales
LN(Employees)
Firm characteristics and Controls
Board Size
Percentage Insiders
Percentage Outsiders over Age 69
LN(Assets)
Growth
ROA
Returns
ROA Volatility
Return Volatility
Capex/Assets
G Index
Compensation Measures
CEO-to-Top 5 Compensation
LN(CEO Total Compensation)
Mean
Median
Standard
Deviation
Minimum
Maximum
N
Lag
Correlation
47.5%
49.0%
20.2%
0.0%
99.7%
1,142
0.77
12.2%
50.3%
59.4%
0.0%
50.0%
75.0%
31.2%
42.0%
42.6%
0.0%
0.0%
0.0%
100.0%
100.0%
100.0%
1,142
1,142
1,142
0.92
0.68
0.73
1.7%
8.07
0.18
29.0%
0.3%
6.00
0.00
26.3%
4.3%
6.54
0.33
22.6%
0.0%
1
0.00
0.0%
49.9%
44
1.00
97.0%
1,142
1,117
1,142
1,142
0.61
0.85
0.73
0.70
0.95
0.39
0.07
0.04
1.88
0.31
0.00
0.00
0.00
1.81
6.80
0.47
0.26
0.10
1.57
0.01
0.00
0.00
0.00
(4.38)
222.88
1.00
1.00
1.33
7.28
1,132
1,142
1,142
1,142
1,138
0.90
0.90
1.00
0.88
0.99
9.41
27.7%
9.7%
7.95
13.1%
4.9%
18.8%
4.3%
43.6%
0.04
9.48
9.20
26.3%
8.0%
7.77
10.4%
4.7%
16.3%
2.2%
34.3%
0.03
9.00
2.28
12.6%
9.7%
1.66
14.2%
6.8%
20.5%
7.9%
34.7%
0.05
2.50
4.67
6.2%
0.0%
3.62
-27.3%
-92.6%
-42.2%
0.1%
4.0%
0.00
2.00
22.60
100.0%
59.3%
14.23
195.6%
39.9%
145.1%
137.4%
419.3%
0.33
18.00
1,142
1,142
1,142
1,142
1,142
1,142
1,142
1,142
1,129
1,142
1,106
0.89
0.79
0.74
0.99
0.29
0.62
(0.01)
0.88
0.78
0.88
0.98
38.3%
8.12
38.4%
8.12
9.2%
0.96
0.0%
0.00
77.7%
10.82
1,142
1,142
0.40
0.73
This table presents the descriptive statistics for our measure of the CEO’s real authority (Percentage CEO Text) and formal authority, CEO characteristics,
determinants of the CEO’s real authority, firm characteristics and controls, and compensation measures. Lag correlation is the Pearson correlation between
the variable and its one year lag value for the pooled sample. See Appendix B for variable definitions.
51
TABLE 4: Pearson Correlations
Panel A: Correlation of CEO's Real Authority Measures with Determinant Variables
Percentage CEO Number
0.93
CEO Present
0.63
0.64
Non-CEO Equity Sensitivity
(0.10)
(0.10)
(0.15)
Product Market Competition
(0.12)
(0.12)
(0.05)
0.04
Regulated Industry
(0.15)
(0.15)
(0.06)
(0.02)
0.25
R&D/Sales
(0.00)
0.01
0.03
0.03
0.13
(0.12)
LN(Employees)
(0.24)
(0.25)
(0.18)
0.10
(0.15)
(0.03)
R&D/Sales
Utilities Industry
Product Market
Competition
Non-CEO Equity
Sensitivity
CEO Present
Percentage CEO
Number
Percentage CEO
Text
N = 1,128
(0.27)
Panel A presents the Pearson correlations between three potential variables which may proxy for the
extent of the CEO’s real authority: Percentage CEO Text (defined in Appendix B), Percentage CEO Number
(the number of comments that the CEO makes divided by the total number of comments made by all
company personnel), and CEO Present (a dummy variable = 1 if the CEO is present on the conference call
and 0 otherwise) and the variables which proxy for the determinants of the CEO’s real authority. Panel
B presents the Pearson correlations for our measure of the CEO’s real authority (Percentage CEO Text)
and measures of the CEO’s formal authority, CEO characteristics, firm controls, and compensation. See
Appendix B for variable definitions. Bold indicates significance at the 5% level.
