ESSAYS IN EXECUTIVE COMPENSATION

By

João Paulo Torre Vieito

A DISSERTATION

Presented to the Faculty of Economics at the University of Porto in the partial fulfilment of requirements for the degree of Doctor

of Business Sciences

Advisor: António Melo Cerqueira

Co­advisor: Elísio Fernando Moreira Brandão

University of Porto Faculty of Economics and Management

Porto, Portugal

May, 2008

I

THE AUTHOR

João Paulo Torre Vieito has a Masters in Finance and a MBA in Management of

Commercial Operations from the Catholic University of Porto. His honours degree is in

Organisation and Business Management from ISCTE. He has been professor of finance

since 1993 at the Polytechnic Institute of Viana do Castelo and Vice­President of the

School of Business Sciences since 2005.

II

ACKNOWLEDGEMENTS

I am particularly indebted to my dissertation advisor Prof. António Melo

Cerqueira, and my co­advisor Prof. Elísio Fernando Moreira Brandão, from the

University of Porto ­ Faculty of Economics and Management, for their valuable

guidance in developing this research. In addition, I would like to thank Prof. Walayet

Khan, from Evansville University, Prof. Kevin Murphy from the University of Southern

of California, Prof. Amir Licht, from the Interdisciplinary Center Herzliya, Prof. Sheng

Huang from Washington University, Prof. Sara Robicheaux, from Birmingham­

Southern College ­ Alabama and Prof. Mário Augusto, from the University of Coimbra,

for their helpful comments on this work.

I would like also to thank my wife, Sandra Manso, for her extraordinary support

during this project with the family, and my children Patricia Vieito and Gonçalo Vieito,

for the moments that I have not spent with them watching them grow up.

Finally, I would also like to dedicate this dissertation to my parents, Filipe Vieito

and Maria José Vieito and to my uncles and aunt Luciano Vieito, Maria de Fátima

Vieito and João Vieito. They were willing to sacrifice so much throughout their lives so

that I could succeed. I feel fortunate to have been blessed with such wonderful parents

and aunts and uncles.

III

ABSTRACT

ESSAYS IN EXECUTIVE COMPENSATION

Advisor: António Melo Cerqueira; Co­advisor: Elísio Fernando Moreira Brandão

This dissertation consists of four parts. In the first part, we review the literature on

executive compensation in the last 13 years and in the other three parts, we investigate a

few critical questions in the executive compensation area as described below.

We analyze whether or not the determinants, the form and total executive compensation

are the same for the new versus old economy firms (first essay), NYSE and NASDAQ

listed firms (second essay) and S&P500, S&PMidCap and S&PSmallCap firms (third

essay). For all these three groups of firms we also analyze whether or not the NASDAQ

crash in 2000 and the Sarbanes­Oxley (SO) Act in 2002 changed the structure of

executive compensation.

In all the cases we find that the factors that explain executive compensation are

generally different. However, in a few instances of common factors the intensity of

these factors (coefficients) is different and this difference is generally statistically

significant. Our results also reveal that the determinants and forms of executive

compensation did indeed change after the NASDAQ crash and the Sarbanes­Oxley Act.

IV

TABLE OF CONTENTS

Page List of tables VII Introduction 1 CHAPTER ONE: Executive Compensation: The Finance Perspective Perspective 5

1. Introduction 6 2. Executive Compensation Trends after 1930 in the USA 7 3. Executive Compensation, Firm Performance and Relative Performances 9 4. Executive Compensation and Agency Theory 18

5. Executive Compensation and Mergers and Acquisitions 23

6. Executive Compensation and Dividend Policy 27 7. Executive Compensation and Capital Structure 28

8. Risk and Executive Compensation 29

9. References 32 CHAPTER TWO: Essay 1 ­ Trends and Determinants of Executive Compensation in the New and Old Economy 44

1. Introduction 45 2. Literature Review and Research Questions 47 3. Data, Sample Selection and Statistics 49 3.1. Data and Sample Selection 49 3.2. Statistics 50 4. Research Design 56 4.1. Dependent Variables 56 4.2. Independent Variables 58 4.2.1. Financial Variables 58 4.2.2. Governance Variables 62 5. Empirical Results 64 5.1. Summary Statistics 64 5.2. Determinants of Executive Compensation in the New and Old Economy 65 6. Conclusions 73 7. References 74 8. Appendix 77

CHAPTER THREE: Essay 2 ­ Executive Compensation: NYSE and NASDAQ listed firms 84

1. Introduction 85 2. Literature Review and Research Questions 86 3. Data, Sample Selection and Statistics 88

V

3.1. Data and Sample Selection 88 3.2. Statistics 89 4. Research Design 95 4.1. Dependent Variables 95 4.2. Independent Variables 96 4.2.1. Financial Variables 96 4.2.2. Governance Variables 100 5. Empirical Results 102 5.1. Summary Statistics 102 5.2. Determinants of Executive Compensation for NYSE and NASDAQ

listed firms 103

5.3. Analysis of the Results 110 6. Conclusions 114 7. References 115 8. Appendix 118

CHAPTER FOUR: Essay 3 ­ Executive Compensation of S&P Listed Firms 125

1. Introduction 126 2. Literature Review and Research Questions 128 3. Data, Sample Selection and Statistics 130 3.1. Data and Sample Selection 130 3.2. Statistics 131 4. Research Design 133 4.1. Dependent Variables 133 4.2. Independent Variables 138 4.2.1. Financial Variables 139 4.2.2. Governance Variables 140 5. Empirical Results 143 5.1. Summary Statistics 143 5.2. Determinants of Executive Compensation in S&P500, S&PMidCap and

S&PSmallCap listed firms 134

5.3. Impact of NASDAQ crash on the determinants of executive compensation 151

5.4. Analysis of the Results 154 6. Conclusions 157 7. References 158 8. Appendix 161

Conclusions and Possible Extensions 168

VI

List of the Tables Chapter 2: Page 1 Number of Items of Compensation by SIC Code 50

2 Mean Total Executive Compensation for New and Old Economy Firms (1992­ 2004)

51

3 Executive Compensation as a Percentage of Total Compensation in New and Old Economy Firms (1992­2004)

53

4 Statistics From Principal Component Analyses 59 5 Summary Statistics 64

6 Fixed Effect Regression: Least of Square Dummy Variables CEOs ­ New Economy Executives 66

7 Fixed Effect Regression: Least of Square Dummy Variables ­ Directors ­ New Economy Executives

67

8 Fixed Effect Regression: Least of Square Dummy Variables ­ CEOs ­ Old Economy Executives

68

9 Fixed Effect Regression: Least of Square Dummy variables ­ Directors – Old Economy Executives 69

10 T Test of Equality of Fixed Effect Regressions Coefficients ­ CEOs 77

11 T Test of Equality of Fixed Effect Regressions Coefficients ­ Directors 80 12 Pearson Correlation of Independent Variables 83 Chapter 3:

1 Average Total Compensation between 1992 and 2004 Adjusted to Inflation 90

2 Yearly Inflation Adjusted Total Compensation Trends of NYSE and NASDAQ Listed Firms Between 1992 and 2004 91

3 Yearly Percentage of Each Compensation Component of NYSE and NASDAQ Listed Firms ( 1992­2004)

92

4 Statistics from Principal Component Analyses 97 5 Statistics from Regression Variables 102 6 Fixed Effect Regression Analysis of Compensation Determinants 105

7 Fixed Effect Regression Analysis of Compensation Determinants of CEO Compensation for NYSE Listed Firms 106

8 Fixed Effect Regression Analysis of Compensation Determinants of CEO Compensation for NASDAQ Listed Firms 107

9 Fixed Effect Regression Analysis of Compensation Determinants of Director Compensation for NYSE Listed Firms 108

10 Fixed Effect Regression Analysis of Compensation Determinants of Director Compensation for NASDAQ Listed Firms 109

11 T Test of Equality of Fixed Effect Regressions Coefficients ­ CEOs (NYSE vs. NASDAQ) 118

VII

12 T Test of Equality of Fixed Effect Regressions Coefficients ­ Directors (NYSE vs. NASDAQ) 121

13 Pearson Correlation 124 Chapter 4:

1 Mean Total Compensation Levels Adjusted for Inflation by Years (1992­ 2004)­Top Five 132

2 Executive Compensation Components as a Percentage of Total Compensation for S&P500, S&PMidCap, S&PSmallCap Listed Firms (1992­2004) 134

3 Statistics from S&P500, S&PMidCap and S&PSmallCap Listed Firms 143

4 Fixed Effect Regression Analysis of Determinants of CEO Compensation for S&P500 Listed Firms

145

5 Fixed Effect Regression Analysis of Determinants of Director Compensation for S&P500 Listed Firms 146

6 Fixed Effect Regression Analysis of Determinants of CEO Compensation for S&PMidCap Listed Firms 147

7 Fixed Effect Regression Analysis of Determinants of Director Compensation for S&PMidCap Listed Firms

148

8 Fixed Effect Regression Analysis of Determinants of CEO Compensation for S&PSmallCap Listed Firms 149

9 Fixed Effect Regression Analysis of Determinants of Director Compensation for S&PSmallCap Listed Firms

150

10 Fixed Effect Regression Analysis of CEO Total Compensation Determinants before and after NASDAQ Crash in 2000 for S&P Listed Firms 152

11 Fixed Effect Regression Analysis of Director Total Compensation Determinants before and after NASDAQ Crash in 2000 for S&P Listed Firms 153

12 T Test of Equality of Fixed Effect Regressions Coefficients ­ CEOs 161

13 T Test of Equality of Fixed Effect Regressions Coefficients ­ Directors 164

14 Pearson Correlation of the Independent Variables 167

1

Introduction

There was little research interest in the area of executive compensation until the

end of 1980s – on average two articles per year before 1985, according to Murphy

(1999). However, in recent years the research interest is growing exponentially. How to

motivate executives to increase shareholders wealth without performing fraudulent acts

is now one of the most interesting and challenging topics of research in the executive

compensation area. Executive compensation has become so important that it is now

investigated in vast areas such as mergers and acquisitions, firm performance, capital

structure and dividend policy.

Several factors have contributed to this phenomenon: a significant number of

privatizations of state controlled enterprises, the reduction in a significant number of

international trade barriers and the free flow of information associated with the

development of new technologies have started to create a small "integrated global

village." The combination of all these factors has led to an unprecedented worldwide

economic growth, and this increase in global competition has intensified the search for

executive talent across the world. Firms now compete for highly qualified executives

globally, hoping that their knowledge will be instrumental in increasing the share value

of the firms that they will manage. The search for good managers has made the average

compensation for Chief Executive Officers (CEOs) in S&P500 listed firms more than

double since 1970, with their compensation in the last few years generally tied to firm

performance, essentially based on stock options.

In this dissertation we first review the empirical and theoretical research in

executive compensation area and than analyze whether or not the factors that explain

executive compensation are the same in three different groups: New versus Old

Economy 1 , NYSE versus NASDAQ listed firms and S&P500, S&P Mid Cap and S&P

Small Cap listed firms. We also analyze whether or not the forms and total value of

executive compensation are the same in these groups and whether or not they changed

after the NASDAQ Crash in 2000 and the Sarbanes­Oxley Act in 2002. We use fixed

1 New and Old economy firms are defined by authors like Anderson, Banker, and Ravindran (2000) and Murphy (2003) as firms competing in the computer, software, Internet, telecommunications, or networking fields.

2

effect regression analyses and collect data from the ExecuComp database from Standard

and Poor’s, which has collected information, since 1992, on the five best paid

executives from firms listed in the S&P1500 indexes.

We receive theoretical as well as empirical motivation to undertake the current

research. Scholars such as Ittner et al. (2003), Anderson et al. (2000), Murphy (2003),

Stathopoulos et al.(2004) and Chen and Hung (2006), document that new economy

firms are fundamentally different in terms of many characteristics they possess

compared to the old economy firms, and if these differences exist, we believe that total

value, the form, and the factors that explain executive compensation can also be

different between these two groups. There is only one study, developed by

Stathopoulos, Espenlaub and Walker (2004), which analyzes the executive

compensation for both new and old economy firms. But this study is confined to the

English market only. Most studies focus on the preponderance of stock options as a

form of executive compensation, without analyzing the remaining components of the

compensation. With the exception of the study developed by Murphy (2003), all other

studies on executive compensation analyze small periods of time; therefore, the

conclusions achieved by these studies must be validated for a longer period of time.

As far as literature review in terms of market structure and executive

compensation is concerned, we find only one research, developed by Firth, Lohen et al.

(1996), that analyzes the factors that explain CEO compensation in Norwegian Stock

Exchange listed firms. As far as we know, no research has yet analyzed whether or not

the factors that explain executive compensation for firms listed on the NYSE versus

NASDAQ are the same. We believe that NASDAQ listed firms are different than the

NYSE listed firms in particular in terms of liquidity and level of cash flows. Sapp and

Yan (2000) document that some small companies listed on NASDAQ changed to

AMEX exhibited improved liquidity. The same results are achieved by Chung et al.

(1999, 2001) and Bacidore and Lipson (2001), who document that NASDAQ listed

companies, which changed to the NYSE, experienced reduced transaction costs. If

NASDAQ and NYSE have different transaction costs, the liquidity of these firms will

be different. Murphy (2003) also reports that NASDAQ listed firms are essentially

technological firms with low levels of cash flows. If the levels of cash flows of these

3

two groups of firms are different, we then expect that factors that explain executive

compensation of these two groups of firms will be different.

Lambert et al. (1990) analyze the relationship between the changes in executive

compensation and the firm size and find that this relationship is positive and significant.

Most of the research on executive compensation inserts a variable of size (which can be

Assets, Market Value or Sales, or even the natural logarithm of this variable) in the

regression analysis to control for firm sizes, but we go further in our study. Instead of

inserting one additional size variable in multiple regressions, we focus on three different

size groups of large firms (S&P 500), medium size firms (S&P Mid Cap), and small

firms (S&P Small Cap) individually.

Given the limitations of the current studies we extend the executive

compensation research by analyzing what factors explain executive compensation in the

three different groups (New and old economy; NYSE and NASDAQ, and S&P500,

S&P Mid Cap and S&P Small Cap). We also analyze whether or not the form and total

values of executive compensation are similar in the above three groups and whether or

not these values changed after the NASDAQ Crash and Sarbanes­Oxley Act.

Essentially, we believe that controlling for firm size and industry effect, the factors that

explain executive compensation may be different because each S&P index represents

different groups of firms with different characteristics, such as net income, sales and

dividends.

Effectively, after the NASDAQ crash, we saw a series of financial scandals

associated with the bankruptcy of some of the large American companies based on

fraudulent accounting practices and executive self­dealing. The Sarbanes­Oxley Act of

2002 was established on July 30th to solve this problem. It introduced sweeping

changes in the governance, reporting, and disclosure requirements of public firms with

the aim of improving accuracy, reliability, and timeliness of the information provided to

investors. This Act contains provisions which have a significant impact on the benefits

and compensation of public company executives. The major changes in this area include

the following provisions: to prohibit publicly­traded companies from making or

arranging loans for their directors and executive officers; to expedite Securities and

Exchange Commission (SEC) reporting to insider traders; to prohibit corporate directors

and executive officers from trading employers` securities during planned blackout

4

periods with respect to those securities and to require an employee retirement Income

Security Act to cover individual account plans to provide a 30­day notice of blackout

periods. So far little is known about the real impact of this legislation in terms of

executive compensation. Narayanan and Seyhun (2005) document that the 2002

Sarbanes­Oxley Act has curtailed, but not eliminated, managerial influencing of the

grant day stock price and Murphy (2003) also reports that some changes have occurred

in terms of the number of stock options granted to executives. Based on these findings,

we expect that the NASDAQ Crash and Sarbanes­Oxley Act have changed the form and

total values of executive compensation.

This dissertation is organized in the following way: Chapter I presents the

review of literature on executive compensation since 1995 based on the best

publications in the area. Chapter II (first essay) analyzes the trends and determinants of

executive compensation in new and old economy firms; Chapter III (essay 2) analyzes

the executive compensation in NYSE and NASDAQ listed firms; Chapter IV (essay 3)

analyzes the executive compensation of S&P listed firms. Finally, we describe the final

conclusions and possible extensions.

5

CHAPTER I

Executive Compensation: The Finance Perspective

6

1. Introduction

Executive compensation is presently one of the most interesting and innovative

fields of research in the finance area. It was only in the 1990s, with the growth of the

world economy, that shareholders felt the need to contract executives and give them

incentives to make firms’ stock market growth increasingly faster each year. Academics

and researchers started searching for the best form of compensation to motivate these

executives. It was not only the values that mattered but also the way in which

executives were paid: with more short term compensation (salary or bonus) or more

long term compensation (stock options, restricted stocks, long­term incentives plans) or

even with other forms of compensation like perks, and the impact of these

compensation policies on all the fields of finance.

In this paper we describe the literature review on executive compensation from

the period between 1995 and 2007. The reason for choosing this period of time is that

we believe that 13 years is enough time to cover a set of research studies that are

representative of executive compensation research lines.

To better understand the state of the art in terms of executive compensation we

aggregate this research by topic. First, we describe the evolution of executive

compensation in America since 1936. After that, we describe the different relationship

between executive compensation, firm performance and relative performance, agency

theory (asymmetric information: adverse selection, moral hazard and double moral

hazard), mergers and acquisitions, dividend policy, capital structure and risk.

7

2. Executive Compensation Trends After 1930 in the USA

The number of empirical studies on executive compensation has increased

exponentially since the beginning of the nineties. Before this period, there is little

knowledge about executive remuneration in America and worldwide.

The most important study that collected information on executive compensation

evolution in America for a long­term period was undertaken by Frydman and Sacks

(2005) based on data since 1936. The authors divide and analyse the period into three

important groups: after World War II, executive compensation decreases slightly; from

the 1940s until the 1970s executive compensation grows slowly, and after the 1990s we

see a fast growth in executive compensation. To these findings we can also add the

information that the NASDAQ crash in 2000 stopped this growth.

There is a generally accepted idea that until the end of the 1980s, probably the

beginning of the 1990s, executives were paid only with salary and bonus and that other

compensation components, like executive stock options or restricted stocks, were then

created in this period to satisfy an increasing desire for pay by firms’ performance,

essentially because shareholders wanted executives to maximise the firms’ values, and

indirectly maximise their wealth. But this is not true. Frydman and Sacks (2005)

documented that between 1934 and the 1950s executives were paid mainly with cash

compensation and in some cases with bonuses. Stock options appeared in 1951 and 18%

of the analysed executives received this kind of compensation component 2 . Due to

advantages in terms of taxes, restricted stock was also introduced at this time in the

USA as a compensation component. Until the 1970s, this kind of compensation

component was used by shareholders to motivate executives to expand the firm’s

production capacity. This was the shareholders’ main goal: to have big companies with

high production capacity. But with the significant economic changes that occurred in

the USA at this time, large organisations found themselves with production excess,

which indirectly led to significant falls in the firms’ market value. With these losses in

terms of the firm’s value, shareholders felt the need to contract executives and motivate

2 The authors report that a small number of companies give stock options to their executives but in the

analysed sample, no executive receives these.

8

them to increase shareholders’ wealth again. If shareholders motivate executives based

on firm performance, by giving part of their compensation in stock options, they give

them the possibility of owning a small part of the firm’s capital in the future. In this

way, it is understood that executives will make greater efforts to increase the firm’s

stock market value to exercise their stock options, and at the same time increase

shareholders’ wealth.

It was only in the nineties, with a significant growth in the world­wide economy,

and with the sprouting of companies associated with new technologies, also called “new

economy” firms, that the academic community, at world­wide level, focused its interest

on the executive compensation problem, first essentially for CEOs and then for the

remaining employees.

With the market in expansion, and great worldwide expectation created for the

stock return of the companies associated with new technologies, the market value of

these companies continued to rise. Financial analysts found strong incongruence

between the market value of these firms and traditional model evaluations. It became

urgent to develop new evaluation models that would determine the real value of the

stocks of this new concept of organisation, which works at world­wide level and

generally does not have a significant sales force. Some authors, like Sanders and Boivie

(2004), also defend that in the case of US Internet firms, market valuation was strongly

associated with the level of executive stock­based incentives compensation.

The new economy firms’ stock prices grew so much that, predictably, in the year

2000, the NASDAQ crash occurred.

Most of the compensation plans based on stock options are now out­of­the­

money and the reason for granting them to executives – to motivate them – has

disappeared.

In an attempt to retain good executives and other employees, many shareholders

reformulated the stock option plans, readjusting the exercise price to an attractive value.

Others opted to attribute new stock option plans.

The first few years of this new century were characterised by a significant number

of fraudulent firm bankruptcy cases and this bankruptcy was, in most cases, related to

compensation policies based on stock options. Executives wanted to increase the firm’s

value so much, in order to exercise their options, that they sometimes lied to the market

9

and manipulated the firm’s accounting data to achieve their goals. To solve these

problems, in 2002 the Sarbanes­Oxley act was created in the USA, which introduced a

significant number of changes in terms of corporate governance.

The real impact of these new corporate governance rules in terms of executive

compensation is still not clear and investigating this is one of the goals of this study.

After this brief description of the evolution of executive compensation since

1936 in the USA, we next describe a group of research studies that relate executive

compensation to different fields in the finance area.

The majority of the studies on executive compensation to date have been based

on data from only one country, and do not compare executive compensation in different

parts of the world; see for example, Lowe et al. (2002). One of the reasons for this is

that, in most countries, information on executive compensation is scarce, disperse and is

aggregated to all the boards and not described for each top executive.

3. Executive Compensation, Firm Performance and Relative Performances

The relationship between executive compensation and firm performance has

been widely analysed in academic publications in the area of accounting, essentially

comparing executive compensation methods with vast groups of accounting items, but it

has not been examined so much in the financial area that focuses on firms’ stock market

price. Some of the findings in this area are described by Devers et al (2007), Bebchuck

and Fried (2004) and Murphy (1999).

Firms that implement executive compensation plans based on performance

generally create more ambitious and difficult strategies (Dow and Raposo, 2005) than

companies that do not give this kind of compensation to their executives, and when the

adoption of these compensation plans for CEOs is announced to the market,

shareholders’ wealth generally increases (Morgan and Poulsen, 2001). In most cases,

the market will respond positively because it believes that the CEO will develop efforts

to increment the firm’s stock market value to the level that will guarantee that stock

options will be exercised.

Short term executive compensation components have been reported as being

negatively related to firms’ corporate social performances and positively related to long­

10

term executive compensation items (Deckop et al. 2006). Conversely, authors like

Sanders (2001a), found better firm performances when firms adopt year­end

readjustments of executive compensation than when firms adopt executive

compensation contracts with predominantly long­term compensation components, such

as stocks and restricted stocks. In other words, executives, essentially CEOs, will

secure better performances for the firm if they know that the company will readjust their

compensation in positive terms at the end of the year, and performances will not be as

good if this compensation is based on long­term compensation components.

Better performances are also achieved in US multinational firms when they

contract CEOs with international experience (Carpenter et al. 2000 and 2001). It has

also been reported that when a CEO is at the same time the Chairman of the Board, this

situation influences, if only in small terms, the firm’s long term performance (Baliga et

al. 1996).

Another interesting finding about firm performances and executive compensation

is that in firms that are controlled by the owners, or in firms where the owner is also the

manager, the relationship between pay and firm performance for all the employees is

higher than in other cases (Wener et al. 2005). But if the CEOs are family CEOs, in

family­controlled firms or the firms have stakeholder management, they generally

receive less total compensation than outsider CEOs. The compensation increases when

family ownership also increases (Gomez­Mejia et al., 2003) but the gap exists even

when firms’ performances increase (Coombs and Gilley, 2005). Performances, in the

case of banks, have been reported by Magnan and St­Onge (1997) as being associated

more with executive compensation in a high managerial discretion context than in a low

managerial one.

The relationship between the change in CEO compensation and firm strategy

has been described as positive when firm performances are low, and negative when firm

performances are higher (Carpenter, 2000). Firms that use defensive strategic

orientations have better firm performances when they pay their executives in cash and

bonuses, and when they evaluate the firm performances based on accounting items.

Firms that adopt prospector strategic orientations will have better performances if they

pay their executives in stock, or stock options, and when they use market measures to

evaluate managerial performance. Furthermore, firms that adopt governance reforms

11

have been reported as achieving higher levels of market performances than firms that do

not adopt governance reforms (Tuschke and Sanders, 2003).

Generally, in firms where agency problems are higher, the performance levels

are lower and these firms pay more to their CEOs than companies with low agency

problems (Core et al, 1999 and Adams et al., 2005). If CEOs’ actions are not monitored,

they will try to extract higher compensation from shareholders than in companies where

the monitoring process is efficient. Pay to board members based on firm performance

has also been described as reducing the likelihood of “White­Collar” crimes being

committed (Schnatterly, 2003).

At present, it is generally accepted that compensation based on performance is

more common for CEOs than for other executives (Ang et al., 2002 and Aggarwall and

Samwick, 2003), because they have more power than other low level executives to

influence a firm’s stock price. The amount that is paid to executives to motivate them is

important, but more important than that is to choose what percentage total compensation

stock options should represent (Kole, 1997 and Mehran, 1995). Some executives may

develop maximum efforts just with a small number of stock options, but to motivate

others, it is necessary to give a significant amount of this compensation component. But

future firm performance is not only dependent on the total compensation, or the

percentage of stock options received, but also on how much is paid to the other

members of the top management team (Carpenter and Sander, 2002, 2004)). In other

words, if the CEO receives a lot more than other top management executives, the latter

will probably not be actively involved in the goal of increasing firm performance and

will pass this responsibility to the CEO. The behaviour and economic view of the gap in

terms of executive compensation between CEOs and other top executives is described

as balanced as predictors of firm performance by Henderson and Fredrickson (2001).

When a firm makes changes in the top management team, the gap between the CEO and

other top management team members’ compensation is positively related to the number

of participants in the firm tournament but the changes in terms of how much is paid to

the executive team has only a small influence on determining company performance

(Conyon et al., 2001). Essentially, this gap must exist but the differences should not be

very significant in order to guarantee that all the members of the management team will

be motivated to increase firm performance. It is logical that hierarchical firm position

12

influences not only the design of the executive compensation plan, but it is most

important that the hierarchical position is related to the level of interaction of each

member with the strategic firm orientation (Boyd and Salamin, 2001). Generally, top

executives will receive more total compensation and have more influence over strategic

firm decisions than low level executives. Total compensation, and long­term

compensation of top executives that are not CEOs, have been reported as positively

related to firm performances (Carpenter and Sanders, 2004) Generally, top executives that have a strong professional reputation are paid

more in terms of performance (Milbourn, 2003) because shareholders know that their

decisions and actions will directly influence a firm’s stock market price. They also

receive more total compensation than non­certified CEOs in cases where firm

performance was high, but less remuneration when performance was poor (Wade et al.,

2006). CEOs who are considered celebrities will internalise such celebrity and tend to

be overconfident about the efficacy of their past actions and their future ability to

increase firm performances (Hayward et al., 2004).

Granting stock options to executives is a way for shareholders to guarantee that

if they lose wealth, so will the executive. Narayanan (1996) also points out that when

executives are remunerated essentially with cash, in the long term they will make fewer

firm investments than optimal, but if they are essentially remunerated with stock

options, the firm’s investment will be higher than optimal value in the long run.

Because of this, the authors defend that the best way to guarantee that executives will

make the optimal firm investment is to create compensation contracts with both

restricted stocks and cash.

As previously said, the shareholders’ main intention, in giving their executives

these kinds of compensation components, is to motivate them to make the firm’s stock

price grow to the desirable level where they will be able to exercise the stock options

and, at the same time, increase shareholders’ wealth. But this is not always the only

reason why stock options are given to executives. Authors like Core and Guay (2001)

also argue that companies give stock options to their executives when they are facing

capital requirements and financing constraints, because this is an expert way to pay

executives without cash. On the other hand, Kato et al. (2005) documented that

companies with more future growth perspectives also give stock options to their

13

executives. The reason for this is not only associated with the fact that they might have

to face small cash flows in order to pay in cash or bonus, but also because pay based on

the firm’s stock price will motivate executives to increase the size of the firm.

If companies give stock options, or restricted stocks 3 , to their executives to

increase firm performance, what happens if these executives cannot make the firm’s

stock grow to the desirable value that will give the capacity to exercise the options?

Generally, in order not to lose their executives, firms with a low level of performance

use repricing, also called resetting, of stock option plans (Chen, 2004 and Brenner et al.,

2000). In other words, companies will reduce the actual exercise price to a more

attractive value where the probability of stock options being exercised is higher in the

future. What shareholders want is to motivate executives again to increase the firm’s

stock market value and, indirectly, their wealth.

Stock option repricing is more common not only in firms with low performance

levels (Chen, 2004 and Brenner et al., 2000), but also in small companies and firms that

have low market notoriety; young firms and companies associated with new technology

(Carter and Lynch, 2001), and firms where boards are essentially composed of insiders

(Chance et al., 2000). The latter authors also find that some companies use stock option

repricing more than once, and when this happens, firms generally have lower

performance in the first year, but in the second year the stock option plans are mostly

in­the­money. If companies must change the exercise price more than once, the desired

incentive effect on the executive is reduced, and this exercise price will be close to the

present market stock price of the firm. This is the reason why the options are generally

in­the­money within two years. Pollock et al. (2002) also note that more visible CEOs,

and CEOs with high stock firm ownership, will have less power to negotiate small

spread between new exercise price and firm stock market value because the market will

think that they will be extracting personal benefits with these changes. Grossman and

Cannella Jr (2006) add the information that CEOs/Chairs that have a significant

ownership tend to maintain the reward policies based on fixed compensation across the

years.

There are some companies that do not contemplate the possibility of being

repriced in their stock option plans. What happens in these situations when the firm’s

3 Restricted stocks are stocks with restrictions on when they can be exercised. The restriction usually lifts in 3 to 5 years when stock vests.

14

stock market price falls to a level at which executives cannot exercise these options and

the desirable incentive effect disappears? Do companies lose their executives? In most

cases, when firms do not want to lose their executives, they give them a new stock

option plan with a more attractive exercise stock price (Chen, 2004).

Some authors defend that stock option repricing should not be used because

firms are paying bad executives, and shareholders are giving these executives a second

chance to get a remuneration that they do not deserve. This is the case of Garvey and

Milbourn (2006), who defend that executives are sometimes paid for “luck” and

sometimes for firm performance. In most cases when companies have bad

performances, shareholders make arrangements to pay these executives and for them to

stay in the job. In the case of emerging markets the situation is different. Executives

from firms with bad performances generally do not have a second chance and lose their

job (Gibson, 2003). In this way, with the exception of emerging markets, the number of

years that a CEO remains in his job (tenure) can be conditioned by firm performance,

and, according to Gomez­Mejia et al., (2001), by the business risk and whether the

company is a family company.

Whether repricing is a good or bad methodology to motivate executives again is

not consensual in terms of finance research. We know that in some cases the reason

why companies present low performance is not associated with bad management but

due to external factors that the CEO cannot influence and this must be one of the

reasons why some authors defend that the repricing methodology must continue to be

considered in executive compensation contracts (Acharya et al., 2000).

One of the fields of research where the relationship between executive

compensation and performance was also analysed was in Hedge Funds, because the

compensation of these executives (also called fund advisors) is, in part, related to fund

performance. The fund advisors will receive an additional compensation when the fund

market price is higher than a certain level called the “high­water mark”. In this way,

executives will try to develop efforts to receive additional compensation and will

sometimes make speculative investments to guarantee that the fund price will rise to the

desirable values that will guarantee they receive this additional compensation. To

achieve these goals, executives sometimes develop speculative investments. The

existence of speculative investments was detected by Golec and Starks (2004) when

15

they investigated the effect of the introduction of regulation, by the American Congress,

to control fund advisors’ compensation and found that after the legislation, most of the

mutual funds changed their risk positions. Authors like Goetzmann et al. (2003) also

add that the existence of “high­water marks” in hedge fund compensation contracts can

be a limit of the performance fee because fund advisors will probably only develop

efforts to achieve this established limit.

Another interesting study associated with hedge funds was developed by

Khorana (2001), who analysed the effect of fund managers’ replacement on future

hedge fund performance. When managers that have implemented low quality decisions

that lead to low fund performance are replaced, hedge fund performance improves.

When managers who have implemented good quality decisions that lead to better fund

performance than average are withdrawn from the management of these funds, hedge

fund performance decreases.

Executive compensation has also been analysed, not only in relation to firms’

performances, but also to firms’ relative performances 4 . Traditional models of pay for

performance give additional remuneration to executives when companies have better

results than the previous year, and to evaluate firms’ performance a vast group of

variables is generally used (Hermalin and Wallace, 2001). In a pay for relative

performance model, the firm’s performance is compared not to last year’s firm results

but to the performance of the main rival firms. The pay for relative performance

methodology is a more demanding model of compensation than the simple traditional

method. In the first case, executives only receive additional compensation if the firm

where they work is one of the best in their area of the market. But the situation can be

difficult when the company has products with a high level of market competition.

Generally, these firms are forced by the market to have low performance and this can

negatively affect executive compensation (Aggarwal and Samwick, 1999).

Pay in terms of relative performance has been defended by authors like Garvey

and Milbourn (2004) as a mechanism for removing the influence of the market­wide

(não falta uma palavara aqui?) on executive compensation, and young and less wealthy

executives have been reported as delegating the stock options risk immunisation to their

4 Relative performances are the firm’s performances compared not with the performances of the

firm in the last few years but with the performance of the main rivals on the market.

16

company. But at present, Rajgopal et al. (2006) and the same authors Garvey and

Milbourn (2006) defend that this theory is not credible because executives can replicate

such indexation in their private portfolios and do not need the help of the companies to

fix the risk associated with receiving compensation based on stock options.

Also regarding executive compensation and firm’s relative performance,

Yermack (2006) documents that firms that give their CEOs the possibility of using firm

airplanes for their private benefit, present, on average, 4% lower performance than the

market average. Perry and Zenner (2001) also report that the introduction of legislation

in America in 1992 limiting the deductibility of executive cash compensation greater

than 1 million dollars (Internal Revenue Code 162 (m)), has made the affected

companies change to a compensation policy based on firm performance. If executives

can extract higher compensation from firms based on salaries without being indexed to

efforts to making firm stock price rise, this will generally be the best compensation

policy for executives. It does not matter what performances firms have because they

will get the same fixed amount in money. This new legislation introduces a fairer way

of paying top executives. They will receive higher fixed compensation in cash, up to

one million dollars, but the other part must be indexed to their personal effort level to

make the firm grow.

In companies where layoff policies are announced, the CEOs receive, on

average, 20% more compensation than companies that do not adopt these policies

(Brookman et al., 2007). Essentially, CEOs that have the hard job of reducing human

resources costs to improve firm performance will receive more than other CEOs.

Another interesting study was developed by Brickley et al. (1999), which documents

that a good way of inducing CEOs to increase firm performance is to give them the

possibility of being on the company board after their retirement. Bernartzi (2003)

analysed why firm employees place a significant part of their compensation in the

firm’s stock and found that, generally, they make these investments because they

believe that good past firm performance will continue in the future, but, according to the

author, past firm performance is not an index of good future firm performance.

Controlling for firm size, performance, and other factors, Balkin et al. (2000)

document that CEO short­term compensation is related to the degree of innovation,

measured by the number of patents and R&D spending, and in the case of low­

17

technology firms no relationship exists between innovation and short, or long­term CEO

compensation. Makri et al. (2006) also find that when a firm’s technological intensity

increases, the total CEO compensation incentive will be more closely aligned with the

impact of the firm’s invention on other related inventions and with the firm's

commitment to scientific research, and bonus compensation will be more related to

financial results.

Tosi, Werner et al. (2000) report that firm performance variance only explains less

than 5% of CEO total compensation and it is effectively the size of the firms that has the

biggest influence on executive wealth (40%). Authors like Beer et al. (2004) report that

some firms are abandoning pay­for­performance policy, because the costs of these

programs will be higher than the associated benefits, and are using other methodologies

like effective leadership, clear objectives, coaching, or training because, in their point of

view, these are better and more efficient investments.

To conclude on executive compensation and firm performance, it may seem that

pay in terms of performance is one of the best methodologies when shareholders want

to increase their wealth because this will reduce agency cost. But this is not true. Most

cases of fraudulent bankruptcy that have occurred in America since the NASDAQ crash

have been indirectly related to executive stock options. Some of these executives had

such a large part of their compensation indexed to firm stock market price that they

made fraudulent accounting transactions and lied to the market with the intention of

successively raising the firm’s stock price to the level where they could exercise their

stock options and receive the high amounts of compensation. The Sarbanes­Oxley Act

has become an important instrument in correcting some of the problems involved.

Because of the associated problems, the use of stock options is decreasing and the use

of other long term compensation components is increasing, such as restricted stock.

Restricted stocks differ from stock options in the sense that they are stocks that are

granted to executives but which can only be sold in the long term. They are less

“dangerous” than stock options because executives will incorporate the new price of

firm stock daily and in the case of the stock options they can lose everything if they can

not raise the firm’s stock market price to the level where stock options can be exercised

and this is, in our point of view, the main problem behind those cases of executives

18

lying to the market and making fraudulent accounting transactions. Quite simply, if they

cannot exercise the options, they will get nothing.

4. Executive Compensation and Agency Theory

Regarding exchanges where principal delegates work for agents, the agency

theory precursors try to develop methods to solve contractual problems associated with

opportunistic agents.

In recent years, agency theory has been analysed in a vast number of situations

in the area of executive compensation following the analyses of Ross (1973) and Jensen

and Meckling (1976). Most of these studies investigate the executive opportunism

associated with the existence of asymmetric information between the executive and the

shareholders.

The asymmetric information problem can be categorised in the following ways:

hidden information, also called the Adverse Selection Problem, and hidden actions, also

called the Moral Hazard Problem. The Moral Hazard Problem may also appear as the

Double Moral Hazard Problem 5 .

Adverse Selection is associated with the fact that executives sometimes have

hidden information that can be omitted when the company makes the compensation

contract to get personal advantages in the future. This hidden information can generally

be associated with the following: executives have access to privileged information about

the firm’s environment; they are experts in the area and shareholders cannot evaluate

their personal knowledge, or it can be associated with the fact that the cost that the

shareholder must pay to get this information is higher than the associated benefits.

Hidden actions, also called moral hazard problems, are described by Katz and

Rose (1998) as: one party, the agent, performs actions that affect the other party

(principal); the principal cannot observe the agent’s actions and also the principal and

agent agree as to what action the executive must develop. The real difference between

the moral hazard and the adverse selection problem is based on the fact that in moral

hazard, executives will develop actions that the principal cannot observe or measure,

5 The two terms have been adopted from literature on insurance.

19

and this action makes the principal lose money, and in an adverse selection problem,

executives have more information than shareholders on some points, which they can use

for their own benefit during the contractual time.

The moral hazard problem can also assume the form of a Double Moral Hazard

problem. Gupta and Romano (1998) define this problem with the example of a

production process involving two parties, where it is difficult to know what the

contribution of each party is to the final product, and each of the parties, during the

production process, may develop a group of actions that cannot be observed by the other

party.

If executives have information that shareholders do not have, shareholders incur

the risk that executives opt not to inform, inform partially or falsify the information and

this will influence executive pay (Goldberg and Idson, 1995).

