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ERASMUS UNIVERSITY ROTTERDAMERASMUS SCHOOL OF ECONOMICSBACHELOR THESIS (IBEB)

Research on the Relationship Between CEO Compensation and Company Performance

By Xiangzi Luo 356786Supervisor: Prof.Dr.Otto.H.SwankJune 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

III.1. Principal-agent theory

III.2. Executive compensation structure

III.3. CEO pay and Corporate governance

IV. Hypotheses

V. Data and Methodology

5.1. Model

5.2. Data

5.3. Key variables

VI. Statistical Results

6.1. Descriptive analysis

6.2. Empirical analysis

6.3. A comparison

VII. 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 pay-

performance 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 pay-

performance 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 TheoryThe 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 StructureDespite 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 pay-

performance relationship.

Ha: There is a significant strengthening effect of firm size on the pay-


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 pay-

performance relationship

Ha: There is a significant strengthening effect of debt ratio on the pay-


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:

Firm performance-Return on Asset (ROA)-Return on Equity (ROE)-Earnings per share(EPS)

Dependent Variable Y:

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 descriptiondependent LN(TM) log of total monetary compensation

LN(TSE) log of total equity based compensation exercised.independent EPS Earnings per share(performance indicator)

ROE Return on Equity(performance indicator)

ROA Return on Asset(performance indicator)Control LN(TA) log of the total asset of a firm

Debt the rate of Debt over total asset

IND dummy variable: 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 equity-

based 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 pay-

performance 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 pay-

performance correlation as the competition faced by the company is fiercer.

Global Industry Classification StandardIND 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 Telecommunication Services 17 1%9 Utilities 62 4%

Total 1662 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. Regardless of the forms of CEO

compensation, it can be concluded that:

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) LN(TS)Variable B t P B t PROE 0.006*** 2.679 0.007 0.016*** 6.716 0.000LN(TA) 0.150*** 8.258 0.000 0.398*** 22.161 0.000Debt 0.166 1.109 0.268 -0.332** -2.249 0.025ROE*LN(TA) 2.291E-05 0.203 0.839 0.000 1.232 0.218ROE*Debt -0.006** -2.480 0.013 -0.017*** -6.659 0.000Adjusted R Square 0.070 0.303F 25.843 145.112Sig F 0.000 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+εiLN(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: LN(TM) LN(TS)Variable B t P B t PROA 0.004 0.520 0.603 -0.035*** -4.317 0.000LN(TA) 0.147*** 7.638 0.000 0.353*** 18.848 0.000Debt 0.145 0.919 0.358 -0.074 -0.481 0.630ROA*LN(TA) -5.349E-05 -0.032 0.975 0.01*** 6.195 0.000ROA*Debt 0.012* 1.651 0.099 -0.008 -1.056 0.291Adjusted R Square 0.071 0.328F 26.525 163.238Sig F 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: LN(TM) LN(TS)Variable B t P B t PEPS 0.022 0.501 0.617 0.221*** 5.083 0.000LN(TA) 0.189*** 9.538 0.000 0.446*** 22.847 0.000Debt -0.332** -2.113 0.035 -0.800*** -5.154 0.000EPS*LN(TA) -0.016*** -3.922 0.000 -0.031*** -7.521 0.000EPS*Debt 0.230*** 6.131 0.000 0.176*** 4.751 0.000

Adjusted R Square 0.093 0.322F 35.009 158.582

Sig F 0.000 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 pay-

performance 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

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1.269a .072 .070

1.08742870404

5415

a. Predictors: (Constant), ROE*Debt, LN(Total Assets), Debt,

ROE*LN(Total Assets), ROE

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 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

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 5.363 .123 43.428 .000

ROE .006 .002 1.217 2.679 .007

LN(Total Assets) .150 .018 .236 8.258 .000

Debt .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


(2) LN(TS)=f(ROE,LN(TA),Debt,ROE*LN(TA),ROE*Debt)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1.552a .305 .303

1.07480090582

5320

a. Predictors: (Constant), ROE*Debt, LN(Total Assets), Debt,

ROE*LN(Total Assets), ROE

ANOVAa


1 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

Model


Standardized

Coefficients


1 (Constant) 5.263 .122 43.122 .000

ROE .016 .002 2.642 6.716 .000

LN(Total Assets) .398 .018 .548 22.161 .000

Debt -.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


(3) LN(TM)=f(ROA,LN(TA),Debt,ROA*LN(TA),ROA*Debt)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1.272a .074 .071

1.08639191403

6239

a. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets),

ROA*LN(Total Assets)

ANOVAa


1 Regression 156.528 5 31.306 26.525 .000b

Residual 1954.490 1656 1.180

Total 2111.018 1661


b. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets), ROA*LN(Total Assets)

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.388 .125 43.157 .000

ROA .004 .008 .042 .520 .603

LN(Total Assets) .147 .019 .232 7.638 .000

Debt .145 .158 .028 .919 .358

ROA*LN(Total Assets) -5.349E-5 .002 -.003 -.032 .975

ROA*Debt .012 .007 .063 1.651 .099


(4) LN(TS)=f(ROA,LN(TA),Debt,ROA*LN(TA),ROA*Debt)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1.575a .330 .328

1.05491593298

7620

a. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets),

ROA*LN(Total Assets)

ANOVAa


1 Regression 908.295 5 181.659 163.238 .000b

Residual 1842.876 1656 1.113

Total 2751.170 1661


b. Predictors: (Constant), ROA*Debt, Debt, ROA, LN(Total Assets), ROA*LN(Total Assets)

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.367 .121 44.276 .000

ROA -.035 .008 -.299 -4.317 .000

LN(Total Assets) .353 .019 .486 18.848 .000

Debt -.074 .153 -.013 -.481 .630

ROA*LN(Total Assets) .010 .002 .517 6.195 .000

ROA*Debt -.008 .007 -.035 -1.056 .291


(5) LN(TM)=f(EPS,LN(TA),Debt,EPS*LN(TA),EPS*Debt)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1.309a .096 .093

1.07373255292

9834

a. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS

Excl*LN(Total Assets), EPS Excl

ANOVAa


1 Regression 201.812 5 40.362 35.009 .000b

Residual 1909.205 1656 1.153

Total 2111.018 1661


b. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS Excl*LN(Total Assets),

EPS Excl

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.347 .138 38.716 .000

EPS Excl .022 .044 .066 .501 .617

LN(Total Assets) .189 .020 .297 9.538 .000

Debt -.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


(6) LN(TS)=f(EPS,LN(TA),Debt,EPS*LN(TA),EPS*Debt)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1.569a .324 .322

1.05991803100

6872

a. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS

Excl*LN(Total Assets), EPS Excl

ANOVAa


1 Regression 890.776 5 178.155 158.582 .000b

Residual 1860.394 1656 1.123

Total 2751.170 1661


b. Predictors: (Constant), EPS Excl*Debt, Debt, LN(Total Assets), EPS Excl*LN(Total Assets),

EPS Excl

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.117 .136 37.537 .000

EPS Excl .221 .043 .579 5.083 .000

LN(Total Assets) .446 .020 .615 22.847 .000

Debt -.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


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