ERASMUS UNIVERSITY ROTTERDAM
ERASMUS SCHOOL OF ECONOMICS
BACHELOR THESIS (IBEB)
Research on the Relationship Between
CEO Compensation and Company Performance
By Xiangzi Luo 356786
Supervisor: Prof.Dr.Otto.H.Swank
June 2014
Abstract
This study aims to analyze the relationship between the CEO compensation (in cash or in
equity based) and company performance (return on asset, return on equity and earnings per
share) of 1662 companies from ExeuComp database in 2012. I use a simple linear regression
and determine a significant positive correlation, which is consistent with the previous done
studies. In the meantime, I also find that there is impact of other factors such as firm size and
debt ratio on the pay-performance relationship.
Table of Contents
I.
Introduction
II.
Literature Reviews
III.
Theoretical Framework
3.1.
Principal-agent theory
3.2.
Executive compensation structure
3.3.
CEO pay and Corporate governance
IV.
Hypotheses
V.
Data and Methodology
VI.
VII.
5.1.
Model
5.2.
Data
5.3.
Key variables
Statistical Results
6.1.
Descriptive analysis
6.2.
Empirical analysis
6.3.
A comparison
Conclusion and Limitations
VIII. Reference
IX.
Annex
I.
INTRODUCTION
The separation of ownership and control in the modern companies has brought about
the so-called agency problem. Basically framed, the shareholders of a firm usually
hire the executives to perform tasks on their behalf. However, with lack of the
information, shareholders cannot observe the behaviors of the CEOs; thereby not
ensure whether the agent will make decisions in the way they would like. To better
align the interests of the two parties, the principle (in this case the shareholders)
comes up with the compensation scheme which will motivate the agents (CEOs) to
maximize the firm’s and in turn their own wealth.
It is not authentic to say that the payment of compensation will actually align the
party’s objectives and eliminate the conflicts. Nevertheless, the upward surge of the
CEO compensation did provoke the public discussions and controversies. According
to Frydman and Jenter (2010), the CEO compensation has increased substantially over
the past 30 years. Survey done on S&P 500 US firms reported that the average annual
CEO compensation has reached at $11.4 million in 2010, and has made an
unbelievable 354 times that of the average workers’ pay. Other studies have also
indicated a disproportionately large share of the compensation pie of CEO, as
compare to a typical employee. Publications over the topic of whether CEO worth that
much of pay and whether the firm performance really linked to the compensation
abound. The most accepted outcome nowadays is that there is a positive relationship
between level of CEO compensation and company performance (Bloom,1998). Hall
and Liebman(1998) add that the major driving strength behind is the equity-based
compensation reward to the CEO. On the other hand, there are also some scholars
holding different views. Gomez-Mejia(1994), for instance, believes that the CEOs are
always overpaid. Brick et al. (2006) even conclude that there is a negative correlation
between the excess compensation and firm performance. Other studies show a
positive but insignificant pay-performance relation which suggests the limitations of
compensation incentive.
In this paper, I want to conduct a research trial to study the correlation between the
amount of CEO pay and company performance. In the meantime, I will also
investigate other factors which may affect the pay-performance relationship, namely
the firm size, debt ratio, industry type and so on. To do so, I analyze both descriptive
and empirical study into different industries and firms obtained from the ExecuComp
database in 2012.
The remainder of this paper will be organized as follows: In section II, I review the
relevant literatures and evidence over the topic of executive compensation. In section
III, the theoretical framework is established and three leading theories are explained
in details. Based on that, I come up with three hypotheses and my expectation regards
to them. The next section describes the data and methodology in which I establish
simple linear regression models with different indicators and discuss the data source
and key variables. Section VI provides the statistic results and gives both descriptive
and empirical analysis with also a comparison among three models. The final section
will conclude and give some limitations and suggestions for further research.
II.
LITERATURE REVIEWS
Despite a vast amount of empirical studies concerning to the topic, no consensus has
ever been reached. The different results with regards to the relationship between
executive compensation and firm performance can be interpreted by multi-factors
such as the sample size, the varied indicators, the different econometric models
applied, and the periodicity of the data etc.
In the prior studies, some main types of performance indicators have been used:
Tobin’s Q, Earnings per share, Return on Equity, Market Value and Return on Assets.
For instance, Mehran (1995) investigates a random sample of 153 manufacturing
firms for the period of 1979-1980. In his comprehensive study, he finds that
performance of a firm, when measured by Tobin’s Q and ROA, is positively linked to
the proportion of equity-based compensation and to the percentage of equity held by
executives, with the correlation coefficients of 0.521 and 2.263 respectively. Ke,
Petroni and Safieddine (1999) also conclude in their paper a positive relationship
between ROA and level of executive pay, but only for the publicly-held insurance
firms. Jensen and Murphy(1990) show in their research the same positive payperformance sensitivity, finding that CEOs receive an extra bonus pay of 2.59$ for
every 1000$ increase in total shareholder’s value. It was then suggested that the
correlation between CEO pay and firm performance, though statistically significant, is
too weak to motivate CEO and this positive but insignificant relation has also been
reported by Ozkan(2007).
