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Statistical model: Determinants of
executive compensation in listed US firms Introduction: The equation
below is a cross-sectional statistical regression model of the determinants
of executive compensation. It shows the variables that are important in
explaining the variation in executive compensation from firm to firm. The
applied variable abbreviations are explained briefly below followed by a link
with more detailed information about definition and theoretical justification
for the inclusion of the variable in the model. This particular model has
been estimated on the entire population of all listed firms in the
where,
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Dependent variable: |
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Variable name |
Elaborated variable definitions and theoretical justifications |
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ExeCompi,t Executive compensation |
A generic abbreviation for the
level of executive compensation measured at time t for firm i. One of the
following two specific measures is applied: 1) top executive compensation by the highest paid executive, and 2) executive team compensation by the sum
of compensation for all top level managers. Executive team compensation is
preferred, because the firm’s management is being executed by a team of
managers rather than a single person. Moreover, evidence from regressions of executive
compensation also favors executive team compensation because this measure
produces more significant parameter estimates and higher adjusted R squares
than similar regressions on top executive compensation. All executive data
are obtained from proxy statements pursuant to Section 14(a) of the US
Securities Exchange Act of 1934. The compensation data include all types of
base salary, bonuses, perquisites, restricted stock awards, and long-term
incentive plans (LTIP) except option based compensation and pensions. The
exclusion of stock options is important because as reported by Jensen, Murphy
and Wruck [2004] stock options represent about 50% of all CEO compensation
for the S&P500 companies in the years 2000, 2001 and 2002. Source: Thomson, One Banker. The SEC database. Proxy
statements. |
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Mechanical variables: The variables listed below are
called mechanical, because managers are paid according to compensation
contracts that increase their compensation if the stock return or the
accounting return increases whether or not the managers are responsible for
the increase in return or not. |
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Variable name |
Elaborated variable definitions and theoretical justifications |
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ROAi,t
Return on assets |
Return
on assets for firm i measured at time t. The preferred and applied return
measure is EBIT (earnings before interest and taxes) to total assets. Earning
on equity is not suited for regressions because equity very often is negative
making such earnings measures meaningless. Total assets are never negative;
however, it is an ultimo measure so the relevant timing is total assets the
previous year. In other words ROAi,t
= EBITi,t / Assetsi,t-1. EBIT is preferred for
three reasons. The first is that earnings need to be measured before interest
expenses because the numerator, total assets, represents investments by both debt
holders and equity holders. It should therefore include return for both kinds
of constituencies. The other reason is that taxes should be excluded from
earnings because they often involve industry subsidies and accumulated tax
benefits that would distort the true company performance if they where
included. The third reason is that EBIT is such a common and well known
earnings measure. There may therefore be more consensus about how to measure
EBIT making it a more reliable earnings measure. One argument in particular can
explain why ROA should determine executive compensation. The reason is that
bonuses in the managerial compensation contract depend on ROA or other
variables that are correlated with ROA such as profit margins or sales
growth. The intention of correlating compensation with ROA is to create additional
incentives for the manager to maximize profit. However, it should be noted
that even in the case where the managers exercise no effort or have no ability
to improve ROA their compensation will still be positively correlated with
ROA because ROA varies for other reasons than managerial effort and prudence.