A significant number of mechanisms have been described to reduce agency

problems between executives and shareholders: the existence of institutional investors

in the company or a large number of blockholders; the use of outside directors on the

boards; the existence of debts in the firm; the managerial labour market and market for

corporate control (Agrawal and Knoeber, 1996). Authors like Burns and Kedia (2006)

defend that the use of stock options is also a good mechanism to align shareholders’ and

executives’ interests, reducing agency costs. Authors like Ang et al. (2000) have

described measures of absolute and relative agency costs to firms with different

ownership and management structures.

One of the above­mentioned mechanisms for reducing the agency cost is the

existence of institutional investors, or blockholders, in the firm. They will defend their

interest more than individual shareholders because their power in terms of firm

ownership is greater. They will also have more control over executive acts and make an

effort not to increase the level of executive compensation (Hartzell and Starks, 2003).

The authors also describe that institutional investors are generally attracted to

companies where there is a strong relationship between pay and performance.

Another mechanism for reducing agency problems related to executive

compensation is the presence of outside directors on the company Board or

Compensation Committee. The theories that argue that executives that are on the Board

or Compensation Committee will try to extract personal benefits in terms of

20

compensation are not consensual. Authors like Hallock (1997) effectively found that

when executives are on two different boards at the same time (interlocked), they can

positively influence their personal compensation and gain more than non­interlocked

executives. On the other hand, Anderson and Bizjak (2003) document that executives

that are also on the Compensation Committee do not experience a decrease in their

personal compensation when they leave these Committees, meaning that they do not

extract opportunistic advantages in terms of compensation. If agency problems exist in

the firm, one of the solutions presented by Borokhovich et al. (1997) is to have boards

with more outside than inside directors. When CEOs are less related to the other board

elements, the probability of their influencing the other members of the board to increase

their personal compensation is less than when boards are composed essentially of inside

directors.

Bank loans have also been defended by authors like Almazan and Suarez (2003)

and Elston and Goldberg (2003) as another mechanism for monitoring executive

opportunism and reducing agency costs. According to the former authors, optimal

executive compensation contracts must be created based on three essential elements:

firm performance, incentives that can highly motivate the executives and bank loans. If

banks give loans to the company, they will frequently monitor the executives’ actions to

guarantee that that money will be paid in the future. They will also force executives not

to extract high compensation from companies to guarantee that firms will have enough

money to pay the loans.

Another study that relates executive compensation and bank loans was carried

out by Osano (2002), who documents that an interesting way to reduce the number of

bank loans that are vested but not paid, and increase the bank market value, is to give

stock options to the executives because they will make efforts for these to increase the

bank stock market price and exercise stock options.

The managerial labour market is another way of reducing agency costs related to

executive compensation. The value that executives receive as compensation must be

congruent with their ability and knowledge and the existence, or not, of workers with

the same knowledge on the market. If there are a significant number of experts in the

area, executives will probably act more in line with shareholders’ interests and will

accept less compensation, because they will know that a lot of other executives want

21

their job. When the number of workers on the labour market with the same knowledge

is small, executives will probably ask for better compensation because they know that

just a small number of people have the same knowledge. Murphy (2003) complements

this information by saying that if a large group of big companies competes on the

market for high quality executives, the compensation contracts that they give to their

executives force the other companies to use the same compensation structure.

The market for corporate control has also been defended as a mechanism to align

shareholders’ and executives’ interests in the sense that if executives are not supervised

by the market, they can probably extract higher compensation then when the market

monitors their actions (Agrawal and Knoeber, 1996). Rajan and Wulf (2006) also

complement this information by saying that if there is high external monitoring,

shareholders will not need to offer their executives perks because their interests will be

aligned with executives’ interests ( Rajan and Wulf, 2006).

The use of stock options has also been defended as a mechanism to reduce

agency problems because it forces executives not to misreport (Kedia, 2006). If

executives receive a significant part of their compensation in stock options, they will try

to develop the best practices to align shareholder and executive interests, and increase

the firm’s stock price to the level at which they will be able to receive this

compensation component in the future. But can we increasingly motivate the executive

to the level where agency problems disappear? If shareholders give their executives

more stock options than they want, in order to motivate them to increase firm

performance, they will diversify their personal portfolio by selling firm stock that they

already have (Ofek and Yermack, 2000).

The main problem is how executives can increase the firm stock market price to

the necessary level to exercise the options. Executives sometimes manipulate the firm’s

accounting, or the reported earnings, to influence the firm’s stock price in positive terms

and achieve their goals, and these situations are more frequent in companies where CEO

compensation is indexed to firm stock price (Bergstresser and Philippon, 2006). Povel

et al. (2007), Yermack (1997), Hu and Noe (2001) and Narayanan (1999) also

document that some executives choose the time to send positive information to the

market to get personal benefits, and that this occurs more frequently in good times. The

relationship between fraud and good times is stronger when investors’ monitoring is

22

low. In an IPO context, Lowry and Murphy (2007) show that executives can also

influence the IPO offer price and its timing.

The relationship between asymmetric information and executive compensation

has also been analysed in a hedge fund context. This is the case of Coles et al. (2000),

who investigated the closed end­funds advisors’ premiums and detected that some

premiums were higher when the executive, also called fund advisor, was related to fund

performance, when the fund assets managed by the fund advisor were highly

concentrated in this fund, and when the executive managed other funds with a weak

relationship between executive compensation and fund performance.

A small group of other studies associated agency problems with executive

compensation. This is the case of Bernardo, Cai and Luo (2001) and Bernardo (2004),

who describe that there are agency problems associated with division managers’

compensation. Division managers sometimes omit the quality of their projects to the

CEO to increase their reputation when this project exceeds expectations and ask for

better compensation in the future. Generally, it is the senior executives who receive

more compensation based on firm performance, who have greater ownership and are

involved in projects that can more greatly influence firm stock price (Barron and

Waddell, 2003). Another study was developed by Goldman (2004), who found that

when the CEO makes the financial budget distribution to the firm department, agency

problems can exist. The reason for this is associated with the fact that when CEOs

receive a significant part of their compensation in stock options, they will send the

highest part of the financial budget to departments that will guarantee that the firm’s

stock price will rise and they can exercise their stock options.

Finally, Aboody and Lev (2000) also describe that one of the biggest sources of

agency problems is the Research and Development department (R&D). Researchers

from these departments can have a significant amount of information about the products

or services that they are developing that nobody else knows, not even the firms’ CEOs.

If they believe that this product, or service, will be a success in the future, they will buy

a significant amount of company stock and will sell these shares at a good profit when

this information is sent to the market.

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5. Executive Compensation and Mergers and Acquisitions

Interest in the way firms pay their executives and their relationship with firm

mergers and acquisitions has been increasing over the last few years. The existing

literature is essentially related to the use of some compensation components as a

mechanism to defend the company, or the executives, from takeover. This research has

made the following findings: it is not indifferent to give stock or stock options in terms

of executive effort to firm’s acquisitions; some of the executives can benefit in financial

terms from successful acquisitions, and the threat of takeover is a mechanism to reduce,

or control, the level of executive compensation. There is further research that points out

the firm’s benefits from the use of stock options to guarantee successful acquisitions

and the importance of the existence of outside directors on the board as an element to

control executive compensation and reduce the takeover hypothesis. Next, we will

describe each of these studies.

The use of stock options as a compensation mechanism for employees has been

reported has defend companies and top executives from takeovers has been analysed by

authors like Pagano and Volpin (2005). The reason why stock options are an efficient

way to protect companies from takeovers is because when employees have company

stock options, when the takeover appears they will defend the organisation and CEO

stability so as not to lose their future remunerations. Another benefit associated with the

use of stock options as a compensation mechanism for top executives is that when they

receive this kind of compensation they will be more careful and generally only buy

good companies. In other words, if shareholders give their CEO a significant part of

their compensation indexed to the firm’s stock market price, when CEOs decide to buy

a company, they generally only acquire firms with future significant growth

perspectives (Datta et al., 2001). Only these companies will guarantee that the firm’s

stock price will increase in the future and that the stock options the executive has can be

exercised. If CEOs buy bad companies, the firm’s stock price will probably decrease in

the future and CEOs will be not able to exercise these options. According to Deutsch et

al. (2007) the best way to improve shareholders' value is to give stock, or stock options,

not only to CEOs but also to outside directors of the boards. This will guarantee that not

only CEOs, but also other board members, will try to buy only good companies and

24

improve shareholders’ wealth. If they buy bad companies, their personal wealth will

also be penalised and not only that of the shareholders. Sanders (2001b) adds that giving

stock or stock options to executives has been described as having the same incentive

effect but, according to the author, the risk properties of these two compensation

components are asymmetrical and they have diametrically opposite effects on firms'

acquisition.

In most cases, when CEOs make successful mergers and acquisitions, they receive

an additional premium from the company that is generally based on cash, or in some

cases, on bonus (Hartzell et al. 2004, Bliss and Rosen, 2001 and Wright et al. 2002).

The size of this payment to CEOs has been described by authors like Grinstein and

Hribar (2004) as related to the power that they have inside the company. High powered

CEOs are able to extract significantly higher compensation from shareholders than

lower powered executives (Coombs and Skill, 2003) and, in this way, the premium that

they receive is related to their power and the intensity of monitoring activities (Wright

et al., 2002). In other words, CEOs can extract high premiums associated with

successful acquisitions if their power inside the company is higher and if the monitoring

level of their actions is small. Otherwise, they can get additional compensation but

perhaps not as much as they desired.

When a company buys another company, what happens to the executives of the

acquired company? Hartzell et al. (2004), describe that if executives from the acquired

company stay in the firm, they normally receive more compensation than before the

acquisition, but a significant number of these executives leave the company in the next

three years and the company gives them severance pay. Most of the other executives

that do not stay in the company retire, and only a few continue in other companies with

executive functions.

Aggrawal and Knoeber (1998) defend that the threat of takeover is also a

mechanism to control the increase in the level of managerial compensation, but this

threat can divert managerial effort from productive to defensive activities (Chakraborty,

and Arnott, 2001). Essentially, when top executives are afraid that another company

will try to buy their company and they will lose their job, they do not try to increase

their compensation so much. But if they are protected, the “white­collar” workers, in

particular, will try to raise their compensations (Bertrand and Mullainathan, 2002).

25

The fear associated with losing their job makes executives resist tender offers. When

takeover occurs and generally when they decide to resist, they gain from this situation

(Cotter and Zenner, 1994). A methodology for managing executives’ fear of takeovers

is to adopt anti­takeover mechanisms (Field and Karpoff, 2002 and Borokhovich et al.,

1997) as is the case of golden parachutes. This is a repellent methodology to reduce the

possibility of successful acquisitions in the sense that the acquirer firm must pay not

only the firm’s value, but also a significant amount of money to the CEO to leave the

company.

The use of golden parachutes has been defended as positively related to top

executive compensation (Field and Karpoff, 2002 and Borokhovich et al., 1997) in the

sense that executives that have this kind of financial protection receive higher

compensation than executives without golden parachutes. Davidson, Pilger and Szakmary (1998) also document that Compensation Committee composition determines

the way the market accepts the adoption of golden parachutes. If the Compensation Committee has more insiders or affiliated outsiders, the market reacts negatively,

reducing the firm’s stock price, but if the Compensation Committee has a higher

percentage of independent outsiders, the firm’s market stock price generally grows.

Boyle et al. (1998) also add the information that when executives have small firm

ownership, ownership is negatively related to the number of extraordinary anti­takeover

mechanisms adopted by firms, but when their ownership is higher, the number of

extraordinary anti­takeover provisions is positively related to executive ownership.

Essentially, when executives have significant firm ownership, they will try to protect

their wealth by introducing alternative choices to get money if takeover is a success.

The use of golden parachutes has also been described as a factor that negatively affects the firm’s stock price (Borokhovich et al., 1997). The adoption of the golden parachutes mechanism can be understood by the market as an act to protect low quality

executives. Evans et al. (1997) confirm this idea and document that banks that adopt golden parachutes effectively present, on average, lower performance than banks with the same average size that do not adopt this protection. In fact, executives with golden parachutes should not be worried either about takeovers or about poor performance.

They are protected, in financial terms, from both situations. If the performance is bad

and shareholders want to get rid of the executives, they are obliged to pay them the

26

golden parachute sum. The same amount will be received if another company buys

their company. In other words, with golden parachutes, executives will be financially

protected from a situation that can negatively affect their wealth.

The existence of a significant outside director on the board of the companies has

also been defended by authors like Harford (2003) as an anti­takeover mechanism.

Companies with a significant number of outside directors have fewer probabilities of

being acquired because they will align the interests of executives more with

shareholders’ interests, reducing agency costs. If outside directors do not monitor

executive actions, takeovers will appear and they will probably lose their board seat in

the future. Conversely, Chatterjee et al. (2003) document that target firms managed by

independent directory boards are likely to ignore the takeover attempt and not refocus

their firms' strategy.

Kroll et al., (1997) further point out that in a manager­controlled firm, the

acquisition announcements of another firm result in negative excess returns to

shareholders, but in the case of firms controlled by the owner, the acquisition

announcements result in positive excess returns to shareholders. This means that when

executives control the firm, they can extract personal benefits from the acquisition, but

not in the other situations.

In a spin­off context, Sewar and Walsh (1996) document that the act of selecting

a new CEO to manage the new firm and the design of their compensation contract are

not strongly correlated with the positive firm stock price reaction to the spin­off

announcements. Certo et al. (2001) also report that founder management has been

described as having a positive impact on IPO underpricing, and executive stock options

and stock option ownership interacted to influence the premiums that investors applied

to IPO firms (Certo et al., 2003). Finally, Hambrick and Finkelstein (1995) document that in management­

controlled firms, where a single major firm owner does not exist, the predominant

compensation policy is to maximise CEO compensation, but when the firms are

externally­controlled or have a major non­manager owner, the predominant

compensation policy is generally to minimise CEO pay, subject to the ability to

attract/retain a good or satisfactory CEO.

27

6. Executive Compensation and Dividend Policy

Until now there has been little research on the relation between executive

compensation and firm dividend policy. The existing bibliography analyses the

relationship between executive compensation, dividends and firm growth, the impact of

the use of the stock options in terms of dividend policies in the USA and Japan and the

impact of the 2003 tax cuts in the USA.

Authors like Smith and Watts (1992) document that those big companies

generally pay higher dividends and higher compensation to executives than small

companies.

When a firm announces that this year it will pay more dividends than last year,

the market normally reacts positively and the firm’s stock price increases. What Lippert

et al. (2000) found is that when executives are paid with a significant part of their

compensation based on stock options, the traditional increase in the firm’s stock market

price is less than in the situation where executives are not paid with stock options. The

authors present two explanations for this situation. One explanation is that pay for

performance and high dividends are both mechanisms that control executive

opportunism. The second reason is based on the behaviour finance theory which says

that when executives have a higher financial and psychological investment in a certain

project, they are more likely to believe that the project will be a success in the future

and will probably inform the market incorrectly about future firm performance. If the

market believes that there is incorrect executive signalling, it will discount this

information from the firm’s stock price and expect the firm’s stock price growth, with

the associated dividend increase, to be less.

The impact of the use of stock options in terms of dividend polices was also

analysed on the Japanese market by Kato, Lemmon et al. (2005). Contrary to the results

of Lippert et al., 2000, they did not find that the adoption of stock based compensation

changed the firm dividend policy.

When top executives have significant ownership, and the firms where they work

have a significant level of agency problems, this is an incentive to increase the firm’s

dividend payment (Fenn and Liang, 2001). If executives have a significant number of

stocks from the firms, and agency problems are high, they will be able to extract more

28

money from the company when they increase the dividends. Brown et al. (2007) also

found that executives with higher ownership were more likely to increase dividends

after the 2003 tax cut in the USA.

7. Executive Compensation and Capital Structure

The present research on the relation between a firm’s capital structure and

executive compensation focuses on the influence of executive ownership in a firm’s

debt structure, the level of executive compensation and the firm’s capital structure, and

on the effect of the market as a mechanism of executive control and the firm’s value

maximisation.

One of the interesting findings that relate executive compensation to a firm’s

capital structure is that executives with significant ownership generally choose short­

term debts for their company and executives with small ownership choose long­term

debt (Datta et al., 2005). Essentially, when part of executives’ compensation is

dependent on the future evolution of the firm’s stock price, they will choose short­term

debts because they are afraid that the market will penalise the firm’s stock price and

their wealth will be negatively affected. Executives that do not hold company stock do

not have these preoccupations and can have debts with higher maturity.

In Japan, firms that give stock options to their executives have, on average,

lower levels of debt than companies that do not adopt this kind of compensation

component (Kato et al., 2005). This suggests that firms with high levels of debt avoid

stock option compensation so as not to reduce agency costs associated with debt. The

authors found only slight evidence that these companies adopt stock option plans to

improve firm performance. Calcagno and Renneboog (2007) complement these ideas

and defend that when a company has risk debt, it is best to give executives incentives

based on the firm’s performance, because they will try to improve the firm’s

performance to the level where they can exercise stock options, rebalancing the capital

structure. Cadenillas et al. (2004) complement this information, defending that the best

way to make executives adopt policies to maximise the firm’s value is to grant stock

with high leverage to good managers and stock options with low leverage, or unlevered,

to not so good managers because the former will have the knowledge and will make

29

efforts to achieve the best methodology to increase firm stock value, while the latter will

not. Lewellen (2006) also describes that firm leverage increases stock volatility, and

higher stock option ownership tends to increase the volatility costs of debt.

Effectively, optimal capital structure has been defended in corporate finance

literature as a way of maximising stock market value, and when companies signal to the

market that they are changing the capital structure, this is generally understood as a

positive sign of the firm’s future performance. These results are congruent with Born

and McWilliams (1997)’s findings, which documented that firm performance decreases

over the years when the firm changes equity to debt. Authors like Berger et al., (1997)

also documented that when executives are not monitored by the market, they will not

choose the optimal capital structure that maximises firm value. Coles et al. (2006) add

the idea that if there is higher sensitivity of CEO wealth to stock volatility, they will

increase firm leverage to get personal advantages and Efendi et al. (2007) also affirm

that the likelihood of a misstated financial statement increases greatly when the CEO

raises new debt so as not to affect executive wealth.

Finally, Phillips (1995) defends that managers’ incentives to maximise

shareholders’ wealth increase following firm recapitalisation; Sundaram and Yermack

(2007) find that CEOs with high debt incentives manage their firms in more

conservative ways and Cumming, Fleming and Suchard (2005) also report that top

executives are better remunerated than venture capitalists in Australia, who raise capital

for the firm to develop their own businesses, meaning that they are extracting wealth

from how they invest capital in the company.

8. Risk and Executive Compensation

Authors like Ross (2004) describe the conditions under which incentive

schedules make agents more or less risk averse.

The most important research that relates executive compensation to risk is

associated with the granting of executive stock options or in some cases with restricted

stock options. The main question about this relationship is exactly how much

compensation indexed to firm stock price shareholders must give to their executives to

motivate them to increase the firm’s stock price. When shareholders give the executive

30

a small, or significant, part of their compensation based on stock options, they will not

guarantee that these executives will receive this amount and the executive cannot refuse

this risk. Only executives with the ability to make the firm’s stock market increase to

the level that will enable them to exercise their options, or make hedging strategies, will

transform risk compensation into fixed compensation.

Another component of executive compensation associated with risk is restricted

stock. Restricted stock is stock that is granted to executives but that cannot be sold for a

certain number of years, normally between 3 and 10 years. The most important

difference between stock options and restricted stock compensation is in terms of the

risk: stock options can only be exercised if the firm’s stock market value increases to a

certain level, otherwise executives will not receive this value, and in the second case, if

executives stay in the company until they are able to sell the stock, they will receive

some value. Whether the value that executives receive is high or low depends on their

ability to make the firm’s stock price grow. Essentially, stock options are a riskier

compensation component then restricted stock because if they do not increase the firm’s

stock price to exercise stock price, they will receive nothing and in the case of restricted

stock they can always receive something.

Authors like Tian (2004) defend that the use of stock options to motivate

executives only works up to a certain level and if this level is exceeded, the incentive

effect decreases. Chen, Steiner and Whyte (2006) effectively documented that since

America’s bank deregulation, banks have increased the use of employed stock option

inducing executive risk­taking. Garvey and Milbourn (2004) add the information that

when executives are young, or have low firm stock ownership, they generally delegate

market risk immunisation of their personal compensations based on stock options to

their company. Jin (2002) also documents that CEO incentives decrease with the risk

associated with the firms but not with market risks.

There are several solutions proposed by authors to manage the relationship

between granting stock options to executives and the risk that they are able to take.

One solution proposed by Brisley (2006) to balance the risk assumed by executives is

to grant not traditional stock options but “progressive performance vesting” stock

options. Essentially, “progressive performance vesting” stock options allow fixed

numbers of options to vest periodically independent of stock price performance. In this

31

way, executives can exercise a certain amount of stock options during a fixed period

and not only at the end. Another methodology is described by Johnson and Tian (2000),

who develop a pricing model of stock options with a strike price indexed to a

benchmark and defend that this model filters out common risks beyond the executive's

control, thereby increasing the efficiency of incentive contracts. Another was defended

by Calvet and Rahman (2006), who argue that the best choice is to give CAPM­based

indexed stock options to executives, which are stock options indexed to the Capital

Asset Price Model, because with this methodology executives will not be involved in

investment projects with high idiosyncratic risk and the compensation model considers

wealth diversification and degree of risk aversion.

According to Prendergast (2002), the empirical investigations that analyse the

existence of negative relationship between risk and incentives are not greatly successful,

because some of these tests have a positive, and not negative, relationship between

uncertainty and incentives. The authors defend that the literature on this relationship

fails because it does not incorporate an important effect of uncertainty on incentives,

which is the employees’ responsibility. When companies work in a certain context,

firms will define what exactly employees have to do and then monitor their actions, but

when the context is uncertain, the responsibility is delegated to the workers and to

reduce their opportunistic actions they will index their compensation to final output.

Miller, Wiseman and Gomez­Mejia (2002) analysed the effects of unsystematic

and systematic firm risk in terms of CEO compensation risk bearing and total

compensation and found that pay in terms of performances and the greater earnings

potential associated with this contingent form of executive pay are highest when

executives can control the performance outcomes. Larraza­Kintana et al. (2007) also

add the information that employment risk and variability in compensation correspond to

greater risk taking, while downside risk and the intrinsic value of stock options

correspond to lower risk taking.

Finally, Tufano (1996), in the context of the gold mining industry, found that in

firms where executives have a significant number of stock options, executives do not

manage gold price risk so well, but in firms where executives have a significant amount

of firm stock, they manage gold price risk better, suggesting that executive risk aversion

affects the policies adopted by executives to manage corporate risk.

32

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44

CHAPTER 2

New vs. Old Economy: Trends and Determinants of Executive

Compensation 6

6 We are grateful to Prof. Amir Licht, from the Interdisciplinary Center Herzliya, paper discussant of this paper at the Financial Management Association/Asia Finance Association Annual Conference, in Hong Kong, in July 2007. We are also grateful to anonymous attendees of the conference for their helpful comments.

45

1. Introduction

In this new study, we examine the determinants and the forms of executive

compensation of new versus old economy US firms over time. We focus our research

on providing answers to the following questions: Are the determinants of executive

compensation for new versus old economy firms the same? Are there significant

differences between the compensation value and the forms of executive compensation

in new versus old economy firms? Does the form of executive compensation change

after the NASDAQ crash and the enactment of the Sarbanes­Oxley act?

According to Anderson et al. (2000) and Murphy (2003), new economy firms are

defined as firms competing in the computer, software, Internet, telecommunications, or

networking fields and as per several researchers such as Ittner et al. (2003), Anderson et

al. (2000), Murphy (2003), Stathopoulos et al. (2004) and most recently Chen and Hung

(2006), new economy firms are fundamentally different in terms of many of the

characteristics they possess compared to the old economy firms.

The present studies on executive compensation have some limitations which

offer a motivation for the current inquiry. One of the limitations is that there is only one

study, by Stathopoulos et al. (2004), which analyses the executive compensation for

both new and old economy firms, but it is confined to the English market only. The

other limitation is that most studies focus on the preponderance of stock options as a

form of executive compensation, without analysing the remaining components of the

compensation. With the exception of the study developed by Murphy (2003), all other

studies on executive compensation analyse short periods of time; therefore, the

conclusions achieved by these studies must be validated for a longer period of time.

Given that there are inherent differences between new and old economy firms,

we believe that executive compensation between old and new firms may be influenced

by different factors, and the trends in terms of compensation may also be different over

time. Based on Narayanan and Seyhun (2005) and Murphy (2003)’s findings, we also

believe that the NASDAQ crash in 2000 and the Sarbanes­Oxley act in 2002

significantly changed the form of compensation for executives in the new and old

economies because a significant number of restrictions in terms of corporate governance

were developed.

46

We also deal with the old problem in executive compensation literature as to

what is the best variable to measure the impact of the firm size: LN (assets), LN (market

value), LN (sales) or these variables without a natural logarithm. Effectively, firm size

is described as one of the most important variables in explaining executive

compensation; nevertheless, in the mind of the researchers, there is generally a doubt as

to whether using one of the above variables, at the exclusion of the others, will produce

inferior results. To solve this problem, we use the Principal Component method and

extract a factor that is the best combination of the three variables to measure the firm

size.

Our results reveal that the number of executives in new economy firms is

considerably smaller than the number of executives in old economy firms. Most of the

new economy executives are from firms associated with Prepackaged Software (26.02%), Semicondutor and Related Devices (17.29%), Computer Programming, Data

Processing (9.46%) and Telecommunications (7.50%).

Executives from new economy firms always receive on average more than those

from old economy firms, but the difference in total compensation has fallen in recent

years. Executive compensation in the new economy firms consists of more than 50%,

and in the case of old economy firms more than 30%, of stock options between 1999

and 2001. After that period, with the NASDAQ Crash and the introduction of the

Sarbanes­Oxley Act in 2002, new and old economy firms instituted a change in the

structure of the components of the executive compensation, reducing the use of stock

options and increasing the use of bonus and restricted stocks.

We also find that the factors that explain executive compensation in new versus

old economy firms are generally different, and in the case of the variables that are the

same, our tests generally rejected the hypothesis that the coefficients related to these

common factors are equal. New economy total executive compensation is influenced by

firm size, the ratio of the number of stock options vested but not exercised and

executive stock ownership, whereas the old economy total executive compensation is

influenced by firm size, executive ownership, one­year total return to shareholders and

the 5­year annual growth rate of firm net income.

47

2. Literature Review and Research Questions

Anderson et al. (2000), Ittner et al. (2003), Murphy (2003) and most recently

Chen and Hung (2006) analyse the issue of executive compensation but only for new

economy firms. Stathopoulos et al. (2004) investigate the executive compensation both

for new and old economy firms in England, but only for a period of two years.

All the studies that analyse new economy executive compensation focus their

attention basically on one component of the executive compensation ­ stock options ­ at

the expense of other components of executive compensation. As discussed earlier, in

this inquiry we take a comprehensive and current view of issues related to executive

compensation.

Use of stock options has been a common and predominant form of executive

compensation; therefore, several studies have focused on this form of compensation.

There are multiple reasons for firms to award stock options to their executives.

Stathopoulos et al. (2004) believe that stock options are given to new economy

executives to align shareholders’ interests with executives’ goals, reduce agency costs,

achieve beneficial tax gains for the company and the employees, and attract and retain

executives with significant knowledge of new technologies. Ittner et al. (2003) are of

the view that new economy firms give stock options to executives because the firms

have difficulty generating enough cash flow to pay high salaries to executives.

Murphy (2003) thinks that a large group of big companies compete in the market

for high quality executives. The compensation contracts that they give to this kind of

executive force the other companies to use the same structure of compensation:

inclusion of stock options as a major component of compensation package. Another

reason invoked is related to what the author calls the "perceived­cost­view"; in other

words, there is the wrong perception that executives compensated with stock options

constitute a cheap form of compensation

From prior studies, we know that until 2000, executives from new economy

firms were predominantly paid with stock options. However, the most important

fraudulent bankruptcy cases, such as Enron and WorldCom, are indirectly related to the

significant number of stock options granted to the executives. We believe that the

NASDAQ crash and the Sarbanes­Oxley Act changed the components of compensation

48

for executives in both new and old economy firms due to the introduction of new rules

in the market place. The new rules contain provisions which have a significant impact

on the benefits and compensation of public company executives. Therefore, given the

above historical context we find reason to test empirically whether the NASDAQ crash

in 2000 and the Sarbanes­Oxley Act in 2002 changed the structure of executive

compensation, and if they did, whether or not the impact of these major events was the

same for the executive compensation of new versus old economy firms.

According to Ittner et al. (2003), Murphy (2003) and Stathopoulos et al. (2004),

new economy companies differ from old economy companies because they present

higher growth rates of sales increase; they spend more money on research and

development; they present low ratios of book­to­market value; they offer lower

dividends per share and a high volatility of share returns. They still hold a smaller

number of employees, a reduced market value and smaller accounting returns than old

economy firms. In addition, they provide larger compensation relative to capital

ownership, have a higher percentage of stock options and a higher percentage of the

volume of stock options not exercised related to the total number of company shares.

Furthermore, Chen and Hung (2006) express the idea that companies listed on

NASDAQ have small boards, and the founder is usually the CEO. They have a large

shareholding of insiders, and the CEOs usually accept lower cash compensation and

higher option compensation in order to convince shareholders of managerial

commitment and future profitability.

Based on these differences, we also develop the idea that if new and old

economy firms have different characteristics, these characteristics can generally lead to

differences in terms of the factors that explain executive compensation. We also believe

that even if some of the factors are equal, their intensity can be different.

49

3. Data, Sample Selection and Statistics

3.1. Data and Sample Selection

Data is from the Standard and Poor´s ExecuComp database 7 , which collects

information about the five most well paid executives from firms listed on S&P Indexes.

We use Unbalanced Panel Data, and our final sample is composed of 67,437

observations of executive compensation for the 13­year period from 1992 to 2004. We

retrieve compensation package details for up to the top five executives in each firm,

including salary, bonus, ex­ante value of options, restricted stock award, Long­term

Incentive plan (LTIP), other annual compensation, all other compensation and several

variables associated with governance and finance.

To develop the sample used in this study, we apply a few restrictions. First, we

remove all the executives whose sum of salary and bonus was equal to zero, in other

words, those who received neither salary nor bonus during the year, receiving instead

some other remuneration types. We also exclude all those executives whose total

compensation is equal to zero. To achieve this final sample we exclude 272 items of

compensation related to executives that received compensation from more than one

company. We delete the compensation values from the company for which the

executives worked less time.

Using the Consumer Price Index (CPI) compiled by the Bureau of Labor

Statistics, with 1982 as the base year, we adjust the monetary variables for inflation.

In order to distinguish between executives from new and old economy firms, we

use the methodology of Murphy (2003), who considers firms from the new economy

with SIC codes 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961,

7370, 7371, 7372 and 7373 and firms from the old economy with SIC codes lower than

4000 unless categorised as new economy firms.

7 The ExecuComp version is from 06­2006.

50

3.2. Statistics

Table 1 presents the number of observations (compensation items) for each SIC

code of new and old economy firms. For example, there are 123 compensation items

from the Computer and Office Equipment industry which represent 0.77% of the total

compensation items in that industry (% of the group) and 0.18% of the total sample of

observations from old and new economy firms. Our sample has nearly 76%

observations from the old economy and 24% observations from the new economy.

Moreover, we can see from Table 1 that our sample observations are dominated by

executives associated with companies from Pre­Packaged Software, Semiconductor,

Related Devices and Telecommunications industries.

Table 1: Number of Items of Compensation by SIC Code

SIC Code SIC Code Description Number of Items of

Compensation % of the Group

% of Total (Old+ New) Economy)

PANEL A: New Economy 3570 Computer and Office Equipment 123 0.77% 0.18% 3571 Electronic Computers 590 3.68% 0.87% 3572 Computer Storage Devices 595 3.71% 0.88% 3576 Computer Communication Equipment 981 6.12% 1.45% 3577 Computer Peripheral Equipment 412 2.57% 0.61% 3661 Telephone & Telegraph Apparatus 972 6.07% 1.44% 3674 Semiconductor and Related Devices 2770 17.29% 4.11% 4812 Wireless Telecommunication 423 2.64% 0.63% 4813 Telecommunications 1201 7.50% 1.78% 5045 Computers and Software Wholesalers 288 1.80% 0.43% 5961 Electronic Mail­Order Houses 562 3.51% 0.83%

7370 Computer Programming, Data Processing 1515 9.46% 2.25%

7371 Computer Programming Service 182 1.14% 0.27%

7372 Prepackaged Software 4168 26.02% 6.18%

7373 Computer Integrated Systems Design 1238 7.73% 1.84% Total New Economy 16020

PANEL B: Old Economy < 4000 and not new ecomomy

51417 76.24%

Total New+Old Economy

67437

Notes: To distinguish between executives from new and old economy firms, we used the methodology of Murphy (2003), which considers firms from the new economy those with SIC code 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961, 7370, 7371, 7372 and 7373 and firms from the old economy those with SIC codes less than 4000 and not yet categorised with the new economy.

51

Table 2 presents the average total compensation of executives and the t­test of

independence of means of executives from new versus old economy firms during the

period from 1992 to 2004.

Table 2 Mean Total Executive Compensation Levels for New and Old Economy Firms (1992­2004)

Our sample includes data from the five most well paid executives associated with the firms listed in the S&P500, S&PMidCap and S&PSmallCap during the period of 1992 to 2004. All the data is from the ExecuComp database. To distinguish between executives from new and old economy firms we used the methodology of Murphy (2003), which considers firms from the new economy those with SIC codes 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961, 7370, 7371, 7372 and 7373 and firms from the old economy those with SIC codes less than 4000 and not yet categorised with the new economy. Total compensation is the sum of salary, bonus, stock options, restricted stocks, long­term incentive plans (LTIP), other annual compensation and all other compensation listed under All other Compensation in the Summary Compensation Table. Values are in thousands of dollars. All the values are adjusted for inflation and are in 2004 dollars.

New Economy Old Economy T test of mean

Year N Mean N Mean Mean Difference t Sig.

1992 485 1477,41 2412 1260,39 217,02 2,134 0,033

1993 887 1399,42 3778 1183,84 215,58 3,177 0,002

1994 958 1668,20 4058 1302,55 365,65 4,640 0,000

1995 1026 828,22 4166 1306,67 521,56 4,529 0,000

1996 1226 2339,72 4319 1542,72 797,01 5,414 0,000

1997 1398 2721,97 4335 1815,28 906,67 5,558 0,000

1998 1495 3438,06 4419 2805,12 1641,51 2,664 0,008

1999 1577 4996,27 4301 2145,37 2850,90 7,368 0,000

2000 1507 6660,90 4091 2570,57 4090,85 9,109 0,000

2001 1417 5159,96 3880 2438,90 2721,07 6,032 0,000

2002 1366 3114,48 3942 2165,15 949,34 5,283 0,000

2003 1373 2346,03 3926 2033,53 312,50 2,863 0,000

2004 1305 2618,61 3790 2420,20 208,42 1,384 0,166

52

From Table 2 we see that, with the exception of the year 1993, the total average

compensation of executives from new and old economies increases from 1992 to 2000

(Nasdaq crash), decreases until 2003 and starts to increase again, but slowly, in 2004.

We can also verify that the mean total compensation difference between the two groups

of executives is very high in the years 1999 and 2000 but drops drastically after 2002,

and it is small in 2004.

Table 3 summarises the evolution of the various compensation components (as a

percentage of total compensation) of the new versus old economy firms during the

period from 1992 to 2004 8 . We use the Independent­Samples T­test to compare the

means of executive compensation components and Levene's test for equality of

variances between the two sub­samples of old versus new economy firms. We also

perform the same test to compare whether the difference between the values of each

component of compensation in the years 2001 to 2002 (NASDAQ crash effect) and

years 2003 to 2002 (Sarbanes­Oxley Act effect) is statistically significant.

8 Last year of information available from Execucomp database when we started this study.

53

Table 3 Executive Components as a Percentage of Total Compensation in New and Old Economy

Firms (1992­2004)

The table displays the percentage of average value of the compensation components for the top five executives, CEOs and directors, in firms that belong to the S&P indexes. Salary is the executive salary for the year. Bonus is the dollar value of bonus (cash and non­cash) earned by the executive officer during the fiscal years. Stock Options is the aggregate value of stock options granted to the executive during the fiscal year as valued using S&P`s Black­Scholes methodology. Restricted Stocks is the value of restricted stock granted during the year (determined as of the date of the grant). LTIP is the amount paid out to the executive under the company's long­term incentive plan. These plans measure company performance over a period of more than one year (generally three years). Values are in thousands of dollars. All the figures are adjusted for inflation and are in 2004 dollars.

PANEL A: Top Five Executives

N Salary Bonus Stock Options Restricted Stocks LTIP Year

New Old New Old New Old New Old New Old New Old

1992 485 2412 45.50% 49.46% 17.92% 18.04% 28.71% 20.43%* 1.69% 3.88%* 2.64% 3.10%*

1993 887 3778 43.80% 48.59%* 18.28% 18.69% 30.52% 20.21%* 1.52% 3.72%* 1.69% 2.88%*

1994 958 4058 40.04% 45.65%* 18.44% 20.29% 34.09% 22.67%* 1.67% 3.27%* 1.22% 2.61%*

1995 1026 4166 38.94% 46.05%* 19.40% 20.18% 33.61% 21.08%* 1.69% 3.90%* 2.03% 3.13%*

1996 1226 4319 35.54% 42.34%* 16.00% 19.41% 40.36% 25.54%* 2.54% 3.89%* 1.55% 3.40%*

1997 1398 4335 35.29% 39.31%* 15.03% 19.94%* 42.92% 27.78%* 1.62% 3.84%* 1.15% 3.89%*

1998 1495 4419 36.37% 39.51%* 13.71% 17.42%* 43.74% 30.85%* 1.42% 3.87%* 0.78% 2.95%*

1999 1577 4301 31.51% 37.57%* 12.48% 18.45%* 50.17% 32.23%* 1.31% 3.72%* 0.83% 2.72%*

2000 1507 4091 27.91% 37.01%* 11.92% 18.21%* 55.02% 32.89%* 1.53% 3.90%* 0.54% 2.44%*

2001 1417 3880 29.72% 37.88%*/(**) 9.46%(*) 14.86%*(*) 54.16% 35.98%*/(*) 1.90% 3.88%* 0.45%/(*) 1.98%*/(*)

2002 1366 3942 35.17% 37.52%* 11.42% 17.39%* 47.29% 31.98%* 1.98% 5.37%* 0.38% 2.18%*

2003 1373 3926 32.93%(*) 38.08%* 15.84%(*) 18.03%*/(**) 40.56%(*) 27.92%*/(*) 3.75%(*) 7.06%*/(*) 0.40%(*) 3.08%*/(*)

2004 1305 3790 29.72% 33.14%* 15.47% 21.51%* 41.22% 26.75%* 6.60% 9.58%* 0.42% 3.41%*

54

Table 3 (cont.)