However, some scholars also hold the belief that CEOs are usually overpaid. Bebchuk
and Fried (2004), for example, criticize in their paper that top executives’
compensation had not been closely tied to the company performance. Sigler (2011)
also argues that both the cash and equity-based compensation may draw some CEOs
to involve in activities that have bad impacts on the company. Cash bonuses, which
are based on annual financial report, might encourage top managers to manipulate the
timing of revenue and expense to maximize their own utilities at the cost of the firm’s.
On the other hand, equity-based compensation may also entice CEOs to outright cheat
and boost accounting numbers before exercising their options for the aim of driving
up the stock price (Conyon, 2006). Devers et al. (2007) also reports his concerns
regarding to the overuse of equity-based pay and the possible illegal behaviors of
CEOs by steering the firms for the sake of their own goods.
Some studies which focus on the impact of other factors on pay-performance
relationship are also worth noting. Scott Schaefer (1998) analyzes the relationship
between company size and the extent to which CEO compensation relies on the
wealth of the firm. He builds an econometric model using nonlinear least squares and
finds that the pay-performance sensitivity, as defined by Jensen and Murphy, is
inversely consistent to the square root of the firm size. Holmstrom (1992) also notes
the finding that the pay–performance sensitivity is decreasing with the size. He even
uses this empirical result to support the agency theory under the assumption that risk
is traded off with incentives at the margin. John and John (1993) argue that the payperformance sensitivity will be decreasing with the debt ratio under a well-formed
compensation structure. Therefore, they suggest the debt ratio of a firm should be
taken into consideration of setting an optimal contract.
In general, although literatures differ on whether the compensation is really linked to
performance, most cases have found a weak but significant relationship between them
(Core, 1999). When taking into account of other factors, the pay-performance
sensitivity will then be changed.
III.
THEORETICAL FRAMEWORK
Principal-Agent Theory
The Principal-agent theory was first separately emerged in the work of Stephen Ross
in the early 1970s. It then turned out to be one of the most used theories to explain the
CEO compensation. Broadly speaking, the principal-agent theory analyzes the
conflicts between the contractual relationship of two parties, namely the principals
and the agents on behalf of those principals. When one party (principal) hires the
other party (agent) to perform a service or work, the two sides may have different
desires or goals and may also have different attitudes towards risks. In this case, either
of the party would only care about its own interests and thereby act for its personal
goods (Denis, 1999).
The principal-agent theory also gives us a clue of why the shareholders would be
willing to give out CEO a fairly large compensation package. When shareholders
(principals) delegate the decision-making rights to the executives (agents), they
expect their CEOs to act in the way that maximize the utility of the company. The
CEOs, on the other hand, is always motivated to work in their own best profits. In the
meantime, as CEOs usually grasp more private information, the shareholder cannot
guarantee that agents would always put his (principal’s) interest in the first place,
especially under situation in which interests of the two are in conflict and the
activities of CEOs are costly for the shareholders to observe.
Figure 1:
To mitigate those clashes, shareholders may either monitor the managerial behaviors
of the CEOs or use compensation packages as a mean of aligning executives and
shareholder interests. Because the monitoring is always cost-prohibitive for
shareholders and because the managerial activities are often unobservable, the
company tends to adopt the compensation scheme (Bloom, 1998).
Executive Compensation Structure
Despite a considerable heterogeneity in CEO pay across industries and firms, most
compensation packages include three basic components: monetary, non-monetary and
equity rights. Monetary compensations include base salaries and annual bonuses in
form of cash. Non-monetary compensations are for instance: increased responsibility,
education opportunities, reputation, etc. Equity rights contain options, stocks, shares
warrants and so on. Usually, the top managers are able to receive several forms of
compensation for their work. The importance of the various types of compensation for
CEO is obvious and worth noting as different component has its own shortcomings. A
mix of the compensation allow the strength of one offset the weakness of another and
may mitigate the activities of the CEOs, which would produce some problems for the
company (Sigler, 2011).
Cash bonuses are traditional ways of providing incentives to the CEOs to focus on
short-run goals. Rewarding this monetary compensation may encourage CEOs to have
undesired behaviors as this type of bonuses are usually paid once at the end of year
and are based on the yearly performance of the firm. Assuming that a CEO of a
company can receive only a base salary with some cash bonuses, to maximize his own
compensation, this top manager is motivated to manipulate the accounting number of
the annual report (Conyon, 2006). Despite of the cheating of financial report, cash
bonuses may also in some cases misdirect the top managers to focus only on short
term performance and may harm the health of the company in long-run perspective.