52
TABLE 4: Pearson Correlations
CEO Founder
(0.02)
CEO Title Concentration
0.04
0.00
CEO Only Insider
0.10
(0.18)
CEO Ownership
(0.01)
0.27
0.04
(0.13)
CEO Tenure
(0.05)
0.47
0.16
(0.15)
0.43
Prestige
(0.22)
(0.08)
0.03
(0.07)
(0.09)
(0.05)
0.04
(0.13)
0.04
0.09
(0.31)
(0.15)
Overconfidence
Board Size
0.36
0.05
(0.23)
(0.11)
(0.00)
(0.15)
(0.13)
(0.15)
0.34
0.01
Percentage Insiders
0.03
0.20
(0.27)
(0.47)
0.26
0.21
(0.08)
(0.16)
(0.12)
Percentage Outsiders > Age 69
(0.04)
0.08
(0.04)
(0.03)
0.04
0.14
0.00
(0.05)
(0.00)
0.01
LN(Assets)
(0.31)
(0.06)
0.11
(0.07)
(0.14)
(0.13)
0.51
0.01
0.64
(0.18)
0.06
Growth
0.02
0.04
0.03
(0.05)
(0.02)
0.06
(0.03)
0.07
(0.09)
0.07
0.09
(0.04)
ROA
0.00
(0.03)
(0.05)
(0.06)
(0.01)
0.06
0.12
0.18
(0.01)
(0.03)
0.01
0.00
(0.05)
Returns
0.02
0.03
0.04
0.03
(0.02)
0.01
0.02
0.11
(0.03)
(0.04)
0.02
0.01
0.27
0.17
ROA Volatility
0.05
(0.01)
(0.05)
0.08
(0.03)
(0.04)
(0.11)
(0.05)
(0.20)
(0.01)
0.00
(0.22)
0.10
(0.27)
0.03
Return Volatility
0.07
0.08
(0.00)
0.06
0.05
0.07
(0.18)
0.00
(0.31)
0.04
(0.02)
(0.29)
0.16
(0.16)
0.21
0.44
Capex/Assets
(0.03)
(0.01)
0.01
(0.00)
0.01
0.05
(0.02)
0.01
(0.13)
0.03
0.06
(0.15)
0.14
0.21
0.03
(0.03)
0.01
G Index
0.02
(0.09)
0.13
0.12
(0.15)
(0.12)
0.03
0.06
0.24
(0.22)
(0.08)
0.13
(0.08)
(0.00)
0.08
(0.10)
(0.17)
0.14
(0.08)
0.20
0.22
(0.19)
(0.05)
0.04
0.15
0.07
(0.21)
(0.01)
0.12
(0.06)
0.11
0.06
(0.05)
(0.11)
0.02
0.15
(0.14)
(0.07)
0.14
0.04
(0.25)
(0.11)
0.42
0.18
0.38
(0.23)
0.03
0.65
0.02
0.18
0.11
(0.08)
(0.14)
(0.06)
0.12
CEO-to-Top 5 Ratio
LN(CEO Total Compensation)
CEO-to-Top 5 Ratio
G Index
Capex/Assets
Return Volatility
ROA Volatility
Returns
ROA
Growth
LN(Assets)
Percentage Outsiders
> Age 69
Board Size
Overconfidence
Prestige
CEO Tenure
CEO Ownership
CEO Only Insider
CEO Title
Concentration
CEO Founder
Percentage CEO
Text
N = 1,077
Percentage Insiders
Panel B: Correlation of All Other Variables
(0.03)
0.53
Panel A presents the Pearson correlations between three potential variables which may proxy for the extent of the CEO’s real authority: Percentage CEO Text (defined in Appendix B),
Percentage CEO Number (the number of comments that the CEO makes divided by the total number of comments made by all company personnel), and CEO Present (a dummy
variable = 1 if the CEO is present on the conference call and 0 otherwise) and the variables which proxy for the determinants of the CEO’s real authority. Panel B presents the
Pearson correlations for our measure of the CEO’s real authority (Percentage CEO Text) and measures of the CEO’s formal authority, CEO characteristics, firm controls, and
compensation. See Appendix B for variable definitions. Bold indicates significance at the 5% level.