PANEL B: CEOs

N Salary Bonus Stock Options Restricted Stocks LTIP Year

New Old New Old New Old New Old New Old New Old

1992 27 170 37.51% 39.02% 25.00% 19.30%** 23.70% 26.28% 2.84% 4.30% 6.20% 6.02%

1993 118 537 35.50% 43.12%* 19.84% 19.80% 37.65% 22.60%* 1.10% 4.80%* 2.18% 3.47%

1994 161 701 32.55% 41.02%* 19.25% 20.91% 40.58% 26.22%* 1.80% 4.25%* 1.39% 2.77%**

1995 167 732 33.31% 40.92%* 18.87% 21.22% 39.46% 26.22%* 1.41% 4.25%* 2.48% 3.32%

1996 175 745 31.12% 37.32%* 17.01% 20.28%** 42.22% 24.65%* 3.81% 4.09% 1.52% 3.77%*

1997 201 751 30.19% 33.96%*** 16.30% 20.74%* 45.67% 29.03%* 1.93% 4.36%* 1.22% 4.46%*

1998 220 761 30.23% 34.52%** 13.72% 18.50%* 49.28% 31.07%* 1.32% 4.36%* 0.76% 3.23%*

1999 278 765 28.14% 31.81%*** 12.56% 18.96%* 52.87% 34.42%* 1.50% 3.77%* 0.94% 3.15%*

2000 270 757 28.09% 31.96%*** 13.09% 18.56%* 53.54% 36.84%* 1.28% 4.64%* 0.39% 2.77%*

2001 249 703 25.59% 32.76%* 8.69%(*) 14.51%*/(*) 57.80% 36.37%*/(*) 2.44%(*) 4.70%* 0.64% 2.07%*

2002 237 700 28.11% 30.99% 10.02% 17.78%* 54.95% 40.48%* 2.07% 6.06%* 0.42% 2.89%*

2003 239 701 30.14% 31.83% 15.70%(*) 18.44%** 44.98%(*) 35.57%*/(*) 5.21%(*) 7.85%**/(*) 0.29%(*) 3.98%*/(**)

2004 239 711 27.34% 28.03% 14.71% 22.73%** 46.83% 32.77%* 7.19% 11.07%* 0.43% 3.73%*

PANEL C: Director s

N Salary Bonus Stock Options Restricted Stocks LTIP Year

New Old New Old New Old New Old New Old New Old

1992 206 1121 42.29% 46.38%** 19.77% 18.49% 29.91% 22.28%* 1.90% 3.76%* 2.88% 3.65%

1993 325 1584 41.24% 45.71%* 18.96% 19.17% 33.49% 22.06%* 1.40% 3.84%* 1.72% 3.08%*

1994 331 1594 37.47% 42.73%* 19.40% 21.03%*** 35.56% 24.87%* 2.06% 3.46%* 1.55% 2.84%*

1995 349 1593 35.39% 42.86%* 19.45% 20.77% 36.32% 22.71%* 1.71% 3.95%* 2.51% 3.45%***

1996 418 1606 34.18% 39.12%* 15.90% 20.03%* 41.76% 27.81%* 2.77% 3.93%* 1.24% 3.59%*

1997 465 1618 32.58% 36.56%* 15.54% 20.57%* 43.88% 29.62%* 2.08% 3.97%** 1.20% 3.94%*

1998 478 1611 34.72% 36.54% 13.81% 18.08%* 44.17% 32.76%* 1.39% 4.03%* 0.69% 3.09%*

1999 483 1492 30.09% 34.04%* 13.10% 19.24%* 49.50% 34.36%* 1.82% 3.65%* 0.99% 2.85%*

2000 464 1362 27.97% 33.59%* 11.70% 18.83%* 53.81% 34.42%* 1.69% 4.64%* 0.43% 2.52%*

2001 416 1229 28.07% 34.75%* 8.52%(*) 15.39%*/(*) 56.32% 37.72%*/(*) 2.04%(*) 4.11%*/(*) 0.56%(*) 1.99%*/(*)

2002 373 1180 29.70% 32.85%*** 10.32% 18.32%* 52.67% 34.88%* 1.76% 5.60%* 0.58% 2.55%*

2003 354 1144 32.15% 32.93% 15.24%(*) 19.51%*/(**) 43.27%(*) 31.10%*(*) 4.31%(*) 7.15%*(*) 0.30%(*) 3.30%*/(*)

2004 343 1065 29.74% 28.92% 15.62% 22.86%* 43.75% 28.49%* 6.66% 10.31%* 0.71% 3.61% *

Note: Difference between the old and new economy is statistically significant at 1% level *, 5% level ** and 10% level ***. In rows for years 2001 and 2003 we also describe whether the differences between each component of compensation between years 2001 and 2000 (Nasdaq crash effect) and 2003 related to 2002 (Sarbanes­Oxley Act effect) are statistically significant. Significance is presented as ( ).

55

If we analyse the compensation for the top five executives, we see that beginning

in 1992, the most important component of compensation was salary for both new and

old economy firms. After 1996, stock options were the most important component of

executive compensation in new economy firms, and in the case of old economy firms

the use of stock options also increased. After the NASDAQ crash in 2000 and the

introduction of the Sarbanes­Oxley Act in 2002, the component weights of executive

compensation significantly changed in both new and old economy firms. The use of

stock options decreased, still significant in both sub­samples, but the use of bonus and

restricted stocks 9 increased. Results for two sample t­tests show that in most cases, the

use of options and restricted stocks significantly changed after the NASDAQ crash and

the Sarbanes­Oxley Act.

In our view, the change from stock options to restricted stock may be due to the

fact that both are compensation components associated with performance of a firm,

whereas salary is not dependent upon performance. More precisely, when a firm grants

stock options to the executives, these options can be cashed in only after a significant

number of years (generally 3 to 10 years) and only if the market price is higher than the

exercise price. Thus, executives have an incentive to manipulate the firm accounting

data to influence the stock price and to refrain from sending less positive information to

the market about the firm’s future performances (Povel, Singh and Winton (2007),

Yermack (1997) and Hu and Noe (2001)).

The main goal of the introduction of the Sarbanes­Oxley Act was essentially to

reduce manipulative acts and fraudulent cases. Restricted stock can be a safer

compensation component than stock options because executives effectively receive

stock and not the possibility of buying stock in the future. In this way, they assume the

daily loss or gain if the stock price decreases or increases. Like restricted stocks, bonus

is also a comparatively safe component of executive compensation though not totally

free from possible manipulation of data by executives. On the other hand, salary is not a

compensation component related to firm performances. In other words, if the firm pays

9 Restricted stocks are stock subject to restrictions on sale and risk of forfeiture until vested by continued employment. Restricted stock typically vests in increments over a period of several years. Dividends or dividend equivalent rights may be paid, and award holders may have voting rights, during the restricted period.

56

more salary, this does not imply that executives will increase their efforts to have better

performances.

Results for the CEO and Director sub­samples show that the CEOs receive more

compensation based on stock options but less salary and bonus than directors. Although

the importance of stock options diminished after the NASDAQ crash and the Sarbanes­

Oxley Act, it is still higher in new economy firms than in old economy firms. Long­

term Incentive Plans continue as a residual component of executive compensation in

both new and old economy firms.

4. Research Design

We now test whether or not the executive compensation in new versus old

economy firms is influenced generally by the same (or different) factors. If some of the

factors influence new and old economy executive compensation at the same time, we

expect that coefficient values will be different and this difference can be statistically

significant.

We used Unbalanced Panel Data and Fixed Effect Regression Model, also called

within estimator or Least Square Dummy Variable model.

4.1. Dependent Variables

The dependent variables are LN (Total Compensation) and LN (Short Term

Compensation) and LN (Option Ratio). LN (Total Compensation) is the total

remuneration gained by the executives and is the sum of salary, bonus, stock options,

restricted stocks, LTIP 10 , other annual compensation and all other compensation. This

variable, without logarithm, was used by Aggarwal and Samwick (1999) to evaluate the

contracts offered to executives in a context of strategic competition between products

and evaluation of relative performance, and by Fields and Fraser (1999) to unmask the

10 A Long Term Incentive Plan (LTIP) is any plan that provides compensation that intends to serve as an incentive for performance and that occurs over a period longer than one year, but not including restricted stock, stock option or stock appreciation rights plans.

57

commercial banks when they attributed compensations to link executives to

performances, and also by Chen and Hung (2006).

LN (Short Term Compensation) is the LN (salary+bonus). Salary and bonus are

considered short­term remunerations, and they are usually received in money. We used

this variable like Stathopoulos, Espenlaub and Walker (2004) and Chen and Hung

(2006).

Finally we also used LN (Option Ratio). We define option ratio as the value of

options received by the executive divided by the total compensation and this variable

was also used by Chen and Hung (2006).

Each one of these dependent variables will be confronted separately with a group

of independent financial and governance variables with the intention of finding, in a

more trustworthy way, possible differences in executive compensation between new and

old economy firms.

Essentially, we analyse:

New Economy

Executive

Compensation

LN(Cash Compensation)

LN(Option Ratio)

LN(Total Compensation)

Old Economy

LN(Cash Compensation)

LN(Option Ratio)

LN(Total Compensation)

0 1

2

3 4

5 6

7 8

( ) *Firm Size Component * ( ) * ( ) ( ) ( ) Re 1

5 * ( ) (1993...2004)

LN Compensation LN Not Exercised Ratio LN Number Mtgs LN Tenure

LN Ownership Sharehold t Y GrowthNI Y LN BsVolatility

YearsDummy f

β β β β β β β β β

ε

= + +

+ + + + + + + +

+ + +

+ + +

The dependent variable LN (Compensation) can assume the values of LN (Total

Compensation), LN (Option Ratio) and LN (Short Term Compensation) and f is the

fixed effect.

58

4.2 Independent Variables

We use two sets of independent variables, financial and governance, as described below.

4.2.1. Financial Variables

Generally, the firm size in executive compensation literature is used as one of

the following variables: LN (Mktval), which is the natural logarithm of the market value

of the company, defined as the closing price for the fiscal year multiplied by the

company's common shares outstanding (millions of dollars) and was used by authors

such as Datta et al. (2005); LN (Sales), which is the natural logarithm of net annual

sales as reported by the company, and was used by authors such as Elston and Goldberg

(2003) and Aggarwall and Samwick (2003) and the LN (Assets), which is the natural

logarithm of the total assets as reported by the company that was used by Anderson and

Bizjack (2003), Elston and Goldberg (2003), Rogers (2002), Fenn and Liang (2001),

Chen (2004) and Yermack (2004). Some authors like Hallock (1997), Sridharan (1996),

Grinstein and Hribar (2004) and Bliss and Rosen (2001), also use these variables

without the natural logarithm. One of the problems in all these studies is that the authors

use one of these variables at the expense of the other variables. Researchers expect to

receive better results by using one variable and not the others in a specific situation. But

there is no sound reason for ignoring one variable and selecting another variable.

Because these variables are highly correlated, and cannot be introduced at the

same time to explain executive compensation, we used Principal Component Analysis

to extract a factor that contains information from the three variables and solves the old

problem of using size variable in executive compensation literature.

Table 4 describes the statistics of Principal Component Analysis.

59

The Principal Component Analysis methodology can be described, in this case,

as:

1 11 12 13

2 21 22 23

1 2 3

( ) ( ) ( )

( ) ( ) ( ) .........

( ) ( ) ( ) p p p p

y a LN Sales a LN Assets a LN Mktval

y a LN Sales a LN Assets a LN Mktval

y a LN Sales a LN Assets a LN Mktval

= + +

= + +

= + +

From Table 4 we can see that variables LN (Sales), LN (Assets), LN (Mktval)

are highly correlated.

Table 4 Statistics from Principal Component Analysis

Panel A: Correlation Matrix (a)

LN(Assets) LN(Sales) LN(Mktval)

LN(Assets) 1 0,92 0,83

LN(Sales) 0,92 1 0,75

LN(Mktval) 0,83 0,75 1

Panel B: Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of

Variance Cumulative

% Total % of

Variance Cumulative

% 1 2.669 88.982 88.982 2.669 88.982 88.982 2 0.266 8.878 97.860 3 0.064 2.140 100.000

To apply the Principal Components Analysis it is necessary to have a high

correlation between the variables. We use the Kaiser­Meyer­Olkin (KMO) test, which

compares the correlation between the variables. From Table 4 we can see that variables

are highly correlated. We only find one factor with Initial Total Eigenvalues superior to

1 that explains 88.98% of the total variance and the vector is:

60

1 11 12 13 ( ) ( ) ( ) y a LN Sales a LN Assets a LN Mktval = + +

or

0,975* ( ) 0,945* ( ) 0,909* ( )

Firm Size Component LN Assets LN Sales LN Mktval

= + + +

We will use the natural logarithm of Firm Size Component to test the impact of

firm size on total compensation, option ratio, and short term compensation of new and

old economy firms.

We also used the variable LN(Not Exercised Ratio), which is the natural

logarithm of the number of unexercised options that the executive held at year end that

were vested divided by the aggregate number of stock options/stock appreciation rights

granted to explain executive compensation. We expect that the number of options

vested but not exercised has a negative relationship with total compensation and options

ratio, meaning that if the executive has stock options that are not exercised, the firm will

probably give fewer stock options in the future. We expect that effect will be more

pronounced in new economy firms because many researchers such as Anderson, Banker

and Ravidran (2000), Ittner et al. (2003), Murphy (2003) and Stathopoulos et al. (2004)

show that new economy firms grant more stock options to the executives.

To analyse the relationship between the risk and executive compensation in new

and old economies, we use the variable LN(Bs Volatility), like Chen (2004) and Palia

(2001), (but without natural logarithm), which is the natural logarithm of the standard

deviation volatility calculated over 60 months with Black and Scholes´ methodology.

We expect a negative relationship between firm risk and cash compensation because if

the volatility is higher, the firm can reward the executives with stock options because

the value will increase with stock return volatility.

We also use the variable Share Ret 1Y, which is the one­year total return to

shareholders, including the monthly reinvestment of dividends. We expect that the

return to shareholders will have a negative relationship with executive compensation

because if the executive has enough return on investments, it is not necessary to give

61

more compensation to executives to align shareholder interests with the interests of the

executive.

We also use the variable LN (Ownership), which is the natural logarithm of the

percentage of the company's shares owned by the named executive officer. This variable

was used, with or without natural logarithm, by authors like Core et al. (1999), Barron

and Waddell (2003) and Chen (2004). We expect, like most of the previous research,

that the percentage of the company shares owned by the executive will have a negative

relationship with executive compensation. According to Chen and Hung (2006), higher

ownership indicates that managers' interests are more aligned with shareholders. Higher

insider ownership is often considered to have a positive impact on corporate

governance, and the more the managerial ownership, the less incentive pay they usually

receive. This is also because if the executive has stock in the firm, he/she will already

be more involved and concerned about improving the firm’s stock price, and it is not

necessary to increase the incentives to reduce agency costs.

To measure the impact of firm growth, we use the variable Growth NI 5Y,

which is the 5­year least square annual growth rate of Net Income. Because new

economy firms, according to the prior authors, have lower cash flows and give more

stock options to executives, we expect that the net income will not be an important

factor in explaining executive compensation in new economy firms. In the case of old

economy firms, we expect that this relationship will positively influence total

compensation.

To control for the effect of time, we use one dummy for each year between

1993 and 2004, like Barron and Waddel (2003) and Grinstein and Hribar (2004). We

expect that the dummy year will be significant in explaining executive compensation,

particularly in the bubble period of 1998 to 2000 and relative to the number of options

granted to new economy firm executives.

62

4.2.2. Governance Variables

LN (Tenure) is the natural logarithm of the number of years that the executive

has been doing the job as CEO. This variable was applied by a significant number of

authors to explain executive compensation such as Chindambaran and Prabhala (2003),

Ryan Jr and Wiggins III (2004), Murphy (1986), Barro and Barro (1990), Hallock

(1997) and Chen (2004) with or without the natural logarithm. We expect a positive

relationship between executive compensation and tenure because in the real world we

observe that more experienced executives command higher compensation.

The influence of the board and composition of the Compensation Committee on

executive compensation is one of the most recent fields of research in the area of

executive compensation. Ryan Jr and Wiggins III (2004) find that CEO compensation is

related to the power and the influence that the CEO has on the board. They find

evidence that firms with external directors on the board pay more compensation based

on stock options and restricted stocks. Anderson and Bizjak (2003) also analyse whether

board independence promotes the shareholders' interests and whether the presence of

the CEO on the Compensation Committee is related to opportunist behaviour. They did

not find evidence that when the executive leaves the compensation committee, the

remuneration decreases.

To analyse the relationship between board members and executive

compensation, we use the variable LN (Number Mtgs), similar to Davidson III et al.

(1998), which is the natural logarithm of the number of board meetings held during the

indicated fiscal year. The number of board meetings is related to the performance of the

firm, and the ability of the executive to make decisions is affected by the number of

meetings of the board during the year. According to Davidson et al. (1998), board

members are more aligned with shareholders´ interests when they have more meetings

during the year. Because of that, we expect a negative relationship between the number

of meetings and executive compensation.

63

Expected correlations

Independent Variables LN(Total Compensation) LN(Option Ratio) LN(Short Term

Compensation) Firm Size Component + + +

LN(Not Exercised Ratio) ­ ­ +

LN(Bs Volatility + + ­

Share Ret 1Y + ­ +

LN(Ownership) ­ ­ ­

Growth NI 5Y + + +

Year Dummy + + +

In Table 12 we present the Pearson Correlation of independent variables. We

find no variables with high correlations in any of the cases.

64

5. Empirical Results

5.1. Summary Statistics Table 5

Summary Statistics

Firm Size Component is a vector that measures the firm size and is a combination of the LN(Assets), LN(Mktval) and LN(Sales) variables. Using these three variables and applying the Principal Component Analysis, we extract a vector that is the best combination of these three variables to analyse the influence of firm size. LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested divided by the aggregate number of stock options/stock appreciation rights granted. LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes’ method. LN (Number Mtgs) is the natural logarithm of the number of board meetings. LN(Dirmtgfee) is the natural logarithm of the value that the executive received for attending board meetings. LN (Tenure) is the natural logarithm of the number of years that the CEO has been doing the job. LN (Ownership) is the natural logarithm of the percentage of the company shares owned by the executive. Sharehold Ret 1Y is the one­year return to shareholder. Sales is the value of the sales of the year. Sales3LS is the 3­year least squares annual growth rate of Sales. Growth NI 1Y is the year firm net Income. Growth I 5Y is the 5­year least squares annual growth rate of net income. Earn­Per­Share is the Earnings per Share (Primary) Excluding Extraordinary Items and Discontinued Operations. ROEAVG is the Net Income before Extraordinary Items and Discontinued Operations divided by the average of the most current year's Total Common Equity and the prior year's Total Common Equity. This quotient is then multiplied by 100. Divyeld is the Dividends per Share by Ex­Date divided by Close Price for the fiscal year. This quotient is then multiplied by 100. Emp. is the number of firm employees. Retyrs is the number of years of credited service the executive has under the company's pension plan and ROA is the Net Income before Extraordinary Items and Discontinued Operations divided by Total Assets. This quotient is then multiplied by 100. Values are in thousands of dollars.

NEW OLD

Dependent Variables N Min. Max. Mean Std. Dev. N Min. Max. Mean Std.

Dev. Firm Size Component 15638 4,96 33,16 19,03 4,61 50839 ­0,46 34,95 19,92 4,22

LN (Not Exercise Ratio) 10605 ­12,61 14,80 0,31 1,40 34406 ­10,63 8,63 0,67 1,16

LN (Bs Volatility) 14883 ­1,98 1,44 ­0,54 0,42 48561 ­2,16 1,42 ­0,98 0,43

LN (Number Mtgs) 14768 0,00 3,58 1,99 0,43 49493 0,00 3,66 1,87 0,37

LN (Dirmtgfee) 10320 ­3,18 1,95 0,45 0,43 40157 ­2,20 2,09 0,33 0,42

LN (Tenure) 4032 ­2,64 4,03 2,23 0,81 13177 ­5,90 4,09 2,24 0,85

LN (Ownership) 3053 ­6,21 5,63 0,32 1,71 8441 ­5,81 4,61 0,35 1,63

Sharehold Ret 1Y 15161 ­99,13 177042,86 146,32 4326,90 50359 ­97,08 531566,66 90,72 5813,84

Sales 15995 0,00 102635,40 3605,55 10589,55 51369 0,00 272941,48 4327,00 13952,08

Sales3LS 15794 ­87,18 19079,17 47,04 281,36 51154 ­86,57 5437,74 12,91 55,88

Growth NI 5Y 5937 ­48,13 1091,80 40,10 64,45 28337 ­67,99 1843,33 15,51 44,07

Growth NI 1Y 15995 ­38468,00 24728,00 131,30 1541,37 51369 ­56121,90 25330,00 179,51 1064,54

Earn­Per­Share 15964 ­91,05 1124,00 0,58 20,32 51257 ­69,83 80,83 1,19 2,93

ROEAVG 15541 ­16671,05 527,99 ­9,65 215,44 50016 ­8563,86 786,15 7,07 120,20

Divyield 15790 0,00 94,12 0,35 2,63 51154 0,00 2777,78 1,69 30,66

Empl 15722 0,01 329,00 11,21 31,90 51004 0,00 750,00 14,48 34,01

Executive Age 1786 31,00 92,00 52,97 8,43 7628 33,00 91,00 56,89 7,75

Retyrs 15164 0,00 46,80 1,52 6,14 49000 0,00 64,70 8,24 12,09

ROA 15995 ­587,97 1100,00 ­1,62 39,95 51364 ­1314,89 218,75 2,84 22,49

65

Ittner, Lambert and Larker (2003), Murphy (2003) and Stathopoulos, Espenlaub

and Walker (2004) are of the view that new economy companies differ from old

economy companies because they present higher growth taxes of sales increase; they

spend more money on research and development; they present low ratios of book­to­

market value, reduced dividends per share and a high volatility of share returns. They

still hold a smaller number of employees, a more reduced market value and smaller

accounting returns than old economy firms. In addition, they provide a larger

compensation proportion based on capital ownership, have a higher percentage of stock

options and a higher percentage of the volume of stock options not exercised related to

the total number of company shares.

Our data also shows that new economy firms have smaller net incomes; the

earning per share and the return on equity are smaller; the new economy executives are

younger; the increase in net income and the yearly returns to shareholders are higher in

new economy firms, and new economy firms have more board meetings, and on

average, board members are paid more per board meeting.

5.2. Determinants of Executive Compensation in New and Old Economy Firms

We now examine whether determinants of executive compensation, in new and

old economy firms, are the same. We first test for correlations among independent

variables, as discussed above, and find the values are relatively low. Tables 6 and 7

present the results of LSDV (least square dummy variables) regressions for the CEOs

and directors of the new economy firms. Tables 8 and 9 present the LSDV regression

results for the CEOs and the directors of the old economy firms.

The regression uses three separate dependent variables: LN(total compensation),

LN(option ratio), and LN(short term compensation). Each of the dependent variables is

potentially explained by various independent variables as discussed earlier. We use

unbalanced panel data because some executives do not necessarily stay with the same

firm throughout our sample period. Standard errors are corrected using period

Seemingly Unrelated Regression (SUR) Panel Corrected Standard Errors (PCSE) which

corrects for both period heteroskedasticity and general correlation of observations

within a given cross section (Beck and Katz, 1995).

66

Table 6 Fixed Effect Regression: Least Square Dummy Variables CEOs

New economy Executives We used Unbalanced Panel Data Fixed Effect Regression Analysis. The dependent variables are LN (Total Compensation), LN(Short Term Compensation) and LN(Option Ratio). Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. LN (Total Compensation) is the natural logarithm of total executive compensation. LN(Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a vector that measures the firm size and is a combination of the LN (Assets), LN (Market Value) and LN(Sales) variables. Using these three variables and applying the Principal Component Analysis, we extract a vector that is the best combination of these three variables to analyse the influence of firm size; LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested divided by the aggregate number of stock options/stock appreciation rights granted; Growth NI 5Y is the 5­year least squares annual growth rate of net income; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes’ method; LN (Number Mtgs) is the natural logarithm of the number of board meetings; LN (Tenure) is the natural logarithm of the number of years that the CEO has been doing the job; LN (Ownership) is the natural logarithm of the percentage of the company shares owned by the executive. We also used a dummy year variable from 1993 to 2004 to control for the year effect. Shareholder Ret 1Y is the one year return to shareholders. To distinguish between executives from new and old economy firms, we used the methodology of Murphy (2003), which considers firms from the new economy those with SIC codes 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961, 7370, 7371, 7372 and 7373 and firms from the old economy those with SIC codes less than 4000 and not yet categorised with the new economy. Independent Variables

LN(Total Comp.)

t Statistics

LN(Option Ratio)

t Statistics

LN(Short Term Comp.)

t Statistics

Constant 3,9468 1,352 ­1,9562 ­1,1438 0,9214 0,3206 Firm Size Component 0,3028* 6,2359 0,1286* 4,184 0,0908*** 1,7004

LN(Not Exercised Ratio) ­0,3179* ­8,6617 ­0,1818* ­6,8661 0,0481 1,2339

LN(Number Mtgs) ­0,0275 ­0,2729 ­0,0005 ­0,0064 0,0185 0,1592 LN(Tenure) ­0,5680 ­0,5656 ­0,1886 ­0,3275 1,3216 1,3628 LN(Ownership) 0,1131** 2,1567 0,0100 0,2450 0,0519 0,7276 Share Ret 1Y ­0,0006 ­1,4682 ­0,0008* ­3,4916 ­0,0001 ­0,1211 Growth NI 5Y 0,0014 1,4230 ­0,0003 ­0,4749 0,0005 0,5452 LN(Bs Volatility) ­0,0912 ­0,2688 ­0,1023 ­0,4549 ­0,7729*** ­1,9145 Year 1993 0,0358 0,0978 ­0,1256 ­0,8451 ­0,0926 ­0,2508 Year 1994 0,2765 0,7954 ­0,1278 ­0,8449 ­0,0647 ­0,1823 Year 1995 0,0831 0,2269 ­0,3816** ­2,2137 ­0,0149 ­0,0392 Year 1996 0,1009 0,2691 ­0,1279 ­0,6924 ­0,2067 ­0,5412 Year 1997 0,2297 0,6046 ­0,3068*** ­1,7328 ­0,1039 ­0,266 Year 1998 0,0992 0,2625 ­0,3574*** ­2,0384 ­0,0537 ­0,1337 Year 1999 0,2952 0,7644 ­0,3330*** ­1,9847 ­0,1910 ­0,4538 Year 2000 0,4027 0,9869 ­0,4372*** ­2,3732 0,0340 0,0781 Year 2001 0,4264 1,0235 ­0,3557*** ­1,803 ­0,3018 ­0,587 Year 2002 0,4445 1,0685 ­0,3512*** ­1,8624 0,1653 0,3581 Year 2003 0,3654 0,8643 ­0,3711*** ­1,8912 0,2043 0,4406 Year 2004 0,3820 0,8871 ­0,4691*** ­2,2527 0,2061 0,4280 Number Obs. 309 309 309 Adjusted R­Square 86.12% 68.19% 64.22% * Significant at 1% level, ** significant at 5% level *** significant at 10%

67

Table 7 Fixed Effect Regression: Least Square Dummy Variables Directors

New Economy Executives We used Unbalanced Panel Data Fixed Effect Regression Analysis. The dependent variables are LN (Total Compensation), LN(Short Term Compensation) and LN(Option Ratio). Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. LN (Total Compensation) is the natural logarithm of total executive compensation. LN(Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a vector that measures the firm size and is a combination of the LN (Assets), LN (Market value) and LN(Sales) variables. Using these three variables and applying the Principal Component Analysis, we extract a vector that is the best combination of these three variables to analyse the influence of firm size; LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; Growth NI 5Y is the 5­year least squares annual growth rate of net income; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes’ method; LN (Number Mtgs) is the natural logarithm of the number of board meetings; LN (Tenure) is the natural logarithm of the number of years that the CEO has been doing the Job; LN (Ownership) is the natural logarithm of the percentage of the company shares owned by the executive. We also used a dummy year variable from 1993 to 2004 to control for the year effect. Shareholder Ret 1Y is the one year return to shareholders. To distinguish between executives from new and old economy firms, we used the methodology of Murphy (2003), which considers firms from the new economy those with SIC codes 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961, 7370, 7371, 7372 and 7373 and firms from the old economy those with SIC codes less than 4000 and not yet categorised with the new economy. Independent Variables

LN(Total Comp.)

t Statistics

LN(Option Ratio)

t Statistics

LN(Short Term Comp.)

t Statistics

Constant 4,7355 1,7959 ­2,2375 ­0,8747 0,2003 0,0795 Firm Size Component 0,2702* 6,7345 0,1238* 4,4973 0,1027** 2,4695

LN(Not Exercised Ratio) ­0,2989* ­10,0186 ­0,1775* ­8,0077 0,0219 0,7446

LN(Number Mtgs) 0,0144 0,1524 0,0327 0,4932 0,0229 0,2291 LN(Tenure) ­0,6834 ­0,732 ­0,1200 ­0,129 1,5270*** 1,7208 LN(Ownership) 0,1243** 2,4721 0,0026 0,0713 0,0465 0,7461 Share Ret 1Y ­0,0005 ­1,3772 ­0,0010* ­4,5157 0,0002 0,5579 Growth NI 5Y 0,0003 0,5012 ­0,0003 ­0,5971 0,0007 1,181 LN(Bs Volatility) ­0,0125 ­0,0433 ­0,0382 0,205 ­0,5566*** ­1,8738 Year 1993 0,0467 0,3124 0,0064 0,0594 ­0,1191 ­0,853 Year 1994 0,3595** 2,4498 0,0504 0,4878 0,0110 0,0809 Year 1995 0,2164 1,3484 ­0,1958*** ­1,6966 0,0199 0,128 Year 1996 0,2414 1,4126 0,0541 0,4663 ­0,0624 ­0,3895 Year 1997 0,3609** 2,0062 ­0,1736 ­1,4341 ­0,0124 ­0,0722 Year 1998 0,2468 1,2597 ­0,2085 ­1,6096 0,0371 0,1915 Year 1999 0,3369 1,5179 ­0,1443 ­1,0652 ­0,2088 ­0,8975 Year 2000 0,4746*** 1,8966 ­0,2836*** ­1,8632 0,0666 0,271 Year 2001 0,6066** 2,2624 ­0,1381 ­0,8059 ­0,1668 ­0,4926 Year 2002 0,5463** 2,0517 ­0,1724 ­1,0773 0,2111 0,7711 Year 2003 0,4771*** 1,7628 ­0,1864 ­1,112 0,2647 0,9578 Year 2004 0,4653*** 1,6794 ­0,3029*** 1,690 0,2772 0,9443 Number Obs. 393 393 393 Adjusted R­Square 83.78% 65.34% 66.42% * Significant at 1% level, ** significant at 5% level *** significant at 10%

68

Table 8 Fixed Effect Regression: Least Square Dummy Variables – CEOs

Old economy Executives We used Unbalanced Panel Data Fixed Effect Regression Analysis. The dependent variables are LN (Total Compensation), LN(Short Term Compensation) and LN(Option Ratio). Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. LN (Total Compensation) is the natural logarithm of total executive compensation. LN(Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a vector that measures the firm size and is a combination of the LN (Assets), LN (Market value) and LN(Sales) variables. Using these three variables and applying the Principal Component Analysis, we extract a vector that is the best combination of these three variables to analyse the influence of firm size; LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested divided by the aggregate number of stock options/stock appreciation rights granted; Growth NI 5Y is the 5­year least squares annual growth rate of net income; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes’ method; LN (Number Mtgs) is the natural logarithm of the number of the board meetings; LN (Tenure) is the natural logarithm of the number of years that the CEO has been doing the job; LN (Ownership) is the natural logarithm of the percentage of the company shares owned by the executive. We also used a dummy year variable from 1993 to 2004 to control for the year effect. Shareholder Ret 1Y is the one­year return to shareholders. To distinguish between executives from new and old economy firms, we used the methodology of Murphy (2003), which considers firms from the new economy those with SIC codes 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961, 7370, 7371, 7372 and 7373 and firms from the old economy those with SIC codes less than 4000 and not yet categorised with the new economy. Independent Variables

LN(Total Comp.)

t Statistics

LN(Option Ratio)

t Statistics

LN(Short Term Comp.)

t Statistics

Constant 5,0932* 6,5267 ­1,3329 ­0,9941 6,1012* 6,8681 Firm Size Component 0,2008* 11,0886 0,0929* 3,7742 0,1242* 7,6246

LN(Not Exercised Ratio) ­0,1985 ­16,6525 ­0,3203* ­19,4289 0,0022 0,2013

LN(Number Mtgs) ­0,0042 ­0,085 ­0,1280** ­1,9981 ­0,054 ­1,186 LN(Tenure) ­0,5738 ­1,4006 ­0,2986 ­0,5463 ­0,7091*** ­1,9516 LN(Ownership) 0,0487** 2,1481 ­0,0979 ­3,3076 0,0399*** 1,9564 Share Ret 1Y 0,0006* 2,7898 ­0,0015* ­5,1449 0,0011* 4,8791 Growth NI 5Y 0,0015* 2,6522 ­0,0008 ­1,1308 0,0007 1,3028 LN(Bs Volatility) 0,0353 0,3513 0,4405* 3,3646 ­0,1995 ­2,1821 Year 1993 0,1884** 1,9655 0,0583 0,3795 0,0763 1,0477 Year 1994 0,3493* 3,5811 0,2739*** 1,7161 0,0969 1,2526 Year 1995 0,2558* 2,5925 0,1096 0,6842 0,0404 0,5021 Year 1996 0,4075* 4,025 0,2054 1,2783 0,0787 0,9663 Year 1997 0,5131* 4,9632 0,3097*** 1,9068 0,0914 1,1104 Year 1998 0,5163* 4,8442 0,3003*** 1,8338 0,0799 0,9421 Year 1999 0,5751* 5,4467 0,3127*** 1,8855 0,1401 1,5819 Year 2000 0,6167* 5,6181 0,2266 1,3306 0,1587*** 1,7721 Year 2001 0,6158* 5,4672 0,3798** 2,1975 0,0844 0,9022 Year 2002 0,6959* 6,1528 0,2490 1,4237 0,2428* 2,601 Year 2003 0,6182* 5,3628 0,3054*** 1,7425 0,1938** 2,0428 Year 2004 0,7819* 6,5267 0,2729 1,5073 0,3220* 3,2358 Number Obs. 1363 1363 1363 Adjusted R­Square 89.34% 76.52% 86.72% * Significant at 1% level, ** significant at 5% level *** significant at 10%

69

Table 9 Fixed Effect Regression: Least Square Dummy Variables Directors

Old economy Executives We used Unbalanced Panel Data Fixed Effect Regression Analysis. The dependent variables are LN (Total Compensation), LN(Short Term Compensation) and LN(Option Ratio). Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. LN (Total Compensation) is the natural logarithm of total executive compensation. LN(Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a vector that measures the firm size and is a combination of the LN (Assets), LN (Market value) and LN(Sales) variables. Using these three variables and applying the Principal Component Analysis, we extract a vector that is the best combination of these three variables to analyze the influence of firm size; LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested divided by the aggregate number of stock options/stock appreciation rights granted; Growth NI 5Y is the 5­year least squares annual growth rate of net income; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes’ method; LN (Number Mtgs) is the natural logarithm of the number of the board meetings; LN (Tenure) is the natural logarithm of the number of years that CEO has been doing the job; LN (Ownership) is the natural logarithm of the percentage of the company shares owned by the executive. We also used a dummy year variable from 1993 to 2004 to control for the year effect. Shareholder Ret 1Y is the one­year return to shareholders. To distinguish between executives from new and old economy firms, we used the methodology of Murphy (2003), which considers firms from the new economy those with SIC codes 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045, 5961, 7370, 7371, 7372 and 7373 and firms from the old economy those with SIC codes less than 4000 and not yet categorised with the new economy. Independent Variables

LN(Total Comp.)

t Statistics

LN(Option Ratio)

t Statistics

LN(Short Term Comp.)

t Statistics

Constant 5,1720* 4,3219 ­1,0845 ­0,7971 6,3836* 4,0755 Firm Size Component 0,2042* 12,5828 0,1007* 4,9939 0,1467* 7,0728

LN(Not Exercised Ratio) ­0,2198 ­19,9261 ­0,3454* ­25,0717 ­0,0318* ­2,4885

LN(Number Mtgs) 0,0062 0,1344 ­0,1681* ­2,9918 ­0,1077** ­2,0766 LN(Tenure) ­0,5212 ­1,0532 ­0,4784 ­0,8453 ­0,9224 ­1,4328 LN(Ownership) 0,0578** 2,5457 ­0,0988* ­3,6193 0,0574** 2,2395 Share Ret 1Y 0,0005** 2,3707 ­0,0015* ­6,2385 0,0009* 4,1489 Growth NI 5Y 0,0013* 2,6242 0,00004 0,0742 0,0005 0,9295 LN(Bs Volatility) 0,0854 0,9394 0,3290* 3,0302 ­0,2454** ­2,2947 Year 1993 ­0,0543 ­0,8618 0,1461*** 1,7231 ­0,0765 ­0,8606 Year 1994 0,0703 1,0810 0,2922* 3,2781 ­0,0076 ­0,0917 Year 1995 ­0,0274 ­0,4200 0,1584 1,8167 ­0,0821 ­0,9385 Year 1996 0,1123** 1,6494 0,2498* 2,8461 ­0,0865 ­0,9097 Year 1997 0,1826** 2,4383 0,2995* 3,2076 ­0,0967 ­0,9593 Year 1998 0,1516** 1,9714 0,2959* 3,2041 ­0,1550 ­1,4683 Year 1999 0,2248* 2,9956 0,3124* 3,2663 ­0,0736 ­0,6915 Year 2000 0,2780* 3,4906 0,2285** 2,2541 ­0,0909 ­0,8108 Year 2001 0,2570* 3,0847 0,3965* 3,795 ­0,1705 ­1,4396 Year 2002 0,3684* 4,406 0,2748* 2,5956 ­0,0022 ­0,0184 Year 2003 0,3039 3,5053 0,3013* 2,8026 ­0,0670 ­0,5512 Year 2004 0,4535* 4,7947 0,2938* 2,5243 0,0106 0,0831 Number Obs. 1711 1711 1711 Adjusted R­Square 87.42% 75.86% 76.79% * Significant at 1% level, ** significant at 5% level *** significant at 10% Note: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

70

Our results reveal that there are significant differences in factors that explain

executive compensation in new versus old economy sub­samples, and generally these

differences are statistically significant based on tests of equality of regression

coefficients (Table 10 and 11 appendix). We find that the factors that explain CEO

compensation in new economy firms also explain Director compensation in new

economy firms but with smaller intensity. We observe the same phenomena in the old

economy firms. We also find that the impact of the common factor (size) to explain

executive compensation is significantly different for old versus new economy firms.

As expected, the firm's size is one of the most important variables in explaining

executive compensation. In the case of the CEO sub­sample, the size variable has a

stronger impact on the executive compensation in new economy firms than in old

economy firms. The difference in impact of the size variable on executive compensation

is statistically significant for new versus old economy firms. The results suggest that as

the firm size increases, new economy executives receive more than executives from the

old economy.

The firm’s size also influences the number of options granted to the executives in

new and old economy firms, but the relationship is stronger in new economy firms. The

results are congruent with the findings of Ittner et al. (2003), Murphy (2003) and

Stathopoulos et al. (2004), Anderson, Banker, and Ravidran (2000) that new economy

firms grant more stock options to executives.

