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The Impact of Executive Compensation and R&D Expenses on Firm Performance: A Comparison between Manufacturing and Services Companies in the United States

Nikolas Karakatsanis|ERASMUS SCHOOL OF ECONOMICS


Master Thesis


December 2015

Author: Nikolaos Karakatsanis

Student Number: 409742

Supervisor: Drs. Fytraki, A.T.

Second Reader: Dr. Keusch, T.

Accounting Auditing and Control

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Abstract

Using a panel of U.S. manufacturing and services companies over the 2000-2014 period, this

study explores the relationships between executive compensation and firm performance, as

well as, the relationship between R&D (research and development) expenses and firm

performance. Moreover, this study presents a comparison of these relations between

manufacturing and services companies. An Ordinary Least Squares (OLS) regression model

was employed to estimate these relations, with control variables of size, total executive

compensation and R&D expenses. My analysis revealed that executive compensation has a

significant positive impact on the performance of manufacturing companies, but do not have

an effect on the performance of services companies. Additionally, R&D expenses positively

affect the performance of manufacturing firms, whereas they have a negative impact on the

performance of services companies.

Keywords: Executive Compensation, R&D Expenses, Firm Performance, Firm Size,

Manufacturing Firms, Services Firms

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Contents1. Introduction.....................................................................................................................- 4 -

1.1 Research Problem & Motivation..................................................................................- 4 -

1.2 Research Objectives.....................................................................................................- 5 -

1.3 Research Methodology.................................................................................................- 7 -

2. Literature Review-Hypothesis Development..................................................................- 8 -

2.1 Executive Compensation and Firm Performance.........................................................- 9 -

2.2 R&D Expenses and Firm Performance......................................................................- 14 -

2.3 Interaction Effects.......................................................................................................- 17 -

3. Research Design............................................................................................................- 21 -

3.1 Variables.....................................................................................................................- 21 -

3.1.1 Independent Variables.........................................................................................- 21 -

3.1.2 Dependent Variable.............................................................................................- 24 -

3.1.3 Control Variables.................................................................................................- 25 -

3.2 Data Collection...........................................................................................................- 25 -

3.3 Methodology...............................................................................................................- 26 -

4. Empirical Results..........................................................................................................- 26 -

4.1 Descriptive Statistics..................................................................................................- 27 -

4.2 Pearson Correlation Matrix........................................................................................- 29 -

4.3 Main Findings.............................................................................................................- 30 -

4.4 Interaction Effects.......................................................................................................- 32 -

5. Conclusion....................................................................................................................- 34 -

5.1 Limitations and Future Research................................................................................- 36 -

References...........................................................................................................................- 37 -

Appendix.............................................................................................................................- 45 -

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1. Introduction One of the most debated issues regarding executive compensation is the one examining the

relationship between CEO compensation and firm performance. For more than seven decades

this particular topic continues to concern the experts in the field of management control, while

the related literature consists of more than three hundred studies (Barkema and Gomez-Mejia,

1998). Many authors have identified in their works a variety of factors that regulate the pay-

performance relationship. In particular, such factors are recorded firm size, firm risk,

ownership structure, socio-cultural factors and demographic factors like executives’ age,

tenure, education and gender. However, something that previous studies have in common and

which operates as a limitation to further testing on the pay-performance sensitivity is that they

focus on notable factors, whereas they ignore other unquantifiable, socio-cultural factors such

as the type of services the firm provides. Therefore, this paper attempts to explain the nature

of the relationship between executive pay and firm performance based on the diversity of

businesses.

Furthermore, research and development expenses are an issue with great importance for the

economic development of companies, hence for the development of national economies. This

importance may stem from the fact that for at least a century economic growth was driven by

increasingly sophisticated technologies, which were developed to a great extent through R&D

expenses (Greenstone, 2011). This importance, at least for the United States, also stem from

the fact that a large proportion of R&D expenses are covered by governmental funds

(Greenstone, 2011).

1.1 Research Problem & MotivationThis modern era corporations have changed the way they pay their employees. Specifically,

companies use more often incentives to motivate their executives and their employees than

they did in the past. The reason for that is to induce them, so as to try harder in order to

achieve a better performance. The basic fact that differentiates CEO and the other executives

from the non-executives employees is the high of their compensation (including basic salary,

bonuses and other compensation). This is happening because of the different responsibilities

they have. Moreover, firms invest in innovation through R&D expenses in order to obtain an

advantage over their competitors. The current study is quite relevant to the content of finance

and specifically to management control which examines the numerous official mechanisms to

influence the decision making process within organizations. Executive’s compensation and

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the decision of the amount of investments in research and development are part of such

mechanisms.

It is observed by Murphy (1999) that CEOs in all countries and especially in the United States

increase continuously their compensations, while worldwide economy faced many recessions

and financial crisis during the second half of the past century. In many cases the performance

of both services and manufacturing companies does not justify these payments. In their work,

Friedman and Friedman (2010) state that the way of CEOs compensation and the conflicts of

interest are the reasons of the catastrophic financial crisis after the Lehman Brothers collapse

in 2008. Moreover, based on a Watson Wyatt survey, Kirkland (2006) found out that

approximately 90% of institutional investors consider top executives to be dramatically

overpaid for what they do. Therefore, it is praiseworthy to wonder if compensation systems

work as they supposed to or not. Additionally, it would beneficiary to examine if executive

compensation affects differently companies that belong in different industries. The last one

could show that manufacturing and services companies might have better performance if they

embrace different compensation packages for their executives.

1.2 Research ObjectivesThere is a plethora of past papers that state the impact of executives’ compensation and R&D

expenses on firms’ performance, which are referred in details in the literature review segment.

The main objective of this study is to investigate the relations between the compensation of

senior business executives (such as CEOs, CFOs and COOs) and firms’ performance on the

one hand and R&D expenses and firms’ performance on the other hand. This investigation

focuses on firms in the United States depending on the type of services they provide. More

specifically, this thesis is going to observe if there is a different impact of executive

compensation and R&D expenses on the total outcome of service enterprises (including banks

and other financial firms) and manufacturing companies in the United States. All these are

incorporated in the following main research question:

RQ: Does executive compensation and R&D expenses have a stronger impact

on the output of services firms than on manufacturing companies in the United

States?