Equity-based compensation is another popular tool for the company to tie pay to
performance by making the executives part of the owners themselves. In principle, as
the equity-based incentives (restricted stocks and option grants) will only be
realizable after a certain period of time, top managers have to work in a way that will
benefit both for personal goods and for the long-term sustainability of the firm.
Nevertheless, it may not be wise to tie most of the components of CEO pay to the
stock price as stock price of a firm will fluctuate under market forces instead of
manager’s efforts.
In general, not a single form of compensation would be good enough to align the
interests of the two sides. Every type of compensation allows for some sorts of
cheating as long as one party gets the privileged access to private information (Sahut,
2010). The complex component of CEO pay is therefore necessary.
CEO Pay and Corporate Governance
It is true that CEO pay can ameliorate agency problem by aligning the interests of
both owners and top managers in some extent. However, when the corporate
governance of a firm is weak such that CEOs may control the amount and structure of
their own rewards, compensation scheme may turn to part of the problem of a
company. Generally speaking, CEOs can still be overpaid and be safeguarded from
their poor performance and thereby, mitigate the relationship between the CEO
compensation and company performance. There are also situations in which
executives themselves are members of the compensation committee, levels of CEO
pay is then directly linked to the number of executives who serve for the
committee(O’Reilly, 1988).
The empirical studies on the CEO compensation and corporate governance are
abundant. Some of the academics conclude that CEO pay levels are consistent with
good firm internal governance in most cases (K, 2002 & Holmstom B, 2003). Others
found that CEOs who also belong to compensation committee tend to receive a higher
stock right with less overall compensation (Anderson R, 2003).
IV.
HYPOTHESES
Due to previous theoretical part and done studies, I come up with three assumptions
regarding to the relationship between the CEO compensation and corporate
performance and other factors which also affect pay-performance relationship.
As stated in the optimal contracting theory, compensation is used to optimize the
agent-principal contract. Under the condition of good corporate governance, I expect
that the compensation committee would set CEO pay consistent with the firm
performance. Simply framed, the better the performance of the company, the higher
pay will top managers receive. I thereby formulate the first hypothesis as below:

H0: There is no significant correlation between CEO compensation and
company performance
Ha: There is a significant positive correlation between CEO compensation
and company performance.
When the size of a firm expands, the complexity of the activities to operate would
also increase; thereby require higher managerial skills of the CEO. On the other side,
company may also take advantage of the economics of scale and utilize the resources
in a much more efficient way to earn extra profits. The higher the profit of the
company, the more willing shareholders are to offer higher compensation regards to
the hard work of CEO and thus, a more significant relationship between the pay and
performance would achieve. So, the second hypothesis is:

H0: There is no significant strengthening effect of firm size on the payperformance relationship.
Ha: There is a significant strengthening effect of firm size on the payperformance relationship.
Among almost all the traded companies, there are basically two ways to raise capital:
equity financing and debt financing. Obviously, different types would lead to different
Asset-liability ratios. Through debt financing, firms are able to raise funds in a
cheaper way and increase its leverage to explore extra profits for shareholders.
However, it also comes with higher risk as leverage amplifies both gains and losses.
To put in another word, if the debt ratio is way too high for the firm to repay, great
finance pressure may eventually force the corporate to go bankruptcy. In order to cure
this financial distress, shareholders are expected to strength the correlation between
the compensation and firm performance. Through this way, CEO will be motivated to
operate better on the business and bring more profits to the company. Therefore, I
expect that the relationship between the CEO pay and company performance is
stronger as the debt rate of a firm increases. The third hypothesis would then be:
H0: There is no significant strengthening effect of debt ratio on the payperformance relationship
Ha: There is a significant strengthening effect of debt ratio on the payperformance relationship.
V.
DATA AND METHODOLOGY
Model
In this section, I develop a linear regression model to analyze the level of CEO
compensation to firm performance after taking into account of other factors:
Y=α+β1X+β2SIZE+β3 Debt +β4 X*SIZE +β5X*DEBT+∑βiINDi+εi
In the equation, I assume that the CEO compensation is determined by the firm
performance, firm size, debt ratio along with the interaction terms and dummy
variables. Here, the dependent variable Y refers to the CEO compensation which in
forms of Monetary and equity-based respectively; the independent variable X
represents the firm performance with three different measures. SIZE is literally the
scale of a firm and the DEBT means the ratio of debt to total asset. The SIZE, DEBT
and the IND will be used as control variables and the interaction terms of X*SIZE &
X*DEBT will serve for the relevant assumptions. For instance, to check my second
assumption regards to the impact of size on level of pay-performance, we progress the
regression under corresponding measurements of each variable. If the signs of β1 and
β4 are the same (whether they are both either positive or negative), this would imply a
strengthening effect. Otherwise, there is a weakening effect.