Our results also reveal that cash compensation is more sensitive to firm size in

old economy firms than new economy firms. That is, large companies from the old

economy pay more in cash than new economy firms.

In the case of the directors, the relationship between firm size and compensation

is similar to the relationship for the CEOs, but the intensity of the coefficients is

smaller, meaning that the impact of the size variable on executive compensation is

smaller than in the CEO sub­sample.

The number of vested stock options that the executives have, but are not

exercised, only affects, in negative terms, the total compensation of the CEOs in new

economy firms. This means that when CEOs can not exercise the options that are

vested, the firm does not increase their incentives. The results also reveal that for both

new and old economy firms when executives have stock options that are vested but not

71

exercised, then firms prefer not to give more stock options. This relationship is more

pronounced in the case of the old economy, meaning that new economy firms are more

averse to granting stock options to executives when there are already unexercised

options. In the case of the directors, the relationship is the same as the CEOs, with the

exception of directors from old economy firms, who are compensated with cash when

the options can not be exercised.

The number of board meetings is negative and statistically significant only in the

case of CEOs and Directors from the old economy. The results are congruent with the

findings of Ryan and Wiggins (2001), and Chen and Hung (2006), who say that more

monitoring power can reduce the need to provide CEOs with incentive compensation.

The total compensation decreases for old economy executives if the board members

attend all the meetings, meaning that, as indicated by Davidson, Pilger and Szakmary

(1998), board members are more aligned with shareholders´ interests when they have

more meetings during the year, and therefore the CEO compensation is more controlled.

The age of the executive (Tenure) is only positive and statistically significant

with total compensation in the case of CEOs from new economy firms and also in the

case of cash compensation from directors. As we expected, more experience is

associated with more remuneration in new economy firms. The results of old economy

firms are not statistically significant, meaning that executive experience does not

influence old economy executive compensation. We also found that CEO tenure is

negatively related to cash compensation, meaning that experienced executives probably

receive compensation in forms other than cash.

Contrary to our expectations, and the results of Chen and Hung (2006), we find

that, with the exception of new economy CEOs, executive ownership has a positive, and

not negative, influence on the total compensation of the CEOs and the Directors of new

as well as old economy firms. The relationship is stronger, however, in the case of

CEOs and Directors of new economy firms. There is a negative association between

executive ownership and the number of options that executives receive. When the

executives have more stocks options from their company, their interests are already

aligned with the shareholders, and it is therefore not necessary to give more stock

options to reduce the agency problem. The same relationship exists in the case of

directors.

72

The one­year return to shareholders is negatively related to the options granted to

CEO in both new and old economy firms, and also in the case of new economy

directors. In the case of old economy directors, it is negatively related to total

compensation, option ratio and short term compensation, and the results are congruent

with the theory that if shareholders are already satisfied with investment returns, they do

not need to pay more to executives because their interests are aligned with executives’

interests.

The increase in the firm’s Net Income in the last five years does not affect CEO

executive compensation in new economy firms. In the case of CEOs and directors from

the old economy, the influence is positive on total compensation. The results are

congruent with Ang et al. (2002) and Aggarwal and Samwick (2003), who find that

CEOs are paid more in terms of performance than other executives and with Ittner et al.

(2003), who find that new economy firms give stock options to executives because the

firms have difficulty generating enough cash flow to pay high salaries to executives.

As we expected, stock return volatility has a strong negative influence on CEO

cash compensation in old economy firms and a less strong influence in old economy

firms. Also in the case of the directors, the stock return volatility is negatively related to

cash compensation in new and old economy firms, and the relationship is stronger in

new economy firms. The results mean that if the volatility increases, firms will reward

their executives with more stock options and less cash as incentives.

We also find that time has a positive effect on total compensation and options

compensation.

73

6. Conclusion

We examine various questions regarding executive compensation of old versus

new economy firms: Are the determinants (and their intensity) of executive

compensation the same in new versus old economy firms? Are the forms of

compensation received by executives in the new and old economies, in terms of salary,

bonus, stock options, restricted stocks and Long­Term Incentive Plans (LTIP) the same

and did the form of compensation change after the 2000 crash of Nasdaq and the 2002

Sarbanes­Oxley Act?

Our results reveal that the number of executives of new economy firms is

considerably smaller than the number of executives of old economy firms, and that most

of the new economy executives are from firms associated with Prepackaged Software (26.02%) Semicondutor and Related Devices (17.29%), Computer Programming, Data

Processing (9.46%) and Telecommunications (7.50%).

Our results also reveal that new economy executives receive more, on average,

than executives from the old economy, but the difference decreases in the last sample

years. In the bubble period, new economy executive compensation is composed of more

than 50% of stock options and in the case of old economy firms with more than 30% of

stock options. After that period, with the NASDAQ Crash and the introduction of the

Sarbanes­Oxley Act we observe a significant change in the structure of the components

of executive compensation ­ reducing the use of stock options and increasing the use of

bonus and restricted stocks.

We also find that the factors that explain executive compensation in new and old

economy firms are generally different, and in the case of the variables that are the same,

our tests generally reject the hypothesis of the equality of regression coefficients related

to these common factors. New economy total executive compensation is influenced by

firm size, the ratio of the number of stock options vested but not exercised, and

executive stock ownership. Old economy total executive compensation is influenced by

firm size, executive ownership, one­year total return to shareholders and 5­year annual

growth rate of firm net income.

74

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8. Appendix

Table 10: T Test of Equality of Fixed Effect Regressions Coefficients ­ CEOs

Panel A: LN (Total Compensation) New Economy Old Economy

Independent Variables N Coef. Std. Error N Coef. Std. Error

Sig.

(Constant) 309 3,9468 2,9192 1363 5,0932 1,0035 No

Firm Size Component 309 0,3028 0,0486 1363 0,2008 0,0181 *

LN(Not Exercised Ratio) 309 ­0,3179 0,0367 1363 ­0,1985 0,0119 *

LN (Number Mtgs) 309 ­0,0275 0,1006 1363 ­0,0042 0,0494 *

LN (Tenure) 309 ­0,5680 1,0042 1363 ­0,5738 0,4097 *

LN (Ownership) 309 0,1131 0,0525 1363 0,0487 0,0227 *

Share Ret 1Y 309 ­0,0006 0,0004 1363 0,0006 0,0002 *

Growth NI 5Y 309 0,0014 0,0010 1363 0,0015 0,0005 *

LN (Bs Volatility) 309 ­0,0912 0,3394 1363 0,0353 0,1006 *

Year1993 309 0,0358 0,3664 1363 0,1884 0,0958 *

Year1994 309 0,2765 0,3476 1363 0,3493 0,0975 *

Year1995 309 0,0831 0,3661 1363 0,2558 0,0987 *

Year1996 309 0,1009 0,3748 1363 0,4075 0,1012 *

Year1997 309 0,2297 0,3798 1363 0,5131 0,1034 *

Year1998 309 0,0992 0,3779 1363 0,5163 0,1066 *

Year1999 309 0,2952 0,3862 1363 0,5751 0,1056 *

Year2000 309 0,4027 0,4081 1363 0,6167 0,1098 *

Year2001 309 0,4264 0,4166 1363 0,6158 0,1126 *

Year2002 309 0,4445 0,4160 1363 0,6959 0,1131 *

Year2003 309 0,3654 0,4228 1363 0,6182 0,1153 *

Year2004 309 0,3820 0,4306 1363 0,7819 0,1198 No

* Significant at level 1%; ** Significant at level 5%; *** Significant at level 10% Note: In this table we analyse whether the coefficient values of the regression are statistically different in new and old economy firms, based on the t test. In most of the regression variables we found that these coefficients are statistically different at 1%(*).

78

Table 10 (Cont.)

PANEL B: LN (Option Ratio) New Economy Old Economy

Independent Variables N Coef. Std.

Error N Coef. Std. Error

Sig.

(Constant) 309 ­1,9562 1,7107 1363 ­1,3329 1,3409 *

Firm Size Component 309 0,1286 0,0307 1363 0,0929 0,0246 *

LN(Not Exercised Ratio) 309 ­0,1818 0,0265 1363 ­0,3203 0,0165 *

LN (Number Mtgs) 309 ­0,0005 0,0756 1363 ­0,1280 0,0640 *

LN (Tenure) 309 ­0,1886 0,5758 1363 ­0,2986 0,5466 *

LN (Ownership) 309 0,0100 0,0408 1363 ­0,0979 0,0296 *

Share Ret 1Y 309 ­0,0008 0,0002 1363 ­0,0015 0,0003 *

Growth NI 5Y 309 ­0,0003 0,0006 1363 ­0,0008 0,0007 *

LN (BS Volatility) 309 ­0,1023 0,2249 1363 0,4405 0,1309 *

YEAR1993 309 ­0,1256 0,1487 1363 0,0583 0,1536 *

YEAR1994 309 ­0,1278 0,1513 1363 0,2739 0,1596 *

YEAR1995 309 ­0,3816 0,1724 1363 0,1096 0,1602 *

YEAR1996 309 ­0,1279 0,1847 1363 0,2054 0,1607 *

YEAR1997 309 ­0,3068 0,1771 1363 0,3097 0,1624 *

YEAR1998 309 ­0,3574 0,1753 1363 0,3003 0,1637 *

YEAR1999 309 ­0,3330 0,1678 1363 0,3127 0,1659 *

YEAR2000 309 ­0,4372 0,1842 1363 0,2266 0,1703 *

YEAR2001 309 ­0,3557 0,1973 1363 0,3798 0,1728 *

YEAR2002 309 ­0,3512 0,1886 1363 0,2490 0,1749 *

YEAR2003 309 ­0,3711 0,1962 1363 0,3054 0,1753 *

YEAR2004 309 ­0,4691 0,2082 1363 0,2729 0,1810 *

* Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

Note: In this table we analyse whether the coefficient values of the regression are statistically different in new and old economy firms, based on the t test. In most of the regression variables we found that these coefficients are statistically different at 1%(*).

79

Table 10 (Cont.)

Panel c: LN (Short Term Compensation) New Economy Old Economy

Independent Variables N Coef. Std.

Error N Coef. Std. Error

Sig.

(Constant) 309 0.9214 2,8743 1363 6,1012 0,8883 *

Firm Size Component 309 0,0908 0,0534 1363 0,1242 0,0163 *

LN(Not Exercised Ratio) 309 0,0481 0,0390 1363 0,0022 0,0108 *

LN (Number Mtgs) 309 0,0185 0,1161 1363 ­0,0540 0,0455 *

LN (Tenure) 309 1,3216 0,9691 1363 ­0,7091 0,3633 *

LN (Ownership) 309 0,0519 0,0713 1363 0,0399 0,0204 *

Share Ret 1Y 309 ­0,0001 0,0005 1363 0,0011 0,0002 *

Growth NI 5Y 309 0,0005 0,0009 1363 0,0007 0,0005 *

LN (BS Volatility) 309 ­0,7729 0,4037 1363 ­0,1995 0,0914 *

YEAR1993 309 ­0,0926 0,3692 1363 0,0763 0,0729 *

YEAR1994 309 ­0,0647 0,3551 1363 0,0969 0,0773 *

YEAR1995 309 ­0,0149 0,3805 1363 0,0404 0,0805 *

YEAR1996 309 ­0,2067 0,3819 1363 0,0787 0,0815 *

YEAR1997 309 ­0,1039 0,3906 1363 0,0914 0,0823 *

YEAR1998 309 ­0,0537 0,4012 1363 0,0799 0,0848 *

YEAR1999 309 ­0,1910 0,4210 1363 0,1401 0,0886 *

YEAR2000 309 0,0340 0,4356 1363 0,1587 0,0895 *

YEAR2001 309 ­0,3018 0,5142 1363 0,0844 0,0936 *

YEAR2002 309 0,1653 0,4615 1363 0,2428 0,0934 NO

YEAR2003 309 0,2043 0,4637 1363 0,1938 0,0949 *

YEAR2004 309 0,2061 0,4815 1363 0,3220 0,0995 * * Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

Note: In this table we analyse whether the coefficient values of the regression are statistically different in new and old economy firms, based on the t test. In most of the regression variables we found that these coefficients are statistically different at 1%(*).

80

Table 11: T Test of Equality of Fixed Effect Regressions Coefficients – Directors

Panel A: LN (Total Compensation) New Economy Old Economy

Independent Variables N Coef. Std.

Error N Coef. Std.

Error

Sig.

(Constant) 393 4,7355 2,6369 1711 5,1720 1,1967 No

Firm Size Component 393 0,2702 0,0401 1711 0,2042 0,0162 *

LN(Not Exercised Ratio) 393 ­0,2989 0,0293 1711 ­0,2198 0,0110 *

LN (Number Mtgs) 393 0,0144 0,0946 1711 0,0062 0,0465 *

LN (Tenure) 393 ­0,6834 0,9336 1711 ­0,5212 0,4949 *

LN (Ownership) 393 0,1243 0,0503 1711 0,0578 0,0227 *

Share Ret 1Y 393 ­0,0005 0,0004 1711 0,0005 0,0002 *

Growth NI 5Y 393 0,0003 0,0007 1711 0,0013 0,0005 *

LN (Bs Volatility) 393 ­0,0125 0,2879 1711 0,0854 0,0909 *

YEAR1993 393 0,0467 0,1495 1711 ­0,0543 0,0630 *

YEAR1994 393 0,3595 0,1468 1711 0,0703 0,0650 *

YEAR1995 393 0,2164 0,1605 1711 ­0,0274 0,0653 *

YEAR1996 393 0,2414 0,1709 1711 0,1123 0,0681 *

YEAR1997 393 0,3609 0,1799 1711 0,1826 0,0749 *

YEAR1998 393 0,2468 0,1960 1711 0,1516 0,0769 *

YEAR1999 393 0,3369 0,2219 1711 0,2248 0,0751 *

YEAR2000 393 0,4746 0,2502 1711 0,2780 0,0796 *

YEAR2001 393 0,6066 0,2681 1711 0,2570 0,0833 *

YEAR2002 393 0,5463 0,2663 1711 0,3684 0,0836 *

YEAR2003 393 0,4771 0,2707 1711 0,3039 0,0867 *

YEAR2004 393 0,4653 0,2770 1711 0,4535 0,0946 No

* Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

81

Table 11 (Cont.)

Panel B: LN (Option Ratio) New Economy Old Economy Independent

Variables N Coef. Std. Error N Coef. Std.

Error

Sig.

(Constant) 393 ­2,2375 2,5580 1711 ­1,0845 1,3606 *

Firm Size Component 393 0,1238 0,0275 1711 0,1007 0,0202 *

LN(Not Exercised Ratio) 393 ­0,1775 0,0222 1711 ­0,3454 0,0138 *

LN (Number Mtgs) 393 0,0327 0,0663 1711 ­0,1681 0,0562 *

LN (Tenure) 393 ­0,1200 0,9305 1711 ­0,4784 0,5660 *

LN (Ownership) 393 0,0026 0,0365 1711 ­0,0988 0,0273 *

Share Ret 1Y 393 ­0,0010 0,0002 1711 ­0,0015 0,0002 *

Growth NI 5Y 393 ­0,0003 0,0006 1711 0,00004 0,0006 *

LN (Bs Volatility) 393 ­0,0382 0,1865 1711 0,3290 0,1086 *

YEAR1993 393 0,0064 0,1079 1711 0,1461 0,0848 *

YEAR1994 393 0,0504 0,1032 1711 0,2922 0,0891 *

YEAR1995 393 ­0,1958 0,1154 1711 0,1584 0,0872 *

YEAR1996 393 0,0541 0,1160 1711 0,2498 0,0878 *

YEAR1997 393 ­0,1736 0,1211 1711 0,2995 0,0934 *

YEAR1998 393 ­0,2085 0,1295 1711 0,2959 0,0924 *

YEAR1999 393 ­0,1443 0,1354 1711 0,3124 0,0956 *

YEAR2000 393 ­0,2836 0,1522 1711 0,2285 0,1014 *

YEAR2001 393 ­0,1381 0,1714 1711 0,3965 0,1045 *

YEAR2002 393 ­0,1724 0,1600 1711 0,2748 0,1059 *

YEAR2003 393 ­0,1864 0,1676 1711 0,3013 0,1075 *

YEAR2004 393 ­0,3029 0,1792 1711 0,2938 0,1164 *

* Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

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Table 11 (Cont.) Panel C: LN (Short Term Compensation)

New Economy Old Economy Independent Variables

N Coef. Std. Error N Coef. Std.

Error

Sig.

(Constant) 393 0.2003 2,5203 1711 6,3836 1,5663 *

Firm Size Component 393 0,1027 0,0416 1711 0,1467 0,0207 *

LN(Not Exercised Ratio) 393 0,0219 0,0294 1711 ­0,0318 0,0128 *

LN (Number Mtgs) 393 0,0229 0,1002 1711 ­0,1077 0,0519 *

LN (Tenure) 393 1,5270 0,8874 1711 ­0,9224 0,6438 *

LN (Ownership) 393 0,0465 0,0623 1711 0,0574 0,0256 *

Share Ret 1Y 393 0,0002 0,0004 1711 0,0009 0,0002 *

Growth NI 5Y 393 0,0007 0,0006 1711 0,0005 0,0006 *

LN (Bs Volatility) 393 ­0,5566 0,2970 1711 ­0,2454 0,1069 *

YEAR1993 393 ­0,1191 0,1396 1711 ­0,0765 0,0889 *

YEAR1994 393 0,0110 0,1358 1711 ­0,0076 0,0834 *

YEAR1995 393 0,0199 0,1557 1711 ­0,0821 0,0875 *

YEAR1996 393 ­0,0624 0,1603 1711 ­0,0865 0,0951 *

YEAR1997 393 ­0,0124 0,1712 1711 ­0,0967 0,1008 *

YEAR1998 393 0,0371 0,1935 1711 ­0,1550 0,1056 *

YEAR1999 393 ­0,2088 0,2327 1711 ­0,0736 0,1064 *

YEAR2000 393 0,0666 0,2457 1711 ­0,0909 0,1121 *

YEAR2001 393 ­0,1668 0,3387 1711 ­0,1705 0,1184 NO

YEAR2002 393 0,2111 0,2738 1711 ­0,0022 0,1190 *

YEAR2003 393 0,2647 0,2763 1711 ­0,0670 0,1215 *

YEAR2004 393 0,2772 0,2936 1711 0,0106 0,1281 *

* Significant at level 1%; ** Significant at level 5%; *** Significant at level 10% Note: In this table we analyse whether the coefficient values of the regression are statistically different in new and old economy firms, based on the t test. In most of the regression variables we found that these coefficients are statistically different at 1%(*).

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Table 12: Pearson Correlation of Independent Variables

Panel A: New Economy 1 2 3 4 5 6 7 8

1­Firm Size Component 1,000 0,189 0,121 0,065 0,012 0,069 ­0,101 ­0,100

2­LN(Not Exercised Ratio) 0,189 1,000 0,081 ­0,041 ­0,182 ­0,086 ­0,160 0,035

3­LN ( Number Mtgs) 0,121 0,081 1,000 ­0,030 ­0,151 ­0,095 0,057 0,097

4­LN (Tenure) 0,065 ­0,041 ­0,030 1,000 0,347 0,076 0,044 ­0,144

5­LN (Ownership) 0,012 ­0,182 ­0,151 0,347 1,000 0,103 0,014 ­0,130

6­Share Ret 1Y 0,069 ­0,086 ­0,095 0,076 0,103 1,000 0,102 ­0,028

7­Growth NI 5Y ­0,101 ­0,160 0,057 0,044 0,014 0,102 1,000 0,071

8­LN (Bs Volatility) ­0,100 0,035 0,097 ­0,144 ­0,130 ­0,028 0,071 1,000

Panel B: Old Economy 1 2 3 4 5 6 7 8

1­Firm Size Component 1,000 0,050 0,085 0,046 ­0,064 ­0,008 ­0,044 ­0,354

2­LN(Not Exercised Ratio) 0,050 1,000 0,049 0,033 ­0,122 ­0,072 ­0,077 ­0,035

3­LN ( Number Mtgs) 0,085 0,049 1,000 ­0,052 ­0,143 ­0,026 ­0,047 0,047

4­LN (Tenure) 0,046 0,033 ­0,052 1,000 0,264 ­0,044 0,034 ­0,102

5­LN (Ownership) ­0,064 ­0,122 ­0,143 0,264 1,000 0,049 0,081 0,030

6­Share Ret 1Y ­0,008 ­0,072 ­0,026 ­0,044 0,049 1,000 0,116 0,120

7­Growth NI 5Y ­0,044 ­0,077 ­0,047 0,034 0,081 0,116 1,000 0,189

8­LN (Bs Volatility) ­0,354 ­0,035 0,047 ­0,102 0,030 0,120 0,189 1,000

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CHAPTER 3

Executive Compensation: NYSE and NASDAQ Listed Firms 11

11 We are grateful to Prof. Sara Robicheaux, from Birmingham­Southern College, discussant of this paper at the Eastern Finance Association Annual Meeting in St Petersburg ­ Florida – USA ­ April 2008. We are also grateful to other anonymous professors present at this conference for their helpful comments.

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1. Introduction

Since the late 80s and early 90s, the world has witnessed the demise of centrally

controlled socialist economies and the rise of the free market capitalist system.

Privatisation of state controlled enterprises, removal of barriers of international trade,

free flow of information, capital and labour, and advances of technology have indeed

created a small, integrated global village producing unparalleled global economic

growth. As a consequence of the growth of the global economy, we have also witnessed

a significant increase in the market value of companies all over the world. Global

growth, market integration, new opportunities, and increased corporate profitability

have intensified the search and competition for executive talent across the world. Firms

now compete for highly qualified executives globally hoping that their knowledge will

be instrumental in increasing the share value of the firms that they will manage.

In this changing global economic and corporate environment, firms, particularly

new economy firms, started paying their executives based on performance, essentially

with stock options. With stock options at their disposal, executives found an added

motivation to increase the value of the stock to raise their chances of exercising their

options later and hence increasing their wealth.

However, in the year 2000, the NASDAQ crash slowed this economic growth

and also some financial scandals came to the fore, along with the bankruptcy of

companies such as Enron and WorldCom, resulting from fraudulent accounting

practices and executive self­dealings. Management, in some widely publicised cases,

distorted the accounting data to manipulate the stock price in order to enhance personal

compensation by exercising options. In order to solve the problems associated with

these scandals, the Sarbanes­Oxley Act of 2002 was created in the USA. It introduced

sweeping changes in governance, reporting, and disclosure requirements of public firms

with the intent of improving accuracy, reliability and timeliness of the information

provided to investors.

Interest in research in executive compensation is recent, but growing. In this

paper, we extend the executive compensation research to NYSE versus NASDAQ listed

firms. We focus our attention on the following questions: Are the total values and the

factors that explain executive compensation for NYSE versus NASDAQ listed firms the

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same? Is the composition of compensation given to these executives different? What

happens to compensation composition and values after the NASDAQ crash in 2000 and

the Sarbanes­Oxley Act in 2002? We analyse data from the years 1992 to 2004.

Our results reveal that NYSE executives receive, on average, more than the

other executives and the differences in total compensation values are statistically

significant. We also find that the forms of compensation for NYSE versus NASDAQ

listed firms are different. In other words, the percentage that each compensation

component represents in total compensation is different in these sub­samples and this

structure changes in all the cases, essentially after the Sarbanes­Oxley Act. Our results

also reveal that the factors that explain CEO and Director compensation on the NYSE

and NASDAQ are generally different, and when some factors are the same, the

coefficients are statistically different.

2. Literature Review and Research Questions

The literature review reveals there is only one study similar and related to our

topic. Firth, Lohen, Ropstad and Sjo (1996), focusing on Norwegian Stock Exchange

listed firms, explore the determinants of Chief Executive Officer (CEO) compensation.

They find a modest positive relationship between CEO compensation and the average

wage level of the company and a strong positive relationship between CEO

compensation and firm size.

Not directly related to the problem of executive compensation and listed firms,

but as a parallel investigation, there are a small number of recent studies associated with

firms listed on the NYSE, NASDAQ and AMEX that may bear important implications

for the findings and conclusions of our study. For example, Sapp and Yan (2000) find

that some of the small firms listed on NASDAQ are changing to AMEX because the

transaction costs are smaller at AMEX (NASDAQ as a competitive multi­dealer system

and AMEX, like NYSE, a monopoly specialist system) and when they change their

liquidity generally improves.

Also Chung, Ness and Ness (1999, 2001) and Bacidore and Lipson (2001), find

evidence that transaction costs on NASDAQ are higher than on the NYSE and due to

this, some NASDAQ firms are also changing to the NYSE to reduce costs. Bacidore

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and Lipson (2001) investigate the effect of opening and closing procedures on the

NYSE and NASDAQ by examining firms that moved from the NASDAQ to the NYSE

and find evidence that opening trades on the NYSE are about 20 percent less costly than

on NASDAQ and these savings on the NYSE opening increase with the size of the

firms.

If NASDAQ and NYSE have different transaction cost structures and the firms

that are listed there have different characteristics, we expect that the factors that explain

executive compensation will also be different. NASDAQ listed firms are essentially

technological firms with low levels of cash flows (Murphy, 2003) and NASDAQ

transaction costs are also generally higher than those of the NYSE. These fundamental

differences can also affect what the company can pay its executives.

Another point that we analyse is associated with the impact of the NASDAQ

crash (2000) and the enactment of the Sarbanes­Oxley Act (2002) on executive

compensation. So far, we know that the NASDAQ crash caused a reduction in

compensation values, but what happened to the components of compensation packages?

Do they still have the same proportion of salary, bonus, stock options, restricted stocks

or LTIP 12 in terms of total compensation as before the NASDAQ crash? And also was

there a significant change in forms and weights of compensation after the Sarbanes­

Oxley Act in 2002?

Effectively, after the NASDAQ crash, there were a series of financial scandals

associated with the bankruptcy of some of the large American companies, based on

fraudulent accounting practices and executive self­dealing. The Sarbanes­Oxley Act of

2002 was established precisely on July 30 th to solve this problem. It introduced

sweeping changes in governance, reporting, and disclosure requirements of public firms

with the intent of improving accuracy, reliability, and timeliness of the information

provided to investors. This Act contains provisions which have a significant impact on

the benefits and compensation of public company executives. The major changes in this

area include the following provisions: to prohibit publicly­traded companies from

making or arranging loans for their directors and executive officers; to expedite

12 Generally, executive compensation is composed of two more components: “other annual compensation” and “all other compensations”. The first case includes the types of compensation not included in salary and bonus and in the second case, all other forms of compensation not included in the other categories. We do not analyse these two forms of compensation because they are residual components and also because they include a large diversity of compensation products.

88

Securities and Exchange Commission (SEC) reporting to insider traders; to prohibit

corporate directors and executive officers from trading employers` securities during

planned blackout periods with respect to those securities and to require an employee

retirement Income Security Act to cover individual account plans to provide a 30­day

notice of blackout periods.

After the Sarbanes­Oxley Act, each of the major US stock markets, the NYSE,

the NASDAQ, and the AMEX adopted new listing standards in an effort to strengthen

the corporate governance practices of listed companies, associated with director

independence, audit committees, compensation committees, nominating committees,

stock option plans, certification, directors/officers and disclosure and foreign issuers.

If a group of new rules of corporate governance were adopted by the NYSE and

NASDAQ, we expect that the way in which companies pay their executives would

change after these important changes.

3. Data, Sample Selection and Statistics

3.1. Data and Sample Selection

Data is from the Execucomp database, which collects information on the five

highest­paid executives from 1500 firms listed on the S&P 500 Index, S&P Mid Cap

Index, and S&P Small Cap Index.

We use Unbalanced Panel Data Analysis. The sample consists of 73,683

observations of compensation, related to the 5 highest­paid executives from S&P 1500

firms between the years 1992 and 2004. This sample is built excluding all executives

whose sum of salary and bonus, and also total compensation, was equal to zero. We

include only longer period compensation (and delete the shorter period compensation)

of executives who receive more than one compensation in the same year. There are a

few instances where an executive switched jobs and received two different

compensations in the same year.

Using the Consumer Price Index (CPI), compiled by the Bureau of Labor

Statistics, with 1982 as a base of 100, we adjust the monetary variables to the price level

of the year 2004. To select between NYSE and NASDAQ firms we use the variable

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EXCHANGE from Execucomp, which classifies NYS as NYSE firms, NAS as

NASDAQ listed firms. Based on Chen and Hung (2006) we make the differentiation

between CEO and Director compensation because, generally, the average compensation

value of CEOs is higher than that of Directors, and also the factors that explain their

compensation can also be different.

3.2. Statistics

In this section we examine the question of whether the executives from firms listed

on the NYSE and NASDAQ are paid differently in terms of total value and

compensation components and whether these items (total compensation value and

components) change after the NASDAQ crash in 2000 and after the introduction of the

Sarbanes­Oxley Act (SO) in 2002. We present the basic statistics in two steps. In the

first step, we analyse the evolution of total compensation through all the observations of

executive compensation between 1992 and 2004 for all top five executives and then for

CEOs and Directors. In the second step, we analyse the percentage that each executive

compensation component represents, in terms of total compensation, year by year. In

this way, we can see the most important executive compensation components and

changes, if any, year by year. We also compare values and components of executive

compensation between 2000 and 2001 (before and after the NASDAQ crash) and 2002

and 2003 (before and after SO) to observe whether the NASDAQ crash and enactment

of the SO act had any impact on executive compensation.

Table 1 presents Independent­Samples T­test to compare the means of executive

compensation components and Levene's test for equality of variances between the two

sub­samples of NYSE and NASDAQ listed firms for the period of 1992 to 2004. The

null hypothesis is that population means are equal; the alternative hypothesis is that

means are different.

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Table 1 Average Total Compensation between 1992 and 2004 Adjusted for Inflation

In this table we describe the total average compensation, between 1992 and 2004, first for all the Top Five Executives and then for CEOs and Directors. Data is from the ExecuComp database. Mean average and mean differences are in thousands of dollars.

Means t­Test Year N Mean Average Mean Difference Significance

PANEL A: Top Five Executives

NYSE 54778 2661.39 1992 to 2004

NASDAQ 18668 2029.29 632.10 *

PANEL B: CEOs NYSE 9305 5781.90 1992 to 2004 NASDAQ 3085 4011.09

1770.82 *

PANEL C: Directors NYSE 19865 4446.18

1992 to 2004 NASDAQ 6524 3023.34

1422.74 *

(*) Mean difference is statistically significant at: (*) 1*% level, (**) 5% level, (***) 10% level.

From table 1, we can see that the average compensation of the five highest paid

NYSE executives is far higher than the average total compensation of the five highest

paid NASDAQ executives, and these differences are statistically significant. Both CEOs

and directors of the NYSE listed firms receive, on average, much more than the CEOs

and directors of NASDAQ listed firms. Mean differences in compensation values are

generally significant.

Table 2 presents the average total compensation of executives of NYSE, and

NASDAQ listed firms, and the Independent­Samples T­test to compare the means of

executive compensation components and Levene's test for equality of variances between

the two sub­samples of NYSE and NASDAQ listed firms, each year for the period of

1992 to 2004. Yearly analysis gives us a better comparison because single average

value based on thirteen yearly observations could be influenced by outlier years. The

null hypothesis will be that all population means are equal; the alternative hypothesis is

that at least one of the means is different.

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Table 2

Year ly Inflation adjusted total compensation trends of NYSE and NASDAQ listed firms between 1992 and 2004

Our sample includes data from the five most well paid executives associated with firms listed on the S&P500, S&P Mid Cap and S&P Small Cap during the period from 1992 to 2004. All the data are from the ExecuComp database. Total compensation is the sum of salary, bonus, stock options, restricted stocks, long­term incentive plans (LTIP), other annual compensation and all other compensation. To differentiate between executives from NYSE and NASDAQ, we used the EXCHANGE variable from the ExecuComp database, which has the following codes: NYS for NYSE firms and NAS for NASDAQ firms. Mean average and mean differences are in thousands of dollars.

NYSE (1) NASDAQ (2) Mean Difference Year

N Mean N Mean (1) and (2)

1992 430 2.584,865 53 1.249,570 1.335,295 *

1993 2499 1.865,280 445 1.091,859 773,421 *

1994 3090 1.717,387 686 1.127,851 589,536 *

1995 3274 1.783,652 777 1.334,982 448,670 *

1996 3398 2.177,596 820 1.605,229 572,367 *

1997 3695 2.805,092 960 1.853,349 951,743 *

1998 3907 3.218,114 1195 2.174,102 1.044,012 **

1999 4211 3.317,494 1513 2.960,016 357,478

2000 4533 3.978,887 1715 3.352,551 626,336 **

2001 4526 3.535,659 1702 3.080,971 454,688 ***

2002 4631 3.079,657 1780 2.100,072 979,585 *

2003 4769 2.812,288 1949 1.764,695 1.047,593 *

2004 4909 3.088,7350 2062 2.001,634 1.087,101 *

(*) Mean difference is significant at: (*) 1% level, (**) 5% level and (***) 10% level.

From table 2 we can see that executives from the NYSE receive, on average, more

than executives from NASDAQ each year during the sample period.

In table 3 we describe the percentage that each executive compensation

component represents in terms of total compensation for NYSE and NASDAQ firms

year by year between 1992 and 2004. In the row for year 2001 and 2003 we describe

whether the differences of the values from year 2001 and 2000 (NASDAQ crash effect)

and 2003 and 2002 (Sarbanes­Oxley Act Effect) are statistically significant.

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Table 3

Year ly Percentages of Each Compensation Component of NYSE and NASDAQ Listed Firms (1992­2004)

This table presents the percentages that each compensation component represents in terms of total compensation by year for the Top Five executives, CEOs and Directors. Salary is the executive salary for the year. Bonus is the dollar value of bonus (cash and non­cash) earned by the executive officer during the fiscal years. Stock options are the aggregate value of stock options granted to the executive during the fiscal year as valued using S & P Black­Scholes methodology. Restricted stocks are the value of restricted stock granted during the year (determined as of the date of the grant). LTIP is the amount paid out to the executive under the company’s long­term incentive plan. We also report in year 2001 and 2003 rows whether the changes between year 2000 and 2001 (NASDAQ crash) and between 2002 and 2003 (Sarbanes­ Oxley Act) are statistically significant. Panel A: Top Five (% of Total Compensation)

Salary Bonus Stock Options Restricted Stocks LTIP Year

NYS NAS NYS NAS NYS NAS NYS NAS NYS NAS

1992 48.69 50.36 19.42 17.76 18.31 24.81 4.58 2.13 4.04 1.19

1993 46.51 48.81 21.41 20.25 17.78 24.64 4.61 1.28 3.92 1.10

1994 43.86 47.34 21.81 20.21 21.31 25.37 4.22 2.19 3.82 0.86

1995 42.95 47.75 22.70 19.46 19.54 25.56 4.71 1.72 4.44 0.09

1996 39.95 44.01 22.38 18.30 23.26 29.82 4.95 2.32 4.38 0.97

1997 36.83 42.47 21.90 17.78 25.83 32.98 5.54 2.07 4.60 0.70

1998 36.07 41.28 19.82 16.04 23.26 29.82 5.68 1.65 4.22 0.50

1999 34.10 37.81 20.02 15.87 32.31 40.67 4.86 1.78 3.59 0.30

2000 32.46 35.78 19.80 16.41 33.25 42.17 5.60 1.98 3.56 0.30

2001 33.63* 36.46* 17.20* 11.87* 35.47* 45.32* 6.03* 2.21* 2.52* 0.40*

2002 33.87 38.74 19.59 14.30 31.08 40.63 7.14 2.34 2.83 0.40

2003 34.65 39.05 20.92* 16.70* 25.32* 35.99* 9.74* 3.55* 3.47* 0.80*

2004 30.48 35.63 23.59 18.15 23.96 36.46 13.32 5.07 4.02 0.71

93

Table 3 (cont.)

Panel B: CEOs (% of Total Compensation)

Salary Bonus Stock Options Restricted Stocks LTIP Year

NYS NAS NYS NAS NYS NAS NYS NAS NYS NAS

1992 36.39 55.29 22.78 12.54 22.89 21.83 6.41 0.68 6.42 5.95 1993 37.64 49.22 23.31 19.42 23.07 17.80 4.62 4.06 5.01 4.45 1994 33.85 43.80 23.22 21.16 28.44 23.10 4.51 4.39 5.14 2.99 1995 31.82 42.46 24.21 23.06 26.76 21.42 5.75 4.99 5.91 3.67 1996 28.59 38.82 22.52 24.13 31.89 23.88 5.96 3.90 5.91 3.99 1997 24.16 36.09 21.93 22.33 36.40 26.05 6.70 5.16 6.04 4.30 1998 23.24 35.02 20.22 20.12 40.27 32.35 5.90 4.49 5.36 3.39 1999 20.33 31.81 19.35 20.57 46.09 35.90 4.94 3.76 4.31 3.10 2000 18.43 30.16 17.93 20.35 47.24 36.92 7.28 4.23 4.19 2.48 2001 18.63 32.12 14.86* 15.40* 51.80** 39.99 7.09 5.52* 3.38* 1.57* 2002 20.03 28.90 17.15 18.97 45.98 38.48 7.90 6.67 4.44 1.78 2003 18.83 30.18 21.68* 21.02** 36.93* 31.08* 11.88* 9.16* 5.56* 2.61* 2004 16.39 27.09 22.99 24.09 36.52 29.73 14.32 11.04 4.79 3.64 Panel C: Directors (% of Total Compensation)

Salary Bonus Stock Options Restricted Stocks LTIP Year

NYS NAS NYS NAS NYS NAS NYS NAS NY NAS

1992 38.98 52.87 21.41 16.58 22.86 16.58 5.35 3.72 5.12 2.52 1993 38.23 49.52 23.22 16.58 22.86 16.58 5.34 3.99 4.63 3.56 1994 35.62 45.44 23.27 21.24 25.98 21.24 5.15 4.34 5.15 2.56 1995 33.59 44.98 23.29 19.59 25.58 19.59 5.91 4.52 5.61 3.08 1996 30.53 40.94 22.41 21.01 30.47 21.01 5.94 4.15 5.47 2.93 1997 26.17 38.95 21.86 23.67 34.62 23.67 6.74 4.76 5.35 2.83 1998 25.40 37.89 20.41 30.41 37.29 30.41 5.82 4.36 5.47 2.59 1999 22.99 34.70 20.23 33.93 42.37 33.93 5.38 3.25 3.97 2.72 2000 20.75 33.15 18.63 34.23 44.06 34.23 6.90 4.31 4.35 1.72 2001 20.73 35.49 16.15* 35.95* 48.53* 35.95 6.47** 5.26* 3.09* 1.35* 2002 21.29 31.57 18.80 36.40 42.08 36.40 8.15 5.96 4.41 1.58 2003 21.10* 33.55* 21.80* 29.74* 35.05* 29.74* 11.56* 7.97* 4.64* 1.86* 2004 18.79 28.84 23.03 28.10 34.25 28.19 14.08 10.56 4.50 2.95 Significant at: (*) 1% level, (**) 5% level and (***) 10% level

94

From table 3 we see that salary, in all the cases, is the most important executive

compensation component of the top five executives until 1998. Between 1999 and 2001,

stock options become the most important component of executive compensation for

NYSE and NASDAQ listed firms.