As I will elaborate below, there are past researches that show the impact of executives’

compensation and R&D expenses on the performance of both services firms and

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manufacturing companies in different countries. What makes my research question unique, is

that aims to gather all of these information in a single work, so as to allow the comparison

between these two different industries by focusing only in the United States economy.

Providing an answer to this research question is very significant. More specifically, it could

modernize compensation systems, especially for the executives based in which industry each

company belongs. Additionally, this could improve companies’ operations; hence it could

reduce costs and increase profitability. Last but not least, the results of this paper will give

American shareholders a better insight about executive compensation and R&D investments.

In details, it will be a helpful tool for the policy makers regarding the design of executive

compensation systems on the one hand and the release of funds in favor of research and

development on the other hand.

The originality of my study lies in the fact that investigates an issue on which there is little or

no relevant existing literature. Additionally, what is special about this paper is that it

compares the compensation systems and R&D investments in different economic sectors to

find the best possible financial mixtures that will maximize firms’ final performance.

Since the purpose of this study is to examine the impact of executives’ compensation and

R&D expenses on American firms’ performance based on the type of services they provide, I

split the companies into two main categories: services companies and manufacturing

companies. At this point, before I continue with my research, there is an imperative need to

clarify some definitions.

Firstly, by the term total output of a company I mean all goods and services that a company

produces for a specific period of time. All the above contribute to form the firms’ profit.

Subsequently, services companies are those companies comprising a wide variety of

industries that do not produce anything but provide services and instead. Such companies are

financial services firms (banks, credit card companies, insurance companies, accountancy

companies, investment funds, real estate funds, etc.), retailers, accommodation industry, etc.

Finally, manufacturing companies are those businesses that use raw materials to make a final

product, which can be sold directly to consumers or to other manufacturing firms to make a

different product, or to provide a certain service. Examples of such companies are vehicle

manufacturers, construction developers, clothing companies, mining and oil-production

companies, large conglomerate, etc.

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Moreover, as stated previously, companies use incentives to compensate their executives.

These incentives can be both monetary when they are based in equity-based compensation

and non-monetary when they are based on behavioral characteristics. Examples of equity-

based incentives are the total number of stock options granted, the total number of restricted

shares granted, long-term incentive payouts, bonuses etc. On the other hand, examples of non-

financial incentives could be the praise and commendation from immediate manager, the

attention receiving from leaders and opportunities to lead projects.

Finally, some things that need to be clarified are some of my choices regarding this project.

My study focuses on firms in the United States for two main reasons: firstly, because of the

size of US economy and secondly, because of the role of the United States as a leading figure-

country-economy of the developed world. Regarding my choice for these particular business

sectors of the United States economy, is because the services and the manufacturing industry

in their broader sense comprise the largest part of the largest economy in the world. In

particular, the industrial sector made up 19.1 percent, while services constituted 79.7 percent

of the United States GDP in 2012 (World Economy Team, 2013).

1.3 Research MethodologyAs a first step towards answering my research question, a thorough existing review of the

relevant literature is conducted. Firstly, I have to present the past theoretical and empirical

findings about executive compensation in general and the way executives are compensated in

the United States in particular. Subsequently, I will focus on the structure of this

compensation; that is the basic components based on which executives are getting paid and

their development through the decades. After that I will make a distinction of the previously

referred subjects between different business sectors and more precisely between the services

sector (including banks and other financial institutions) and the manufacturing sector,

comprising mainly of companies belonging in the automotive industry, mining and aerospace

companies, etc. Secondly, I have to present the existing literature that is relevant to research

and development (R&D) expenses in general, their use from U.S. companies and their impact

on both manufacturing and services companies. Finally, another stream of literature that I will

examine is which specific metrics could act as control variables that affect the examined

relations.

After that, I will collect my data which I will extract from the Wharton Research Data

Services system. For my research, I will need annual data for executive compensation for

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companies in the United States that are available through the COMPUSTAT ExecuComp

Annual Compensation database. Moreover, for the annual data about R&D expenses and

ROA (firm performance) I will use the COMPUSTAT Fundamentals Annual database. The

same database I will use to extract data about sales turnover which I will use to measure the

control variable of firm size. Lastly, I will compare the impact of executive compensation and

R&D expenses on firm performance for all the U.S. manufacturing and services companies.

My sample period is from 2000 until 2014.

The main findings of my research reveal a significant positive association between executive

payments and the performance of manufacturing companies. On the contrary, there is not an

association between executive payments and the performance of services companies.

Moreover, the analysis showed that research and development expenses have a positive

impact on the performance of companies belonging to manufacturing sector. In contrast, the

impact of R&D expenses on the performance of services companies is negative. Also, the

impact of executive compensation on firm performance is more intense in manufacturing than

in services companies. In addition, the impact of R&D expenses on firm performance is

bigger in manufacturing than in services companies. Finally, the results show that the

interaction effects between executive compensation and firm size and R&D expenses and firm

size is positive for both manufacturing and services firms (except for the interaction of

executive payments and firm size in services companies, which does not apply at all).

The paper is organized as follows. In the next section, there is a thorough presentation of the

literature that is related to both the executive compensation-firm performance relation and the

R&D expenses-firm performance relation. In the same section, the construction of the

research hypotheses is made. This is followed by the research design that includes data

collection and methodology. Then, my findings and the basic conclusions are presented,

followed by the research limitations and some recommendation for further research.

2. Literature Review-Hypothesis DevelopmentIn this section there is an attempt to collect and present all the past theoretical and empirical

studies which are relevant to the relationship between executive compensation and firm

performance on the one hand and R&D (research and development) expenses and firm

performance on the other hand. For this purpose, there has been an overview of published,

working and research papers and an endeavor to be used both the market and accounting ways

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to measure firm performance. To choose which papers should be discussed, I used mainly

those that are included in the top ten accounting journals as they ranked in both the British

and the Australasian rankings. I did that because I consider it as the best way to support the

ideas that my thesis promotes. Additionally, there has been an extensive use of Google

Scholar, so as to find other relevant papers. Also, I choose to use papers that on average cover

chronologically a period of two decades, so as to observe the changes in both practice and

theory of the pay-performance sensitivity and the performance-R&D expenses relationship

throughout the years.