Figure 2: Conceptual Framework
Independent Variable X:

Dependent Variable Y:

Firm performance
-Return on Asset (ROA)
-Return on Equity (ROE)
-Earnings per share(EPS)
CEO Compensation
-Monetary pay LN(TM)
-Equity-based pay LN(TSE)
Control Variable:



Firm Size (SIZE)
Debt Ratio(DEBT)
Industry(IND)
Data
In order to investigate the hypotheses, I collect data from the Standard & Poor’s
(McGraw-Hill) ExecuComp database via Wharton. The ExecuComp provides annual
data of the executive compensation as well as the firm-specific financial statement
information. It tracks corporates in the S&P 1000 from 1992 onwards, and is also the
most widely used data source over the relevant topics.
In this paper, I use a sample size of 1662 companies classified under the Global
Industry Classification Standard (GISC). More specifically, I have groups as 10
(Energy), 15 (Materials), 20 (Industrials), 25 (Consumer Discretionary), 30
(Consumer Staples), 35 (Health Care), 40 (Financials), 45 (Information Technology),
50 (Telecommunication Services) and 55 (Utilities). I prefer the cross-sectional
analysis to the time-series analysis, as the former allows a broader industry coverage
at a single time point. The construction of the cross section will be based on the year
of 2012 with more samples compared to 2013.To assess the CEO compensation, I
gather variables such as salary, bonus, stock option from the AnnComp data table. In
the meantime, I use data such as total assets, stockholder’s equity, EPS, ROA and
ROE from the Codirfin datasheet to collect the company information and performance.
Key Variables
variable type
variable
description
dependent
LN(TM)
log of total monetary compensation
LN(TSE)
log of total equity based compensation exercised.
EPS
Earnings per share(performance indicator)
ROE
Return on Equity(performance indicator)
ROA
Return on Asset(performance indicator)
LN(TA)
log of the total asset of a firm
Debt
the rate of Debt over total asset
IND
dummy variable:
independent
Control
IND=1,if it belongs to a corresponding industry; otherwise, IND=0
CEO compensation: Though the compensation exists in many forms, it is
acknowledged that cash and stock options are the most widely used in companies.
Debate over which would serve as the optimal contract for the agency problem has
still been extending. As a proxy for the executive remuneration, I thereby use two
measures: total monetary compensation and total equity-based compensation. Both of
the indicators are given in thousands and are represented in the form of nature
logarithm to minimize the disturbance of heteroskedasiticity. The total cash
compensation LN(TM) consists base salary and bonus, while the alternative equitybased compensation LN(TSE) contains stocks and options that are assessed using the
value realized from the option exercise or stock vesting.
Company performance: I decide in this paper to adopt three variables: ROA, ROE
and EPS as a measurement of firm performance. Return on Asset (ROA) is the ratio
of net income before extraordinary items and Discontinued Operations to total assets.
It shows how profitable of a firm is relative to total assets and is used as one of the
indicators of company performance. However, the drawback of using ROA is that the
number of ROA can vary substantially as it dependents highly on industries. Return
on Equity (ROE) is the ratio of net income before extraordinary items and
Discontinued Operations to total common equity and allows for comparison among
different firms. ROE alone can also not be a proper measurement as it can be driven
up intentionally by the degree of leverage of a firm (ECB, 2012). Therefore, I also
have my third indicator: earnings per share, which is the portion of the firm’s earning
allocated to each outstanding share of the common stock. In principle, the higher the
EPS is, the higher is the profit of the company. Nevertheless, EPS may be affected by
many economic factors and is open to manipulation. To sum up, each measure has its
own advantages and disadvantages. Hence, I apply three of them to adjust the
unpleasant effects.
Firm Size: Previous research shows that firm size is a major determinant of executive
compensation. There are also some literatures investigate the effect of size on the payperformance relationship. For instance, Schaefer (1997) uses market value and total
asset as two measures of firm size and finds the pay-performance sensitivity moves
inversely proportional to the square root of size. Cichello (2005) also estimates the
relation between size and pay-performance sensitivity and applies three variables to
represent the firm size, namely total assets, total sales and number of employees. Sum
up the early documents, I determine to use the natural logarithm of total asset LN(TA)
as the indicator of the firm size regard to the second hypothesis.
Debt ratio: The term is defined as the ratio of total debt to total asset, calculated in
percentage. It shows the proportion of a firm’s asset which is financed by debt and is
also used as a measurement of leverage degree of a company. It seems that the debt
ratios vary significantly across industries. In general, industries with capital-intensive
businesses will usually have higher debt ratios compared to others. This also explains
why Industrials & Utilities industries tend to own such a high debt ratio compared to
Technology. Back to this study, as higher debt ratio replies to higher risk-taking, I
expect a positive effect of debt rate on the relationship of CEO compensation and
company performance.
Industry: As mentioned above, I have classified 1662 sample into 10 industries and
will use IND as a dummy variable in my model. Though I have no assumption regards
to the types of industry. I will also give a descriptive analysis and expect a larger payperformance correlation as the competition faced by the company is fiercer.