This is why ROA is categorized as a mechanical variable; it varies positively
with managerial compensation whether or not the managers are excellent or
not. Source: Thomson, One Banker. The
SEC, CS, WS and EX databases. |
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ROSi,t
Return on stocks |
Return
on common stocks for firm i measured at time t. Stock return is measured as the
percentage gain or fall in the firm’s market capitalization plus dividend
yield. The use of market capitalization means that the measure is fully
adjusted for capital changes. Managerial compensation is
hypothesized to be an increasing function of the return on stocks (ROS). The
explanation is that an important part of the managers’ compensation consists
of awards of stocks and stock options and the value of these awards will tend
to be higher the higher the stock return. This is so because many firms award
a fixed quantity of stocks to the management, not a fixed dollar amount of
stocks. It is also possible that the compensation contract specifies that
more stocks are awarded the higher the realized market return. This would
further intensify the positive correlation between stock return and
managerial compensation. It should be noted that even if the managers
exercise no effort or have no ability to improve ROS their compensation will
still be positively correlated with ROS, because ROS varies for other reasons
than managerial effort and prudence. This is why ROS is categorized as a
mechanical variable in the compensation function. Source: Thomson, One Banker. The
SEC, DS, CS, WS and EX databases. |
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NoExei,t Number of executives in team |
This
is a simple count of the number of executive salaries that have been used to
calculate the dependent variable executive team compensation. The variable is
used to control for the fact that different firms report executive
compensation for a different number of top managers in their proxy reports. The
variable is not included in regressions using top executive compensation. It should be needless to say that
the more executive compensations that are added, the higher is the executive
team compensation. This is also why NoExe
is characterized as a mechanical variable. Source: Thomson, One Banker. The
SEC database. Proxy statements. |
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Common sense variables: The variables below are called
common sense variables, because the theory behind the inclusion of these
variables is predominantly based on classic efficiency arguments. |
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Variable name |
Elaborated variable definitions and theoretical justifications |
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Labori,t Number of employees |
Labor
is the number of employees in the firm. This number normally includes part
time employees. Only one database supplier, namely, Extel has specific
numbers on full time and part time employment. The measurement error from
pooling part and full time employment is unevenly distributed throughout the
sample because some industries relies more on part time employment than
others. Retail stores may for example have many part time employees so the
1.4 million employees of Wal Mart Stores Inc in 2002 may be inflated. Another
source of measurement error in this variable is that some firms report
average number of employees during the year and others report ultimo numbers. The number of employees is a
classic determinant of executive compensation. It should be positively
correlated and there are several explanations for this. 1) The marginal productivity theory: According to this theory it
is efficient for firms to pay their managers (indeed any employee) a salary
that is equal to their marginal productivity. Furthermore, managers should be
expected to have a much higher productivity than other workers because their
decisions affect the productivity of all who work below the managers. In
larger firms there are more people who work below the top management and as a
result the managerial productivity is much higher in large firms. In larger
firms it is therefore more important to attract the most competent managers
in the market for managerial labor, and consequently larger firms need to pay
higher executive compensations (for a detailed presentation of this theory
see Rosen [1982, 1992, Section 2]). 2)
The budget argument: Managers are paid more in firms with more employees
because it is economically possible to do so. After all, the cost of
management must be born by revenues generated from lover level employees. In
other words, more employees mean higher revenues and this makes it more
possible to pay higher compensations for the management whether or not they
are worth it. Source: Thomson, One Banker. The
CS, WS and EX databases. |
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MarkCapi,t Market capitalization |
The firm’s market capitalization is the market value of all
outstanding shares measured at year end (Market price year end * Common
shares outstanding). The most difficult measurement aspect of this variable
is to determine the correct number of common shares outstanding. This may
change during the year and the databases may not be up to date on this
information and it could be further complicated by the presence of several
types of common shares. Market capitalization determines executive
compensation for the same reasons as those mentioned for size measured by
number of employees. However, market capitalization captures a different
dimension of company size than labor does. Market capitalization says
something about the expected potential future size of the company from the
owners’ point of view, whereas labor captures a dimension of size that is
present in nature and that is known to be considered when setting the level
of executive compensation. Both of these dimensions should have a positive
impact on managerial compensation. Source: Thomson, One Banker. The
SEC, DS, CS, WS and EX databases. |
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Owi,t Executive & director ownership
in % |
Ownership
is measured by total percentage ownership by all executives and directors.
This is calculated as total shares held by executives and directors as a
group divided by common shares outstanding. A difficult measurement aspect of
this variable is to determine the correct number of common shares
outstanding. This may change during the year and the databases may not be up
to date on this information and it could be further complicated by the
presence of several types of common shares. Manager ownership is hypothesized
to be negatively related to executive compensation for at least two reasons. 1) The substitution argument: The
more a manager owns of the company the more income does the manager have from
dividends and capital gains and the less important is the income from
management compensation. Furthermore, managerial ownership includes all the
important incentive properties that can be found in managerial compensation.