2.1 Executive Compensation and Firm PerformanceTo begin with, my dissertation is connected to the literature that gives information about the

association between executive compensation and firm performance. But before investigating

the above relation it is crucial to observe the importance of executive compensation as it is an

issue that gathered a lot of attention for over five decades around the globe and in the United

States in particular. Several factors triggered this extensive interest, factors that are

undoubtedly associated with the change of executive compensation.

In their study, Frydman and Saks (2010) examined the profound change in the level of

executive payments during a long period of time from 1936 to 2005. More precisely, after a

sharp drop during WWII, the level of payments increased slowly from the mid-1940s to the

mid-1970s, but they increased rapidly from the 1970s to the present. But which reasons lead

to this change? Murphy (1999) tries to answer this question by citing some factors that cause

the previously referred fact. Firstly, chief executive officers (CEOs) obtained more power in

the board of directors which lead to the continuous rise of their compensation. Secondly,

during 1980s, it was believed widely that the high payments of CEOs lead to plant closures

and proliferation of layoffs which in turn lead to a corporate downsizing. Finally, during

1990s it was observed a tight bond between the payments of CEOs and the stock price

performance of the companies.

Furthermore, the importance of executive compensation is illustrated also by the unbreakable

link to firm performance. At this point, it is of great importance to observe if there is an

interaction between these two components. More precisely, it is crucial to be examined if

executive compensation affects firm’s performance and parallel it is affected by this.

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To begin with, Doucouliagos et al. (2007) showed in their paper that executive compensation

and performance of Australian banks are not related when the period is one lag. However, this

relation seems to exist when there is a period of two lags and more. Zhou et al. (2011) showed

that neither, there is a relation between managers’ compensation and bank’s performance, nor

compensation changes influence firm performance. However, the pay-performance sensitivity

that is examined in this study is lowered by the state control ownership of Chinese banks.

Similarly, in their work, Firth et al. (2006) found that in firms in which the state holds the

majority of shares, pay-performance sensitivity does not exist, whereas firms in which private

equity holders or SOEs represent the major shareholders appear to have a stronger pay-

performance link. Wei (2000) tested 765 listed companies and found no significant positive

association between firm performance and executive compensation. In Japan during the

period from 1977 to 2000, Kubo and Saito (2008) examined the pay-performance sensitivity

and they found that it decreased substantially after 1990 due to bureaucracy.

Relevant to this, is the agency theory which describes the association between two parties: the

principal (shareholders) and the agent (executives). This relationship happens when

shareholders hire managers to do the day-to-day business in the company. For example, the

chief executive officer (CEO) possesses the highest position among executives in a company

and is the one who makes all the important decisions. These decisions may be about policy

and strategy, expansion, acquisitions, etc. The chief operating officer (COO) is responsible for

the day-to-day operation of the company. Finally, the chief financial officer (CFO) of a firm

is responsible for the financial planning and other similar staff. According to this theory there

is not an alignment of shareholders’ interest with those of executives. Specifically, executives

regardless whether they are working in the services or the manufacturing industry, desire to

maximize their wealth whether firm performance is good or bad. This, in most cases, contrast

shareholders’ interests.

On the other hand, there is past literature that points out the effect of executive compensation

on the firm performance based on several factors. Specifically, Wallsten (1999) in his

empirical work found that executive compensation is linked to firm’s performance and in fact

this link is growing stronger based mainly on the executive’s rank in the company. On the

contrary, firm’s performance seems to have no effect on executive payments, as executives

are rewarded in profitable years but are not punished in non-profitable years. Similarly, Gaver

and Gaver (1998), Iyengar (2000) and Shaw and Zhang (2010) stated that executives are not

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punished in poorly performing years because the board of directors is reluctant to that,

showing that pay-performance sensitivity does not apply in poorly performing years.

Coughlan and Schmidt (1985) in their work focused among others on the significant relation

between rates of change in pay and firm performance as it is depicted by the stock price

performance. Likewise, Frydman and Jenter (2010) pointed out that this relation between

executive pay and stock price performance increased more intensively in the 90’s. Brick et al.

(2006) were more precise on this subject as they evidenced that overcompensation of directors

and CEOs has a negative impact on firm performance and lead to underperformance.

Moreover, there is further past literature that points out the existence of relation between

executive’s compensation and firm performance. According to previous researches, this

relation might be either positive or negative. The majority of the researchers in one way or

another found a positive effect of executives’ payments on firm’s performance. Among them

are Mehran (1995) and Hubbard and Palia (1995) who both in their work found that

executive’s compensation influences firm’s performance; the former by examining the

executive compensation structure of 153 randomly-selected manufacturing companies

between 1979-1980 , whereas the latter by testing 147 banks during 1980. The option

valuation model as being modified by Merton (1973) to include also firm dividends was used

by Black and Scholes (1973), Core and Guay (2002) and Daniel et al. (2013). According to

Manmohan and Mukund (1988) the relationship between executive compensation and firm

performance is characterized by complexity, but they found that there is a positive relation by

examining the changes in CEOs’ compensation in 1985 and 1986.

In addition, Sun et al. (2013) stated that firm performance and CEOs’ compensation are

associated, when performance is measured in terms of revenue efficacy. To test their

hypothesis, they used a sample of United States property-liability companies from the

insurance industry. Moreover, Zajac (1990) stressed that there is a strong connection between

executive compensation and firm performance, as companies in which CEOs align their

personal wealth to the wealth of the shareholders, hence their firms, tend to be more

profitable. To extract his findings, the author used data for CEOs from the Forbes 500, the

Fortune 500 largest industrial firms and large firms with annual sales of one billion dollars

that are not listed in Forbes 500 in 1987. In their work, Canarella and Gasparyan (2008) found

a positive relation between executive compensation and firm performance. They used a

sample of computer and software companies from 1996 to 2002.