Examining the impact of the NASDAQ crash on the compensation of the top

five executives, we find that NASDAQ listed firms exhibit a decrease in the value that

bonus represents in total compensation from 16.41% in 2000 to 11.87% in 2001, and

this decrease is compensated with a small increase in terms of stock options (42.17% in

2000 and 45.32% in 2001) and restricted stocks (1.98% in 2000 to 2.31% in 2001).

NYSE listed firms maintain practically the same composition of compensation for the

top five executive sub­sample.

To evaluate the impact of the Sarbanes­Oxley Act in 2002 on executive

compensation, we compare the results for 2002 to 2003 and observe that NYSE and

NASDAQ executives are paid with fewer stock options but with more restricted stocks

and bonuses. We also see that, in the case of the NYSE and NASDAQ, the importance

of restricted stocks in total compensation increases in the later years.

Results for panel B show that for NYSE listed firms, salary is the most

important compensation component between 1992 and 1995 and after that, stock

options are the most important component of CEO compensation. Essentially in the

later years, bonus also becomes an important component of CEO compensation.

In the case of NASDAQ listed firms, the most important compensation

component is also salary, but after 1996 stock options have practically achieved a

similar level of importance.

If we analyse the impact of the NASDAQ crash on CEO compensation,

comparing the change in percentage that each compensation component represents in

terms of total compensation between the year 2000 and 2001, we see that NYSE

executives receive more stock options and less bonus, and NASDAQ executives receive

more stock options, more salary and less bonus.

In terms of the impact of the Sarbanes­Oxley Act, comparing results for 2002

and 2003, we see that NYSE executives receive fewer stock options and more bonus

and restricted stocks. NASDAQ executives also receive fewer stock options and more

bonus, salary and essentially restricted stocks.

95

If we compare the compensation components between CEOs and Directors, we

see that directors receive more salary than CEOs. NASDAQ listed firms reveal that

bonus is a more important component of compensation for Directors than for CEOs but

in the case of the NYSE this difference is small. In all the cases, CEOs receive more

stock options than Directors. The use of restricted stock becomes an important

component after the Sarbanes­Oxley Act in 2002 for NYSE and NASDAQ listed firms.

4. Research Design

We first examine the determinants of executive compensation. We believe that if

firms listed on the NYSE are significantly different from firms listed on NASDAQ, the

factors that explain the executive compensation in these two groups may also be

different. In this section we make this analysis.

We used Unbalanced Panel Data analysis and the Fixed Effect Regression

Model, also called within estimator or the Least of Square Dummy Variable (LSDV).

4.1. Dependent Variables

The dependent variables are LN (Total Compensation) and LN (Short Term

Compensation) and LN (Option Ratio).

The dependent variable LN (Total Compensation) is the natural logarithm of the

sum of salary, bonus, stock options, restricted stocks, LTIP, other annual compensation

and all other compensation, LN (Short term Compensation) is the natural logarithm of

the sum of salary and bonus. The other dependent variable is the option ratio LN (option

ratio), which is the natural logarithm of stock options granted to the executive divided

by the total compensation.

Based on Fama and French (1997) we control for industry effect inserting the 48

industry classification dummies. We also control for time effect inserting one dummy

for each year between 1993 and 2004. We expect that time will have a positive effect on

explaining executive compensation during the analysed period.

96

Based on Ang, Lauterbach and Scheiber (2002) 13 and Cheng and Hung (2006),

we also separate the analysis for CEOs and Directors because these two groups can

have different characteristics in terms of compensation.

The model that we use is:

0 1

2

3

4 5

6 7 8

9

( ) * F irm S iz e C om p o n e n t * ( ) * ( ) * ( ) * ( ) * 1 * 5 * * * (1 9 9 3 ..2 0 0 4 ) *

LN C om p en s a t io n LN N o t E xe r c is e d R a t io LN BS Vo la t i l i ty LN N umm tg s LN Ten u r e T r s t r Sa le s ls P d ir p e n s In te r lo c k Ye a r s D umm ys In d

β β β β β β β β β β β β

= + +

+ +

+ +

+ + +

+ + + +

+ + +

+ u s t r y D umm ys f ε + +

LN (Compensation) can assume the values of LN (total compensation), LN (Option ratio) or

LN (Cash compensation) and f is the fixed effect.

4.2. Independent Variables

To explain the factors that affect the executive compensation of the NYSE and the

NASDAQ listed firms we use a group of financial and governance variables.

4.2.1. Financial Variables

Firm size has been reported as one of the most important variables in explaining

executive compensation. To measure the impact of firm size on executive

compensation, researchers generally use the variables Assets, Market Value or firm

Sales with our without natural logarithm. But which of these variables is best for

measuring the impact of firm size on executive compensation? There is no empirical

answer to this question. Researchers use only one of these variables, at the expense of

other variables, to capture the size effect which they believe will produce the results

most consistent with their research design. Each of the size variables has an impact on

executive compensation but these variables are highly correlated, and cannot be

13 This analysis was made with 166 American Banks between 1993 and 1996.

97

introduced at the same time to explain executive compensation in the regression model.

We therefore introduce a new technique by using the Principal Component Analysis

to extract a factor that contains information from the three variables and resolve this old

problem in executive compensation research.

The factor that measures firm size will be composed of the following variables:

Y1 = a11LN (Sales) +a12 LN (Assets) +a13LN (Market Value)

Essentially, Principal Component Analysis solves the problem of a number of

variables that are highly correlated and cannot be introduced at the same time in a

model. In this way, from table 4 we can see that variables LN (Sales), LN (Assets), LN

(Market Value) are highly correlated and from Kaiser­Meyer­Olkin and Bartlett`s test

that this correlation is higher and statistically significant.

Table 4 Statistics from Principal Component Analysis

Panel A: Corr elation Matr ix (a) LN (Market Value) LN (Assets) LN (Sales)

LN (Market Value) 1 0.820 0.796 LN (Assets) 0.820 1 0.845 LN (Sales) 0.796 0.845 1 Panel B: Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared

Loadings

Total % of

Variance Cumulative

% Total % of

Variance Cumulative

% 1 2.640 88.010 88.010 2.640 88.010 88.010 2 0.208 6.936 94.946 3 0.152 5.054 100.000

Panel C: KMO and Bartlett' s Test Kaiser­Meyer­Olkin Measure of Sampling Adequacy. 0,763* Significant at 1% level

From table 4 we can see that there is only one factor with Initial Total

Eigenvalues superior to 1 that explains 88.01% of the total variance and the vector is:

Firm Size Component = 0.929* LN (Market Value) +0.947* LN (Assets) + +0.938* LN (Sales)

98

We expect that firm size component will have a positive relationship with all

three executive compensation dependent variables. Authors like Noguera and Highfield

(2007) also report that because larger firms have more complex operations and will be

more difficult to monitor, the use of incentive­based compensation is therefore practised

more in large firms than in small firms. We thus expect a stronger relationship between

dependent variable and firm size in the case of NYSE listed firms, which are essentially

large firms.

We also use the variable LN (Not Exercised ratio), which is the natural logarithm

of the number of unexercised options that the executive holds at the end of the year that

were vested, divided by the aggregate number of stock options/stock appreciation rights

granted. We expect a negative relationship between this ratio and total executive

compensation and the volume of stock options granted, but a positive relationship with

short term cash compensation. If executives cannot exercise their options, the company

will probably have to give additional compensation, essentially in cash, to increase their

motivation. We also expect that this relationship will be higher in NASDAQ listed firms

because, as (Murphy (2003), Anderson, Banker and Ravidran (1998), and Stathopoulos,

Espenlaub and Walker (2004)) note, new economy executives are compensated more

with stock options. They will lose most of their compensation if the exercise price

remains below the market price. So to protect them against this risk the executives are

rewarded with more stock options.

To analyse the relationship between the firm risk and the executives’ total

compensation, option ratio and short term compensation, we also use the variable LN

(BS Volatility), which is the natural logarithm of the standard deviation volatility

calculated over 60 months with Black and Scholes methodology. We expect a positive

relationship between the two dependent variables (total compensation and option ratio)

with firm risk and a negative relationship with short term compensation. If the volatility

is high, the firm’s stock price will also be high and companies will probably prefer to

compensate their executives with more stock options. In this way, firms will probably

reduce compensation based on cash compensation (short term compensation) and

increase compensation based on stock options. We also expect that the relationship

between stock return volatility and option ratio will be higher for NASDAQ listed firms

99

because, as discussed above, executives from new technology firms are more

compensated with stock options.

Trs1yr, one­year total return to shareholders including the monthly reinvestment

of dividends, is also used in our investigation to analyse the impact of shareholders’

return on executive compensation. If shareholders receive a high return on their

investments in the company, they do not need to give more stock options to executives

to align executives’ interests with the owners’ interest to reduce the agency cost. Based

on this, we expect a negative relationship between option ratio, total compensation and

the one­year shareholders’ return and a small positive relationship with cash

compensation in the sense that companies will probably give some money to

compensate executives’ efforts, but they do not need to give more stock options to

motivate them.

We also test the effect of firm growth on executive compensation. The variable

that we use to test this effect is the 5­year least squares annual growth rate of sales

(Sale5ls). We expect, like Ryan and Wiggins (2001), that executives will receive higher

incentive pay in firms with higher growth opportunities.

In this investigation, we also control, like Barron and Waddel (2003) and

Grinstein and Hribar (2004), for the time effect on executive compensation in the sense

that some compensation changes can be associated merely with time effect. To control

for time we create a dummy variable for each year between 1993 and 2004 assuming

the value of 1 if the compensation is from the year and 0 if not. We believe that time

will have a strong effect on explaining executive compensation in all the cases. In boom

years we expect higher compensation and in recessionary years we expect lower

compensation. Moreover, during technology boom years we expect an increase in

compensation.

To control for industry effect on executive compensation, and based on Fama

and French (1997) industry classifications we also create, for each industry, a dummy

that assumes the value of 1 when the executive is associated with a specific industry

sector and 0 when the executive is not associated with that specific sector. We also

believe that some industries pay their executives more than others therefore creating

industry specific effects.

100

4.2.2. Governance Variables

In the past few years we have witnessed a significant number of studies that

have analysed the relationship between board activity, board composition and executive

compensation. Authors like Ryan Jr and Wiggins III (2004) find that CEO

compensation is related to the power and influence that s/he has on the board, and firms

with external directors on the board pay more compensation based on stock options and

restricted stocks. Anderson and Bizjak (2003) also analyse whether board independence

promotes shareholders´ interests and whether the presence of the CEO on the

Compensation Committee is related to opportunistic behaviour.

To analyse this relationship between board activity and executive compensation

we use the variable LN (Number Mtgs), which is the natural logarithm of the number of

board meetings held during the indicated fiscal year, and the dummy variable Interlock,

which assumes the value one when it is “true” that the executive serves on another

board and zero if not.

We expect the number of board meetings to be negatively related to executive

compensation because more control reduces the ability to increase compensation and it

aligns the interests of shareholders and executives. Davidson III, Pilger and Szakmary

(1998) and Ryan and Wiggins (2001) also conclude that more monitoring power can

reduce the need to provide CEOs with incentive compensation.

Like Core, Holthausen and Larker (1999) and Hallock (1997), we expect that if

executives are interlocked, they can influence their personal compensation in positive

terms.

The number of years that an executive is CEO (LN (Tenure)) has also been

documented as an important variable in explaining executive compensation. Authors

like Chidambaram and Prabhala (2003), Ryan Jr and Wiggins III (2004), Murphy

(1986) and Barro and Barro (1990) use this variable. Like Ryan and Wiggins, (2001),

Conyon and He (2004) and Kang et al. (2004), we expect that CEO entrenchment due to

tenure will lead to higher cash compensation and lower incentive compensation (stock

options).

Finally we use the dummy variable Pdirpens, which assumes the value equal to 1

when it is "TRUE" that the company pays a pension/retirement plan to a director. We

101

expect that if the company has a director pension plan, it may have the ability to pay

less to its executives.

Expected correlations

Dependent Variables Independent Variables LN (Total

Compensation) LN(Option ratio) LN(Short term Compensation)

Firm Size Component + + + LN (Not Exercised Ratio) ­ ­ + LN(Bs Volatility) + + ­ LN(Number Mtgs) ­ ­ ­ LN (Tenure) + ­ + Trs1yr ­ ­ + Sales5ls + + + Pdirpens ­ ­ ­ Interlock + + + Year Dummy + + +

102

5. Empirical Results

5.1. Summary Statistics

In table 5 we describe the statistics of key financial and corporate governance

variables that can help us to understand the differences between companies listed on the

NYSE and NASDAQ.

Table 5 Statistics from Regression Variables

The table displays some statistics from firms that belong to NYSE and NASDAQ listed firms. Firm Size Component is a factor extracted from Principal Component Analysis, composed of information from variables LN (Assets), LN (Market Value) and LN (Sales), which are used to analyse firm size. LN (Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN (BS Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings held during the indicated fiscal year. LN (Tenure) is the natural logarithm of the number of years as CEO. Trs1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends. Sale5ls is the 5­year least squares annual growth rate of sales. Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not. Interlock is a dummy that assumes the value of 1 when the named officer is involved in a relationship requiring disclosure in the "Compensation Committee Interlocks and Insider Participation" section of the proxy and 0 when not. Values are in thousands of dollars.

NYSE NASDAQ Independent variables N Min. Max. Mea

n Std. Dev. N Min. Max. Mean Std.

Dev. Firm Size Component 9384 10.94 35.97 22.75 4.06 2815 10.24 31.86 18.96 3.70

LN (Not Exercised Ratio) 9384 ­7.13 14.80 0.82 1.20 2815 ­10.10 8.92 0.81 1.34

LN(Bs Volatility) 9384 ­2.17 0.89 ­1.14 0.38 2815 ­1.90 1.26 ­0.70 0.44

LN(Number Mtgs) 9384 0.00 3.61 1.93 0.36 2815 0 3.09 1.83 0.39

LN (Tenure) 9384 ­5.90 4.06 2.09 0.90 2815 ­2.05 3.78 2.18 0.91

Trs1tr 9384 ­95.32 890.39 18.74 44.25 2815 ­94.01 24828 41.56 475.82

Sales5ls 9384 ­37.67 251.13 11.94 16.56 2815 ­36.35 903.16 26.83 37.12

Pdirpens 9384 0 1 0.20 0.40 2815 0 1 0,03 0.17

Interlock 9384 0 1 0,05 0.22 2815 0 1 0,05 0.22

We can see that, NYSE firms are, on average, bigger than NASDAQ firms. The

average number of executive stock options vested (but not exercised) and also the

103

average numbers of executives interlocked are practically the same in both situations.

NASDAQ listed firms generate a higher return to shareholders and sale increases than

NYSE firms. NYSE firms pay more to executive pension plans than NASDAQ listed

firms.

5.2. Determinants of Executive Compensation for NYSE and NASDAQ Listed

Firms

In this section we test the hypotheses that there are differences in forms and

determinants of executive compensation for firms listed on the NYSE and NASDAQ.

As we see from the table above, these two groups of companies have different

characteristics; therefore, we believe that the factors that explain executive

compensation in these two groups can also be different.

To determine what factors influence executive total compensation, option ratio

and short term compensation (the three dependent variables), we use Unbalanced Panel

Data and Least Squares Dummy Variable Regression (LSDV). In all the regressions,

Standard errors are corrected using period Seemingly Unrelated Regression (SUR) –

Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity

and general correlation of observations within a given cross section (Beck and Katz,

1995).

We also check whether correlation between independent variables is significant

based on the Pearson Correlation test and find that the correlation between the variables

is small.

As a first step we investigate whether the listing place (exchange) and the job

title of the executive influences the compensation. If listing exchange (NASDAQ versus

NYSE firms) and job title (CEO versus Director) are significant variables then we will

have an in­depth analysis of executive compensation for NYSE versus NASDAQ listed

firms and also for CEOs versus Directors. We run a fixed effect regression on each of

the three dependent variables using all the explanatory variables stated above, including

year and industry dummy variables, and on top of that, we add two dummy variables ­

“NASDAQ Dummy” (1 if the executive is from a NASDAQ listed firm, otherwise zero)

and “Pexecdirp” (1 if the executive is a Director, otherwise zero). Regression results

104

show that both variables are significant. Based on the above finding, we proceed with

our analysis and run separate fixed effect regressions on NYSE CEOs, NASDAQ

CEOs, NYSE Directors, and NASDAQ Directors in order to identify the determinants

of executive compensation. These regressions will show us whether the factors that

explain the compensation of CEOs and Directors are the same for NYSE versus

NASDAQ listed firms. Results are presented in tables 6 through 10. We further

extended our analysis in tables 11 and 12 (shown in appendix) and perform the tests of

equality of coefficients for regressions on NASDAQ CEOs versus NYSE CEOs and

NASDAQ Directors versus NYSE Directors. That is, we examine whether the factor

intensity on executive compensation is the same for NASDAQ versus NYSE sub­

samples for CEOs and Directors.

105

Table 6 Fixed Effect Regression Analysis of Compensation Determinants

Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a factor extracted from Principal Component Analysis, composed of information from variables LN (Assets), LN (Market Value) and LN (Sales), which are used to analyse firm size. LN (Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted. LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method. LN (Number Mtgs) is the natural logarithm of the number of board meetings held during the indicated fiscal year. LN (Tenure) is the natural logarithm of the number of years as CEO. Trs1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends. Sales5ls is the 5­year least squares annual growth rate of sales. Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not. Interlock is a dummy that assumes the value of 1 when the executive is on two different boards at the same time and 0 when not. Nasdaq is a dummy that assumes the value of 1 when the executive is in a firm listed on NASDAQ and 0 when not. Pexecdir is a dummy that assumes the value of 1 when the executive is a director and 0 when not. We also control for time effect in terms of executive compensation using a dummy for each year between 1993 and 2004 and for industry effect using the Fama and French (1997) 48 industry classifications.

Ln(Total Compensation) T Statist ics LN(Option

Ratio) T

Statistics LN(Short Term Compensation)

T Statistics

Constant 1,429* 3,312 ­2,72* ­6,263 2,890* 6,522 Firm Size Component 0,172* 30,587 0,032* 5,513 0,105* 19,971 LN (Not Exercised Ratio) ­0,240* ­51,573 ­0,302* ­62,794 ­0,002 ­0,496 LN(Bs Volatility) 0,184* 5,510 0,333* 9,725 ­0,123* ­3,933 LN(Number Mtgs) ­0,008 ­0,446 ­0,029 ­1,578 ­0,105* ­6,251 LN (Tenure) 1,039* 5,316 0,504** 2,557 0,689* 3,412 Trs1yr ­0,000001 ­0,042 ­0,0001* ­3,604 0,0001* 4,527 Sales5ls 0,002* 4,747 0,001* 2,579 0,0004 1,481 Pdirpens ­0,164* ­7,087 ­0,047** ­1,965 ­0,067* ­3,072 Interlock 0,098* 3,146 0,094* 2,935 0,038 1,326 Year1993 0,065* 2,635 0,031 1,027 0,043* 2,210 Year1994 0,276* 10,337 0,231* 7,783 0,140* 6,679 Year1995 0,289* 10,409 0,161* 5,266 0,129* 5,699 Year1996 0,428* 14,787 0,342* 11,064 0,167* 7,088 Year1997 0,532* 17,728 0,346* 10,806 0,208* 8,277 Year1998 0,586* 19,268 0,427* 13,378 0,265* 10,206 Year1999 0,728* 22,971 0,528* 16,135 0,308* 11,154 Year2000 0,808* 23,548 0,458* 12,816 0,368* 12,385 Year2001 0,876* 25,256 0,603* 16,753 0,281* 8,888 Year2002 0,949* 27,137 0,579* 15,826 0,436* 13,778 Year2003 0,921* 25,044 0,513* 13,385 0,479* 14,535 Year2004 1,105* 28,990 0,573* 14,398 0,586* 17,049

NASDAQ Dummy 0,435 1,599 0,372 1,328 ­0,775* ­3,052 Pexecdir Dummy 0,410* 1,925 0,049* 2,244 0,370* 18,632 Apparel Dummy ­0,562 ­1,311 0,335 0,761 ­0,870** ­2,092 Business Dummy ­0,988** ­2,200 ­0,016 ­0,035 ­0,844*** ­1,891 Candy Dummy ­0,304 ­0,612 0,462 0,790 ­0,408 ­1,075 Computer Dummy 1,070** 2,031 2,237* 4,103 0,547 1,060 Construction Dummy 0,333 1,160 ­0,317 ­1,053 0,287 1,047 Consumer Dummy 1,163** 2,242 ­0,511 ­0,927 0,738*** 1,722 Medical Dummy 0,578 1,596 0,474 1,173 0,466 1,477 Trading Dummy ­0,650 ­1,409 0,192 0,417 0,913*** 1,890 Wholesales Dummy ­2,467* ­4,920 ­1,579* ­3,081 ­0,840 ­1,635

N 12225 12225 12225 Adjusted R­square 84.00% 68.05% 76.30% *Significant at 1% level, ** significant at 5% level, *** significant at 10%. Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995).

106

Table 7 Fixed Effect Regression Analysis of the Determinants of CEO Compensation for NYSE

Listed Firms

Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a factor extracted from Principal Component Analysis, composed of information from variables LN (Assets), LN (Market Value) and LN (Sales), which are used to analyse firm size; LN (Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings held during the indicated fiscal year; LN (Tenure) is the natural logarithm of the number of years as CEO; Trs1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; Sales5ls is the 5­year least squares annual growth rate of sales; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy that assumes the value of 1 when the executive is on two different boards at the same time and 0 when not; Nasdaq is a dummy that assumes the value of 1 when the executive is in a firm listed on NASDAQ and 0 when not; Pexecdir is a dummy that assumes the value of 1 when the executive is a director and 0 when not. We also control for time effect in terms of executive compensation using a dummy for each year between 1993 and 2004 and for industry effect using the Fama and French (1997) 48 industry classifications.

LN(Total Compensation)

t Statistics

LN(Option Ratio)

t Statistics

LN(Short Term Compensation)

t Statistics

Constant 0.346 0.524 ­2.099 ­2.897 4.578* 7.225 Firm Size Component 0.165* 19.770 0.010 1.087 0.124* 18.165 LN (Not Exercised Ratio) ­0.220* ­35.360 ­0.344* ­47.105 0.002 0.325 LN(Bs Volatility) 0.190* 4.374 0.188* 3.733 ­0.123* ­3.508 LN(Number Mtgs) 0.002 0.097 ­0.037 ­1.363 ­0.071* ­3.461 LN (Tenure) 1.633* 6.058 0.379 1.289 ­0.147 ­0.559 Trs1yr 0.00004 0.293 ­0.002* ­10.267 0.001* 13.136 Sales5ls 0.001*** 1.921 0.003* 3.897 ­0.0002 ­0.277 Pdirpens ­0.103* ­3.633 ­0.079** ­2.455 ­0.042*** ­1.881 Interlock 0.083** 2.254 0.142* 3.274 ­0.001 ­0.022 Year1993 0.117* 2.937 ­0.002 ­0.039 0.058** 2.045 Year1994 0.351* 8.539 0.202* 3.367 0.152* 5.137 Year1995 0.362* 8.664 0.148** 2.426 0.091* 2.936 Year1996 0.512* 11.766 0.293* 4.766 0.136* 4.215 Year1997 0.609* 13.430 0.316* 5.011 0.158* 4.738 Year1998 0.663* 14.548 0.411* 6.441 0.239* 6.963 Year1999 0.800* 17.210 0.543* 8.436 0.298* 8.361 Year2000 0.827* 16.864 0.496* 7.337 0.308* 8.151 Year2001 0.899* 17.939 0.628* 9.236 0.245* 6.068 Year2002 0.989* 19.466 0.611* 8.874 0.399* 9.899 Year2003 0.987* 18.803 0.600* 8.452 0.368* 8.988 Year2004 1.151* 21.427 0.611* 8.434 0.496* 11.773 Apparel Dummy ­0,168 ­0,446 0,008

0,255 0,431 ­0.589*** ­1.916

Business Dummy ­1.547 ­3.443 0,255 0,413

0,515 ­0,186 ­0,471 Candy Dummy ­0,570 ­1,420 0,413 0,615 ­0.541*** ­1.912 Computer Dummy 2.719* 4.417 1.711** 2.492 ­1.206** ­2.142 Construct Dummy 1.380* 3.889 ­0,541 ­1,332 ­0,310 ­1,002 Medical Dummy 0,061 0,170 0.270 0,601 0.479*** 1.684 N 6124 6124 6124 Adjusted R­Square 86.35% 68.36% 81.77% *Significant at 1% level, ** significant at 5% level *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

107

Table 8 Fixed Effect Regression Analysis of the Determinants of CEO Compensation for

NASDAQ listed firms Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a factor extracted from Principal Component Analysis, composed of information from variables LN (Assets), LN (Market Value) and LN (Sales), which are used to analyse firm size; LN (Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings held during the indicated fiscal year; LN (Tenure) is the natural logarithm of the number of years as CEO; Trs1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; Sales5ls is the 5­year least squares annual growth rate of sales; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy that assumes the value of 1 when the executive is on two different boards at the same time and 0 when not; Nasdaq is a dummy that assumes the value of 1 when the executive is in a firm listed on NASDAQ and 0 when not; Pexecdir is a dummy that assumes the value of 1 when the executive is a director and 0 when not. We also control for time effect in terms of executive compensation using a dummy for each year between 1993 and 2004 and for industry effect using the Fama and French (1997) 48 industry classifications.

LN(Total Compensation)

t Statistics

LN(Option Ratio) t Statistics LN(Short Term

Compensation) t

Statistics

Constant 1.786* 2.658 ­2.927* ­4.624 4.481* 5.926

Firm Size Component 0.248* 19.816 0.035* 2.889 0.116* 9.394

LN (Not Exercised Ratio) ­0.295* ­23.322 ­0.281* ­21.437 0.011 0.896

LN(Bs Volatility) 0.100 0.948 0.223** 2.092 ­0.424* ­3.913

LN(Number Mtgs) ­0.011 ­0.270 ­0.047 ­1.168 ­0.046 ­1.130

LN (Tenure) 0.609** 2.213 0.598** 2.323 ­0.102 ­0.325

Trs1yr ­0.00003 ­1.604 ­0.00002 ­1.125 0.00003 1.584

Sales5ls 0.0002 0.397 ­0.00008 ­0.146 ­0.001 ­1.107

Pdirpens ­0.072 ­0.428 ­0.127 ­0.696 ­0.028 ­0.170

Interlock 0.098 1.455 0.031 0.471 0.129* 1.971

Year1993 0.036 0.258 0.380** 2.527 ­0.047 ­0.510

Year1994 0.193 1.382 0.392* 2.654 0.051 0.553

Year1995 0.012 0.084 0.326** 2.192 ­0.015 ­0.143

Year1996 0.122 0.835 0.394** 2.451 ­0.077 ­0.763

Year1997 0.139 0.955 0.415* 2.755 ­0.027 ­0.272

Year1998 0.148 1.012 0.492* 3.257 0.020 0.195

Year1999 0.287** 1.914 0.576* 3.740 ­0.004 ­0.039

Year2000 0.358** 2.342 0.584* 3.737 0.091 0.811

Year2001 0.360** 2.333 0.661* 4.183 ­0.062 ­0.536

Year2002 0.334** 2.181 0.647* 4.077 0.025 0.220

Year2003 0.220 1.419 0.570* 3.563 0.048 0.408

Year2004 0.391** 2.495 0.640* 3.971 0.132 1.098

N 1877 1877 1877

Adjusted R­Square 85.43% 74.42% 67.96%

(*) Significant at 1% level, (**) significant at 5% level, (***) significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995).

108

Table 9 Fixed Effect Regression Analysis of Determinants of Director Compensation for

NYSE listed firms

Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a factor extracted from Principal Component Analysis, composed of information from variables LN (Assets), LN (Market Value) and LN (Sales), which are used to analyse firm size; LN (Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings held during the indicated fiscal year; LN (Tenure) is the natural logarithm of the number of years as CEO;Trs1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; Sales5ls is the 5­year least squares annual growth rate of sales; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy that assumes the value of 1 when the executive is on two different boards at the same time and 0 when not; Nasdaq is a dummy that assumes the value of 1 when the executive is in a firm listed on NASDAQ and 0 when not; Pexecdir is a dummy that assumes the value of 1 when the executive is a director and 0 when not. We also control for time effect in terms of executive compensation using a dummy for each year between 1993 and 2004 and for industry effect using the Fama and French (1997) 48 industry classifications.

LN(Total Compensation) t

Statistics LN(Option Ratio) t Statistics LN(Short Term

Compensation) t Statistics

Constant 1.253 0.827 ­0.483 ­0.307 0.437 0.265 Firm Size Component 0.144* 18.820 0.026* 3.117 0.099* 13.391 LN (Not Exercised Ratio) ­0.228* ­40.640 ­0.333* ­54.353 ­0.007 ­1.285 LN(Bs Volatility) 0.168* 4.200 0.297* 6.952 ­0.138* ­3.638 LN(Number Mtgs) ­0.008 ­0.351 ­0.052** ­2.192 ­0.115* ­5.516 LN (Tenure) 1.475** 2.196 ­0.408 ­0.586 1.872** 2.553 Trs1yr 0.0003** 2.185 ­0.001* ­11.749 0.002* 14.304 Sales5ls 0.003* 4.831 0.002** 3.178 0.002* 2.605 Pdirpens ­0.111* ­4.356 ­0.059 ­2.156 ­0.054** ­2.195 Interlock 0.093* 2.631 0.129* 3.359 ­0.007 ­0.218 Year1993 0.062** 2.171 0.009 0.242 0.048** 2.105 Year1994 0.282* 9.111 0.209* 5.929 0.161* 6.629 Year1995 0.310* 9.775 0.175* 4.750 0.115* 4.408 Year1996 0.482* 14.252 0.352* 9.548 0.189* 6.755 Year1997 0.606* 17.099 0.396* 10.100 0.206* 6.873 Year1998 0.669* 18.720 0.438* 11.096 0.302* 9.477 Year1999 0.806* 21.711 0.560* 14.068 0.354* 10.750 Year2000 0.871* 21.844 0.497* 11.603 0.389 10.852 Year2001 0.973* 23.855 0.620* 14.092 0.346* 8.964 Year2002 1.077* 25.895 0.588* 13.042 0.539* 13.989 Year2003 1.081* 25.054 0.592* 12.542 0.530* 13.481 Year2004 1.268* 28.436 0.610* 12.467 0.671* 16.439 Apparel Dummy ­0,352 ­0,067 ­0,379 ­0,649 ­0,077 ­0,131 Business Dummy ­1.474*** ­1.719 1.045 1,174 ­2.207** ­2.402 Candy Dummy ­0,035 ­0,067 ­0,049 ­0,073 ­0,047 ­0,114 Computer Dummy 2.033 1.567 0,513 0,379 2.796** 1.989 Construct Dummy 0,896 1.252 ­1,237 ­1,619 1.259*** 1.674 Medical Dummy 0,083 0.168 0,685 1,182 ­0,355 ­0,793 Consumer Dummy 1.330** 2.287 ­0,930 ­1,455 1.314** 2.524 Fabricat Dummy 2.444* 4.298 1.217** 2.065 1.268** 2.118 Trading Dummy ­0,152 ­0.168 ­1,03 ­1,102 2.435** 2.695 N 8281 8281 8281 Adjusted R­Square square

84.16% 66.57% 74.67% *Significant at 1% level, ** significant at 5% level, *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995).

109

Table 10 Fixed Effect Regression Analysis of the Determinants of Director Compensation

for NASDAQ listed firms

Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and based on the 1982 base of 100, we adjust to inflation the monetary variables reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: Firm Size Component is a factor extracted from Principal Component Analysis, composed of information from variables LN (Assets), LN (Market Value) and LN (Sales), which are used to analyse firm size. LN (Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings held during the indicated fiscal year; LN (Tenure) is the natural logarithm of the number of years as CEO;Trs1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; Sales5ls is the 5­year least squares annual growth rate of sales; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy that assumes the value of 1 when the executive is on two different boards at the same time and 0 when not; Nasdaq is a dummy that assumes the value of 1 when the executive is in a firm listed on NASDAQ and 0 when not; Pexecdir is a dummy that assumes the value of 1 when the executive is a director and 0 when not. We also control for time effect in terms of executive compensation using a dummy for each year between 1993 and 2004 and for industry effect using the Fama and French (1997) 48 industry classifications.

LN(Total Compensation) t Statistics LN(Option Ratio) t Statistics LN(Short Term

Compensation) t Statistics

Constant 1.295** 2.054 ­2.760* ­5.708 3.487* 4.952

Firm Size Component 0.246* 21.748 0.043* 4.162 0.124* 11.724

LN (Not Exercised Ratio) ­0.274* ­26.534 ­0.256* ­29.042 0.028* 2.861

LN(Bs Volatility) 0.210** 2.426 0.319* 4.601 ­0.219* ­2.644

LN(Number Mtgs) ­0.020 ­0.528 ­0.018 ­0.540 ­0.077** ­2.181

LN (Tenure) 0.818* 3.034 0.566* 2.732 0.254 0.840

Trs1yr ­0.00001 ­0.572 ­0.00002 ­1.395 0.00005** 2.645 Sales5ls 0.0004 0.949 0.0004 1.053 ­0.0003 ­0.793

Pdirpens 0.015 0.112 ­0.001 ­0.013 0.103 0.843

Interlock 0.083 1.281 ­0.003 ­0.057 0.102*** 1.704

Year1993 0.062 0.801 0.143** 1.815 0.030 0.589

Year1994 0.260* 3.168 0.155** 2.030 0.150* 2.670

Year1995 0.147** 1.660 0.130** 1.687 0.127*** 1.946

Year1996 0.222* 2.527 0.240* 3.132 0.042 0.705

Year1997 0.264* 2.958 0.201* 2.612 0.079 1.216

Year1998 0.267* 2.924 0.272* 3.504 0.144** 2.297

Year1999 0.383* 4.039 0.341* 4.217 0.122*** 1.715

Year2000 0.440* 4.340 0.323* 3.837 0.188** 2.474

Year2001 0.441* 4.311 0.453* 5.262 0.042 0.539

Year2002 0.459* 4.500 0.423* 4.925 0.172** 2.180

Year2003 0.289* 2.747 0.350* 3.954 0.149*** 1.785

Year2004 0.484* 4.470 0.430* 4.732 0.248* 2.856

N 2543 2543 2543

Adjusted R­Square 82.68% 72.46% 66.17%

*Significant at 1% level, ** significant at 5% level ,*** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

110

5.3. Analysis of the Results

As we expected, the above results show that executive compensation for NYSE

versus NASDAQ listed firms is explained by different factors. We also analyse in tables

11 and 12 whether the factor intensity (coefficients of the regressions of the CEOs and

Directors of NYSE and NASDAQ listed firms) is the same. We find that in all the cases

the values of the coefficients are significantly different.

Prior research has shown that firm size is one of the most important variables in

explaining executive compensation. From tables 7 and 8, we can see that the size

variable is significant in explaining variation in executive total compensation; however,

it has a stronger impact in the case of NASDAQ listed firms than that of NYSE listed

firms. Firm size has a positive influence on option ratio for CEOs in the case of

NASDAQ listed firms, meaning that size influences the number of stock options

granted to NASDAQ executives. This relationship is not statistically significant for the

NYSE. The firm’s size also positively influences the CEO’s short term executive

compensation (salary and bonus).

In the case of Directors, all three executive compensation dependent variables

are positively related to the size of the firm but the directors’ compensation for

NASDAQ listed firms is more sensitive to firm size than for the NYSE listed firms.

Not exercised ratio, which represents the number of options not exercised but

vested, is negatively related to CEOs´ total compensation and option ratio both for

NYSE and NASDAQ listed firms and, as we expected, it is positively related to short

term CEO compensation in both cases, meaning that when executives have stock

options they cannot exercise, the firms give them additional compensation, in cash, to

increase executive motivation. In the case of Directors, we also find a negative

relationship between total compensation and option ratio and a positive relationship

between short­term compensation and option ratio, but this relationship is only

significant, in the latter case, in NASDAQ listed firms, meaning that Directors from

these firms cannot exercise the stock options that they have and, therefore, firms give

more cash compensation to increase their motivation.

The stock return volatility influences total compensation positively with the

exception of CEOs from NASDAQ, where the relationship is not statistically

111

significant. The option ratio also has a positive and significant relationship with firm

stock return volatility. The results are congruent with Yermack (1995) and Bryan and

Hwang’s (2000) findings. We also find a negative relationship between cash

compensation and stock return volatility, as did Core et al. (1999). In the case of

Directors, we find a positive relationship between total compensation, option ratio and

firm stock return volatility and a negative relationship with short­term compensation in

both cases, meaning that when volatility increases, firms prefer to give more stock

options to the executive and less cash compensation. These relationships are stronger in

the case of NASDAQ than for the NYSE.

Noguera and Highfield (2007) are of the view that the board of directors is the

central internal mechanism of corporate governance in place at any corporation. Some

authors include the size of the board, others the composition of the board or the

influence of the CEO on the board as an explanatory variable of executive

compensation. Davidson III et al. (1998) use the number of board meetings and find

that it is negatively related to executive compensation because more control reduces the

ability to increase compensation and thus align the interests of shareholders and

executives. Also, Ryan and Wiggins (2001) conclude that more monitoring power can

reduce the need to provide CEOs with more incentive compensation. Our results are

congruent with previous findings in the case of NYSE CEO short term compensation

and NYSE and NASDAQ Directors in the case of option ratio and short term

compensation.

We also find that tenure strongly affects, in positive terms, CEO compensation

for NYSE listed firms but only slightly affects NASDAQ firms. Only in the case of

NASDAQ listed firms do we find that the option ratio is positively and significantly

related to CEO tenure. Our results are not consistent with the results of Ryan and

Wiggins (2001) and Conyon and He (2004), who find that CEO entrenchment due to

tenure would lead to higher cash compensation and lower incentive compensation

(stock options). We only find consistent results in the case of Directors’ tenure in which

case there is a strong positive relationship between tenure and total and short­term

compensation for NYSE listed firms. In the case of NASDAQ listed firms, option ratio

is positively related to tenure but the results are not congruent with the previous

112

findings that executives will prefer a more certain compensation (cash compensation)

over less certain compensation (stock options).

The one­year return to shareholders negatively affects CEO option ratio for

NYSE listed firms, and positively affects the cash compensation that these CEOs

receive. In the case of NASDAQ listed firms, the relationships are not statistically

significant. As we expected, if shareholders are satisfied with the return of their

investments, they do not need to give their executives more incentives based on stock

options. We do not find a negative relationship between cash compensation and one­

year shareholders’ return, meaning that NYSE companies compensate their CEOs with

more cash compensation when they receive higher returns of investments. In the case of

Directors, we find a positive relationship with total compensation and short term

compensation for NYSE listed firms and a negative relationship with option ratio. In the

case of NASDAQ listed firms, this relationship is only positive and statistically

significant with cash compensation.