Global Industry Classification Standard
IND
code
IND type
Sample
N
proportion
0
Energy
99
6%
1
Materials
111
7%
2
Industrials
228
14%
3
Consumer Discretionary
285
17%
4
Consumer Staples
85
5%
5
Healthcare
162
10%
6
Financials
306
18%
7
Information Technology
307
18%
8
9
Telecommunication Services
Utilities
Total
17
62
1662
1%
4%
100%
VI.
STATISTICAL RESULTS
Descriptive Analysis
Table 4.1 : Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
LN(TM)
1662
-6.91
10.34
6.68
1.13
LN(TS)
1662
-6.91
13.93
8.33
1.29
ROE
1662
-3050.00
7038.46
13.71
213.71
ROA
1662
-143.77
170.30
4.17
11.14
EPS Excl
1662
-21.48
45.48
1.90
3.38
LN(TA)
1662
2.34
14.67
7.99
1.77
Debt
1662
-.73
1.00
.56
.22
Valid N (listwise)
1662
First of all, I take a glance at the descriptive analysis of the data. Table 4.1 shows the
relevant statistics on the compensation level and firm performance with also other
control variables in 2012. It summarizes the dependent variable of CEO pay which
has a mean of 6.68 for LN(TM) and 8.33 for LN(TS), respectively. In general, I can
conclude that the equity-based compensation account for a larger proportion of the
total compensation. The standard deviation which measures the spread shows that
there is a slightly more deviation from the mean of the equity-based compensation
(1.29) as compared to the cash bonus (1.13). The fact that LN(TM) ranged from -6.91
to a maximum of 10.34 and LN(TS) ranged from -6.91 to a high of 13.93 also tells us
that different pay levels exist across firms.
In the meantime, the performance statistics of the company also gives us information.
It can be reported from the table that the mean of the three indicators are 13.71%,
4.17%, and 1.90% respectively. So, the ROE has an increment of 9.54% compared to
ROA and it also owns the highest standard deviation of 213.71 among three.
To further explore how different industries behavior, I introduce the table 4.2 and 4.3
with statistics in reference to industry types.
compensation, it can be concluded that:
Regardless of the forms of CEO
a) Compensation levels are different among industries according to the
fluctuating mean values.
b) Compensation levels are also different across firms within each industry based
on the various minimum and maximum ranges.
One possible explanation for the distinctions would be that there are different
characteristics among industries. For instance, utilities have more government
involvement, and finance firms have different business nature compare to others.
Table 4.2
LN(TM)
Industry
Mean
N
Minimum
Maximum
0
6.75
99
-6.91
9.83
1
6.78
111
5.28
7.82
2
6.73
228
-6.91
8.96
3
6.78
285
-6.91
10.34
4
6.71
85
-6.91
8.94
5
6.65
162
-6.91
8.10
6
6.83
306
4.09
10.25
7
6.33
307
-6.91
8.52
8
6.86
17
5.99
8.09
9
6.78
62
6.20
7.34
Total
6.68
1662
-6.91
10.34
Table 4.3
LN(TS)
Industry
Mean
N
Minimum
Maximum
0
8.61
99
5.64
13.93
1
8.49
111
6.37
11.41
2
8.44
228
5.01
11.42
3
8.44
285
3.50
12.45
4
8.49
85
4.88
11.49
5
8.34
162
.69
11.36
6
8.30
306
5.11
11.23
7
7.94
307
-6.91
11.94
8
8.44
17
7.05
10.26
9
8.55
62
7.06
10.19
Total
8.33
1662
-6.91
13.93
Empirical Analysis
On the second part of the statistical analysis, I focus more on the empirical regression
results. There are 3 models relating to the two main compensation forms using three
different independent indicators: (i) relationship between CEO pay and ROE; (ii)
relationship between CEO pay and ROA; (iii) relationship between CEO pay and EPS.