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On the contrary, there is literature like the work of Chen and Jermias (2014) which stated that

firm’s performance is negatively affected by executive’s compensation, when this certain

compensation policy is not in line with the business strategy of the firm.

Based on these facts, I constructed the following hypotheses:

H1a1: Executives’ compensation has a significant positive association with the

performance of a manufacturing company in the United States.

H1b1: Executives’ compensation has a significant positive association with the

performance of a service company in the United States.

Another stream of literature that is relevant to my thesis is the one that regards executive

compensation in firms which are based in the United States. As I mentioned before, the

importance to examine the impact of executives’ compensation on firms’ performance partly

lies in the size of the United States economy, so as the results can be more visible. Besides the

above, the leading role that the United States play in the “Western”, developed world is of

great importance for this study’s results, in the sense that if these results are applicable in the

world’s largest economy they will be applicable everywhere.

In the relevant literature, Bebchuk et al. (2001) referred that CEOs in the United States were

compensated more compared to their colleagues in other countries. According to them,

various factors lead to this phenomenon. As example they pointed out the larger influence of

American CEOs compared to foreigners CEOs. Moreover, they suggested that the American

market is characterized by bigger competition, which lead American CEOs to get paid more

than their counterparts in other countries.

Next, to understand better the relation between the executives’ payments and firms’

performance, it would be beneficiary to examine the exact structure of the executive

compensation. As I stated in the introduction companies in modern economies exploit the use

of incentives to compensate their executives and to induce them, so as to succeed larger

profits. The type of incentives depends on whether they are based in equity-based

compensation or on behavioral characteristics; the former are pecuniary incentives, whereas

the latter are non-pecuniary incentives.

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In his work, Murphy (1999) stated that despite the different compensation systems that

companies may use, managers are getting paid mainly in four different ways: the basic salary,

the short-term incentives, the long-term incentives and the perquisites. This composition has

changed as well following the change in the magnitude of executive compensation. In

particular, since 1950 executive payments changed significantly, as forms of incentive

payments became the larger parts of the total compensation (Frydman and Saks, 2010). Also,

Mehran (1995) in his empirical work stressed that companies in which managers own shares

perform better. Likewise, Core et al. (2003) pointed out that is better for firm performance if

executives are also owners of the companies they work, by holding certain equity. In her

study, Frye (2004) suggested that firms which use compensation systems based on equity for

their employees (executive and non-executive perform better.

It is also interesting to observe that the exact structure of executive compensation could be

related to the composition of the board of directors. In details, it is a common fact that firms

with more outside directors tent to compensate their executives with a higher percentage of

equity-based forms of payments (Mehran, 1995).

Moreover, there is existing literature that focuses on the impact of executive compensation on

firms of two specific industries; the services companies and manufacturing companies. My

expectations to find different results between these two sectors lie in the important role that

the finance industry plays, as the most significant pillar in every country’s economy. But is

there actually any possibility of having different results between different industries? And if

yes, which are the characteristics that differentiate each industry from the other and lead to

these results?

According to Zhou et al. (2009) “services companies differ from manufacturing in labor

intensive, in intangibility, in service heterogeneity, in customer involvement, in customer-

supplier duality, in simultaneity of production and in consumption”. The financial sector

(Services Industry) differs from other industries in its complexity which can lead to a greater

impact of executive compensation on the firm’s performance of financial institutions than in

other companies (Macey and O’Hara, 2003; Doucouliagos et al., 2007; John and Qian, 2003).

The aforementioned complexity exists because of the special role that the financial

institutions have, regarding the progress of American markets and worldwide markets in

general. By this I mean that banks and other companies in the finance industry are the main

pillars in any economy as they supply with funds all other business sectors.

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However, financial institutions are regulated at a higher degree than manufacturing firms

(John and Qian, 2003) and this regulation is an alternative way of monitoring and a way of

providing incentives to managers (Hirschey and Pappas, 1981; Carroll and Ciscel, 1982).

Based on this regulation, the pay-performance sensitivity in financial institutions is expected

to be lower than it is in manufacturing companies. John and Qian (2003) stressed also that

pay-performance sensitivity in the banking industry is lower than it is in the manufacturing

industry because of its high leverage.

Now based on these arguments I will construct the hypotheses that will help to compare and

contrast the impact of executive compensation on companies in the service industry and in the

manufacturing industry. More specifically:

H2: The significant positive association of executives’ compensation with firm

performance is bigger in manufacturing than in services companies in the

United States.

2.2 R&D Expenses and Firm Performance Secondly, the importance of R&D (research and development) expenses should not be

considered less significant, as it is a core part of the modern business world. Businesses often

use research and development for future growth by emerging new products or processes, in

order to increase and advance their operations. Although R&D expenses are a synonymous

with high-tech companies, many firms from all industries spend large amounts of money to

improve their products/services.

Ericson and Jacobson (1992) stressed that firms in order to obtain an advantage over their

current or future competitors make use of R&D expenses. In this way, they manage to prevent

imitations and to increase substantially their profits. Similarly, Hayes and Abernathy (1980)

claimed that superior technologically products will help firms to succeed and to gain an edge

over their rivals. Additionally, Chao-Hung Wang (2011) focused on the survival of firms in

such a competitive environment and explained that R&D expenses can play a significant role

in it. In particular, he pointed out that R&D expenses are outweighed by the benefits they

have in innovation and performance. In his work, Drucker (2005) pointed out that firms

should increase their R&D investments in order to gain in competitiveness.