The sales increase in the last five years has a positive and statistically significant

relationship but only with the CEO total compensation and option ratio for NYSE listed

firms. The results are consistent with the findings of Ryan and Wiggins (2001) and

Anderson et al., (2000), which show that executives receive higher incentive pay in

firms with higher growth opportunities. However, these results are inconsistent with the

findings of Ghosh and Sirmans (2005), who find a negative relationship between

executive total compensation and firm growth opportunities. In the case of NASDAQ

listed firms, the relationship is not statistically significant. In the case of NYSE

Directors, the sales increase in the last 5 years influences, in positive terms, all the

dependent variables. NASDAQ director compensation is not affected by the sales

increase in the last five years.

As we expected, the existence of firm pension plans influences, in negative

terms, total compensation, option ratio and short term compensation, but only in the

case of NYSE listed firms, meaning that if the firm has already put money into

executives’ pension plans, they are able to justify a reduction in executive compensation

during the year. In the case of Directors, only the total and short­term compensation in

NYSE listed firms are affected by the existence of Directors’ pension plans in the

company.

113

As we also expected, interlocked executives will receive more total

compensation and stock options, but the results are only statistically significant for

NYSE listed firms. The results are congruent with Core, Holthausen and Larcker (1999)

and Hallock (1997), who find that interlocked executives can positively influence their

personal compensation. In the case of Directors, we find a positive relationship with

total compensation and option compensation for NYSE listed firms and a positive

relationship with short term compensation for NASDAQ listed firms.

We also achieve interesting results in terms of the effect of the years on CEO

and Director compensation. The year effect strongly explains the total executive

compensation in NYSE listed firms for CEOs and Directors but not for NASDAQ listed

firms. Only after 1999 do we find a positive and statistically significant relationship in

NASDAQ listed firms, but in smaller terms than in NYSE listed firms. When we

analyse the option ratio, we find that both groups of CEOs are always influenced by the

effect of time, but the relationship is stronger for NYSE listed firms. In terms of short­

term compensation, this relationship is only positive and statistically significant for

NYSE listed firms. In the case of Directors, the time effect strongly influences total

compensation, option ratio and short term compensation for NYSE listed firms but the

time effect has less significance for NASDAQ listed firms.

Finally, we also analyse the industry effect, using the 48 Fama and French

(1997) industry classifications and find that some of these dummies can also explain

executive compensation. This is the case of Business, which has a negative relationship

with CEO total compensation, and computer and construction, which have a positive

relationship with total compensation. The computer industry also has a positive

relationship with the NYSE CEO option ratio. We also find a negative and statistically

significant relationship with short­term compensation and the apparel, candy and

computer industries and a positive relationship in the medical industry.

In the case of Directors, we find a negative relationship with the business

industry and total compensation for NYSE listed firms and a positive relationship

between total compensation and the consumer and manufacturing industries, also for

NYSE listed firms. In the case of option ratio, we find a positive relationship with the

manufacturing industry also for NYSE and a negative relationship with the Business

industry and cash compensation for NYSE. We also find a positive relationship between

114

cash compensation and the computer, construction, consumer, manufacturing and

trading industries for NYSE listed firms.

6. Conclusion

This is the first paper that analyses whether executive compensation for NYSE

and NASDAQ 14 listed firms is explained by the same factors. Using a one­way fixed

effect regression in an unbalanced panel sample for the period of 1992 to 2004 we also

investigate the trends in terms of executive compensation in NYSE and NASDAQ listed

firms, the forms of the compensation and whether the forms and weights of

compensation changed after the NASDAQ crash in 2000 and the Sarbanes­Oxley Act in

2002.

Our results reveal that executive compensation is influenced by different factors

for NYSE and NASDAQ listed firms, and when some of the factors are the same, the

intensity of the coefficients is different, and this difference is statistically significant.

We also verify that NYSE and NASDAQ CEO and Director Compensation are

composed of different components. The percentage that salary represents in terms of

total compensation in NYSE listed firms is higher for Directors than CEOs. Bonus is a

more important compensation component for Directors than CEOs in NASDAQ listed

firms, but in the case of NYSE firms, the difference is small. In all cases, CEOs receive

more stock options than Directors. The used of restricted stock increases essentially in

the last few years. We also find that after the NASDAQ Crash in 2000, and essentially

after the Sarbanes­Oxley Act in 2002, the forms and weights of CEO and Director

compensation change for NYSE and NASDAQ listed firms.

14 We did not analyse the factors that explain CEO and Director compensation in AMEX listed firms because the number of items of compensation is very small, making it impossible for us to make the regression analysis.

115

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8. Appendix

Table 11 T Test of Equality of Fixed Effect Regressions Coefficients – CEOs

(NYSE versus NASDAQ)

Panel A: LN (Total Compensation) NYSE NASDAQ t test

Dependent Variables N Coef. Std. Error N Coef. Std. Error Sig.

Constant 6124 0,346 0,661 1877 1,786 0,672 *

Firm Size Component 6124 0,165 0,008 1877 0,248 0,013 *

LN (Not Exercised Ratio) 6124 ­0,220 0,006 1877 ­0,295 0,013 *

LN(Bs Volatility) 6124 0,190 0,043 1877 0,100 0,105 *

LN (Number Mtgs) 6124 0,002 0,024 1877 ­0,011 0,040 *

LN (Tenure) 6124 1,633 0,270 1877 0,609 0,275 *

Trs1yr 6124 0,00004 0,000 1877 0,00003 0,000 *

Sales5ls 6124 0,001 0,001 1877 0,0002 0,001 *

Pdirpens 6124 ­0,103 0,028 1877 ­0,072 0,168 *

Interlock 6124 0,083 0,037 1877 0,098 0,067 *

Year1993 6124 0,117 0,040 1877 0,036 0,140 *

Year1994 6124 0,351 0,041 1877 0,193 0,139 *

Year1995 6124 0,362 0,042 1877 0,012 0,145 *

Year1996 6124 0,512 0,043 1877 0,122 0,146 *

Year1997 6124 0,609 0,045 1877 0,139 0,145 *

Year1998 6124 0,663 0,046 1877 0,148 0,147 *

Year1999 6124 0,800 0,046 1877 0,287 0,150 *

Year2000 6124 0,827 0,049 1877 0,358 0,153 *

Year2001 6124 0,899 0,050 1877 0,360 0,154 *

Year2002 6124 0,989 0,051 1877 0,334 0,153 *

Year2003 6124 0,987 0,052 1877 0,220 0,155 *

Year2004 6124 1,151 0,054 1877 0,391 0,157 *

Apparel Dummy 6124 ­0,168 0,376

Busines Dummy 6124 ­1,547 0,449

Candy Dummy 6124 ­0,570 0,406

Computer Dummy 6124 2,719 0,616

Construct Dummy 6124 1,380 0,355

Medical Dummy 6124 0,061 0,357 * Difference is statistically significant at 1% (*), 5% level (**) or 10% level(***)

119

Table 11 (Cont.)

PANEL B: Option Ratio NYSE NASDAQ T test

Dependent Variables N Coef. Std. Error N Coef. Std. Error Sig,

Constant 6124 ­2,099 0,724 1877 ­2,927 0,633 *

Firm Size Component 6124 0,010 0,010 1877 0,035 0,012 *

LN (Not Exercised Ratio) 6124 ­0,344 0,007 1877 ­0,281 0,013 *

LN(Bs Volatility) 6124 0,188 0,050 1877 0,223 0,107 *

LN(Number Mtgs) 6124 ­0,037 0,027 1877 ­0,047 0,040 *

LN (Tenure) 6124 0,379 0,294 1877 0,598 0,257 *

Trs1yr 6124 ­0,002 0,0001 1877 ­0,00002 0,00002 *

Sales5ls 6124 0,003 0,001 1877 ­0,00008 0,001 *

Pdirpens 6124 ­0,079 0,032 1877 ­0,127 0,183 *

Interlock 6124 0,142 0,043 1877 0,031 0,066 *

Year1993 6124 ­0,002 0,061 1877 0,380 0,150 *

Year1994 6124 0,202 0,060 1877 0,392 0,148 *

Year1995 6124 0,148 0,061 1877 0,326 0,149 *

Year1996 6124 0,293 0,061 1877 0,394 0,161 *

Year1997 6124 0,316 0,063 1877 0,415 0,151 *

Year1998 6124 0,411 0,064 1877 0,492 0,151 *

Year1999 6124 0,543 0,064 1877 0,576 0,154 *

Year2000 6124 0,496 0,068 1877 0,584 0,156 *

Year2001 6124 0,628 0,068 1877 0,661 0,158 *

Year2002 6124 0,611 0,069 1877 0,647 0,159 *

Year2003 6124 0,600 0,071 1877 0,570 0,160 *

Year2004 6124 0,611 0,072 1877 0,640 0,161 *

Apparel Dummy 6124 0,008 0,431

Business Dummy 6124 0,255 0,515

Candy Dummy 6124 0,413 0,615

Computer Dummy 6124 1,711 0,687

Construct Dummy 6124 ­0,541 0,406

Medical Dummy 6124 0,270 0,449

Difference is statistically significant at 1%(*), 5% level (**) or 10% level(***)

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Table 11 (cont.)

PANEL C: LN(Short Term Compensation) NYSE NASDAQ t test

Dependent Variables N Coef. Std. Error N Coeff. Std. Error Sig.

Constant 6124 4.578 0,634 1877 4,481 0,756 *

Firm Size Component 6124 0,124 0,007 1877 0,116 0,012 *

LN (Not Exercised Ratio) 6124 0,002 0,005 1877 0,011 0,012 *

LN(Bs Volatility) 6124 ­0,123 0,035 1877 ­0,424 0,108 *

LN(Number Mtgs) 6124 ­0,071 0,021 1877 ­0,046 0,041 *

LN (Tenure) 6124 ­0,147 0,262 1877 ­0,102 0,314 *

Trs1yr 6124 0,001 0,0001 1877 0.00003 0,0001 *

Sales5ls 6124 ­0,0002 0,001 1877 ­0,001 0,001 *

Pdirpens 6124 ­0,042 0,022 1877 ­0,028 0,166 *

Interlock 6124 ­0,001 0,031 1877 0,129 0,065 *

Year1993 6124 0,058 0,029 1877 ­0,047 0,092 *

Year1994 6124 0,152 0,03 1877 0,051 0,093 *

Year1995 6124 0,091 0,031 1877 ­0,015 0,103 *

Year1996 6124 0,136 0,032 1877 ­0,077 0,100 *

Year1997 6124 0,158 0,033 1877 ­0,027 0,101 *

Year1998 6124 0,239 0,034 1877 0,020 0,102 *

Year1999 6124 0,298 0,036 1877 ­0,004 0,110 *

Year2000 6124 0,308 0,038 1877 0,091 0,112 *

Year2001 6124 0,245 0,04 1877 ­0,062 0,115 *

Year2002 6124 0,399 0,04 1877 0,025 0,114 *

Year2003 6124 0,368 0,041 1877 0,048 0,118 *

Year2004 6124 0,496 0,042 1877 0,132 0,120 *

Apparel Dummy 6124 ­0,588 0,307

Business Dummy 6124 ­0,186 0,394

Candy Dummy 6124 ­0,541 0,283

Computer Dummy 6124 ­1.206 0,563

Construct Dummy 6124 ­0,310 0,310

Medical Dummy 6124 0,479 0,284

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Table 12 T Test of Equality of Fixed Effect Regressions Coefficients – Directors

Panel A: LN (Total Compensation) NYSE NASDAQ t test

Dependent Variables N Coef. Std. Error N Coef. Std. Error Sig.

Constant 8281 1,253 1,515 2543 1,295 0,630 *

Firm Size Component 8281 0,144 0,008 2543 0,246 0,011 *

LN (Not Exercised Ratio) 8281 ­0,228 0,006 2543 ­0,274 0,010 *

LN(Bs Volatility) 8281 0,168 0,040 2543 0,210 0,087 *

LN(Number Mtgs) 8281 ­0,008 0,022 2543 ­0,020 0,037 *

LN (Tenure) 8281 1,475 0,672 2543 0,818 0,270 *

Trs1yr 8281 0,0003 0,0001 2543 ­0,00001 0,00002 *

Sales5ls 8281 0,003 0,001 2543 0,0004 0,0004 *

Pdirpens 8281 ­0,111 0,026 2543 0,015 0,135 *

Interlock 8281 0,093 0,035 2543 0,083 0,065 *

Year1993 8281 0,062 0,029 2543 0,062 0,078 *

Year1994 8281 0,282 0,031 2543 0,260 0,082 *

Year1995 8281 0,310 0,032 2543 0,147 0,089 *

Year1996 8281 0,482 0,034 2543 0,222 0,088 *

Year1997 8281 0,606 0,035 2543 0,264 0,089 *

Year1998 8281 0,669 0,036 2543 0,267 0,091 *

Year1999 8281 0,806 0,037 2543 0,383 0,095 *

Year2000 8281 0,871 0,040 2543 0,440 0,101 *

Year2001 8281 0,973 0,041 2543 0,441 0,102 *

Year2002 8281 1,077 0,042 2543 0,459 0,102 *

Year2003 8281 1,081 0,043 2543 0,289 0,105 *

Year2004 8281 1,268 0,045 2543 0,484 0,108 *

Apparel Dummy 8281 ­0,352 0,568

Business Dummy 8281 ­1,474 0,858

Candy Dummy 8281 ­0,035 0,517

Computer Dummy 8281 2,033 1,298

Construct Dummy 8281 0,896 0,716

Medical Dummy 8281 0,083 0,496

Consumer Dummy 8281 1,330 0,581

Fabricant Dummy 8281 2,444 0,569

Trading Dummy 8281 ­0,152 0,904

* Difference is statistically significant at 1%(*), 5% level (**) or 10% level(***)

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Table 12 (Cont.)

Panel B: LN (Option Ratio) NYSE NASDAQ t test

Dependent Variables N Coef. Std. Error N Coef. Std. Error Sig.

Constant 8281 ­0,483 1,571 2543 ­2,760 0,483 *

Firm Size Component 8281 0,026 0,008 2543 0,043 0,009 *

LN (Not Exercised Ratio) 8281 ­0,333 0,006 2543 ­0,256 0,009 *

LN(Bs Volatility) 8281 0,297 0,043 2543 0,319 0,069 *

LN(Number Mtgs) 8281 ­0,052 0,024 2543 ­0,018 0,033 *

LN (Tenure) 8281 ­0,408 0,697 2543 0,566 0,207 *

Trs1yr 8281 ­0,001 0,0001 2543 ­0,00002 0,00002 *

Sales5ls 8281 0,002 0,001 2543 0,0004 0,0003 *

Pdirpens 8281 ­0,059 0,027 2543 ­0,001 0,109 *

Interlock 8281 0,129 0,038 2543 ­0,003 0,054 *

Year1993 8281 0,009 0,036 2543 0,143 0,079 *

Year1994 8281 0,209 0,035 2543 0,155 0,076 *

Year1995 8281 0,175 0,037 2543 0,130 0,077 *

Year1996 8281 0,352 0,037 2543 0,240 0,077 *

Year1997 8281 0,396 0,039 2543 0,201 0,077 *

Year1998 8281 0,438 0,039 2543 0,272 0,078 *

Year1999 8281 0,560 0,040 2543 0,341 0,081 *

Year2000 8281 0,497 0,044 2543 0,323 0,084 *

Year2001 8281 0,620 0,044 2543 0,453 0,086 *

Year2002 8281 0,588 0,045 2543 0,423 0,086 *

Year2003 8281 0,592 0,047 2543 0,350 0,089 *

Year2004 8281 0,610 0,049 2543 0,430 0,091 *

Apparel Dummy 8281 ­0,379 0,585

Business Dummy 8281 1,045 0,890

Candy Dummy 8281 ­0,049 0,666

Computer Dummy 8281 0,513 1,356

Construct Dummy 8281 ­1,237 0,764

Medical Dummy 8281 0,685 0,580

Consumer Dummy 8281 ­0,930 0,639

Fabricant Dummy 8281 1,217 0,590

Trading Dummy 8281 ­1,030 0,935

* Difference is statistically significant at 1%(*), 5% level (**) or 10% level(***)

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Table 12 (Cont.)

PANEL C: LN (Short Term Compensation) NYSE NASDAQ t test

Dependent Variables N Coef. Std. Error N Coef. Std. Error Sig.

Constant 8281 0,437 1,653 2543 3,487 0,704 * Firm Size Component 8281 0,099 0,007 2543 0,124 0,011 * LN (Not Exercised Ratio) 8281 ­0,007 0,005 2543 0,028 0,010 * LN(Bs Volatility) 8281 ­0,138 0,038 2543 ­0,219 0,083 * LN(Number Mtgs) 8281 ­0,115 0,021 2543 ­0,077 0,035 *

LN (Tenure) 8281 1,872 0,734 2543 0,254 0,302 *

Trs1yr 8281 0,002 0,000 2543 0,00005 0,00002 *

Sales5ls 8281 0,002 0,001 2543 ­0,0003 0,0003 *

Pdirpens 8281 ­0,054 0,024 2543 0,103 0,122 *

Interlock 8281 ­0,007 0,033 2543 0,102 0,060 *

Year1993 8281 0,048 0,023 2543 0,030 0,051 *

Year1994 8281 0,161 0,024 2543 0,150 0,056 *

Year1995 8281 0,115 0,026 2543 0,127 0,065 *

Year1996 8281 0,189 0,028 2543 0,042 0,059 *

Year1997 8281 0,206 0,030 2543 0,079 0,065 *

Year1998 8281 0,302 0,032 2543 0,144 0,063 *

Year1999 8281 0,354 0,033 2543 0,122 0,071 *

Year2000 8281 0,389 0,036 2543 0,188 0,076 *

Year2001 8281 0,346 0,039 2543 0,042 0,079 *

Year2002 8281 0,539 0,039 2543 0,172 0,079 *

Year2003 8281 0,530 0,039 2543 0,149 0,083 *

Year2004 8281 0,671 0,041 2543 0,248 0,087 *

Apparel Dummy 8281 ­0,077 0,592

Business Dummy 8281 ­2,207 0,919

Candy Dummy 8281 ­0,047 0,411

Computer Dummy 8281 2,796 1,406

Construct Dummy 8281 1,259 0,752

Medical Dummy 8281 ­0,355 0,448

Consumer Dummy 8281 1,314 0,521

Fabricant Dummy 8281 1,268 0,599

Trading Dummy 8281 2,435 0,986

* Difference is statistically significant at 1%(*), 5% level (**) or 10% level(***)

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Table 13: Pearson Correlation of the Independent Variables

Panel A: NYSE 1 2 3 4 5 6 7 8 9

1­ Firm Size Component 1,000 0,023 ­0,308 0,268 ­0,042 ­0,030 ­0,043 0,152 ­0,058

2­ LN(Not Exercised Ratio) 0,023 1,000 0,001 ­0,033 0,125 ­0,036 ­0,024 0,013 0,042

3­ LN (Bs Volatility) ­0,308 0,001 1,000 ­0,057 ­0,132 0,057 0,188 ­0,271 ­0,036

4­ LN ( Number Mtgs) 0,268 ­0,033 ­0,057 1,000 ­0,030 ­0,043 ­0,021 0,151 ­0,060

5­ LN (Tenure) ­0,042 0,125 ­0,132 ­0,030 1,000 0,016 0,030 0,121 0,134

6­ Trs1yr ­0,030 ­0,036 0,057 ­0,043 0,016 1,000 0,042 ­0,025 0,002

7­ Sales5ls ­0,043 ­0,024 0,188 ­0,021 0,030 0,042 1,000 ­0,139 0,095

8­ Pdirpens 0,152 0,013 ­0,271 0,151 0,121 ­0,025 ­0,139 1,000 ­0,002

9­ Interlock ­0,058 0,042 ­0,036 ­0,060 0,134 0,002 0,095 ­0,002 1,000

Panel B: NASDAQ 1 2 3 4 5 6 7 8 9

1­ Firm Size Component 1,000 0,127 ­0,305 0,054 ­0,025 0,001 ­0,079 0,115 ­0,059

2­ LN(Not Exercised Ratio) 0,127 1,000 ­0,024 0,001 0,187 ­0,027 ­0,068 0,034 0,005

3­ LN (Bs Volatility) ­0,305 ­0,024 1,000 0,168 ­0,101 0,098 0,198 ­0,188 ­0,035

4­ LN (Number Mtgs) 0,054 0,001 0,168 1,000 ­0,080 ­0,008 0,039 0,075 ­0,043

5­ LN (Tenure) ­0,025 0,187 ­0,101 ­0,080 1,000 ­0,003 0,079 0,039 0,096

6­ Trs1yr 0,001 ­0,027 0,098 ­0,008 ­0,003 1,000 0,000 ­0,006 ­0,004

7­ Sales5ls ­0,079 ­0,068 0,198 0,039 0,079 0,000 1,000 ­0,067 0,003

8­ Pdirpens 0,115 0,034 ­0,188 0,075 0,039 ­0,006 ­0,067 1,000 0,019

9­ Interlock ­0,059 0,005 ­0,035 ­0,043 0,096 ­0,004 0,003 0,019 1,000

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CHAPTER 4

Executive Compensation: An Examination of S&P Listed Firms 15

15 We are grateful to Professor Sheng Huang, from Washington University, discussant of this paper at the Midwest Finance Association in Texas (USA) in February of 2008 and to other anonymous professors present at the conference for their helpful comments. We are also grateful to Professor Mário Augusto, from the University of Coimbra, discussant of the paper at the 18 th Luso ­ Spanish Conference on Management in February in Porto.

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1. Introduction

In this age and era of technological advances, regulatory changes, and

increased cross­border flow of information, capital and labour, firms search for

executive talent globally to find the best fit for jobs. The emergence of the global

village, removal of barriers to free trade, and the rise of Internet technology have indeed

created an increased demand for corporate executives. Perhaps, this is the reason that

there is a new and growing interest among academics as well as practitioners to explore

the numerous dimensions of executive compensation. Researchers are examining

questions related to the determinants and forms of executive compensation and potential

differences in compensation in numerous situations including new versus old economy

firms and men versus women executives. Firm size has been defended by a significant

number of authors as being one of the most important factors that influence executive

compensation. Therefore, research inquiries related to executive compensation can

produce misleading results if the size effect is not accounted for. For example,

comparison of compensation between men versus women executives can be more

meaningful if size effect is accounted for.

The motivation to develop this investigation is based on the findings of Lambert

et al. (1990), who analyse the relationship between the percentage that executive

compensation changes and the percentage that firm size changes and found that this

relationship is positive and significant but is smaller than when absolute values and not

percentages are used to analyse the relationship. According to the authors, the result

means that the changes in executive compensation are not primarily influenced by firm

size. To analyse the impact of firm size in terms of executive compensation, most

authors adjusted for size effect in multiple regression setting using variables like Sales,

Market Value, Assets or the natural logarithm of these values, but we go a step further

in our study. Instead of inserting one additional size variable in multiple regressions, we

focus on three different size groups of large firms (S&P 500), medium size firms (S&P

Mid Cap), and small firms (S&P Small Cap), individually.

Based on the findings of Lambert et al. (1990) that changes in executive

compensation are not primarily influenced by changes in terms of firm size, we control

for firm size and industry effect and analyse whether the factors that influence executive

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compensation (and the forms of compensation) are the same for the S&P500, S&P Mid

Cap and S&P Small Cap index listed firms between the period of 1992 and 2004 and

also if this changed after the NASDAQ crash. We also examine whether the forms of

executive compensation changed after the NASDAQ crash in 2000 and the Sarbanes­

Oxley Act in 2002. Based on Narayanan and Seyhun (2005) and Murphy (2003)’s

findings, we also believe that the NASDAQ crash in 2000 and the Sarbanes­Oxley Act

in 2002 significantly changed the form of compensation because a significant number of

restrictions in terms of corporate governance were developed.

We believe that, controlling for firm size and industry effect, the factors that

explain executive compensation may be different because each S&P index represents

different groups of firms with different characteristics such as net income, sales and

dividends. We want to focus on each of the three sets of S & P firms – small, medium,

and large firms and compare and contrast the differences in executive compensation, if

any, among these different size firms. We also separate the analysis between CEOs and

Directors based on the investigation of Cheng and Hung (2006), who found significant

differences between these two groups.

Our motivation to develop this investigation is also due to the fact that we

believe that the changes introduced by the Sarbanes­Oxley Act in 2002 and also the

NASDAQ crash in 2000 changed the structure of executive compensation not only in

terms of total values, but also in terms of the percentage or fraction that each

compensation component represents in the total compensation.

Our results reveal that the NASDAQ crash in 2000 and, especially, the

Sarbanes­Oxley Act in 2002, did indeed change the forms of the components of

executive compensation. We also find that the factors that explain executive

compensation in S&P500, S&P Mid Cap and S&P Small Cap listed firms are generally

different: if some variables are equal, their intensity is different and this difference is

generally statistically significant. We also document that the NASDAQ crash changes

the influence of the factors that explain executive compensation.

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2. Literature Review and Research Questions

Firm size has been used by a vast number of authors to analyse diverse situations

in the executive compensation area. Datta, Iskandar­Datta and Raman (2001), use firm

size to explain how executive compensation determines corporate acquisitions.

Bushman, Indjejikian and Smith (1996) analyse individual performance and the CEOs’

annual incentive plans and find that firm size is positively related to salary and bonuses

and negatively related to long­term compensation. Bertrand and Hallock (1999) find

that the gender pay gap among highest­paid executives is positively related to firm size.

Some authors also analyse the impact of firm size on the use of resetting, also

called repricing, of stock options plans. Bens et al. (2003) find a negative relationship

between stock option repurchase and firm size; Brenner et al. (2000), Carter and Lynch

(2001,2003) and Chance et al. (2000), find stock option resetting is more common in

smaller firms. The same results are discovered by Chidambaran and Prabhala (2003).

Also Chance et al. (2000) say that firms that reprice stock options plans are generally

younger, associated with high technology and have out­of­the­money stock options

plans.

Ueng et al. (2000) describe that CEO pay in large firms is a function of the

influence of this CEO on the board, the firm size and firm’s performances but do not

find that CEO influence on the board in small companies affects CEO pay. Kostiuk

(1990), analysing the impact of firm size on executive compensation, describes that

there is significant variability in the level of compensation between firms of the same

size, which may indicate that CEOs and other executives from the firms listed on

S&P500, MidCap and Small Cap have different forms and values of compensation.

Hermalin and Wallace (2001), like Aggarwall and Samwick (1999), use the

variable size to determine the relationship between compensation and performance and

find a positive relationship between these two variables and stock options granted, and a

negative relationship with the use of golden parachutes, restricted stocks and with

supplemental pension plans. Ryan and Wiggins (2004) also document a positive

relationship between total compensation and equity based compensation. Kato et al.

(2005) find the same results on the Japanese market. Morgan and Poulsen (2001) find a

129

positive relation between firm size and the vote returns for management­sponsored

compensation proposals.

As we can see, firm size can explain a lot of situations in terms of executive

compensation, but what happens if we fix this effect in terms of executive

compensation, making separate analyses for firms listed on S&P500, S&P Mid Cap and

S&P Small Cap?

In this paper, we analyse the following two major sets of questions: Is the value

of total compensation packages across S&P firms the same? Are the forms of total

compensation packages across S&P firms the same? Has there been a change in the

forms of compensation among S&P firms since the NASDAQ crash (2000) and the

enactment of the Sarbanes­Oxley Act (2002)? If there has been a change, is it the same

across the S&P firms?

If firms listed on S&P500, S&P Mid Cap and S&P Small Cap indexes are

different, we expect that the factors that explain executive compensation in these firms

can also be different, and if some of the variables are the same, the intensity of these

factors can be different and statistically significant.

The second set of questions explores the following questions: Are the

determinants of executive compensation the same across S&P firms? Is the intensity

(impact) of the factors explaining executive compensation across S & P firms the same?

Have the factors that explain executive compensation changed since the NASDAQ

Crash in 2000?

The NASDAQ crash is also effectively associated with a group of financial

scandals and the bankruptcy of some large American companies, based on fraudulent

accounting practices and executive self­dealing. To solve some of these corporate

governance problems, the Sarbanes­Oxley Act was created in the USA in 2002,

introducing changes in governance, reporting, and disclosure requirements of public

firms with the intent of improving accuracy, reliability, and timeliness of the

information provided to investors.

With these significant changes in terms of corporate governance rules, we

believe that the percentage that each compensation component (salary, bonus, stock

options, restricted stocks, long­term incentive plans) represents in total compensation

has changed since the NASDAQ crash in 2000 and essentially since the Sarbanes­Oxley

130

Act in 2002, in these three groups. We also expect that these changes, in terms of the

structure of executive compensation, will be different in S&P500, S&P Mid Cap and

S&P Small Cap listed firms.

3. Data, Sample Selection and Statistics

3.1. Data and Sample Selection

We use data from the Standard ExecuComp database, which contains

information about executive compensation for public U.S. companies from 1992 to

2004. The dataset includes all the S&P index listed firms. Together, these firms

represent over 80% of the total market capitalisation of U.S. public firms 16 . This

database contains information about the five most well paid executives from each of the

firms in the database. In terms of compensation, the database categorises the various

components into salary, bonus, ex­ante value of options, restricted stocks award, long­

term incentive plan (LTIP), other annual compensation and all other compensation.

We use unbalanced panel data because not all executives stay with the same firm

during the sample period. The sample constitutes 79,650 observations of compensation

related to the 5 most highly paid executives from these 1500 firms 17 from 1992 to 2004.

This sample is built excluding the entire executive observations whose sum of salary

and bonuses, and also total compensation, was equal to zero. We also exclude

observation from executives who have remunerations more than once in the same year,

deleting the observations with lower remunerations than the executive receives.

Using the Consumer Price Index (CPI), compiled by the Bureau of Labor

Statistics, and using 1982 as base year, we adjust the monetary variables to the price

level of the year 2004.

16 The data is from 1992 to 2004 because 1992 is the first year of information in the ExecuComp database and 2004 was the last complete year of information when we started this work. In this database, there are executives with data for all of the years and others with only a small number of years of information. 17 The three groups of executives that we are studying (S&P500, S&PMidCap and S&PSmallCap listed firms), were achieved with the variable SPCODE index from ExecuComp database, which classifies the firms listed in the S&P500 with the code “SP”, the firms listed in the S&PMidCap 400 Index with the code “MD” and the firms listed in the S&PSmallCap Index with the code “SM”.

131

3.2. Statistics

Table 1 presents the statistics of average total compensation of executives for

firms listed in the S&P500, S&P Mid Cap and S&P Small Cap index. We use One­Way

Analysis of Variance to test the null hypothesis of the equality of three means against

the alternative hypothesis that at least one mean is different. 18

18 We use the Bonferroni correction, which is a multiple­comparison correction used when several dependent or independent statistical tests are being performed simultaneously.

132

Table 1

Descriptive Statistics

Mean Total Compensation Levels Adjusted for Inflation by Year (1992­2004) ­ Top Five

This table presents the average total compensation of the five most well paid executives associated with firms listed in the S&P500, S&P Mid Cap and S&P Small Cap index during the period from 1992 to 2004 and the results for One­Way­Anova to test whether the mean differences are statistically different. Data are from ExecuComp database. Monetary variables are adjusted to inflation and are stated in 2004 dollars. Total compensation is the sum of salary, bonus, stock options, restricted stocks, other annual compensation and all other compensation. Salary is the executive salary for the year. Bonus is the dollar value of bonus (cash and non­ cash) earned by the executive officer during the fiscal years. Stock option is the aggregate value of stock options granted to the executive during the fiscal year as valued using the Black­ Scholes methodology. Restricted stocks are the value of restricted stock granted during the year (determined as of the date of the grant). LTIP is the amount paid out to the executive under the company’s long­term incentive plan. Other annual compensation is the dollar value of other annual compensation not properly categorised as salary or bonus. All other compensation is the amount listed under “All other Compensation” in the Summary Compensation Table. Mean average and mean difference are in thousands of dollars.

S&P500 (1) S&PMidCap(2) S&PSmallCap(3) Mean Difference Mean Test (ANOVA) (Bonferroni Test)

Year

N Mean N Mean N Mean (1) and (2)

(1) and (3) (2) and (3) (1) and (2)

(1) and (3)

(2) and (3)

1992 1416 1963.48 678 875.84 539 801.26 1087.64 1314.67 227,.02 * * ***

1993 2007 1951.39 1044 908.05 916 960.44 1043.34 1267.07 223.73 * * **

1994 2106 2179.17 1132 1019.63 1061 731.25 1159.54 1447.93 288.39 * * *

1995 2167 2380.44 1231 1084.13 1220 712.24 1296.30 1668.19 371.89 * * *

1996 2250 2975.18 1370 1254.44 1386 814.20 1720.75 2160.98 440.23 * * *

1997 2354 3937.39 1524 1511.26 1674 900.35 2426.12 3037.04 610.91 * * *

1998 2448 4807.43 1637 161.16 1964 958.87 3192.26 3848.56 656.30 * *

1999 2515 5557.27 1703 1872.61 2131 1000.30 3684.65 4556.97 872.32 * * *

2000 2583 6697.17 1820 2152.70 2223 1347.02 4544.46 5350.15 805.67 * * **

2001 2595 6039.35* 1845 1962.25* 2337 1157.00* 4077.10 4882.35 805.25 * * *

2002 2636 4663.58 1934 2062.78 2539 1038.81 2600.81 3624.77 1023.97 * * *

2003 2690 4332.43* 2012 1744.33* 2696 1002.22 ***

2588.11 3330.21 742.10 * * *

2004 2672 4767.34 1957 1975.63 2671 1181.24 2791.71 3586.10 794.39 * * *

Note: Difference is statistically significant at (*) 1% level, (**) 5% level and (***) 10% level.

From table 1, we can see that, on average, the top five executives from the

S&P500 firms consistently receive higher compensation during the period of 1992 to

2004. The means differences of total compensation between S&P500, S&P Mid Cap,

133

and S&P Mid Cap are significant each year during the sample period except for S&P

Mid Cap and S&P Small Cap listed firms in 1998.

Table 2, summarises the time series percentage changes that each compensation

component represents in terms of total compensation for all S & P listed firms during

the period from 1992 to 2004 19 . We also perform the One­Way ANOVA analysis of

variance to test the hypothesis that the means of each component of executive

compensation for S&P500, S&P Mid Cap and S&P Small Cap index listed firms are

equal. In most cases, we reject the null hypotheses that the means are equal 20 .

We also test in Table 2 whether the differences in executive compensation

between year 2001 in relation to year 2000 (NASDAQ crash effect) and the year 2003

in relation to 2002 (Sarbanes­Oxley Act effect) are statistically significant. Based on a t

test we find that in most cases, these differences are statistically significant.

19 Last year of information available from Execucomp database when we started this investigation. 20 We also use the Levene's test for homogeneity of variance and Bonferroni correction, which is a multiple­comparison correction used when several dependent or independent statistical tests are being performed simultaneously.

134

Table 2

Executive Compensation Components as a Percentage of Total Compensation for S&P 500, S&P Mid Cap and S&P Small Cap Listed Firms (1992­2004)

This table presents the percentages that each compensation component represents in terms of total compensation by year. Our sample includes data from the five most well paid executives associated with firms listed in the S&P500, S&P Mid Cap and S&P Small Cap during the period from 1992 to 2004. Salary is the executive salary for the year. Bonus is the dollar value of bonus (cash and non­cash) earned by the executive officer during the fiscal years. Stock Option is the aggregate value of stock options granted to the executive during the fiscal year as valued by using S & P’s Black­ Scholes methodology. Restricted stocks are the value of restricted stock granted during the year (determined as of the date of the grant). LTIP is the amount paid out to the executive under the company’s long­term incentive plan. Other annual compensation is the dollar value of other annual compensation not properly categorised as salary or bonus. All other compensation is the amount listed under “All other Compensation” in the Summary Compensation Table. We describe S&P500 listed firms as SP, S&P Mid Cap listed firms as MD and S&P Small Cap firms as SM.