Besides that, the superscripts ***, **, and * denote the 1%, 5%, and 10% levels of
significance in the regression respectively. I hereby use each model to test the
hypotheses and get results as below:
Regression Analysis on Model 1:
LN(TM)
Variable
B
ROE
0.006***
LN(TA)
0.150***
Debt
0.166
ROE*LN(TA)
2.291E-05
ROE*Debt
-0.006**
Adjusted R Square 0.070
F
25.843
Sig F
0.000
t
2.679
8.258
1.109
0.203
-2.480
P
0.007
0.000
0.268
0.839
0.013
LN(TS)
B
0.016***
0.398***
-0.332**
0.000
-0.017***
0.303
145.112
0.000
t
6.716
22.161
-2.249
1.232
-6.659
P
0.000
0.000
0.025
0.218
0.000
In model 1 of the regression, I take ROE as the measurement of the independent
variable. Two equations are derived as follow:
LN(TM)= β0+β1*ROE+ β2*LN(TA)+ β3*Debt +β4ROE*LN(TA)+β5ROE*Debt+∑βiINDi+εi
LN(TS)= β0+β1*ROE+ β2*LN(TA)+ β3*Debt +β4ROE*LN(TA)+β5ROE*Debt+∑βiINDi+εi
The results in the table shows that there is a strong positive relationship between the
CEO remuneration (both in cash or in equity-based) and ROE. The t statistics for the
slope are significant at 0.01 critical level, with t1=2.679 and t2=6.716. Thus, Ho can
be rejected and so I accept Ha for my first hypothesis. This positive finding is also
consistent with the previous studies (Sigler, 2011). For the second hypothesis, I will
analyze by looking at the sign of coefficients of ROE and ROE*LN(TA). However, as
t statistics for neither LN(TM) nor LN(TS) are significant according to the table, H0
cannot be rejected, which implies that there is no significant strengthening impact of
firm size on the pay-performance relationship. On the same line of reasoning, I test
my third hypothesis and exhibit a weakening instead of strengthening effect of debt
ratio on pay-performance correlation, as the signs of β1 and β5 are different. In this
case, I also not reject Ho.
Regression Analysis on Model 2:
Variable
ROA
LN(TA)
Debt
ROA*LN(TA)
ROA*Debt
Adjusted R Square
F
Sig F
LN(TM)
B
P
LN(TS)
B
t
t
P
0.004
0.520
0.603
-0.035***
-4.317
0.000
0.147***
7.638
0.000
0.353***
18.848
0.000
0.145
0.919
0.358
-0.074
-0.481
0.630
-5.349E-05
-0.032
0.975
0.01***
6.195
0.000
0.012*
1.651
0.099
-0.008
-1.056
0.291
0.071
0.328
26.525
163.238
0.000
0.000
In model 2 of my regression, the measurement is done by adopting ROA, ceteris
paribus. The corresponding equations are thereby:
LN(TM)= β0+β1*ROA+ β2*LN(TA)+ β3*Debt +β4ROA*LN(TA)+β5ROA*Debt+∑βiINDi+εi
LN(TS)= β0+β1*ROA+ β2*LN(TA)+ β3*Debt +β4ROA*LN(TA)+β5ROA*Debt+∑βiINDi+εi
It is important to note in this model that t value of neither the ROA nor ROA*LN (TA)
is significant for the type of monetary compensation. Furthermore, with a p-value of
0.099 and a positive coefficient (0.012) of the ROA*Debt, I can see only a slightly
significant strengthening effect of debt ratio on the pay-performance relationship. On
the contrary, there are significant evidence for testing the first two hypotheses using
LN (TS). Nevertheless, I find an oddly negative correlation between the ROA and LN
(TS), and thereby also a weakening effect of the LN(TA) on pay-performance
correlation. Same result can also be tracked from the other paper (Cooper, 2009).
Lastly, no significant effect of debt ratio on the ROA-LN (TS) correlation as p-value
of only 0.291, which is higher than 0.1 critical level.
Regression Analysis on Model 3:
Variable
EPS
LN(TA)
Debt
EPS*LN(TA)
EPS*Debt
LN(TM)
B
t
P
LN(TS)
B
t
P
0.022
0.501
0.617
0.221***
5.083
0.000
0.189***
9.538
0.000
0.446***
22.847
0.000
-0.332**
-2.113
0.035
-0.800***
-5.154
0.000
-0.016***
-3.922
0.000
-0.031***
-7.521
0.000
0.230***
6.131
0.000
0.176***
4.751
0.000
Adjusted R Square 0.093
F
35.009
Sig F
0.000
0.322
158.582
0.000
***Significance at 0 .01 level
**significance at 0.05 level
*significance at 0.1 level
In the last model, a simple regression is performed to determine the relationship
between the CEO compensation and EPS:
LN(TM)= β0+β1*EPS+ β2*LN(TA)+ β3*Debt +β4EPS*LN(TA)+β5EPS*Debt+∑βiINDi+εi
LN(TS)= β0+β1*EPS+ β2*LN(TA)+ β3*Debt +β4EPS*LN(TA)+β5EPS*Debt+∑βiINDi+εi
One advantage using model 3 is that almost all the statistics are within the significant
level of 5%. Concerning the first hypothesis, there is a positive significant relationship
between the LN(TS) and EPS, which is the same as my result in model 1. I would
thereby reject the first hypothesis and accept the alternative one. No significant
evidence supports the positive correlation between LN(TM) and EPS. Furthermore,
statistics in the table also indicates a significant but negative effect of the LN(TA) on
the pay-performance level regardless of the forms of the dependent variables. Finally,
I will reject Ho and accept Ha for my third hypothesis, as it exhibits a significant
strengthening effect of debt ratio on the pay-performance relationship in both cases.