Indeed, at 100% managerial ownership the managerial compensation become
completely redundant since any dollar that are paid as managerial compensation
simply reduces the manager’s gains from ownership with the same dollar
amount. This is ceteris paribus because there may be specific tax benefits
from doing it one way or another. 2)
The indirect size argument: It is a stylized fact that managerial
ownership is strongly and negatively correlated with company size and it is
also a stylized fact that managerial compensation is strongly and positively
correlated with company size. It is therefore possible that the hypothesized
negative relation between managerial ownership and managerial compensation is
an indirect result of size correlations. That is, high managerial ownership
means small size which again means low managerial compensation. To minimize
this kind of omitted variable error the compensation regression should be
controlled for size. Source: Thomson, One Banker. The
SEC database. Proxy statements. |
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ExeCoRiski,t Executive compensation risk |
Executive compensation risk is measured slightly different depending
on whether compensation is defined as the highest paid executive or the sum
of compensation for all top level executives. When executive compensation is
defined as the highest paid executive then executive compensation risk has
been calculated as the two-year standard deviation of top executive compensation
divided by the two-year average of top executive compensation. When executive
compensation is measured by the sum of all executive compensation, then the executive
compensation risk has been calculated as follows: Only the three highest paid
executives are considered. For each of these the executive compensation risk
is calculated in the same way as it was calculated for the highest paid executive.
Then the average of these three numbers is used as the final executive
compensation risk. It is hypothesized that executive
compensation risk is positively correlated with executive compensation. The
argument is the classic risk aversion
argument. According to this argument a risk adverse person need to be
paid a higher average compensation the riskier his compensation in order to
get the same utility from his compensation package (Copeland and Weston
[1988, pages 96-102]). Source: Thomson, One Banker. The
SEC database. Proxy statements. |
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Political variables: These variables are called
political because the theory used to explain their inclusion in the
compensation model mainly supports a political theory of managerial
rent-seeking. |
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Variable name |
Elaborated variable definitions and theoretical justifications |
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ExeAgei,t Average top level executive age |
Executive
age is defined as the average top level executive age. This measure is
preferred to other measures, such as the age of the CEO, because the
management of the firm is being executed by a team of executives rather than
a single person. It is therefore also more relevant to consider the average
age of that team rather than the age of a single person. This argument is furthermore
supported by the fact that the executive team compensation measure produces
the regressions with the most significant parameter estimates and the highest
adjusted R square when compared to regressions that use compensation by the highest
paid executive. Average executive age is expected
to be positively correlated with executive compensation for at least two
reasons. 1) The influence argument:
The argument is that older executives have more time to build up trust and
influence at the board of directors and at key shareholders that need to
approve their compensation contracts. Older teams of executives are therefore
more able to increase their compensation than younger executive teams. 2) The experience argument: In
addition, it could be argued that older executives are more capable and have
more experience in securing a profitable compensation contract. Both of these
arguments are to some degree based on an assumption that executive age is a
proxy for executive tenure, i.e. the length of executive service. However, it
should be noted that the kind of experience that secures high levels of compensation
could also be gained outside the company in other companies. In other words,
tenure could be low and management compensation high, as long as age is high.
Source: Thomson, One Banker. The SEC
database. The company’s
10-K or 20-F filings. |
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StockRi,t Consensus stock recommendation |
The consensus stock recommendation is calculated as the
mean of several similarly scaled grades of common stock delivered by stock
analysts from various investment banks to First Call previously called I/B/E/S.