Panel A: Top Five (% of Total Compensation)

Salary Bonus Stock Options Restricted Stocks LTIP Year

SP MD SM SP MD SM SP MD SM SP MD SM SP MD SM

1992 41.67 55.90 59.54 20.38 17.25 18.40 22.63 16.46 13.95 5.25 3.33 2.45 4.78 2.76 1.54

1993 40.66 51.02 56.26 22.45 20.28 19.51 21.90 16.30 15.68 4.88 3.55 2.56 4.33 3.47 1.30

1994 37.90 48.30 53.57 22.35 20.79 20.59 25.39 20.06 17.68 4.47 3.79 2.63 4.69 2.78 0.94

1995 36.02 47.15 54.78 23.21 21.86 20.24 24.50 18.75 15.96 5.34 3.78 2.23 5.39 2.96 1.53

1996 33.02 43.28 51.32 21.81 23.31 19.12 29.74 20.71 20.31 5.57 4.17 2.59 5.07 3.20 1.71

1997 29.54 40.63 48.21 20.92 21.18 20.65 33.45 24.17 22.22 6.23 4.76 2.46 5.30 3.25 1.82

1998 27.84 40.26 47.00 19.84 18.39 17.87 35.72 29.43 26.00 6.36 4.13 2.87 5.08 2.52 1.74

1999 25.10 36.51 45.83 19.16 19.27 18.21 41.93 33.00 27.18 5.12 3.85 2.83 4.12 2.43 1.28

2000 23.05 35.04 44.19 18.31 20.30 18.37 42.53 33.63 29.29 7.58 3.86 2.45 4.42 2.10 1.05

2001 23.71 (*)

36.3 44.84 15.95 (*)

16.44 (*)

14.80 (*)

46.09 (*)

35.92 (*)

31.06 (*)

6.97 (*)

4.82 (*)

3.36 (*)

3.10 (*)

1.46 (*)

1.06

2002 25.77 34.95 45.52 18.18 18.95 17.23 39.32 34.20 27.74 6.46 5.65 3.89 3.91 1.42 0.91

2003 25.07 (*)

36.09 (*)

46.91 21.18 (*)

19.99 (**)

17.93 (**)

32.49 (*)

28.91 (*)

24.05 (*)

10.94 (*)

7.71 (*)

4.96 (*)

4.77 (*)

2.02 (*)

1.02 (*)

2004 21.80 32.46 42.09 23.08 22.98 20.22 31.98 26.45 23.92 13.28 10.06 7.12 4.79 3.13 1.29

135

Panel B: CEOs (% of Total Compensation)

Salary Bonus Stock Options Restricted Stocks LTIP Year

SP MD SM SP MD SM SP MD SM SP MD SM SP MD SM

1992 36.39 55.29 63.11 22.78 12.54 19.32 22.89 21.83 12.16 6.41 0.68 0.00 6.42 5.95 1.11

1993 37.64 49.22 52.17 23.31 19.42 21.22 23.07 17.80 16.71 4.62 4.06 4.05 5.01 4.45 0.96

1994 33.85 43.80 50.59 23.22 21.16 21.06 28.44 23.10 19.31 4.51 4.39 3.61 5.14 2.99 1.10

1995 31.82 42.46 51.36 24.21 23.06 21.53 26.76 21.42 18.21 5.75 4.99 2.32 5.91 3.67 1.88

1996 28.50 38.82 47.18 22.52 24.13 20.67 31.98 23.88 22.04 5.96 3.90 2.50 5.91 3.99 2.56

1997 24.16 36.09 43.77 21.93 22.33 23.10 36.40 26.05 23.53 6.70 5.16 2.96 6.04 4.30 2.35

1998 23.34 35.02 40.81 20.22 20.12 19.15 40.27 32.35 29.01 5.90 4.49 4.03 5.36 3.39 2.31

1999 20.33 31.81 40.49 19.35 20.57 19.15 46.09 35.90 30.01 4.94 3.76 3.78 4.31 3.10 1.48

2000 18.43 30.16 38.65 17.93 20.35 19.94 47.24 36.92 31.54 7.28 4.23 3.63 4.19 2.48 1.36

2001 18.63 32.12 39.44 14.86

(*) 15.40 (*)

14.99 (*)

51.80 (**)

39.99 35.01 (**)

7.09 5.52 3.92 3.38 (*)

1.57 (*)

1.16 (*)

2002 20.03 28.90 39.63 15.92 18.97 17.88 45.98 38.48 31.65 7.90 6.67 4.41 4.44 1.78 1.28

2003 18.83 30.18 40.25 17.35

(*) 21.02 (**)

18.91 36.93 (*)

31.08 (*)

29.04 (**)

11.88 (*)

9.16 (*)

5.99 (*)

5.56 (*)

2.61 (*)

1.29

2004 16.39 27.09 35.95 16.30 24.09 21.28 36.52 29.73 27.50 14.32 11.04 8.36 4.79 3.64 1.60

Panel C: Directors (% of Total Compensation)

Salary Bonus Stock Options Restricted Stocks LTIP Year

SP MD SM SP MD SM SP MD SM SP MD SM SP MD SM

1992 38.98 52.87 56.55 21.41 17.89 20.33 23.29 18.90 14.50 5.35 3.72 2.64 5.12 2.52 1.17

1993 38.23 49.52 54.31 23.22 21.12 21.05 22.86 16.58 16.10 5.34 3.99 2.97 4.63 3.56 1.08

1994 35.62 45.44 50.59 23.27 22.35 21.22 25.98 21.24 19.84 5.15 4.34 3.11 5.15 2.56 1.11

1995 33.59 44.98 51.29 23.29 22.80 21.00 25.58 29.59 17.87 5.91 4.52 2.23 5.61 3.08 1.58

1996 30.53 40.94 46.49 22.41 25.64 19.91 30.47 21.01 23.74 5.94 4.15 3.43 5.47 2.93 1.53

1997 26.17 38.95 44.59 21.86 23.66 22.56 34.62 23.67 23.32 6.74 4.76 2.87 5.35 2.83 2.60

1998 25.40 37.89 43.13 20.41 19.61 18.69 37.29 30.41 28.07 5.82 4.36 3.33 5.47 2.59 1.40

1999 22.99 34.70 42.30 20.23 20.47 18.93 42.37 33.93 29.02 5.38 3.25 3.15 3.97 2.72 1.24

2000 20.75 33.15 41.86 18.63 20.47 19.71 44.06 34.23 29.29 6.90 4.31 2.93 4.35 1.72 1.17

2001 20.73 (*)

35.49 (**)

41.11 (*)

16.15 (*)

16.58 (*)

15.92 (*)

48.53 (*)

35.95 32.68 (*)

6.47 (***)

5.26 (*)

3.84 (*)

3.09 (*)

1.35 (*)

0.98 (**)

2002 21.29 31.57 41.11 18.80 19.06 18.89 42.08 36.40 29.72 8.15 5.96 4.05 4.41 1.58 0.96

2003 21.10 33.55 41.24 21.81

(*) 21.12 (*)

19.90 35.05 (*)

29.74 (*)

26.74 (*)

11.56 (*)

7.97 (*)

5.73 (*)

4.64 1.86 (*)

1.13 (*)

2004 18.79 28.84 37.10 23.02 24.01 21.78 34.25 28.19 26.26 14.08 10.56 7.47 4.50 2.95 1.51

Note: Differences between years 2001 and 2000 (presented in year 2001 line) and between 2003 and 2002 (presented in 2003 line) are statistically significant at (*) 1% level, (**) 5% level and (***)10% level.

136

If we analyse the top five executives, we conclude that the most important

component of executive compensation in S&P Small Cap listed firms is salary. In the

case of S&P500 listed firms, until 1997, the most important component of executive

compensation is also salary, but after 1997 stock options become the most important

component, representing over 46% of the total compensation in year 2001. If we

analyse S&P Mid Cap listed firms, we see a decrease in salary (as a fraction of the total

compensation) from 55.90% in 1992 to 32.46% in 2004 and an increase of stock options

from 16.46% in 1992 to 33.63% in 2000 and 26.45% in 2004.

Essentially after year 2002 (Sarbanes­Oxley Act), firms give fewer stock options

and more restricted stock and bonuses and generally these differences are statistically

significant (see the significance levels in table 2 comparing the values of 2003 with

those of 2002). Salary continues to decrease in all the cases but it is still the most

important component of executive compensation in S&P Small Cap listed firms

(42.09% of total compensation in 2004).

If we analyse CEOs and directors separately, we conclude that CEOs receive

more stock options than Directors and this compensation component represents over

50% of total executive compensation in S&P500 listed firms and over 30% in the case

of S&P Mid Cap and S&P Small Cap listed firms. The weight of stock options

decreases from 1992 to 2004 but still represents over 30% in S&P500 and S&P Mid

Cap companies and over 20% in S&P Small Cap companies in 2004.

Directors now receive more salary than CEOs and this is more significant in

small companies. They also receive more bonuses than CEOs, but the difference is not

so pronounced as is in the case of salary. In the case of stock options, CEOs receive, in

the later years, a little more than Directors and this is also the case for restricted stocks.

From these results we can conclude that essentially after the introduction of

Sarbanes­Oxley in 2002, the structure of executive compensation changed, reducing the

use of stock options and increasing the use of bonus and restricted stocks.

137

4. Research Design

We control for firm size making an individual analysis for each of the three S &

P listed index firms ­ S&P 500, S&P Mid Cap, and S&P Small Cap index. We attempt

to identify the determinants of executive compensation in each of S & P sub­groups. We

also perform separate analysis for CEOs and Directors, as did Cheng and Hung (2006),

because both groups of executives have different characteristics.

We use unbalanced panel data with Fixed Effect Regression Model, also called

within estimator or Least Squares Dummy Variable (LSDV).

4.1. Dependent Variables

We use three different classifications for executive compensation, the dependent

variables, which are LN (Total Compensation), LN (Short Term Compensation) and LN

(Option Ratio).

LN (Total Compensation) is the total of the remunerations received by the

executives and is the sum of salary, bonus, stocks options, restricted stocks, LTIP, other

annual compensation and all other compensation. We used this variable like Chen and

Hung (2006). This variable, without logarithms, was also used by Aggarwal and

Smawick (1999) to evaluate the contracts offered to executives in a context of strategic

competition between products and evaluation of relative performance, and by Fields and

Fraser (1999) to unmask the commercial banks when they attributed compensations to

link executives to the performances. LN (Short Term Compensation) is the LN

(salary+bonus). Salary and bonus are considered short­term remunerations, and they are

usually received in money. We used this variable in a similar way to Aggarwal and

Samwick (1999), Stathopoulos et al. (2004) and Chen and Hung (2006). Finally, we

also used LN (Option Ratio). We define option ratio as the value of options received by

the executive divided by the total compensation and this variable was also used by Chen

and Hung (2006).

Each one of the above dependent variables will be confronted separately with a

group of independent financial and governance variables with the intention of finding,

138

in a more reliable way, possible differences of executive compensation between

S&P500, S&P Mid Cap and S&P Small Cap listed firms.

Essentially, we analyse:

S&P500

LN(Cash Compensation)

LN(Option Ratio)

LN(Total Compensation)

S&PMidCap

LN(Cash Compensation)

LN(Option Ratio)

LN(Total Compensation

Executive

Compensation

S&PSmallCap LN(Cash Compensation)

LN(Option Ratio)

LN(Total Compensation)

0 1

2 3

4 5

6 7 8 9

( ) * ( ) * ( ) * 1 * ( ) * ( ) * ( ) * * * * * (1993...2004)

LN Compensation LN Not Exercised Ratio LN Ajex Tra yr LN Bs Volatility LN Number Mtgs LN Tenure ROA Pdirpens Interlock Industry Dummys Years Dummy

f

β β β β β β β β β β β β

= + + + + +

+ + + + + + +

+ + + + + ε

The dependent variable LN (Compensation) can assume the values of LN

(Total Compensation), LN (Option Ratio) and LN (Short Term Compensation) and f is the fixed effect.

4.2. Independent Variables

In order to explore the determinants of executive compensation for firms from

the S&P 500, S&P Mid Cap and S&P Small Cap listed indexes, we use the following

independent variables.

139

4.2.1. Financial Variables

To analyse the relationship between firm performance and executive

compensation, we use the variables ROA, which is the net income before extraordinary

items and discontinued operations divided by Total Assets. This variable was employed

by Zenner (2001) as one of the most commonly used variables to analyse the

relationship between executive compensation and performance. We expect a positive

relationship between executive compensation and firm performance, because better

returns can lead to higher compensation.

We also use the variable LN(Not Exercised Ratio), which is the natural

logarithm of the number of unexercised options that the executive held at year end that

were vested, divided by the aggregate number of stock options/stock appreciation rights

granted. We expect this variable to have a negative relationship with total compensation

and options ratio, meaning that if the executive has stock options that are not exercised,

the firm will probably give fewer stock options in the future.

We also analyse the relationship between the volatility of firm stock return and

executive compensation. To do this we use the variable LN (BS Volatility), which is the

natural logarithm of the standard deviation volatility calculated over 60 months with the

methodology of Black and Scholes. This variable was used by authors like Chen (2004)

without natural logarithm. If the volatility of firms’ stock returns increases, the value of

stock options will also increase and eventually shareholders will prefer to provide

incentives for their executives with more stock options and with less salary and bonus

(short­term compensation). In this way, we expect a positive relationship between risk

and option ratio, an inherent positive relationship with total compensation, and a

negative relationship with short­term compensation.

We also analyse the relationship between the return to shareholders and

executive compensation. We use the variable (Trs1yr), which is the one­year total return

to shareholders, including the monthly reinvestment of dividends. We expect that, if

shareholders receive a high return from their investments in the company, they do not

need to give more stock options to executives to align their interests with executives’

interests. Based on this, we expect a negative relationship between option ratio, total

compensation and the one­year shareholders’ return and a small positive relationship

140

with cash compensation in the sense that companies will probably give some money to

compensate executives’ efforts, but they do not need to give more stock options to

motivate them.

LN (Ajex) is the natural logarithm of the ratio used to adjust per­share data for

all stock splits that have occurred subsequent to the end of the company’s fiscal year.

We expect that executives might influence the determination of this ratio and they will

probably do so in the sense of having personal benefits. Based on this, we expect a

positive relationship between LN (Ajex) and the dependent variables.

We also use a dummy variable for each year between 1993 and 2004, as did

Barron and Waddel (2003) and Grinstein and Hribar (2004), to control for the effect of

the time in terms of executive compensation because we expect that time will be one of

the important factors in explaining the evolution of executive compensation during the

analysed period. Based on Fama and French (1997) industry classification we also used

dummy variables to fix the industry effect.

4.2.2. Governance Variables

We also tested the influence of a number of variables related to corporate

governance in executive compensation.

One of the most important topics in terms of corporate governance and executive

compensation is the influence of the Board and Compensation Committee’s

composition on executive compensation. This relationship was analysed by Ryan Jr and

Wiggins III (2004), who found that the determination of CEO compensation was related

to the power and influence that s/he has on the Board. They found evidence that firms

with external directors on the Board pay more compensation based on stock options and

restricted stocks. Anderson and Bizjak (2003) also analysed whether board

independence promotes shareholders’ interests and whether the presence of the CEO on

the Compensation Committee is related to opportunistic behaviour. They did not find

evidence that when executives leave the Compensation Committee, their remuneration

decreases.

To analyse this relationship we used two variables: LN (Number Mtgs) and

Interlock. LN (Number Mtgs) is the natural logarithm of the number of board meetings

141

held during the indicated fiscal year. The ability of the executive to take decisions is

affected by the number of board meetings during the year. According to Davidson,

Pilger and Szakmary (1998), board members align more with shareholders` interests

when they have more meetings during the year.

We also used the dummy variable Interlock, which assumes the value one if the

named executive is on two different Boards or Compensation Committees, and zero if

not. Based on the findings of Hallock (2007), we expect that the executives who are on

two boards (interlocked) will receive more compensation than the executives who are

not.

We also analyse the influence of the number of years of the tenure of a CEO

(LN (Tenure)) on executive compensation. This variable was applied by a significant

number of authors to explain executive compensation including Chindambaran and

Prabhala (2003), Ryan and Wiggins (2004), Murphy (1986) and Barro and Barro

(1990). We expect a positive relationship between executive compensation from

S&P500, S&P Mid Cap and S&P Small Cap and the number of years as CEO.

Finally, we test the influence in terms of compensation if the company has a

director pension/retirement plan (Pdirpens). This is a dummy variable that assumes the

value equal to 1 when it is “true” and 0 when it is not. We expect a negative relationship

between Pdirpens and executive compensation because, if the firm already pays a

pension plan for the executive, this will give the firm reason not to increase the present

compensation.

142

Expected correlations

Dependent Variables Independent Variables LN(Total

Compensation) LN(Option ratio) LN(Short term Compensation)

LN (Not Exercised Ratio) ­ ­ + LN(Ajex) + + + Trs1yr ­ ­ + LN(Bs Volatility) + + ­ LN(Number Mtgs) ­ ­ ­

LN (Tenure) + ­ +

ROA + + + Pdirpens ­ ­ ­ Interlock + + + Year Dummy + + +

Table 14 describes Pearson correlations between independent variables and we

do not find high correlations between these variables.

143

5. Empirical Results

5.1. Summary Statistics

Table 3 presents some statistics to better understand the differences between the

firms listed on S&P500, S&P Mid Cap and S&P Small Cap indexes.

Table 3 Statistics from S&P500, S&P Mid Cap and S&P Small Cap Listed Firms

The table displays descriptive statistics from firms that belong to the S&P 500, Mid­Cap 400 and Small­Cap 600 indexes. LN (BS Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method. LN (Number Mtgs) is the natural logarithm of the number of the board meetings. LN(Shrownpc) is the natural logarithm of executive ownership. LN (Tenure) is the natural logarithm of the number of years as CEO. LN (Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted. Trs1tyr is the one­year total return to shareholders, including the monthly reinvestment of dividends. ROA is the net income before extraordinary items and discontinued operations divided by total assets. Interlock is a dummy variable that assumes the value 1 when true, indicating that the executive is involved in a relationship requiring disclosure in the Compensation Committee Interlocks and Insider participation and 0 when not. Empl is the number of firm employees. Divyield is the dividends per share by ex­date divided by close price for the fiscal year. Ni5ls is the 5­year least squares annual growth rate of net income. Page2 is the executive age. Pdirpens is a dummy that assumes the value equal to 1 when it is true that the company pays into a directors’ pension plan and 0 when not.

S&P500 S&P MidCap S&P SmallCap

LN (BSs Volatility) ­1.148 ­1.040 ­0.881 LN(Number Mtgs) 1.968 1.870 1.829 LN(Shrownpc) 0.437 0.419 0.342 LN(Tenure) 2.112 2.161 2.119 LN (Not Exercised Ratio) 0.736 0.690 0.677 Trs1yr 26.838 28.166 25.903 ROA 5.357 5.036 4.605 Interlock 0.020 0.030 0.0314 Empl 40.758 10.610 4.939 Divyield 1.668 1.501 1.271 Ni5ls 18.613 19.333 18.664 Page 2 56.431 55.910 55.300 Pdirpens 0.183 0.0970 0.0671

From table 3, we can conclude that S&P500 listed firms have a higher stock

return volatility and the boards have more meetings during the year. Executives from

large companies also have more stock firm ownership than executives from S&P Mid

144

Cap and S&P Small Cap listed firms. The number of years of the executives’ tenure

(Tenure) and also the executive average age is similar in all the three groups.

Executives from large companies have more stock options vested but not exercised than

executives from other sub­groups. In terms of the one­year return to shareholders the

medium size companies produce higher returns to shareholders.

The ROA is higher in S&P 500 firms than S&P Mid Cap or S&P Small Cap

listed firms and the executives from Small Cap listed firms are more interlocked.

Large companies give more dividends than small and medium size companies,

however; the medium size companies exhibit higher increase in net income.

Finally, firms from S&P500 give better pension plans to executives than other

companies.

5.2. Determinants of Executive Compensation in S&P500, S&P Mid Cap and S&P

Small Cap Listed Firms

Based on the summary statistics described in table 3, we develop the idea that if

S&P500 listed firms are so different in terms of growth of net incomes, dividends, etc,

from S&P Mid Cap and Small Cap companies, the factors that explain executive

compensation in these three groups will probably be different.

We use the Hausman Test to detect whether it is better to use a fixed or random

effect regression model. The results are favourable for using fixed effect regression

analysis, also called within method or Least of Square Dummy Variable Regression

(LSDV). In all the regressions, Standard errors are corrected using period Seemingly

Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for

both period heteroskedasticity and general correlation of observations within a given

cross section (Beck and Katz, 1995). In this section, the results of the multivariate

analyses are described in tables 4 to 11, and in tables 12 and 13 we describe whether the

difference between the coefficients values of these Fixed Effect Regressions are

statistically significant.

145

Table 4 Fixed Effect Regression Analysis of Determinants of CEO Compensation for

S&P500 Listed Firms Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and 1982 as base year, we adjust the monetary variables for inflation reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN(Ajex) is the natural logarithm of the ratio used to adjust per­share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; Tts1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings. LN (Tenure) is the natural logarithm of the number of years as CEO. ROA is the net income before extraordinary items and discontinued operations divided by total assets. Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not. Interlock is a dummy variable that assumes the value equal to 1 when the executive is on two different boards at the same time and 0 when not. We control for time effect inserting one dummy for each year between 1993 and 2004 and for industry effect using one dummy for each industry per Fama and French (1997) industry classification. To distinguish between executives from S&P500, S&PMidcap and S&PSmallCap, we used the SPCODE variable from the ExecuComp database that considers firms from S&P500 those with code SP, S&PMidCap with code MD and S&PsmallCap with code SM.

LN (Total Compensation) t Statistics LN (Option

Ratio) t

Statistics LN (Short Term Compensation)

t Statistics

Constant 8.840* 22,735 ­3,256 ­1.171 8,560* 4,391 LN (Not Exercised Ratio) ­0.245* ­26.930 ­0.315* ­35.246 0.007 0.920 LN(Ajex) ­0.227* ­8.313 ­0.035 ­1.354 ­0.028 ­1.203 Trs1yr 0.001* 3.749 ­0.001* ­8.031 0.001* 10.004 LN(Bs Volatility) ­0.028 ­0.430 0.192* 3.100 ­0.317* ­5.600 LN(Number Mtgs) ­0.033 ­0.972 ­0.046 ­1.438 ­0.074* ­2.373 LN(Tenure) ­0.265 ­0.276 1,027 0,897 ­0,666 ­0.830 ROA 0.001 1.330 ­0.0006 ­0.630 0.006* 6.952 Pdirpens ­0.043 ­1.136 ­0.101* ­2.788 ­0.027 ­0.868 Interlock 0.136** 2.574 0.108** 2.124 ­0.015 ­0.326 Year1993 0.108** 2.524 0.002 0.025 0.043 1.431 Year1994 0.314* 6.793 0.226* 3.763 0.139* 4.143 Year1995 0.368* 7.715 0.162* 2.589 0.113* 3.096 Year1996 0.574* 11.504 0.292* 4.429 0.182* 4.758 Year1997 0.739* 13.508 0.334* 5.049 0.276* 6.874 Year1998 0.846* 15.478 0.437* 6.614 0.402* 9.556 Year1999 1.072* 18.803 0.613* 9.102 0.478* 10.070 Year2000 1.163* 18.899 0.556* 7.801 0.589* 11.830 Year2001 1.296* 20.491 0.669* 9.276 0.552* 9.912 Year2002 1.301* 20.282 0.620* 8.416 0.673* 12.170 Year2003 1.317* 19.764 0.568* 7.478 0.707* 12.825 Year2004 1.510* 22.735 0.622* 8.160 0.856* 15.541 Candy Dummy ­0,500 ­1,148 ­0,396 0,644 ­0,512*** ­1,694 Computer Dummy 0,242 0,133 3,019 1,402 ­1,312 ­0,866 Electronic Dummy ­0,758 ­1,112 ­0,398 ­0,599 ­1,480** ­2,358

N 3841 3841 3841 Adjusted R­ square 80.30% 71.07% 74.49%

*Significant at 1% level, ** significant at 5% level *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

146

Table 5 Fixed Effect Regression Analysis of Director Compensation Determinants in

S&P500 Listed Firms Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and 1982 as base year, we adjust the monetary variables for inflation reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN(Ajex) is the natural logarithm of the ratio used to adjust per­share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; Tts1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings; LN (Tenure) is the natural logarithm of the number of years as CEO. ROA is the net income before extraordinary items and discontinued operations divided by total assets; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy variable that assumes the value equal to 1 when the executive is on two different boards at the same time and 0 when not. We control for time effect inserting one dummy for each year between 1993 and 2004 and for industry effect using one dummy for each industry per Fama and French (1997) industry classification. To distinguish between executives from S&P500, S&PMidcap and S&PSmallCap, we used the SPCODE variable from the ExecuComp database that considers firms from S&P500 those with code SP, S&PMidCap with code MD and S&PSmallCap with code SM.

LN(Total Compensation)

t Statistics LN(Option Ratio t

Statistics LN(Shor t Term Compensation)

t Statistics

Constant 8.035* 2,930 ­3,093 ­1,083 7,982* 3,490 LN (Not Exercised ratio) ­0.246* ­30.827 ­0.299* ­41.115 0,004 0,552 LN(Ajex) ­0.195* ­7.679 ­0.064* ­2.901 ­0.019 ­0,757 Trs1yr 0.001* 4.607 ­0.001* ­8.084 0.001* 9.880 LN(Bs Volatility) ­0.117*** ­1.896 0.244* 4.606 ­0,367* ­6.196 LN(Number Mtgs) ­0.003 ­0.098 ­0.044 ­1.544 ­0.100* ­3.277 LN(Tenure) ­0.063 ­0.052 1,026 0,822 ­0.051 ­0.521 ROA 0.004* 4.799 ­0.001 ­0.954 0.006* 6.341 Pdirpens ­0.057*** ­1.695 ­0.057*** ­1.901 ­0.020 ­0.611 Interlock 0.103** 2.085 0.093** 2.068 ­0,026 ­0,563 Year1993 0.091* 2.684 0.020 0.475 0.063** 2.282 Year1994 0.298* 7.867 0.214* 5.465 0.181* 6.151 Year1995 0.361* 9.029 0.179* 4.141 0.167* 5.066 Year1996 0.543* 12.605 0.334* 7.405 0.245* 6.886 Year1997 0.749* 16.131 0.392* 8.518 0.331* 8.599 Year1998 0.884* 18.931 0.468* 10.230 0.468* 11.070 Year1999 1.111* 22.397 0.628* 13.501 0.553* 12.203 Year2000 1.263* 23.493 0.565* 11.171 0.678* 13.612 Year2001 1.403* 25.109 0.680* 13.181 0.656* 12.024 Year2002 1.426* 25.156 0.623* 11.797 0.820* 15.083 Year2003 1.427* 24.303 0.585* 10.648 0.869* 16.015 Year2004 1.621* 27.645 0.632* 11.367 1.015* 18.474 Candy Dummy ­0,024 ­0,045 ­0,050 ­0,074 ­0,064 ­0,150 Computer Dummy 0,212 0,094 3,398 1,452 ­0,904 ­0,478 Consumer Dummy ­0,829 ­1,013 ­0,940 ­1,201 ­1,426*** ­1,861 Eelectronic Dummy ­0,841 ­1,092 ­0,350 ­0,486 ­1,515** ­2,087

N 5207 5207 5207

Adjusted R­Square 78.75% 70.40% 67.06% *Significant at 1% level, ** significant at 5% level *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

147

Table 6 Fixed Effect Regression Analysis of Determinants CEO Compensation for S&P

Mid Cap Listed Firms Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and 1982 as base year, we adjust the monetary variables for inflation reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN(Ajex) is the natural logarithm of the ratio used to adjust per­share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; Tts1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings; LN (Tenure) is the natural logarithm of the number of years as CEO. ROA is the net income before extraordinary items and discontinued operations divided by total assets; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy variable that assumes the value equal to 1 when the executive is on two different boards at the same time and 0 when not. We control for time effect inserting one dummy for each year between 1993 and 2004 and for industry effect using one dummy for each industry per Fama and French (1997) industry classification. To distinguish between executives from S&P500, S&PMidcap and S&PSmallCap, we used the SPCODE variable from the ExecuComp database that considers firms from S&P500 those with code SP, S&PMidCap with code MD and S&PSmallCap with code SM.

LN (Total Compensation)

t Statistic

s

LN (Option Ratio

t Statistics

LN (Shor t Term Compensation)

t Statistics

Constant 3.753* 5.994 ­2.934* 4.959 5.482* 12.203

LN (Not Exercised Ratio) ­0.225* ­19.624 ­0.353* ­26.917 ­0.010 ­1.195

LN(Ajex) ­0.186* ­4.341 ­0.157* ­3.241 ­0.058*** ­1.756 Trs1yr ­0.00002 ­1.096 ­0.00001 ­0.702 0.0003*** 1.722 LN(Bs Volatility) ­0.050 ­0.574 0.229** 2.358 ­0.114*** ­1.674 LN(Number Mtgs) 0.038 0.904 0.036 0.770 ­0.028 ­0.855 LN(Tenure) 1.435* 5.630 0.914* 3.788 0.357*** 1.898 ROA 0.009* 6.288 0.0002 0.146 0.012* 10.873 Pdirpens

0.040 0.604 ­0.056 ­0.757 0.065 1.319 Interlock ­0.052 ­0.831 0.076 1.069 0.059 1.250 Year1993 0.121 0.694 ­0.045 ­0.285 0.138** 2.088 Year1994 0.336*** 1.954 0.101 0.631 0.203* 3.178 Year1995 0.338*** 1.944 0.009 0.056 0.230* 3.518 Year1996 0.507* 2.907 0.160 1.000 0.300* 4.579 Year1997 0.633* 3.625 0.139 0.856 0.384* 5.715 Year1998 0.747* 4.280 0.318*** 1.961 0.466* 6.955 Year1999 0.918* 5.228 0.412** 2.546 0.538* 7.902 Year2000 1.090* 6.116 0.321** 1.911 0.658* 9.251 Year2001 1.149* 6.432 0.472* 2.841 0.610* 8.357 Year2002 1.236* 6.893 0.428** 2.566 0.743* 10.072 Year2003 1.222* 6.804 0.394** 2.334 0.749* 9.919 Year2004 1.403* 7.784 0.430** 2.547 0.898* 11.583

N 1997 1997 1997

Adjusted R­Square 72.64% 67.70% 70.19%

*Significant at 1% level, ** significant at 5% level, *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

148

Table 7 Fixed Effect Regression Analysis of Determinants of Director Compensation for

S&P Mid Cap Listed Firms Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and 1982 as base year, we adjust the monetary variables for inflation reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN(Ajex) is the natural logarithm of the ratio used to adjust per­share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; Tts1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings. LN (Tenure) is the natural logarithm of the number of years as CEO. ROA is the net income before extraordinary items and discontinued operations divided by total assets. Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not. Interlock is a dummy variable that assumes the value equal to 1 when the executive is on two different boards at the same time and 0 when not. We control for time effect inserting one dummy for each year between 1993 and 2004 and for industry effect using one dummy for each industry per Fama and French (1997) industry classification. To distinguish between executives from S&P500, S&PMidcap and S&PSmallCap, we used the SPCODE variable from the ExecuComp database that considers firms from S&P500 those with code SP, S&PMidCap with code MD and S&PsmallCap with code SM.

LN (Total Compensation) t Statistics LN (Option

Ratio t

Statistics LN (Short Term Compensation)

t Statistics

Constant 3.421* 5.983 ­2.437* ­4.211 4.739* 9.878 LN (Not Exercised Ratio) ­0.245* ­23.625 ­0.333* ­30.245 ­0.008 ­0.956 LN(Ajex) ­0.193* ­5.021 ­0.217* ­5.374 ­0.035 ­1.105 Trs1yr 0.0000003 ­0.015 ­0.00001 ­0.730 0.000** 2.245 LN(Bs Volatility) 0.036 0.509 0.364* 4.898 ­0.153** ­2.571 LN(Number Mtgs) 0.048 1.242 ­0.004 ­0.104 ­0.066** ­2.051 LN(Tenure) 1.659* 6.627 0.820* 3.234 0.700* 3.332 ROA 0.010* 8.550 0.001 0.784 0.009* 10.037 Pdirpens 0.086 1.445 ­0.010 ­0.153 0.065 1.300 Interlock 0.023 0.380 0.100 1.524 0.045 0.874 Year1993 0.064 1.003 ­0.065*** ­0.931 0.052 1.160 Year1994 0.300* 4.673 0.139 1.954 0.146* 3.205 Year1995 0.321* 4.696 0.058* 0.859 0.179* 3.631 Year1996 0.518* 7.550 0.225* 3.278 0.277* 5.522 Year1997 0.675* 9.510 0.214* 2.901 0.372* 7.415 Year1998 0.776* 10.749 0.311* 4.351 0.438* 8.411 Year1999 0.909* 12.622 0.413* 5.841 0.498* 0.935 Year2000 1.064* 13.522 0.294* 3.613 0.643* 11.119 Year2001 1.156* 14.567 0.446* 5.713 0.604* 10.116 Year2002 1.258* 15.689 0.386* 4.852 0.757* 12.512 Year2003 1.224* 15.221 0.381* 4.694 0.760* 12.346 Year2004 1.435* 17.390 0.429* 5.230 0.889* 13.803

N 2669 2669 2669 Adjusted R­square 70.49% 64.98% 64.63%

*Significant at 1% level, ** significant at 5% level *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995).

149

Table 8 Fixed Effect Regression Analysis of Determinants of CEO Compensation for S&P

Small Cap Listed Firms

Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and 1982 as base year, we adjust the monetary variables for inflation reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN(Ajex) is the natural logarithm of the ratio used to adjust per­share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; Tts1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings; LN (Tenure) is the natural logarithm of the number of years as CEO. ROA is the net income before extraordinary items and discontinued operations divided by total assets; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy variable that assumes the value equal to 1 when the executive is on two different boards at the same time and 0 when not. We control for time effect inserting one dummy for each year between 1993 and 2004 and for industry effect using one dummy for each industry per Fama and French (1997) industry classification. To distinguish between executives from S&P500, S&PMidcap and S&PSmallCap, we used the SPCODE variable from the ExecuComp database that considers firms from S&P500 those with code SP, S&PMidCap with code MD and S&PSmallCap with code SM.

LN (Total Compensation)

t Statistics

LN (Option Ratio t Statistics LN (Shor t Term

Compensation) t

Statistics Constant 6.492* 5.054 4.527* 3,233 ­2,158** ­2,238 LN (Not Exercised Ratio) ­0.243* ­22.273 ­0.332* ­25.975 0.008 0.826 LN(Ajex) ­0.309* ­6.236 ­0.310* ­5.380 ­0.046 ­1.110 Trs1yr 0.001* 3.773 ­0.001* ­5.404 0.001* 7,095 LN(Bs Volatility) 0.161** 2.194 0.020 0.237 ­0.119*** ­1,957 LN(Number Mtgs) 0.070*** 1.811 ­0.064 ­1.144 ­0.017 ­0.515 LN(Tenure) 0.375 0,699 ­2.190* ­3.791 3.443* 8.541 ROA 0.003* 3.187 ­0.002*** ­1.914 0.004* 5.605 Pdirpens 0.092 1.096 0.067 0.702 0.119*** 1.697 Interlock 0.098 1.458 0.214* 2.839 0.060 1.021 Year1993 ­0.183 ­1.191 ­0.005 ­0.018 0.146 1.431 Year1994 0.039 0.254 ­0.015 ­0.053 0.289* 2.832 Year1995 ­0.012 ­0.078 ­0.056 ­0.201 0.276* 2.603 Year1996 0.092 0.586 0.014 0.050 0.313* 2.983 Year1997 0.155 0,988 ­0.015 ­0.053 0.361* 3.434 Year1998 0.214 1.357 ­0.005 ­0.019 0.450* 4.260 Year1999 0.328** 2.084 0.060 0.213 0.502* 4.715 Year2000 0.410* 2.579 0.038 0.137 0.565* 5.244 Year2001 0.496* 3.123 0.135 0.482 0.533* 4.931 Year2002 0.508* 3.191 0.164 0.585 0.629* 5.779 Year2003 0.533* 3.350 0.141 0.506 0.665* 6.104 Year2004 0.770* 4.803 0.119 0.424 0.807* 7.369 Apparel ( Dummy) ­0,619 ­1,266 ­1,330** ­2,514 1,582* 4,082

N 2209 2209 2209

Adjusted R­Square 75.49% 71.91% 70.60%

*Significant at 1% level, ** significant at 5% level *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

150

Table 9 Fixed Effect Regression Analysis of Determinants of Director Compensation for

S&P Small Cap Listed Firms

Data is from the ExecuComp database from 1992 to 2004. We used Unbalanced Panel Data ­ Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and 1982 as base year, we adjust the monetary variables for inflation reporting the values to the year 2004. Dependent variables are LN (Total Compensation), LN (Short Term Compensation) and LN (Option Ratio). LN (Total Compensation) is the natural logarithm of total executive compensation. LN (Short Term Compensation) is the natural logarithm of Salary and Bonus. LN (Option Ratio) is the natural logarithm of the value of options granted to the executive divided by total compensation. The independent variables are: LN(Not Exercised Ratio) is the natural logarithm of the number of unexercised options that the executive held at year end that were vested, divided by the aggregate number of stock options/stock appreciation rights granted; LN(Ajex) is the natural logarithm of the ratio used to adjust per­share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; Tts1tr is the one­year total return to shareholders, including the monthly reinvestment of dividends; LN (Bs Volatility) is the natural logarithm of standard deviation volatility calculated over 60 days with the Black Scholes method; LN (Number Mtgs) is the natural logarithm of the number of board meetings; LN (Tenure) is the natural logarithm of the number of years as CEO. ROA is the net income before extraordinary items and discontinued operations divided by total assets; Pdirpens is a dummy that assumes the value equal to 1 when it is true and zero when not; Interlock is a dummy variable that assumes the value equal to 1 when the executive is on two different boards at the same time and 0 when not. We control for time effect inserting one dummy for each year between 1993 and 2004 and for industry effect using one dummy for each industry per Fama and French (1997) industry classification. To distinguish between executives from S&P500, S&PMidcap and S&PSmallCap, we used the SPCODE variable from the ExecuComp database that considers firms from S&P500 those with code SP, S&PMidCap with code MD and S&PsmallCap with code SM.

LN (Total Compensation)

t Statistics

LN (Option Ratio)

t Statistics

LN (Shor t Term Compensation)

t Statistics

Constant 6.091* 4.318 3,305** 2,202 ­1.785 ­1.511

LN (Not Exercised Ratio) ­0.235* ­25.758 ­0.318* ­32.193 0.009 1.141 LN(Ajex) ­0.281* ­6.772 ­0.331* ­7.313 ­0.050 ­1.400 Trs1yr 0.0005* 4.011 ­0.0008* ­5.955 0.001* 8.423 LN(Bs Volatility) 0.178* 2,825 0.158** 2.242 ­0.055 ­1.015 LN(Number Mtgs) 0.017 0.501 ­0.025 ­0.662 ­0.078* ­2.543 LN(Tenure) 0,467 0.749 ­1.818* ­2.747 3.524* 6.769 ROA 0.004* 4.698 ­0.0002 ­0.256 0.005* 7.583 Pdirpens 0.079 1.030 0.085 1.038 0.041 0.616 Interlock 0.084 1.346 0.139** 2.097 0.077 1.398 Year1993 ­0.024 ­0.326 0.066 0.824 0.035 0.685 Year1994 0.216* 2,891 0.159*** 1.868 0,176 1,398 Year1995 0.184* 2.406 0.098 1.161 0.166* 2.904 Year1996 0.324* 4.243 0.246* 3.053 0.210* 3,680 Year1997 0.415* 5.448 0.191** 2.314 0.257* 4,212 Year1998 0.426* 5.501 0.183** 2.211 0.359* 6.196 Year1999 0.571* 7.307 0.236* 2.815 0.397* 6.546 Year2000 0.654* 8.013 0.201** 2.370 0.448* 7.201 Year2001 0.766* 9.470 0.308* 3.572 0.440* 7.028 Year2002 0.818* 10.053 0.333* 3.802 0.574* 8.958 Year2003 0.830* 10.088 0.305* 3.480 0.582* 8.964 Year2004 1.098* 12.982 0.317* 3.519 0.760* 11.497 Apparel Dummy ­0,695* ­1,297 ­0,972*** ­1,670 0,955 2,173

N 3003 3003 3003

Adjusted R­Square 72.29% 69.64% 66.96% *Significant at 1% level, ** significant at 5% level *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995)

151

In tables 12 and 13, we analyse whether the regression coefficients, which explain

the variation in executive compensation, are the same for each of three S&P index

firms. That is, we are interested in determining whether explanatory factors have a

similar impact on response variables across all S&P firms. In most cases, the

coefficients are different and this difference is statistically significant at 1% level.

5.3. Impact of NASDAQ Crash on the Determinants of Executive Compensation

In tables 10 and 11 we analyse whether the NASDAQ crash changed the

determinants and the associated impact on total executive compensation.

152

Table 10

Fixed Effect Regression Analysis of CEO Total Compensation Determinants Before and After NASDAQ Crash in 2000 for S&P Listed Firms

S&P500 S&P MidCap S&PSMallCAP

1992 to 2000 2001 to 2004 1992 to 2000 2001 to 2004 1992 to 2000 2001 to 2004 Independent

Variables Ln(Total Comp.)

(t statistics)

Ln(Total Comp.) (t statistic)

Ln(Total Comp.)

(t statistic)

Ln(Total Comp.)

(t statistic)

Ln(Total Comp.)

(t statistic)

Ln(Total Comp.)