A Comparison
A quick comparison of the three models would help give an overall picture of the
empirical outcome:
(i)
Within each model, under the same indicator of firm performance, the
adjusted R-squared will always be larger for the LN(TS) compared to
LN(TM). This may imply that a higher percentage of the deviation of
equity-based compensation can be explained by the independent variable
compared to the cash reward.
(ii)
All of the three models are significant with small value of F-statistics,
which shows a strong correlation between performance and compensation
after taking into account of other factors.
(iii)
In model 1 and model 3, I get a similar positive result regards to my first
two assumptions but a totally opposite outcome regards to the third one. In
contrast, statistics in model 2 are only significant for the analysis of the
equity type of compensation, but it shows an oddly negative payperformance correlation.
VII.
CONCLUSION AND LIMITATIONS
Conclusion
Nowadays, the existence of a correlation between the CEO pay and performance has
been widely disputed. This paper aims to study not only the correlation but also the
impacts of other factors such as firm size and debt ratio on the pay-performance
relationship. To do so, I collect data from the ExecComp database and use a simple
linear regression to test the relevant hypotheses. In general, there is a significantly
positive pay-performance correlation, which is consistent with the previous studies.
Impacts of firm size and debt on the pay-performance relationship are different
according to different models. Model 1 suggests a weakening effect of the debt ratio,
while model 3 indicates an opposite strengthening effect. As for the firm size, model 3
concludes a weakening effect on the pay-performance relationship for both monetary
and equity based compensation. Other models, however, imply that there is no
significant strengthening impact of firm size on the pay-performance relationship. The
different results may be explained by the fact that each measurement of the company
performance has disadvantages. ROA varies substantially as it dependents highly on
industries; ROE can be driven up intentionally by the degree of leverage of a firm;
EPS may also be affected by many economic factors and is open to manipulation.
Limitations
The major limitation in this paper is that I only infer the correlation instead of
causality between the factors. Correlation is symmetry, while causation is asymmetry.
Also, there might be confounders, which influence both the CEO compensation and
company performance. For instance, some of the CEOs obtain larger pay package due
to the fact that they start with healthier institutions. In this case, a positive relation
between the two factors would not necessary indicate the effect of compensation on
the consequence of performance, but the other way around.
The second limitation relates to the data and methodology part of the paper. To be
more specific, it is due to the lack of latest data and the mere use of cross-sectional
analysis. It is noted that a sample size of only 1662 companies from 2012 is processed.
To achieve a better accurate result for the future research, some of the suggestions
will be proposed. First, relevant causation analysis is highly recommended. Second, a
larger data sample can be collected by including the time-series study. Last but not
least, the performance should be measured by not only the profitability but also other
indicators such as “quality of assets, risk associated to the production value and the
funding capacity” (ECB, 2012).
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ANNEXS
(1) LN(TM)=f(ROE,LN(TA),Debt,ROE*LN(TA),ROE*Debt)
Model Summary
Adjusted
Model
1
a.
R
R Square
Square
.269a
.072
.070
Predictors:
(Constant),
ROE*Debt,
R Std. Error of the
Estimate
1.08742870404
5415
LN(Total
Assets),
Debt,
ROE*LN(Total Assets), ROE
ANOVAa
Model
1
Sum of Squares df
Mean Square
F
Sig.
Regression
152.796
5
30.559
25.843
.000b
Residual
1958.222
1656
1.183
Total
2111.018
1661
a. Dependent Variable: LN(TM)
b. Predictors: (Constant), ROE*Debt, LN(Total Assets), Debt, ROE*LN(Total Assets), ROE
Coefficientsa
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
5.363
.123
ROE
.006
.002
LN(Total Assets)
.150
Debt
Model
1
t
Sig.
43.428
.000
1.217
2.679
.007
.018
.236
8.258
.000
.166
.149
.032
1.109
.268
ROE*LN(Total Assets)
2.291E-5
.000
.043
.203
.839
ROE*Debt
-.006
.003
-1.216
-2.480
.013
a. Dependent Variable: LN(TM)
(2) LN(TS)=f(ROE,LN(TA),Debt,ROE*LN(TA),ROE*Debt)
Model Summary
Adjusted
Model
1
a.
R
R Square
Square
.552a
.305
.303
Predictors:
(Constant),
ROE*Debt,
R Std. Error of the
Estimate
1.07480090582
5320
LN(Total
Assets),
Debt,
ROE*LN(Total Assets), ROE
ANOVAa
Model
1
Sum of Squares df
Mean Square
F
Sig.
Regression
838.164
5
167.633
145.112
.000b
Residual
1913.006
1656
1.155
Total
2751.170
1661
a. Dependent Variable: LN(TS)
b. Predictors: (Constant), ROE*Debt, LN(Total Assets), Debt, ROE*LN(Total Assets), ROE
Coefficientsa
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
5.263
.122
ROE
.016
.002
LN(Total Assets)
.398
Debt
Model
1
t
Sig.