53
Table 5: Determinants of the CEO's Real Authority
Percentage CEO Text
Independent variables
(1)
Determinants of CEO's Real Authority
Non-CEO Equity Sensitivity
(Incentive effects)
(2)
(3)
(4)
(5)
(0.185) ***
(6.83)
Product Market Competition
(Urgency effects)
(2.890) **
(2.22)
Regulated Industry
(8.379) ***
(Task importance effects)
(16.630) **
(Expertise effects)
(2.30)
LN(Employees)
(Span of control effects)
(0.170) ***
(0.128) ***
(6.11)
(5.96)
(2.008)
(2.079)
(1.38)
(1.42)
(1.56)
(10.870) ***
(4.21)
(4.61)
(18.770) **
(19.390) **
(2.49)
(0.790)
CEO's Formal Authority
CEO Founder
(7)
(9.580) ***
(3.92)
R&D/Sales
(6)
(1.612) ***
(2.85)
(2.54)
(1.288) **
(2.28)
(0.092)
(0.04)
CEO Title Concentration
2.997 *
CEO Only Insider
2.437
1.95
1.50
CEO Characteristics
CEO Ownership
(0.664) *
(1.89)
2
CEO Ownership
0.025 ***
CEO Tenure
0.494 *
CEO Tenure2
(0.024) **
Prestige
(4.774) **
Overconfidence
(0.151)
2.67
1.66
(2.38)
(2.25)
(0.05)
(Continued)
54
Table 5, (Continued)
Firm Characteristics and Controls
Board Size
(0.730) *
(0.716) *
(0.673) *
(0.747) **
(0.675) *
(0.687) *
(1.93)
(1.89)
(1.77)
(1.96)
(1.78)
(1.80)
(1.22)
Percentage Insiders
(0.021)
(0.031)
(0.041)
(0.035)
(0.034)
(0.044)
0.076
(0.43)
(0.65)
(0.86)
(0.74)
(0.73)
(0.92)
Percentage Outsiders over Age 69
(0.047)
(0.047)
(0.045)
(0.045)
(0.047)
(0.059)
(0.019)
(0.69)
(0.68)
(0.66)
(0.67)
(0.69)
(0.87)
(0.28)
LN(Assets)
(3.248) ***
(3.217) ***
(3.235) ***
(3.418) ***
(2.984) ***
(2.207) ***
(0.463)
1.47
(2.203) ***
(6.61)
(6.45)
(6.52)
(6.98)
(5.23)
(3.49)
(3.36)
Growth
0.970
1.256
0.034
2.167
(0.450)
(0.198)
(0.688)
ROA
(0.013)
(0.16)
(0.49)
(0.71)
(0.81)
(0.02)
(0.61)
0.03
Returns
0.022
0.021
0.029
0.015
0.020
0.012
0.012
ROA Volatility
(0.024)
(0.41)
(0.52)
(0.46)
Return Volatility
(0.017)
(0.016)
(0.024)
G Index
0.502 **
0.25
0.69
(0.98)
2.03
Intercept
76.790 ***
16.77
Industry Fixed Effects?
Heteroscedasticity Robust SE?
Adj. R2
N
0.32
0.01
(0.042)
(0.062)
0.67
0.92
(0.030)
(0.028)
(0.92)
(1.37)
0.447 *
0.519 **
1.82
2.09
78.270 ***
77.550 ***
17.17
16.92
0.55
(0.068)
0.48
0.019
0.35
(0.006)
(0.30)
0.455 *
1.84
79.210 ***
16.97
(0.12)
(0.05)
(0.18)
(0.002)
(0.050)
0.002
0.64
(0.024)
(0.40)
(0.018)
(1.01)
0.543 **
2.21
75.700 ***
15.74
0.38
0.39
0.031
0.026
0.58
(0.011)
(0.56)
0.584 **
2.39
73.380 ***
14.83
0.47
(0.008)
(0.44)
0.465 *
1.90
65.160 ***
11.64
N
Y
N
Y
N
Y
N
Y
N
Y
N
Y
N
Y
9.8%
1,092
9.8%
1,100
10.5%
1,100
9.7%
1,100
9.7%
1,096
12.1%
1,088
14.8%
1,067
This table presents the regression results examining the CEO’s real authority. In each model the dependent variable is our measure the CEO’s real authority,
Percentage CEO Text. Models (1) through (5) examine each of the determinants of the CEO’s real authority separately with firm controls. In model (6)—our formal
test of H1—we include all determinants of the CEO’s real authority. In model (7) we test H1a by including variables for the CEO’s formal authority and CEO
characteristics. See Appendix B for variable definitions. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.