In particular, common stock is graded on a discrete scale from 1 to 5, where
mark 1 is a strong buy, 2 is a buy, 3 is a hold, 4 is a sell and 5 is a
strong sell. The consensus stock recommendation
is hypothesized to be negatively correlated with executive compensation for
at least one reason. The expectations
argument: The explanation is that managers are more able to succeed in
getting high compensation packages when their company looks good in the stock
market. After all, if you ask the board and the shareholders to approve a
huge increase in compensation it should be more likely to go through when the
firm is rated a strong buy rather than a strong sell (Mathiesen [2005,
Section 6.2]). Source: Thomson, One Banker. The
FC database (previously called I/B/E/S). |
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SelfMani,t Self-management by fraction of
directors that are also executives |
Self-management is defined as the fraction of directors that are executives
as well. For example, if self-management is equal to 0.75 it implies that 75%
of the directors also functions as executives. There are at least two arguments
that can explain why self-management is correlated with executive
compensation. 1) The self-dealing
argument: According to this argument executive compensation is a positive
function of self-management. The idea is that the more the executives are in
direct control of the board of directors the more able are they to award
themselves high compensation packages. 2)
The alibi argument: If you want to commit an offense without getting captured
you need an alibi. The alibi argument claims that the self-dealing argument
is not working in practice because it would be too easy for unhappy
shareholders to sue a self-dealing management and win. Instead, it is claimed
that management compensation is a negative function of self-management. The
argument is that if you really want to increase managerial compensation, much
beyond what is reasonable given the size and the economic performance of the
firm, then you need an alibi to avoid being sued afterwards. The perfect
alibi is low self-management. The claim is that the board can be controlled
indirectly just as well as if the executives had the board positions themselves,
but it is practically impossible to sue the management if the board is
controlled indirectly because it is very difficult to prove indirect control
(Mathiesen [2005, Section 6.2]). Source: Thomson, One Banker. The
SEC database. The company’s
10-K or 20-F filings. |
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Dummy variables: |
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Variable name |
Elaborated variable definitions and theoretical justifications |
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dIndust1i,t,j SIC industry dummies |
This is industry dummies created from the primary four-digit
SIC code. The classification contains 213 dummies including 26 financial
industry dummies. It should be noted that the industry is the primary
industry among several industries that the firm operates in. This is
sometimes misleading if the primary industry is not the majority industry of
the firm. For example, the primary industry of the General Electric Company
is 6159 a financial industry. It is well known that General Electric is
foremost an industrial conglomerate and for that reason it is more
appropriate to reassign the SIC to 9997. The distinction between financials
and non-financials is important because in most circumstances it would be
wrong to make a regression on a sample including both financials and
non-financials because they are too different to be treated alike. The point
is, that in order to produce better industry dummies it is necessary to use
an error correcting procedure that corrects the most obvious SIC
classification errors. It is hypothesized that compensation is determined by the type of
industries because different industries over time may have created a
tradition for certain compensation levels. Source: Thomson, One Banker. The SEC, CS, WS and EX databases. |
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dIncorpi,t,h Dummies for US state of
incorporation |
Dummies for incorporation in the These dummies are hypothesized to be determinants of executive
compensation because of differences in the legal environment from state to
state both with regard to disclosure requirements and with regard to taxes. Source: Thomson, One Banker. The SEC, CS, WS and EX databases. |
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dSubi,t,l Substitution dummies |
Substitution dummies are dummy variables that are
included to control for the effect of substituting missing observations in a
variable with a constant so that a single missing observation does not lead
to the deletion of the entire observation set from the regression sample. To
be sure, if for example advertising expense to sales is missing the trick is
to substitute a constant, such as 0%, and include a new regression dummy with
the value one each time a missing value is substituted and zero otherwise. It
should be emphasized that the choice of constant has no effect on the
regression estimates of the model with the exception of the estimator of the
substitution dummy. There is therefore no need to worry much about the choice
of constant (however, a sensible choice is to pick a value (such as the
median) that has minimal impact on the descriptive statistics of the
substituted sample). The substitution technique may be very important for
maintaining a large sample size available for regression. For example,
advertising intensity is missing for about 73% of all publicly listed US
companies in 2002. In other words, unless the substitution technique is used
73% of the sample will be lost and it will therefore most certainly be
biased. This problem of losing data and getting biased samples because of
missing observations increases when other variables are considered. To be
sure, the research and development intensity is missing for about 66% of all
publicly listed US companies in 2002 and when it is combined with missing
values from advertising and if we assume that missing observations are
randomly distributed we will loose about 91% of the sample
(1-[1-0.73]*[1-0.66] = 0.9082)! The substitution technique does a fine job of preserving sample size
and avoiding sample biases and for that reason it has been applied on all
variables apart from the dependent variable, incorporation, industry, total
assets and market capitalization. |
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- Copyright 1997-2010, ViamInvest. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Legal notice. |
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