43.122
.000
2.642
6.716
.000
.018
.548
22.161
.000
-.332
.148
-.057
-2.249
.025
ROE*LN(Total Assets)
.000
.000
.225
1.232
.218
ROE*Debt
-.017
.003
-2.827
-6.659
.000
a. Dependent Variable: LN(TS)
(3) LN(TM)=f(ROA,LN(TA),Debt,ROA*LN(TA),ROA*Debt)
Model Summary
Adjusted
Model
1
R
R Square
Square
.272a
.074
.071
R Std. Error of the
Estimate
1.08639191403
6239
a. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets),
ROA*LN(Total Assets)
ANOVAa
Model
1
Sum of Squares df
Mean Square
F
Sig.
Regression
156.528
5
31.306
26.525
.000b
Residual
1954.490
1656
1.180
Total
2111.018
1661
a. Dependent Variable: LN(TM)
b. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets), ROA*LN(Total Assets)
Coefficientsa
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
5.388
.125
ROA
.004
.008
LN(Total Assets)
.147
Debt
Model
1
t
Sig.
43.157
.000
.042
.520
.603
.019
.232
7.638
.000
.145
.158
.028
.919
.358
ROA*LN(Total Assets)
-5.349E-5
.002
-.003
-.032
.975
ROA*Debt
.012
.007
.063
1.651
.099
a. Dependent Variable: LN(TM)
(4) LN(TS)=f(ROA,LN(TA),Debt,ROA*LN(TA),ROA*Debt)
Model Summary
Adjusted
Model
1
R
R Square
Square
.575a
.330
.328
R Std. Error of the
Estimate
1.05491593298
7620
a. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets),
ROA*LN(Total Assets)
ANOVAa
Model
1
Sum of Squares df
Mean Square
F
Sig.
Regression
908.295
5
181.659
163.238
.000b
Residual
1842.876
1656
1.113
Total
2751.170
1661
a. Dependent Variable: LN(TS)
b. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets), ROA*LN(Total Assets)
Coefficientsa
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
5.367
.121
ROA
-.035
.008
LN(Total Assets)
.353
Debt
Model
1
t
Sig.
44.276
.000
-.299
-4.317
.000
.019
.486
18.848
.000
-.074
.153
-.013
-.481
.630
ROA*LN(Total Assets)
.010
.002
.517
6.195
.000
ROA*Debt
-.008
.007
-.035
-1.056
.291
a. Dependent Variable: LN(TS)
(5) LN(TM)=f(EPS,LN(TA),Debt,EPS*LN(TA),EPS*Debt)
Model Summary
Adjusted
Model
1
R
R Square
Square
.309a
.096
.093
R Std. Error of the
Estimate
1.07373255292
9834
a. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS
Excl*LN(Total Assets), EPS Excl
ANOVAa
Model
1
Sum of Squares df
Mean Square
F
Sig.
Regression
201.812
5
40.362
35.009
.000b
Residual
1909.205
1656
1.153
Total
2111.018
1661
a. Dependent Variable: LN(TM)
b. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS Excl*LN(Total Assets),
EPS Excl
Coefficientsa
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
5.347
.138
EPS Excl
.022
.044
LN(Total Assets)
.189
Debt
Model
1
t
Sig.
38.716
.000
.066
.501
.617
.020
.297
9.538
.000
-.332
.157
-.065
-2.113
.035
EPS Excl*LN(Total Assets)
-.016
.004
-.467
-3.922
.000
EPS Excl*Debt
.230
.038
.409
6.131
.000
a. Dependent Variable: LN(TM)
(6) LN(TS)=f(EPS,LN(TA),Debt,EPS*LN(TA),EPS*Debt)
Model Summary
Adjusted
Model
1
R
R Square
Square
.569a
.324
.322
R Std. Error of the
Estimate
1.05991803100
6872
a. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS
Excl*LN(Total Assets), EPS Excl
ANOVAa
Model
1
Sum of Squares df
Mean Square
F
Sig.
Regression
890.776
5
178.155
158.582
.000b
Residual
1860.394
1656
1.123
Total
2751.170
1661
a. Dependent Variable: LN(TS)
b. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS Excl*LN(Total Assets),
EPS Excl
Coefficientsa
Standardized
Unstandardized Coefficients
Coefficients
B
Std. Error
Beta
(Constant)
5.117
.136
EPS Excl
.221
.043
LN(Total Assets)
.446
Debt
Model
1
t
Sig.
37.537
.000
.579
5.083
.000
.020
.615
22.847
.000
-.800
.155
-.137
-5.154
.000
EPS Excl*LN(Total Assets)
-.031
.004
-.774
-7.521
.000
EPS Excl*Debt
.176
.037
.274
4.751
.000
a. Dependent Variable: LN(TS)