Another fact that demonstrates the importance of R&D expenses is the existence of RJV

(research joints ventures) which are created based on a cooperative agreement between firms

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that share the costs and the results of the research project. Specifically, the importance seems

from the fact that firms which may be competitors in an industry, cooperate in R&D

investments in order to minimize the costs and maximize the potential profits. According to

Siebert (1996), firms that cooperate in R&D expenses show higher profitability. Similarly,

Motta (1992) showed that a cooperative R&D has a higher welfare level than a non-

cooperative one. In the same range, Rosenkranz (1995) stressed that firms choose to cooperate

in their R&D expenses, because otherwise their profitability will be low or even zero due to

the uncertainty of their rival’s strategy. On the contrary, Choi (1993) suggested that this kind

of cooperation depends on the spillover rates and when these rates are high a cooperative

R&D is more preferable than a non-cooperative one, but the profit decreases. The reason for

that is the intensified competition that lies in the post-innovation period.

Next, there is the literature that shows if there is a positive or negative relationship between

R&D expenses and firm performance. In their work, Cooper et al. (2008) found a positive

relation between R&D expenses and firm performance in terms of future returns. Similarly,

Lev and Sougiannis (1999) found that R&D expenses become risk factors when they are

added to a firm’s capitalization and they have a positive impact on firm’s future returns.

Hirschey and Weygandt (1985) stressed that corporate expenses for innovation (R&D) need a

period of five to ten years to have a deferred effect. In addition, Toivanen et al. (2002)

showed that R&D investments positively affect market value. Liang and Zhang (2005) and

Cheng et al. (2006), by examining samples of 72 and 96 high technology companies

respectively, found also a positive relationship. However, there must be the appropriate

understanding and management of the knowledge produced by R&D investments, in order to

lead to an increase in firm’s future performance.

On the contrary, there are past papers that reveal no or negative relationship between R&D

expenses and firm performance. Donelson and Resutek (2012) claimed that expenses in

research and development and firm’s profits are not related. Hsu et al. (2013) showed that

R&D expenses increase net sales, but also increase operating costs which tend to decrease

operating income. Finally, Cooper et al. (2008) suggested that there is a negative relation

between R&D expenses and firm’s earnings when this relation is examined from finance

perspective, but it is positive in accounting.

Based on these facts, I constructed the following hypotheses:

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H1a2: Research and development expenses have a significant positive

association with the performance of a manufacturing company in the United

States.

H1b2: Research and development expenses have a significant positive

association with the performance of a service company in the United States.

Another stream of literature that is relevant to my thesis focuses on the use of R&D expenses

by American companies. It is interesting to observe how these companies consider

investments in R&D given that in the United States have their headquarters some of the

largest, most profitable and influential manufacturing and services firms in the world.

To begin with, Greenstone (2011) pointed out the importance of R&D expenses for the

United States, explaining that for at least a century America faced a continuous economic

growth due to innovation, which in turn increased the standard of living. Research and

Development investments lead to this innovation and to the competitiveness of the U.S.

economy. Another fact that reveals the importance of R&D expenses is that both the private

and the public sector spend large amounts of money on such investments (Greenstone, 2011).

In a comparative analysis between French and American manufacturing firms, Mulkay et al.

(2000) found that in U.S. firms the effect of cash flows and profits on R&D investments is

more intense. That practically means that in the United States it is easier to get access to

funding for research and development.

Furthermore, there is existing literature that focuses on the impact of research and

development expenses on firms of specific industries, such as manufacturing companies and

non-manufacturing companies. The latter are companies that do not produce anything and

therefore they may call services companies (including financial services companies).

Ehie and Olibe (2010) stressed that companies from different industries tend to approach

differently R&D expenses and to make use of different mixtures, in order to achieve their

goals. Specifically, manufacturing companies want to improve their production systems

through these investments, whereas services companies use R&D expenses for other

developments. Likewise, Mansfield and Lee (1996) stated that manufacturing companies may

be significantly benefitted by investments in R&D, since these investments can improve the

production, and hence can minimize the production costs. Ho et al. (2005) examined the

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impact of R&D expenses on firm performance in both manufacturing and services companies

for a one-year lag. They found that the performance of manufacturing firms was positively

affected in contrast with the performance of services companies.

Based on these facts, I constructed the following hypothesis:

H3: The significant positive association of research and development expenses

with firm performance is bigger in manufacturing than in services companies in

the United States.

2.3 Interaction EffectsFinally, there is the literature that is relevant to which metrics could be used as control

variables in the executive compensation and firm performance relation on the one hand and in

the R&D expenses and firm performance relation on the other hand. There is a plethora of

past papers that are referred to firm size1 as a control variable, meaning that affect both the

executive compensation and the firm performance.

Consistent with previous studies Cyert et al. (2002) found out that firm size is the most

important explanatory variable for all forms of executive compensation. Likewise Hubbard

and Palia (1995) showed the positive relation between the size of financial institutions and

their executive compensation. Besides all these, it is observed that in large companies the

productivity growth rate is linked to the firm size as it is shown by its working capital, the

number of employees, the amount of tangible assets and its sales (Halkos and Tzeremes,

2007). However, there are researchers who stress that pay-performance sensitivity should be

inversely related to firm size. More specifically, political influence could lead to lower pay-

performance sensitivity in large firms (Jensen and Murphy, 1990). Moreover, Schaefer

(1998), by using a model and empirical evidence, argued that pay-performance sensitivity

drops when firm size rises.

Based on these arguments, I constructed the following hypothesis:

H4a: The impact of executives’ compensation on the performance of a

manufacturing company in the United States should be greater in larger firms.

1 Firm size is used as control variable when testing H1-H3, while is used as moderator variable when testing the interaction effects in H4-H5.

17


H4b: The impact of executives’ compensation on the performance of a service

company in the United States should be greater in larger firms.

There is also a plethora of past literature that includes firm size as a control variable when

testing the relationship between R&D expenses and firm performance. To begin with, Crépon

et al. (1998) found a significant correlation between firm size and R&D expenses. Similarly,

Hsu et al. (2013) stressed a positive relationship between firm size and R&D expenses. Ehie

and Olibe (2010) showed that R&D expenses positively influence firm performance after

controlling for firm size. Finally, Ayaydin and Karaaslan (2014) found that firm size has a

positive impact on the profitability of companies.

Based on the above, I constructed the following hypothesis (here firm size is used:

H5a: The impact of research and development expenses on the performance of

a manufacturing company in the United States should be greater in larger

firms.