55
Table 6: Analysis of the CEO's Formal Authority
CEO
Founder
Measures of Formal Authority
CEO Title
CEO Only
Concentration
Insider
Independent variables
(1)
Determinants of CEO's Real Authority
Non-CEO Equity Sensitivity
(2)
(0.012)
(Incentive effects)
Product Market Competition
(2.22)
1.533
1.098
0.157
(6.687) ***
(Task importance effects)
(2.69)
0.38
5.359
1.11
(10.300)
(7.801)
(0.65)
(0.53)
(Expertise effects)
LN(Employees)
(0.368) **
(2.42)
0.74
R&D/Sales
(0.390) **
(0.26)
(Urgency effects)
Regulated Industry
(3)
(0.938)
2.689 **
3.38
5.507
0.37
1.558
(Span of control effects)
(0.99)
CEO Characteristics
CEO Ownership
0.947
(0.294)
(0.702)
1.21
(0.44)
(1.01)
2
CEO Ownership
(0.018)
(0.69)
CEO Tenure
1.641 ***
3.75
CEO Tenure
2
0.008
0.55
2.46
0.06
15.270 ***
0.036 **
2.31
3.307 ***
6.55
(0.061) ***
1.41
0.029
1.28
(0.004)
(0.01)
(0.015)
(3.94)
(1.11)
(2.444)
(2.674)
Prestige
(5.038) *
(1.65)
(0.58)
(0.64)
Overconfidence
(3.110)
4.029
1.349
(0.79)
0.65
0.25
(Continued)
56
Table 6, (Continued)
Firm Characteristics and Controls
Board Size
(0.760)
(1.35)
Percentage Insiders
0.218 ***
Percentage Outsiders over Age 69
0.039
3.06
0.39
LN(Assets)
2.255 **
2.37
(2.410) ***
(3.25)
(0.951) ***
(4.049) ***
(6.00)
(1.543) ***
(9.71)
(15.89)
(0.288) **
(0.008)
(2.15)
2.820 **
2.39
(1.544)
(1.33)
Growth
(2.039)
ROA
(0.227) *
(1.68)
(3.23)
(2.39)
Returns
0.052
0.009
0.058
ROA Volatility
(0.064)
(0.66)
(1.98)
0.71
Return Volatility
0.039
0.038
0.007
G Index
(0.016)
(0.33)
1.06
1.19
(0.05)
Intercept
(17.740) **
(2.16)
Industry Fixed Effects?
Heteroscedasticity Robust SE?
Adj. R2
N
17.070 *
(0.07)
1.86
(0.554) ***
0.14
(0.286) **
0.88
1.630 ***
3.21
38.050 ***
3.39
(10.670)
(1.31)
(0.410) **
0.94
0.072
0.18
0.834 *
1.83
144.600 ***
14.72
N
Y
N
Y
N
Y
23.9%
1,067
16.9%
1,067
28.4%
1,067
This table presents the regression results examining three measures of the CEO’s formal authority. The
dependent variables (CEO formal authority proxies) are listed above each of the columns. We include our
measures for the determinants of the CEO’s real authority and CEO characteristics in each model. See
Appendix B for variable definitions. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.