(t statistic)

Constant 9,434* (2,970) 14,339*

(4,955) 7,193* (29,674)

8,734* (39,759)

7,863* (3,358)

7,584* (40,093)

LN (Not Exercised Ratio)

­0,246* (­22,937)

­0,300* (­13,835)

­0,201* (­12,825)

­0,260* (­12,383)

­0,236* (­15,017)

­0,284* (­15,752)

LN(Ajex) ­0,283* (­8,791)

­0,269* (­2,771)

­0,236* (­3,864)

­0,144 (­1,444)

­0,271* (­3,989)

­0,337* (­2,863)

Trs1yr 0,001* (4,496)

­0,0004 (­1,144)

0,00007 (0,317)

­0,00001 (­1,056)

0.0005** (2,431)

0,00005 (0,189)

LN(Bs Volatility) 0,177** (2,087)

0,045 (0,234)

­0,070 (­0,610)

0,345 (1,619)

0.345* (3,378)

­0,052 (­0,320)

LN(Number Mtgs) 0,065 (1,311)

­0,099*** (­1,844)

0,066 (1,122)

­0,067 (­0,994)

­0.010 (­0,159)

0,113** (2,012)

LN(Tenure) ­0,442 (­0,373)

­2,385 (­1,644)

­0.072 (­0,079)

ROA 0,011* (5,971)

­0,004* (­3,265)

0,011* (4,637)

0,002 (0,876)

0.003* (2,585)

0,006* (3,345)

Pdirpens 0,002 (0,041)

­0,141 (­1,281)

0,053 (0,701)

­0,112 (­0,567)

0.029 (0,255)

0,010 (0,031)

Interlock 0,080 (1,310)

­0,015 (­0,129)

­0,063 (­0,804)

­0,008 (­0,0559)

0.169 (1,291)

­0,018 (­0,189)

Year1993 0,114* (2,660)

0,144 (1,988)

­0.150 (­1,031)

Year1994 0,323* (7,038)

0,329** (1,988)

0.066 (0,456)

Year1995 0,387* (8,076)

0,330** (1,965)

0.021 (0,143)

Year1996 0,610* (11,62)

0,478* (2,828)

0.144 (0,966)

Year1997 0,790* (14,209)

0,608* (3,571)

0.238 (1,600)

Year1998 0,871* (15,711)

0,727* (4,280)

0.267*** (1,764)

Year1999 1,078* (18,279)

0,892* (5,207)

0.389** (2,576)

Year2000 1,147* (17,538)

1,086* (6,201)

0.456* (2,959)

Year2001

Year2002 0,003 (0,085)

0,097** (2,364)

0,015 (0,431)

Year2003 0,087** (2,079)

0,149* (3,337)

0,064*** (1,689)

Year2004 0,286* (5,646)

0,397* (7,012)

0,269* (5,936)

Candy Dummy ­0,475 (­1,098)

Computer Dummy ­0,149 (­0,066)

N 2391 1450 1187 951 1089 1173

Adjusted R­ Square

83.13% 81.31% 76.04% 77.32% 78.47% 81.22%

*Significant at 1% level, ** significant at 5% level *** significant at 10% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) – Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995).

153

Table 11 Fixed Effect Regression Analysis of Determinants of Directors´ Total Compensation Before and After NASDAQ Crash for S&P Listed Firms

S&P500 S&P MidCap S&PSmallCap

1992 to 2000 2001 to 2004 1992 to 2000 2001 to 2004 1992 to 2000 2001 to 2004 Independent Variables Ln(Total

Comp.) (t statistics)

Ln(Total Comp.)

(t statistic)

Ln(Total Comp.) (t statistic)

Ln(Total Comp.)

(t statistic)

Ln(Total Comp.)

(t statistic)

Ln(Total Comp.) (t statistic)

Constant 8,522** (2,441)

13,848* (4,889)

7,205* (57,585)

3,191* (3,396)

6,569** (2,509)

7,348* (56,320)

LN (Not Exercised Ratio)

­0,251* (­27,623)

­0,294* (­19,719)

­0,226* (­21,824)

­0,291* (­15,055)

­0,264** (­20,984)

­0,271* (­18,994)

LN(Ajex) ­0,244* (­8,511)

­0,297 (­3,945)

­0,247 (­6,402)

­0,127 (­1,554)

­0,234* (­4,326)

­0,225* (­2,590)

Trs1yr 0,001* (4,929)

0,000** (0,394)

0,0001 (0,788)

­0,00002 (­0,885)

0,0008* (4,371)

­0,0002 (­1,002)

LN(Bs Volatility) 0,083 (1,089)

­0,290 (­2,040)

0,067 (0,908)

­0,021 (­0,101)

0,372* (4,226)

­0,113 (­0,836)

LN(Number Mtgs) 0,125* (2,916)

­0,060 (­1,367)

0,020 (0,499)

­0,020 (­0,325)

­0,012* (­0,243)

0,071 (1,538)

LN(Tenure) ­0,271 (­0,195)

2,492** (­1,695) ­­­ 0,336*

(0,316)

ROA 0,014* (7,914)

­0,0002 (­0,242)

0,012* (8,150)

0,005* (3,725)

0,003 (2,906)

0,008* (5,449)

Pdirpens ­0,011 (­0,293)

­0,099 (­1,140)

0,057 (0,964)

­0,293*** (­1,802)

0,196 81,852)

­0,179 (­0,653)

Interlock 0,010 (0,180)

0,087 (0,923)

0,039 (0.639)

­0,059 (­0,478)

0,107 (1,209)

­0,010 (­0,120)

Year1993 0,103* (3,103)

0,086*** (1.948)

0,009 (­0,131)

Year1994 0,312* (8,499)

0,315* (6.147)

0,252 (3,380)

Year1995 0,387* (9,872)

0,308* (6,147)

0,223 (2,918)

Year1996 0,587* (13,613)

0,478* (9,400)

0,391 (5,088)

Year1997 0,802* (17,212)

0,624* (11,553

0,505 (6,509)

Year1998 0,914* (19,510)

0734* (13,736)

0,511 (6,404)

Year1999 1,139* (22,668)

0,834* (14,596)

0,669 (8,254)

Year2000 1,257* (22,473)

1.027* (16.44388)

0,760 (8,932)

Year2001

Year2002 0,075* (2,638)

0,149* (3,838)

0,040 (1,525)

Year2003 0,108* (3,118)

0,153* (3,446)

0,109* (3,664)

Year2004 0,293* (7,375)

0,367* (6,528)

0,295* (7,651)

Apparel Dummy ­0,747 (­0,927)

Candy Dummy 0,007 (0,013)

Computer Dummy ­0,280 (­0,107)

Consumer Dummy ­0,244 (­0,323)

N 2241 2547 1343 1655 1909

Adjusted R­ square 80.46% 79.52% 70.00% 75.64% 83.52%

154

5.4. Analysis of the Results

In tables 4 through 11 we compare compensation for the CEOs and Directors of

S&P 500, S&P Mid Cap and S&P Small Cap listed firms. We find that the factors that

explain their compensation are not all the same and, in the case of the factors that are

the same, the intensity of the coefficients is different and this difference is statistically

significant (tables 12 and 13).

The number of stock options vested has a negative influence on executive

compensation in S&P 500, S&P Mid Cap and S&P Small Cap listed firms, and also on

the number of stock options that are granted to the executive. Essentially, when

executives have stock options that go unexercised due to market price staying below the

exercise price, companies reduce compensation based on stock options and increase

cash compensation with the purpose of not losing the executives and giving them

incentive again.

The ratio used to adjust per­share data for all stock splits has a negative

influence on total compensation for all the executives of S&P listed firms; however, it

has higher impact on small size companies. In the case of S&P Mid Cap and S&P Small

Cap firms, this ratio also negatively affects the number of stock options and the cash

compensation that executives receive.

The one­year return to shareholders positively affects, on a small scale, both the

total and cash compensation of CEOs and directors of S&P 500 listed firms. But it

impacts only cash compensation for all S&P firms. The one­year return to shareholder

is negatively related to the option ratio for S&P 500 and S&P Small Cap listed firms

meaning that when shareholders are satisfied with the returns generated by the firm on

their investment, they do not feel the need to give more incentive to the executives with

stock options to align executive interests with their own.

The volatility of stock returns has a positive relationship with the number of

stock options and a negative relationship with cash compensation for the executives of

S&P 500 and S&P Mid Cap listed companies, meaning that when volatility is high,

companies pay more with stock options and less with cash compensation. However,

only large companies have a positive and statistically significant relationship. We find

a positive relationship between stock return volatility and total compensation in the case

155

of S&P Small Cap CEOs and a negative relationship with S&P500 Directors. Option

ratio is positively influenced by stock return volatility in all the cases except CEOs and

Directors from S&P SmallCap listed firms. Stock return volatility is negatively related

to cash compensation in all the situations excluding S&P Small Cap Directors. We can

conclude that when volatility is higher, shareholders prefer to grant stock options to

executives and reduce the use of cash compensation.

The number of board meetings is generally negatively related to cash

compensation of CEOs and Directors for S&P500 firms, but for Directors only for S&P

Mid Cap and S&P Small Cap firms. Our findings are congruent with those of Ryan and

Wiggins (2001), and Chen and Hung (2006), in the sense that monitoring power can

reduce the necessity to motivate executives with more compensation to reduce agency

problems. According to Davidson, Pilger and Szakmary (1998), board members are

more aligned with shareholders´ interests when they have more meetings during the

year, and therefore CEO compensation is more controlled. We only find a positive

relationship between total compensation and the number of meetings in the case of

CEOs from S&P Small Cap listed firms, meaning that when the number of board

meetings is higher, CEOs from these firms will receive more.

We also analyse the impact of the number of years as CEO (Tenure) on total

compensation, option ratio and short term compensation. The number of years that

executives are leading the company (Tenure) has a strong and positive influence on

CEO and Directors’ total compensation in the case of medium size companies. The

number of stock options that they receive is also positively influenced by CEO tenure.

In the case of large size companies, the relationship is negative, meaning that when

executives have more years of experience as CEO, compensation decreases, as does the

number of stock options that they receive. The number of stock options granted is

positively related to CEO and Directors’ tenure in S&P Mid Cap firms. We also find a

positive relationship between cash compensation of CEO and Directors and tenure for

S&P Mid Cap and S&P Small Cap listed firms. We can conclude, like Chen and Hung

(2006) and Chung and Pruitt (1996), that tenure influences executive compensation.

ROA has a positive influence on total compensation in the case of all CEOs and

Directors, with the exception of CEOs from S&P500, but has a negative influence on

the number of stock options granted to executives for small size companies.

156

As we expected, the effect of the existence of a firm’s pension plan on total

compensation is negative, but only for CEOs and Directors from S&P500 and S&P

Small Cap listed firms. In the case of S&P Mid Cap listed firms, the coefficients are not

statistically significant. The results imply that when firms already contribute to a

pension plan for the executives, they do not increase the compensation. In the case of

CEOs and Directors of S&P 500 firms, the number of stock options granted to the

executives is also negatively influenced by the existence of a pension plan.

We find, like Hallock (1997), that CEO interlocking, being a member of two

different boards at the same time, positively influences the total compensation of CEOs

of S&P 500 firms. The number of options that executives receive is also positively

related to interlocking for CEOs and Directors of S&P 500 and only CEOs for S&P

Small Cap Listed firms. We can conclude that CEOs and Directors from big companies

can essentially extract benefits from interlock relationships.

Time has a positive effect on total compensation, the number of options and

short­term compensation. Based on Fama and French (1997) industry classification, we

also analysed the effect of the industry in terms of pay to executives.

In tables 10 and 11 we find interesting results about the impact of the NASDAQ

crash on the variables that explain executive compensation. In the case of CEOs of S&P

500 firms, after the NASDAQ crash, the variables one­year return to shareholders and

volatility are not significant, and the variable number of board meetings changes its sign

from positive to negative while explaining total compensation. This relationship is

statistically significant. In the case of S&P listed firms the most important changes are:

LN (Ajex) is not statistically significant after the NASDAQ crash but ROA becomes

statistically significant. In the case of S&P Small Cap firms, after the NASDAQ crash,

volatility is not statistically significant but the number of meetings becomes significant.

With regard to the sub­sample of S&P 500 Directors, LN (Ajex), the number of

board meetings and ROA are not statistically significant after the NASDAQ crash. In

the case of S&P Mid Cap firms, the number of board meetings and executive

interlocking are not statistically significant after the NASDAQ crash. The dummy

variable if firms have pension plans for executives (Pdirpens) changes from positive

influence before the NASDAQ crash to negative influence after the crash. Finally, in the

case of S&P Small Cap firms, the one­year return to shareholders and the number of

157

board meetings are not statistically significant after the NASDAQ crash, but ROA is

now positively related to total compensation and this relationship is statistically

significant.

6. Conclusion

In this paper we analyse whether the total value and the components of executive

compensation across S&P500, S&P Mid Cap and S&P Small Cap listed firms are

statistically the same or different during the period from 1992 to 2004. We also examine

whether the total compensation and forms of compensation change after the NASDAQ

crash and the Sarbanes­Oxley Act in 2002. Furthermore, we control for firm size and

industry effect based on the Fama and French (1997) industry classifications and

analyse whether the determinants that explain executive compensation are the same or

different across S&P large, medium, and small firms.

Our results reveal that the mean executive compensation and the component

weights (forms of compensation) are significantly different for firms across S&P500,

S&P Mid Cap and S&P Small Cap indexes. Total compensation and forms of

compensation change after the NASDAQ crash and enactment of the SO Act. In

general, salary and stock options decrease and the use of restricted stocks and bonuses

essentially increase after the Sarbanes­Oxley Act, among S&P500 and also S&P Small

Cap firms, implying the effectiveness of the Act. Corporate salaries and stock option

awards were subject to a lot of public debate before the introduction of the SO Act and

it appears that firms made significant changes in their compensation packages in the

spirit of the Act.

Our results also reveal that, controlling for firm size and industry effect, the

factors that explain CEO and Director compensation in S&P500, S&P Mid Cap and

S&P Small Cap listed firms are, in general, not all the same and if some factors are the

same, the intensity of the coefficients is significantly different. We also find that the

NASDAQ crash changed the influence of some of the factors that explain executive

compensation.

158

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161

8. Appendix

Table 12 T Test of Equality of Fixed Effect Regressions Coefficients – CEOs

In these tables we compare, with the t test, whether the values of CEO regression coefficients are equal or not, and whether the difference is statistically significant. (*) means that the difference is statistically significant at 1%; ** at 5% level and (***) that it is statistically significant at 10%. Panel A: LN (Total Compensation)

CEO­S&P500 (1) CEO­S&PMidCap (2) CEO­S&PSmallCap (3) T test f

equality of Coef. Independent

Variables N Coef. Std.

Error N Coef. Std. Error N Coef. Std.

Error

(1) vs. (2)

(1) vs. (3)

(2) vs. (3)

Constant 3841 8.840 2,334 1997 3.753 0,626 2209 6.492 1,285 * *

LN (Not Exercised Ratio) 3841 ­0.245 0,009 1997 ­0.225 0.011 2209 ­0.243 0,011 * * *

LN(Ajex) 3841 ­0.227 0,027 1997 ­0.186 0.043 2209 ­0.309 0,0001 * * *

Trs1yr 3841 0.001 0,0002 1997 ­0.00002 0,00002 2209 0.001 0,074 * * *

LN(Bs Volatility) 3841 ­0.028 0.066 1997 ­0.050 0,088 2209 0.161 0.074 * * *

LN(Number Mtgs) 3841 ­0.033 0.034 1997 0.038 0.088 2209 0.070 0.038 * * *

LN(Tenure) 3841 ­0.265 0.961 1997 1.435 0.042 2209 0.375 0.537 * * *

ROA 3841 0.001 0.001 1997 0.009 0.255 2209 0.003 0.0008 * * *

Pdirpens 3841 ­0.043 0.038 1997 0.040 0.001 2209 0.092 0.083 * * *

Interlock 3841 0.136 0.053 1997 ­0.052 0.066 2209 0.098 0.067 * * *

Year1993 3841 0.108 0.043 1997 0.121 0.062 2209 ­0.183 0.153 * * *

Year1994 3841 0.314 0.046 1997 0.336 0.173 2209 0.039 0.155 * * *

Year1995 3841 0.368 0.048 1997 0.338 0.172 2209 ­0.012 0.156 * * *

Year1996 3841 0.574 0.051 1997 0.507 0.174 2209 0.092 0.158 * * *

Year1997 3841 0.739 0.055 1997 0.633 0.174 2209 0.155 0.157 * * *

Year1998 3841 0.846 0.055 1997 0.747 0.175 2209 0.214 0.158 * * *

Year1999 3841 1.072 0.057 1997 0.918 0.174 2209 0.328 0.157 * * *

Year2000 3841 1.163 0.062 1997 1.090 0.176 2209 0.410 0.159 * * *

Year2001 3841 1.296 0.063 1997 1.149 0.178 2209 0.496 0.159 * * *

Year2002 3841 1.301 0.064 1997 1.236 0.179 2209 0.508 0.159 * * *

Year2003 3841 1.317 0.067 1997 1.222 0.179 2209 0.533 0.159 * * *

Year2004 3841 1.510 0.066 1997 1.403 0.180 2209 0.770 0.160 * * *

Candy Dummy 3841 ­0,500 0,436 1997

Computer Dummy 3841 0,242 1,823 1997

Electronic Dummy 3841 ­0,758 0,681 1997

APPAREL Dummy 3841 ­ ­ ­ ­ ­ 2209 ­0,619 0,488

Note: * Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

162

Table 12 (Cont.) PANEL B: LN (Total Option Ratio)

CEO­S&P500 (1) CEO­S&PMidCap (2) CEO­S&PSmallCap (3) Equality of Coef. Independent Variables

N Coeff. Std. Error N Coef. Std.

Error N Coef. Std. Error

(1) vs. (2)

(1) vs. (3)

(2) vs. (3)

Constant 3841 ­3,256 2,780 1997 ­2,934 0,592 2209 4,527 1,400 * * *

LN (Not Exercised Ratio) 3841 ­0,315 0,009 1997 ­0,353 0,013 2209 ­0,332 0,013 * * *

LN(Ajex) 3841 ­0,035 0,026 1997 ­0,157 0,048 2209 ­0,310 0,058 * * *

Trs1yr 3841 ­0,001 0,000 1997 0,00001 0,000 2209 ­0,001 0,000 *

LN(Bs Volatility) 3841 0,192 0,062 1997 0,229 0,097 2209 0,020 0,084 * * *

LN(Number Mtgs) 3841 ­0,046 0,032 1997 0,036 0,046 2209 ­0,064 0,044 * * *

LN(Tenure) 3841 1,027 1,145 1997 0,914 0,241 2209 ­2,191 0,578 * * *

ROA 3841 ­0,0006 0,001 1997 0,0002 0,001 2209 ­0,002 0,001

Pdirpens 3841 ­0,101 0,036 1997 ­0,056 0,074 2209 0,067 0,095

Interlock 3841 0,108 0,051 1997 0,076 0,071 2209 0,214 0,075 *

Year1993 3841 0,002 0,063 1997 ­0,045 0,159 2209 ­0,005 0,277 * * *

Year1994 3841 0,226 0,060 1997 0,101 0,160 2209 ­0,015 0,280 * * *

Year1995 3841 0,162 0,063 1997 0,009 0,160 2209 ­0,056 0,279 * * *

Year1996 3841 0,292 0,066 1997 0,160 0,160 2209 0,014 0,277 * * *

Year1997 3841 0,334 0,066 1997 0,139 0,162 2209 ­0,015 0,278 * * *

Year1998 3841 0,437 0,066 1997 0,318 0,162 2209 ­0,005 0,279 * * *

Year1999 3841 0,613 0,067 1997 0,412 0,162 2209 0,060 0,279 * * *

Year2000 3841 0,556 0,071 1997 0,321 0,168 2209 0,038 0,279 * * *

Year2001 3841 0,669 0,072 1997 0,472 0,166 2209 0,135 0,280 * * *

Year2002 3841 0,620 0,074 1997 0,428 0,167 2209 0,164 0,280 * * NO

Year2003 3841 0,568 0,076 1997 0,394 0,169 2209 0,141 0,280 * * *

Year2004 3841 0,622 0,076 1997 0,430 0,169 0,119 0,280 * * *

Candy Dummy 3841 0,396 0.615

ComputerDummy 3841 3,019 2,153

ElectronicDummy 3841 ­0,398 0,665

ApparelDummy 2209 ­1,330 0,529

Note: * Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

163

Table 12 ( Cont.) PANEL C: LN (Short Term Compensation)

CEO S&P500 (1)

CEO S&PMidCap (2)

CEO S&PSmallCap (3)

Equality of Coef. Independent

Variables N Coef. Std.

Error N Coef. Std. Error N Coef. Std. Error

(1) vs. (2)

(1) vs. (3)

(2) vs. (3)

CONSTANT 3841 8,560 1,950 1997 5.482 0,449 2209 ­2,158 0,964 * * *

LN (Not Exercised Rat) 3841 0,007 0,008 1997 ­0.010 0,009 2209 0.008 0,009 * NO *

LN (Ajex) 3841 ­0,028 0,0233 1997 ­0.058 0,0329 2209 ­0.046 0,041 * * *

Trs1yr 3841 0,001 0,0001 1997 0.0003 0,000015 2209 0.001 0,0001 * * *

LN (Bs Volatility) 3841 ­0,317 0,0566 1997 ­0.114 0,068 2209 ­0.119 0,061 * * *

LN (Number Mtgs) 3841 ­0,074 0,0311 1997 ­0.028 0,033 2209 ­0.017 0,033 * * *

LN (Tenure) 3841 ­0,666 0,8022 1997 0.357 0,188 2209 3.443 0,403 * * *

ROA 3841 0,006 0,0009 1997 0.012 0,001 2209 0.004 0,0007 * * *

Pdirpens 3841 ­0,027 0,0306 1997 0.065 0,05 2209 0.119 0,07 * * *

Interlock 3841 ­0,015 0,0461 1997 0.059 0,048 2209 0.060 0,058 * * *

Year1993 3841 0,044 0,0307 1997 0.138 0,066 2209 0.146 0,102 * * *

Year1994 3841 0,139 0,034 1997 0.203 0,064 2209 0.289 0,102 * * *

Year1995 3841 0,113 0,036 1997 0.230 0,065 2209 0.276 0,106 * * *

Year1996 3841 0,182 0,038 1997 0.300 0,065 2209 0.313 0,104 * * *

Year1997 3841 0,276 0,04 1997 0.384 0,067 2209 0.361 0,105 * * *

Year1998 3841 0,402 0,042 1997 0.466 0,067 2209 0.450 0,105 * * *

Year1999 3841 0,478 0,047 1997 0.538 0,068 2209 0.502 0,106 * * *

Year2000 3841 0,589 0,049 1997 0.658 0,071 2209 0.565 0,108 * * *

Year2001 3841 0,552 0,0557 1997 0.610 0,073 2209 0.533 0,108 * * *

Year2002 3841 0,673 0,0553 1997 0.743 0,074 2209 0.629 0,109 * * *

Year2003 3841 0,707 0,055 1997 0.749 0,075 2209 0.665 0,109 * * *

Year2004 3841 0,856 0,055 1997 0.898 0,078 2209 0.807 0,387 * * *

Candy Dummy 3841 ­0,512 0,302

Computer Dummy 3841 ­1,315 1,519

Electronic Dummy 3841 ­1,480 0,627

Note: * Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

164

Table 13 T Test of Equality of Fixed Effect Regressions Coefficients – Directors

In these tables we compare, with the t test, whether the Director regression coefficients are different, and whether the difference is statistically significant. (*) means that the difference is statistically significant at 1%; ** at 5% level and (***) that is statistically significant at 10%. Panel A: LN (Total Compensation)

S&P500 (1) S&PMidCap(2) S&PSmallCap (3) Equality Coefficients Independent

Variables N Coef. Std.

Error N Cof. Std. Error N Coef. Std.

Error

(1) vs. (2)

(1) vs. (3)

(2) vs. (3)

CONSTANT 5207 8,035 2,742 2669 3,421 0,572 3003 6,091 0,536 *

LN (Not Exercised Rat) 5207 ­0,246 0,008 2669 ­0,245 0,010 3003 ­0,235 0,009 * * *

LN (Ajex) 5207 ­0,195 0,026 2669 ­0,193 0,038 3003 ­0,281 0,041 * * *

TRS1YR 5207 0,001 0,000 2669 0,0000003 0,000 3003 0,0005 0,0001 * * *

LN (Bs Volatility) 5207 ­0,117 0,062 2669 0,036 0,072 3003 0,178 0,063 * * *

LN (Number Mtgs) 5207 ­0,003 0,031 2669 0,048 0,039 3003 0,017 0,034 * * *

LN (Tenure) 5207 ­0,063 1,200 2669 1,659 0,250 3003 0,467 0,622 * * *

ROA 5207 0,004 0,001 2669 0,010 0,001 3003 0,004 0,001 * * *

Pdirpens 5207 ­0,057 0,034 2669 0,086 0,059 3003 0,079 0,077 * * *

Interlock 5207 0,103 0,050 2669 0,023 0,061 3003 0,084 0,062 * * *

Year1993 5207 0,091 0,034 2669 0,064 0,064 3003 ­0,024 0,072 * * *

Year1994 5207 0,298 0,038 2669 0,300 0,064 3003 0,216 0,075 * * *

Year1995 5207 0,361 0,040 2669 0,321 0,068 3003 0,184 0,077 * * *

Year1996 5207 0,543 0,043 2669 0,518 0,069 3003 0,324 0,076 * * *

Year1997 5207 0,749 0,046 2669 0,675 0,071 3003 0,415 0,076 * * *

Year1998 5207 0,884 0,047 2669 0,776 0,072 3003 0,426 0,077 * * *

Year1999 5207 1,111 0,050 2669 0,909 0,072 3003 0,571 0,078 * * *

Year2000 5207 1,263 0,054 2669 1,064 0,079 3003 0,654 0,082 * * *

Year2001 5207 1,403 0,056 2669 1,157 0,079 3003 0,766 0,081 * * *

Year2002 5207 1,426 0,057 2669 1,258 0,080 3003 0,818 0,081 * * *

Year2003 5207 1,427 0,059 2669 1,224 0,080 3003 0,830 0,082 * * *

Year2004 5207 1,621 0,059 2669 1,435 0,083 3003 1,098 0,085 * * *

Candy Dummy 5207 ­0,024 0,529

Computer Dummy 5207 0,212 2,254

Consumer Dummy 5207 ­0,829 0,818

Electronic Dummy 5207 ­0,841 0,771

Apparel Dummy 3003 ­0,695 0,536

Note: * Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

165

Table 13 (Cont.) PANEL B: LN (Option Ratio)

S&P500 (1) S&PMidCap(2) S&PSmallCap (3) Equality Coefficients

Independent Variables N Coef. Std.

Error N Coef. Std. Error N Coef. Std.

Error

(1) vs. (2)

(1)vs. (3)

(2) vs. (3)

CONSTANT 5207 ­3,093 2,855 2669 ­2,437 0,579 3003 3,305 1,501 * * *

LN (Not Exercised Rat.) 5207 ­0,299 0,007 2669 ­0,333 0,011 3003 ­0,318 0,01 * * *

LN (Ajex) 5207 ­0,064 0,022 2669 ­0,217 0,04 3003 ­0,331 0,045 * * *

Trs1yr 5207 ­0,001 0,0007 2669 0,00001 0,00002 3003 ­0,0008 0,0001 * * *

LN (Bs Volatility) 5207 0,244 0,053 2669 0,364 0,074 3003 0,158 0,068 * * *

LN (Number Mtgs) 5207 ­0,044 0,028 2669 ­0,004 0,04 3003 ­0,025 0,037 * * *

LN (Tenure) 5207 1,026 1,249 2669 0,820 0,254 3003 ­1,818 0,662 * * *

ROA 5207 ­0,001 0,001 2669 0,001 0,001 3003 ­0,0002 0,001 * * *

Pdirpens 5207 ­0,057 0,03 2669 ­0,010 0,064 3003 0,085 0,082 * * *

Interlock 5207 0,093 0,045 2669 0,100 0,066 3003 0,139 0,066 * * *

Year1993 5207 0,020 0,041 2669 ­0,065 0,070 3003 0,066 0,08 * * **

Year1994 5207 0,214 0,039 2669 0,139 0,071 3003 0,159 0,085 * * *

Year1995 5207 0,179 0,043 2669 0,058 0,067 3003 0,098 0,085 * * *

Year1996 5207 0,334 0,045 2669 0,225 0,069 3003 0,246 0,081 * * *

Year1997 5207 0,392 0,046 2669 0,214 0,074 3003 0,191 0,083 * * *

Year1998 5207 0,468 0,046 2669 0,311 0,071 3003 0,183 0,083 * * *

Year1999 5207 0,628 0,047 2669 0,413 0,071 3003 0,236 0,084 * * *

Year2000 5207 0,565 0,051 2669 0,294 0,081 3003 0,201 0,085 * * *

Year2001 5207 0,680 0,052 2669 0,446 0,078 3003 0,308 0,086 * * *

Year2002 5207 0,623 0,053 2669 0,386 0,080 3003 0,333 0,088 * * *

Year2003 5207 0,585 0,055 2669 0,381 0,081 3003 0,306 0,088 * * *

Year2004 5207 0,632 0,056 2669 0,429 0,082 3003 0,317 0,09 * * *

Candy Dummy 5207 ­0,05 0,669

Computer Dummy 5207 3,398 2,341

Electronic Dummy 5207 ­0,940 0,782

Apparel Dummy 5207 ­0,350 0,72 3003 ­0,972 0,582

* Significant at level 1%; ** Significant at level 5%; *** Significant at level 10%

166

Table 13 ( Cont.) PANEL C: LN (Short Term Compensation)

S&P500 (1) S&P MidCap(2)

S&P SmallCap (3) Equality Coefficients

Independent Variables

N Coef. Std. Error N Coef. Std. Error N Coef. Std.

Error (1)

vs. (2)

(1) vs.(3)

(2) vs. (3)

CONSTANT 5207 7,982 2,287 2669 4,739 0,48 3003 ­1,785 1,181 * * *

LN (Not Exercised Rat) 5207 0,004 0,008 2669 ­0,008 0,009 3003 0,009 0,008 * * No

LN (Ajex) 5207 ­0,019 0,025 2669 ­0,035 0,032 3003 ­0,050 0,035 * * *

Trs1yr 5207 0,001 0,0001 2669 0,0004 0,0002 3003 0,001 0,0001 * * *

LN (Bs Volatility) 5207 ­0,367 0,059 2669 ­0,153 0,059 3003 ­0,055 0,054 * * *

LN( Number Mtgs) 5207 ­0,100 0,031 2669 ­0,066 0,032 3003 ­0,078 0,03 * * *

LN (Tenure) 5207 ­0,521 1,000 2669 0,700 0,210 3003 3,524 0,521 * * *

ROA 5207 0,006 0,001 2669 0,009 0,001 3003 0,005 0,001 * * *

Pdirpens 5207 ­0,020 0,033 2669 0,065 0,05 3003 0,041 0,066 * * *

Interlock 5207 ­0,026 0,047 2669 0,045 0,051 3003 0,077 0,055 ** * *

Year1993 5207 0,063 0,028 2669 0,052 0,044 3003 0,035 0,052 * * *

Year1994 5207 0,181 0,029 2669 0,146 0,045 3003 0,176 0,056 * * *

Year1995 5207 0,167 0,033 2669 0,179 0,049 3003 0,166 0,057 * * *

Year1996 5207 0,245 0,036 2669 0,277 0,05 3003 0,210 0,057 * * *

Year1997 5207 0,331 0,038 2669 0,372 0,05 3003 0,257 0,061 * * *

Year1998 5207 0,468 0,042 2669 0,438 0,052 3003 0,359 0,058 * * *

Year1999 5207 0,553 0,045 2669 0,498 0,053 3003 0,397 0,061 * * *

Year2000 5207 0,678 0,050 2669 0,643 0,058 3003 0,448 0,062 * * *

Year2001 5207 0,656 0,055 2669 0,604 0,06 3003 0,440 0,063 * * *

Year2002 5207 0,820 0,054 2669 0,757 0,061 3003 0,574 0,064 * * *

Year2003 5207 0,869 0,054 2669 0,760 0,062 3003 0,582 0,065 * * *

Year2004 5207 1,015 0,055 2669 0,889 0,064 3003 0,760 0,066 * * *

Candy Dummy 5207 ­0,064 0,425

Computer Dummy 5207 ­0,904 1,891

Electronic Dummy 5207 ­1,426 0,766

Apparel Dummy 5207 ­1,515 0,726 3003 0,955 0,44

167

Table 14: Pearson Correlation of the Independent Variables Panel A: S&P500 1 2 3 4 5 6 7 8 9

1­ LN( Not Exercised Ratio) 1,000 ­0,016 ­0,069 0,023 ­0,015 0,142 ­0,011 ­0,004 0,015

2­LN(Ajex) ­0,016 1,000 0,203 ­0,086 ­0,075 0,186 0,126 0,157 0,025

3­Trs1yr ­0,069 0,203 1,000 0,093 ­0,021 0,032 0,125 ­0,035 0,001

4­LN(Bs Volatility) 0,023 ­0,086 0,093 1,000 ­0,028 ­0,123 ­0,165 ­0,306 ­0,049

5­LN(Number Mtgs) ­0,015 ­0,075 ­0,021 ­0,028 1,000 ­0,006 ­0,121 0,165 ­0,021

6­B89LN(Tenure) 0,142 0,186 0,032 ­0,123 ­0,006 1,000 0,061 0,132 0,110

7­ROA ­0,011 0,126 0,125 ­0,165 ­0,121 0,061 1,000 0,042 0,004

8­Pdirpens ­0,004 0,157 ­0,035 ­0,306 0,165 0,132 0,042 1,000 ­0,014

9­Interlock 0,015 0,025 0,001 ­0,049 ­0,021 0,110 0,004 ­0,014 1,000

Panel B: S&PMidCap 1 2 3 4 5 6 7 8 9

1­ LN( Not Exercised Ratio) 1,000 ­0,115 ­0,019 ­0,014 ­0,007 0,124 ­0,015 0,047 0,009

2­LN(Ajex) ­0,115 1,000 0,042 ­0,057 ­0,119 0,161 0,145 0,013 0,104

3­Trs1yr ­0,019 0,042 1,000 0,105 ­0,017 ­0,002 0,008 ­0,013 0,001

4­LN(Bs Volatility) ­0,014 ­0,057 0,105 1,000 ­0,015 ­0,108 ­0,173 ­0,271 0,016

5­LN(Number Mtgs) ­0,007 ­0,119 ­0,017 ­0,015 1,000 ­0,051 ­0,145 0,113 ­0,022

6­LN(Tenure) 0,124 0,161 ­0,002 ­0,108 ­0,051 1,000 0,038 0,073 0,151

7­ROA ­0,015 0,145 0,008 ­0,173 ­0,145 0,038 1,000 0,005 0,033

8­Pdirpens 0,047 0,013 ­0,013 ­0,271 0,113 0,073 0,005 1,000 0,014

9­Interlock 0,009 0,104 0,001 0,016 ­0,022 0,151 0,033 0,014 1,000

PANEL C: S&PSmallCAP 1 2 3 4 5 6 7 8 9

1­ LN(Not Exercised Ratio) 1,000 ­0,017 ­0,031 ­0,038 ­0,057 0,152 0,023 0,023 0,084

2­LN (Ajex) ­0,017 1,000 0,153 ­0,094 ­0,070 0,076 0,166 0,077 0,073

3­Trs1yr ­0,031 0,153 1,000 0,110 ­0,034 ­0,002 0,134 ­0,039 ­0,023

4­LN (Bs Volatility) ­0,038 ­0,094 0,110 1,000 0,077 ­0,041 ­0,178 ­0,154 ­0,100

5­LN (Number Mtgs) ­0,057 ­0,070 ­0,034 0,077 1,000 ­0,114 ­0,113 0,039 ­0,109

6­LN (Tenure) 0,152 0,076 ­0,002 ­0,041 ­0,114 1,000 0,021 0,027 0,139

7­ROA 0,023 0,166 0,134 ­0,178 ­0,113 0,021 1,000 0,011 0,045

8­Pdirpens 0,023 0,077 ­0,039 ­0,154 0,039 0,027 0,011 1,000 0,050

9­Interlock 0,084 0,073 ­0,023 ­0,100 ­0,109 0,139 0,045 0,050 1,000

168

Conclusions and Possible Extensions

In this dissertation we organise the literature on executive compensation from

the last 13 years based on the best publications in the area. In the other parts, we present

three essays associated with executive compensation. These three essays analyse

whether the factors that explain executive compensation are different or not in firms

from the new and old economy, NYSE and NASDAQ and also in S&P500,

S&PMidCap and S&PSmallCap, respectively. We also analyse for these three groups

whether total executive compensation and the form of this compensation are the same or

not for CEOs and Directors and whether they changed after the NASDAQ crash in 2000

and Sarbanes­Oxley Act in 2002.

Our results reveal that in the case of new and old economy firms, new economy

executives receive, on average, much more than executives from the old economy,

primarily due to stock options, but in the last few years the difference in compensation

between the executives of both groups has been decreasing. The NASDAQ crash and

the Sarbanes­Oxley Act were significant in terms of the way in which executives are

paid in both new and old economy firms. Firms in both groups have reduced the use of

stock options and have instead increased the use of bonuses and restricted stocks. We

also find that the factors that explain executive compensation in new and old economy

firms are generally different, and in the case of the variables that are the same, such as

firm size component, the intensity of the factors is different. Our results also reveal that

new economy executives receive more, on average, than executives from the old

economy, but the difference decreases in the last sample years. In the bubble period,

new economy executive compensation is composed of more than 50% of stock options

and in the case of old economy firms of more than 30% of stock options. After that

period, with the NASDAQ Crash and the introduction of the Sarbanes­Oxley Act, we

observe a significant change in the structure of the components of executive

compensation – a reduction in the use of stock options and an increase in the use of

bonus and restricted stocks.

In the case of S&P500, S&P Mid Cap and S&P Small Cap executives our results

reveal that the mean executive compensation and the component weights (forms of

compensation) are significantly different. Total compensation and forms of

169

compensation change after the NASDAQ crash and enactment of the SO Act. In

general, salary and stock options decrease and the use of restricted stocks and bonuses

essentially increases after the Sarbanes­Oxley Act, among S&P500 and also S&P Small

Cap firms, implying the effectiveness of the Act. Corporate salaries and stock option

awards were subject to a lot of public debate before the introduction of the SO Act and

it appears that firms made significant changes in their compensation packages in the

spirit of the Act.

We also find that that in S&P500, S&P Mid Cap and S&P Small Cap firms the

factors that explain executive compensation are generally not the same. As in the new

and old economy situation, if some of the factors are simultaneously significant for both

cases, the intensity of these factors is generally different and this difference is

statistically significant. In this case, we also find that the NASDAQ crash changed the

influence of some of the factors that explain executive compensation, and also the way

in which executives are paid.

In the case of NYSE and NASDAQ listed firms, executive compensation is

generally driven by different factors and the way in which executives are paid is also

different. The percentage that salary represents in terms of total compensation in NYSE

listed firms is higher for Directors than for CEOs. Bonus is a more important

compensation component for Directors than for CEOs in NASDAQ listed firms, but in

the case of NYSE firms, the difference is small. In all cases, CEOs receive more stock

options than Directors. The used of restricted stock increases essentially in the last few

years. We also find that after the NASDAQ Crash in 2000, and essentially after the

Sarbanes­Oxley Act in 2002, the forms and weights of CEO and Director compensation

change for NYSE and NASDAQ listed firms.

The limitations of this investigation arise from the fact that we do not know

whether the achieved results will be the same when we divide executives by gender.

Another limitation of this study is that it does not analyse the three most important stock

exchanges in America (NYSE, NASDAQ and AMEX), but only the first two. The

reason for this is based on the fact that, in the latter, the number of executive

compensation observations is too small and makes it impossible to run regression

analyses with a significant number of variables.

170

This investigation can be extended to analyse whether men’s and women’s

compensation are explained by the same factors or not. Another interesting point to

investigate is to analyse the relationship between gender, executive compensation and

firms’ performances, and also gender, executive compensation and dividend policy. In

other words, can firms managed by women CEOs be more profitable than firms

managed by men CEOs? Are dividend policy, executive remuneration and gender

related?