H5b: The impact of research and development expenses on the performance of

a service company in the United States should be greater in larger firms.

Additionally, R&D expenses and executive compensation could be used as control variables

to see their influence on the relationship between executive compensation and firm

performance and R&D expenses and firm performance respectively.

According to Cheng (2004), there is a positive association between changes in R&D expenses

and changes in CEOs’ total annual compensation, when CEO is about to retire (horizon

problem) and when firms face small earnings reduction (myopia problem). On the contrary,

Bizjak et al. (1993) found a negative association between R&D expenses and CEO’s

compensation. One possible explanation for these contradictory findings is the danger of

overinvestment in R&D expenses, which makes it unclear for the compensation committees,

if they should motivate more R&D spending. Artz et al. (2010) showed that there is a positive

impact of R&D expenses on firm performance. Moreover, researchers like Salavou (2002)

and Prajogo (2006) stated that product innovation as a result of R&D expenses leads to firm

growth and performance increase. Similarly, in their works Roberts (1999) and Cho and Pucik

(2005) stressed a very positive association between innovation as a result of R&D expenses

and performance of manufacturing firms in the United States. Last but not least, there are few

18


sources that indicate spending for research and development in the banking industry. Thomke

(2003) pointed out the experiment conducted by the Bank of America, one of the leading U.S.

banks, which has been running a series of ongoing R&D processes to create a new service

concept for retail banking. The results show a significant increase in the performance of the

bank.

Finally, the board of directors that is responsible for the level and the composition of

executive payments could be another control variable to test how it affects the relation

between executive compensation and firm performance.

Specifically, Doucouliagos et al. (2007) found out that there is a positive relation between the

directors’ and the CEO’s compensation. Also as it is referred above, when the majority in the

board of directors is outsiders, executives tent to be compensated basically with equity

(Mehran, 1995). This fact points out that the board of directors play a key role in the setting of

compensation systems. Lastly, CEO compensation monitoring is related to firm performance

and this relationship get stronger when firms are controlled by management rather than by

their owners.

After studying the already existing literature, I have found significant evidence that will

contribute to complete this particular survey. First of all, there is a plethora of previous

researches that examining the impact of executives’ compensation and R&D expenses on

firms’ performance.

Initially, the vast majority of studies related to executive compensation and firm performance

show that there is a strong relationship between them. However, there are some exceptions

where this link does not exist. More specifically, it is observed that in countries such as China

in which the government has a significant influence in many companies (including banks),

executives’ compensation does not affect firms’ performance at all (Zhou et al., 2011).

Furthermore, there is extensive literature about how this relation is applied in the United

States. There is also past literature related to the impact of executive compensation on firm’s

performance in both the finance and the manufacturing industry.

Continuously, most of the literature regarding R&D expenses and firm performance reveal a

strong positive relation between them with some exceptions. Following, there is literature

relevant to the use of research and development expenses in the United States. Last but not

19


least, there are past researches that investigate the impact of R&D expenses on manufacturing

and non-manufacturing firms.

As far as control variables are concerned, I choose to use firm size because it has been

previously shown to have an effect on both the relations I am testing. Moreover, I choose to

use R&D expenses and executive compensation as control variables when executive

compensation and R&D expenses stand as my independent variables respectively. I did that

because R&D expenses are used to boost firm’s performance and as a competitive advantage

against competitors. Also many times they are manipulated so as to have an impact on

executive’s compensation (Cheng, 2004). All these are important for my thesis as they will

help me to interpret this study’s final findings and possibly to add something new in the

standing literature.

However, I have found no literature comparing the impacts between these two industries. For

this reason this work is important. Firstly, it can add to the existing literature regarding

executives’ compensation and firms’ performance on the one hand and R&D expenses and

firms’ performance on the other hand. Secondly it has important implications in both theory

and practice. Regarding the theory, this study offers important information in the content of

management control that has to do with decision making on administrative issues. Such issues

are executives’ compensation and their structure. This paper has also some important practical

implications. In particular, it can assist companies by modernizing their executive

compensation systems and by improving their operations. Moreover, it points out the

importance of R&D expenses for companies as far as their economic progress and their

competitiveness are concerned.

My thesis contributes to the already existing literature that is relevant to the impact of

executive compensation and R&D expenses on firm performance by examining and

comparing these relationships between companies from the services sector and the

manufacturing sector. Based on the existing literature I expect a strong impact of executive

compensation and R&D expenses on firm’s performance. Moreover, I expect the relation of

executive compensation on firm performance to be stronger for companies in the

manufacturing sector than for companies in the services sector. Similarly, I expect that the

impact of R&D expenses on firm performance will be greater on manufacturing than on

services companies.

20


3. Research DesignIn this section, I will present and further elaborate the basic components-variables that I

intend to use in order to examine the research question of this thesis. More specifically, I will

elucidate what is and how I am going to measure firm performance, executive compensation,

R&D expenses and firm size. Moreover, in this section I will give information about data

collection. Finally, I will explain all about the methodology and the basic model I will use.

3.1 Variables A complete presentation of all the variables is available in Table 1 in the Appendix.

3.1.1 Independent VariablesIn this study, the independent variables are CEO remuneration and R&D expenses.

CEO RemunerationThe already existing literature provides little guidance for empirical researchers on

determining in the most appropriate way the compensation of executives and especially

CEOs. Most studies use nothing but a market or accounting measure of performance to

calculate executives’ payments. But how shall we determine the payments of business

executives? To understand this issue better and thus to find the most suitable answer, we

should determine first what is executive compensation and what components on average may

constitute such a compensation.

As stated in the introduction, companies in the modern economic environment compensate

their executives by using different incentives, so as to make them perform better by securing

and increasing shareholders’ wealth. Depending on whether the incentives are equity-based or

behavior-based, they are divided into two categories: the monetary and non-monetary

incentives. Both these categories have a common goal, which is rewarding employees for

doing their job well, but it is slightly different based on what they are designed to achieve.

More specifically, monetary incentives are something that employees can struggle for, in

contrast with non-monetary incentives.