57
Table 7: CEO Compensation Regressions
CEO-toTop 5
Total Compensation
CEO-toCEO-toTop 5
Top 5
LN(Compensation)
Cash Only
CEO-toLN(CompTop 5
ensation)
Independent variables
(1)
Determinants of CEO's Real Authority
Percentage CEO Text
(2)
(3)
8.654 ***
7.509 ***
5.70
CEO's Formal Authority
CEO Founder
4.76
(1.192)
0.55
(1.34)
2.049 ***
2.90
2.952 ***
0.816
1.53
0.739
1.764 ***
3.16
1.591 ***
2.185 *
1.71
(0.320)
(0.48)
1.276 ***
2.81
(0.087)
3.98
3.84
1.35
2.66
(0.463) **
(0.409) *
(0.472) *
(0.315) *
(2.12)
(1.88)
(1.78)
(1.78)
(1.01)
0.006
0.004
0.007
0.004
0.003
1.14
0.75
1.21
1.14
0.260 *
1.75
CEO Tenure
4.62
0.399
3.22
2
5.735 ***
(0.27)
3.110 ***
CEO Tenure
2.27
(0.288)
CEO Only Insider
CEO Ownership
3.162 **
(6)
(0.22)
2.323 ***
2
(5)
(0.237)
CEO Title Concentration
CEO Characteristics
CEO Ownership
(4)
0.233
1.63
0.198 **
2.11
0.258 **
2.53
(0.18)
(0.241)
0.57
0.151 *
1.94
(0.005)
(0.003)
(0.004)
(0.003)
(0.94)
(0.68)
(1.09)
(0.94)
(0.67)
(1.580)
(0.156)
(1.098)
(0.669)
Prestige
(2.015) *
(1.95)
(1.59)
(0.19)
Overconfidence
1.137
1.073
1.824
0.86
0.81
1.64
(Continued)
58
(1.38)
2.223 **
2.11
(0.002)
(0.85)
1.873 *
1.86
Table 7, (Continued)
Firm Characteristics and Controls
Board Size
(0.256)
(0.095)
(0.048)
0.186
0.091
(1.46)
(0.51)
(0.27)
1.29
0.67
(0.014)
(0.021)
(0.097) ***
(4.18)
(0.56)
(0.84)
(2.62)
(0.64)
(0.31)
Percentage Outsiders over Age 69
(0.012)
(0.021)
(0.018)
(0.006)
0.028
0.027
(0.44)
0.984 ***
3.79
(0.75)
(0.64)
0.788 ***
1.030 ***
2.59
3.32
18.25
(3.771) *
(3.728) *
1.994
Growth
(3.296)
ROA
0.099 **
Returns
0.029 **
ROA Volatility
0.045
1.42
1.74
1.75
Return Volatility
(0.020) **
(0.022) **
(0.021) **
G Index
0.282 **
(1.52)
2.44
2.02
(1.96)
2.32
Intercept
29.130 ***
10.57
Industry Fixed Effects?
Heteroscedasticity Robust SE?
Adj. R2
N
(0.29)
4.360 ***
(1.78)
(1.75)
0.131 ***
0.122 ***
1.24
0.180 ***
(0.012)
2.51
Percentage Insiders
LN(Assets)
(0.048) ***
0.321 **
1.27
0.387 *
1.69
(0.226)
(0.12)
0.102 ***
(0.005)
1.42
2.606 ***
12.97
1.020
0.59
0.101 ***
3.14
2.95
5.72
3.16
3.36
0.023
0.022
0.026
0.013
0.017
1.47
1.49
0.057 *
0.057 *
(2.25)
0.237 *
1.93
27.620 ***
9.53
1.59
0.076 **
2.48
0.009
(2.12)
1.20
0.199
0.108
1.63
1.13
22.370 ***
7.01
39.500 ***
13.10
1.05
0.025
0.99
(0.015) **
(2.40)
0.225 **
2.53
22.310 ***
9.03
1.06
(0.005)
(0.35)
(0.002)
(0.45)
0.177 **
2.25
41.540 ***
15.87
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
15.2%
1,100
17.0%
1,077
19.3%
1,077
59.8%
1,077
19.9%
1,077
48.4%
1,077
This table presents the regression results examining CEO compensation. In models (1) through (4) we use the CEO’s total compensation as the
dependent variable; while in models (5) and (6) we examine cash compensation (salary and bonus) only. In models (1), (2), (3), and (5) we scale the
CEO’s compensation by the compensation of the CEO and other top four executives; while in models (4) and (6) the dependent variable is natural log
of CEO’s compensation without scaling. We multiplied LN(Compensation) by a factor of 10 for easier readability of the coefficients in the tables. See
Appendix B for variable definitions. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.