Another way to classify and understand the compensation of executives is to distinguish it in

the four main categories that compose it. These main components are the basic salary, short-

term incentives, long-term incentives and perquisites:

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Basic salary

The basic salary is considered a “fixed” element of executives’ payments and usually it is not

affected by the company’s performance.

Short-term incentives

Short-term incentives or annually awarded incentives are used to compensate executives for

achieving the company’s short-term business goals set by the board. These types of incentives

are mostly financial metrics in the form of bonuses. However, many companies tend to use

non-financial metrics as well. In most cases executives in higher-level positions receive

higher of these award opportunities and vice versa. Finally, short-term incentives are usually

planned to provide minimum, desired and maximum levels of performance which are used to

create minimum, desired and maximum levels of payment.

Long-term incentives

Long-term incentives are mixtures of equity and cash compensation that is used to reward

executives for achieving the company’s long-term goals that will increase shareholders’

wealth. Examples of long-term incentives which are referring to a performance period that

usually is between three to five years are restricted stocks, shares, stock options, etc. Long-term

incentive objectives differ among companies but most of the times are focused on return

measures (return on assets) and operational measures (earnings per share). In any case

executives do not receive any further payment (from the incentives), but in the end of the

specific period. Similar to annual incentives, long-term incentives are usually designed to

have minimum, desired and maximum levels of performance. Finally, these kinds of

incentives are considered an important part of executives’ compensation because they are the

largest part of it (over 60 percent for the median S&P 500) and they guarantee an alignment

with the shareholder interest.

Benefits/Perquisites

The last component of executives’ total compensation package consists of a wide variety of

benefits and perquisites or “perks”. This form of compensation is used to recognize how

important to the company each executive is and include company cars, club membership,

spouse travel, housing accommodation etc.

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Because of the complexity to quantify and measure benefits such as “perks” due to lack of

reliability of data, this paper following past studies will focus solely on monetary incentives.

Moreover, they tend to be more effective and influential than other non-monetary incentives.

Research and development (R&D) expenses Another independent variable in this study is research and development expenses (R&D).

R&D expenses are a type of tax deductible operating expense, used to find and create new

products and/or services. These expenses can be of both minor and major significance

especially for large corporations. The already existing literature gives contradictory results

regarding the association between executive compensation and R&D expenses. According to

Cheng (2004), this is happening because compensation committees based on the fear of

overinvestment in R&D hesitate to allow further expenditures in the field of research and

development. Furthermore, there is extensive literature that shows the impact that R&D

expenses have on firm performance especially in the manufacturing industry. For example,

DeCarolis and Deeds (1999) and Hua and Wemmerlov (2006) pointed out the positive effect

that innovation through R&D expenses has on the performance of U.S. manufacturing firms.

Likewise, Wakelin (2000) showed the significant effect that R&D expenses have on the

performance of manufacturing firms in the United Kingdom.

There are three types of R&D. These are the following:

Basic Research: This kind research that applies to the industrial sector improves the scientific

knowledge without any specific commercial purposes. However, such research may be in the

interest to the company, present or future.

Applied Research: Industries use this type of research to gain new knowledge about products,

processes and services, with specific commercial purposes.

Development: Development is the systematic use of obtaining knowledge or understanding

through research to the production of products, systems or methods (with the design and

development of prototypes to be included).

23


3.1.2 Dependent VariableIn this study, the dependent variable is firm performance.

Firm PerformanceWhat is firm performance depends on how someone perceives the term “firm performance”.

That is for example when someone uses marketing, operations, finance, human resource

management, productivity, profitability or another metric to answer this question. In general,

as firm performance could be considered the total output of a company; that is, all goods and

services that a company produces for a specific period of time which leads to the firms’ profit.

There is extensive literature that presents several measures of a company’s performance, both

market-based and accounting-based.

One difficulty that comes with comparing firm performance between different industries is

the different metrics they use to do it. In particular, banks and manufacturing firms may use

different ways to measure their performance. For example, banks can use non-performing

loans (NPL) to measure their performance as this can increase the competitiveness within the

banking system (Zhou et al, 2011). On the other hand, manufacturing firms may use their

productivity or return on sales (Firth et al, 2006; Mengistae and Xu, 2002) as metrics.

To avoid this problem, it is necessary to find a metric that can be used by both financial

institutions and manufacturing companies. There are many conventional measures that can be

used for companies in both the finance and the manufacturing industries. For example Brick

et al. (2008) used stock returns, while Buck et al. (2008) total shareholder returns, pre-tax

profits and returns on assets. Return on assets (ROA) and return on equity (ROE) are used

extensively for the assessment of the performance of financial institutions (Knapp et al, 2005;

Choi and Hasan, 2005; Shen and Chang, 2006) only in terms of profitability. Moreover, they

are performance metrics that can be used to compare the performance of banks and

manufacturing firms (Mishra and Nielsen, 2000).

Although ROE is the prevalent measure of firm performance, it can obscure some potential

difficulties. For example, some companies may increase their debt leverage levels to keep

their ROE falsely healthy. This could increase significantly risk with worse results for such

companies. On the contrary, ROA is a better metric of financial performance, as distortion

such as mentioned before (Brown et al., 2010). In my thesis, I choose to measure firm

performance in terms of ROE and ROA.

24


3.1.3 Control VariablesIn this study, control variables are firm size, R&D expenses and executive compensation.

Firm sizeFirm size is an important control variable that influences the payments of CEOs, a

relationship that is examined extensively by many researchers (Jensen and Murphy, 1990;

Gregg et al., 1993; Bliss and Rosen, 2001). It is expected that managers in larger firms will

receive higher payments because of the higher degree of complexity of their duties. Every

industry has an optimal firm’s size based on its particular production technologies, including

capital intensities and scale economies (Kumar et al., 2001). All executive compensation

relevant literature includes firm size as a control variable, but measures it in different ways.

For example, Mishra and Nielsen (2000) used total capital, while Mengistae and Xu (2002)

used current sales. Furthermore, Brunelloa et al. (2001) used both revenue and employment as

size measures. This study uses the logarithm of sales turnover to measure size.