59
Table 8: CFO Compensation Regressions
CFO-toTop 5
Total Compensation
CFO-toLN(CompTop 5
ensation)
Cash Only
CFO-toLN(CompTop 5
ensation)
Independent variables
(1)
Determinants of CFO's Real Authority
Percentage CFO Text
(2)
1.933 **
1.470 *
2.27
CFO Characteristics
CFO Ownership
(3)
1.75
6.830 ***
4.81
CFO Ownership2
(2.112) ***
CFO on Board of Directors
2.709 ***
(2.70)
3.83
CEO's Formal Authority
CEO Founder
(4)
1.226
1.34
6.022 ***
3.55
(2.373) ***
(2.69)
1.740 ***
2.62
(5)
2.079 ***
3.06
4.008 ***
3.71
(1.154) *
(1.93)
2.679 ***
5.59
(0.717)
(0.316)
0.160
(1.25)
(0.55)
0.40
CEO Title Concentration
(0.189)
0.290
CEO Only Insider
0.804 **
(0.51)
2.12
CEO Characteristics
CEO Ownership
CEO Ownership2
CEO Tenure
0.78
0.346
0.77
(0.329)
(1.33)
1.114 ***
3.82
2.371 ***
2.94
4.041 ***
3.62
(0.916)
(1.29)
1.934 ***
3.74
(0.267)
(0.67)
0.449 *
1.67
0.542 *
1.72
0.006
(0.045)
(0.047)
0.06
(0.49)
(0.59)
0.043
(0.001)
(0.001)
0.002
(0.001)
(0.46)
(0.42)
1.37
(0.90)
(0.072)
0.045
(0.132)
(0.90)
0.68
(1.98)
(0.62)
0.003
0.001
0.68
(0.030)
CEO Tenure2
0.003
1.08
(0.45)
1.33
0.50
Prestige
0.414
0.791
0.227
0.254
0.88
1.54
Overconfidence
0.733
1.574
(0.338)
0.776
1.05
2.27
(0.68)
1.51
(Continued)
60
(0.001)
0.65
0.62
Table 8 (Continued)
Firm Characteristics and Controls
Board Size
0.102
0.095
1.24
1.13
Percentage Insiders
0.021 **
Percentage Outsiders over Age 69
0.028 **
0.025 **
1.98
2.08
0.027 *
2.01
LN(Assets)
(0.555) ***
1.94
(0.414) ***
(4.84)
(3.09)
Growth
(0.303)
(0.119)
(0.26)
(0.10)
ROA
(0.018)
(0.022)
(0.75)
(0.84)
Returns
0.010
0.007
ROA Volatility
(0.037) *
(1.76)
(1.39)
Return Volatility
0.004
0.004
1.39
1.02
(0.027)
0.66
G Index
Intercept
0.76
Adj. R2
N
1.97
(0.018)
(1.31)
0.017
1.08
4.024 ***
27.65
4.218 ***
3.51
0.111 ***
4.44
0.032 ***
4.19
0.054 **
2.54
0.022 ***
3.99
(0.008)
1.84
0.005
0.015
0.52
0.018 *
1.79
(0.253) **
19.53
(0.56)
(0.019)
(1.27)
0.009 *
2.59
0.031 ***
1.75
5.41
0.000
0.73
0.01
0.001
0.005
0.35
(0.57)
1.12
(0.24)
20.04
1.37
0.050 ***
0.008
(0.22)
33.130 ***
2.512 ***
1.134
(0.011)
9.15
2.24
(0.440)
0.066
14.970 ***
1.61
0.024 **
(2.49)
(0.034)
16.850 ***
0.135 *
(0.13)
(0.013)
12.98
Industry Fixed Effects?
Heteroscedasticity Robust SE?
0.176 **
16.850 ***
14.07
1.29
0.092 **
2.04
36.940 ***
28.56
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
7.3%
1,080
11.9%
1,058
69.2%
1,058
15.1%
1,058
64.6%
1,058
This table presents the regression results examining CFO compensation. In models (1) through (3) we use the CFO’s total
compensation as the dependent variable; while in models (4) and (5) we examine cash compensation (salary and bonus) only. In
models (1), (2), and (4) we scale the CFO’s compensation by the compensation of the CEO, CFO and other top three executives;
while in models (3) and (5) the dependent variable is natural log of CFO’s compensation without scaling. We multiplied
LN(Compensation) by a factor of 10 for easier readability of the coefficients in the tables. See Appendix B for variable definitions. *,
**, *** indicate significance at the 10%, 5% and 1% levels, respectively.
61
Table 9: Robustness Tests of the Determinants of the CEO's Real Authority
Percentage CEO
Text w/o CFO
Percentage CEO Text
Independent variables
Determinants of CEO's Real Authority
Non-CEO Equity Sensitivity
(Incentive effects)
Product Market Competition
(Urgency effects)
Regulated Industry
(Task importance effects)
R&D/Sales
(Expertise effects)
(1)
(2)
(3)
(4)
(0.190) ***
(0.526) *
(0.286)
(0.358)
(6.88)
(0.87)
(1.20)
(2.644)
(2.105)
(1.898)
(1.44)
(1.44)
(10.570) ***
(10.890) ***
(1.40)
(9.363) ***
(1.79)
(2.999) **
(2.32)
(9.554) ***
(3.22)
(4.62)
(4.04)
(4.14)
(25.840) **
(19.200) **
(16.160) **
(17.490) ***
(2.52)
(2.42)
(1.280) **
(1.027) **
(1.35)
(2.26)
(2.01)
(1.03)
Control variable for firm size
Assets
Assets
Assets
Sales
N
1,065
1,066
1,204
1,204
LN(Employees)
(Span of control effects)
(2.49)
(0.976)
(2.66)
(0.687)
This table presents robustness tests of model (7) from Table 5. We made the following modifications to the regressions:
(1)
Same as model (7) in Table 5 except the dependent variable is the percentage of CEO text after removing the text
spoken by the CFO from the denominator of the calculation.
(2)
Same as model (7) in Table 5 except Microsoft has been removed from the sample. Bill Gates’ stock holdings create
a substantial outlier in the Non-CEO Equity Sensitivity variable.
(3)
Same as model (2) in this table except we do not require a minimum of 2 years of data. After we remove this
restriction, this introduces a significant outlier (Arqule, Inc.) for the variable R&D/Sales, which we remove.
(4)
Same as model (3) in this table except we use LN(Sales) as the firm size control variable instead of LN(Assets).
All control variables from Table 5 are included in the regression but are not reported for brevity. See Appendix B for
variable definitions. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.
62
Table 10: Number of Segments as a Determinant of the CEO's Real Authority
Percentage CEO Text
Independent variables
(1)
Number of Segments Variables
Number of Segments
(2)
0.818 **
2.37
(3)
0.795 **
2.28
Number of Segments2
1.980 **
2.53
(0.135) *
(1.85)
Determinants of CEO's Real Authority
Non-CEO Equity Sensitivity
(0.132) ***
(Incentive effects)
Product Market Competition
(6.40)
(6.35)
(6.30)
(1.501)
(1.419)
(0.70)
(0.99)
(0.94)
(10.060) ***
(10.670) ***
(10.910) ***
(Task importance effects)
R&D/Sales
(4.11)
(4.25)
(4.31)
(19.820) ***
(21.810) ***
(22.570) ***
(Expertise effects)
(2.64)
LN(Employees)
(Span of control effects)
N
(0.130) ***
(1.032)
(Urgency effects)
Regulated Industry
(0.131) ***
1,022
(2.88)
(3.02)
(1.029) *
(0.999) *
(1.77)
(1.71)
1,020
1,020
This table presents the results of model (7) from Table 5 after including the number of operating
segments reported by the firm. Model (1) excludes the LN(Employees), whereas Models (2) and (3)
include this variable. Model (3) also includes a squared term for the number of segments to
account for a potential nonlinear relation. All control variables from Table 5 are included in the
regression but are not reported for brevity. See Appendix B for variable definitions. *, **, ***
indicate significance at the 10%, 5% and 1% levels, respectively.
63