3.2 Data CollectionThe data used in this study were collected from Wharton Research Data Services system.

More specifically, our data consists of all manufacturing and services firms listed on the

COMPUSTAT ExecuCOMP Annual Compensation Database and the COMPUSTAT

Fundamentals Annual files from 2000 to 2014. To measure executive compensation, I used

total compensation (TDC1) which includes salary, bonus, other annual, total value of

restricted stock granted, total value of stock options granted (using Black-Scholes), long-term

incentive payouts and all other total. To measure R&D expenses, I used all costs incurred

during the year that relate to the development of new products or services (XRD). For

measuring firm performance, I used ROA by dividing net income (NI) with total assets (AT).

Finally, to measure firm size, I used net sales turnover (SALE).

The impact of executive compensation and R&D expenses on firm performance for all U.S.

manufacturing and services firms (including banks and other financial services companies) is

compared. I selected the companies based on their SIC (Standard Industrial Classification)

code. Particularly, I used all manufacturing firms that have a SIC code between 2000 and

3990 (analytical manufacturing companies with their SIC codes are listed in Table 2 in the

Appendix) and all services companies that have a SIC code between 6012 and 8900

(analytical services companies with their SIC codes are listed in Table 3 in the Appendix).

Lastly, my sample period is from 2000 until 2014, as I want to examine this effect in this time

25


during which took place some of the most important political, economic and more events. For

example, the September 11 attacks in 2001 which shook among others the U.S. economy, the

introduction of the euro in 2002 as a competitive to the dollar currency and the 2008 Global

Financial Crisis.

3.3 MethodologyIn this section I will present how I am going to test my hypotheses about the relationship

between the executive compensation and firm performance on the one hand and R&D

expenses and firm performance on the other hand. My model is based on the basic model

analysis of compensation (Doucouliagos et al., 2007; Zhou et al, 2011), which is an Ordinary

Least Squares (OLS) analysis. This will be used to run the following regression:

PERF¿=β0+β1∗¿¿¿+β2∗R∧D ¿+β3∗PAY ¿+ε ¿¿

where PAY ¿ and R∧D¿ represents the executive compensation and the research and

development expenses for company i (manufacturing or services company) in year t. ¿¿¿¿

represents the size of each company measured in terms of sales turnover (operating expenses).

PERF¿ represents the performance of each company i in year t. Finally, ε ¿ represent the error

term.

Additionally, to measure the interaction effects in H4a-H5b, I will modify my regression

model, so as to include the interaction terms:

PERF¿=β0+β1∗R∧D¿+ β2∗PAY ¿+ β3∗¿¿¿+β4∗PAY ¿∗¿¿¿+ε¿¿¿

PERF¿=β0+β1∗PAY ¿+β2∗R∧D¿+ β3∗¿¿¿+β4∗R∧D¿∗¿¿¿+ε¿¿¿

where PAY ¿, R∧D¿, ¿¿¿ , PERF¿¿ and ε ¿ represent the same as before. PAY ¿∗¿¿¿¿ and

R∧D¿∗¿¿¿¿ represent the interaction effects between executive compensation and firm size

and R&D expenses and firm size respectively. Here ¿¿¿¿ is the moderating variable.

4. Empirical ResultsIn this chapter, I am presenting the results for the relations that I examine, based on statistical

tests. In the beginning, I will analyze the descriptive statistics and the Pearson correlation

matrices, while in the end I will present the results of my regression analysis, followed by the

acceptance or rejection of my hypotheses.

26


4.1 Descriptive StatisticsTable 4 reports summary statistics for the key variables for manufacturing and services firms

used in this study. Panel A reports mean, median, standard deviation, minimum and

maximum values of return on assets, total executive compensation, research and development

expenses and sales turnover for manufacturing companies while Panel B for services

companies. As it is observed, in both Panels the variables that represent total executive

compensation, research and development expenses and sales turnover are substantially

skewed to the right with a mean significantly greater than the median. This skewness can be

described as asymmetry in a statistical distribution.

TABLE 4 - Summary statistics

Panel A: Descriptive Statistics for Manufacturing Companies

Variables Mean MedianStandard

DeviationMinimum Maximum

ROA 0.05 0.06 0.66 -8.63 24.09

TotalExComp 2,625.84 1,381.38 4,556.13 0.00 377,996.50

RDExp 407.48 57.80 1,160.48 0.03 12,183.00

SaleTurn 9,358.28 1,991.14 29,040.42 0.04 433,526.00

Panel B: Descriptive Statistics for Services Companies



ROA 0.02 0.05 0.21 -2.00 0.84

TotalExComp 2,699.01 1,262.59 5,778.70 0.00 120,060.70

RDExp 386.48 62.52 1,224.45 0.10 9,811.00

SaleTurn 3,841.24 756.87 13,297.23 1.74 106,916.00The examined samples consists of 22,442 manufacturing and 4,828 services firm-year observations from 2000

to 2014 respectively. Data about executive compensation, research and development expenses and sales

turnover for the examined period are collected from Compustat Execucomp and Compustat Annual

Fundamentals. ROA was constructed using data obtained from Compustat Annual Fundamental database.

Total compensation (TDC1 data item on Execucomp) includes salary, bonus, other annual, total value of

restricted stock granted, total value of stock options granted (using Black-Scholes), long-term incentive

payouts and all other total and referred to the total compensation of the top executives of manufacturing firms.

27


Following Core and Guay (1999) and Karuna (2007) I will use logarithmic transformation to

mitigate this positive skewness. Table 5 present the new summary statistics for ROA and the

logarithms of the rest of the key variables. Panel A and Panel B report mean, median,

standard deviation, minimum and maximum values of return on assets, the logarithm of total

executive compensation, the logarithm of R&D expenses and the logarithm of sales turnover

for manufacturing and services companies respectively. It is observed that the mean and the

median values are much closer than before, which practically means that now the distribution,

is more normal.

TABLE 5 - Summary statistics (logarithmic transformation)

Panel A: Descriptive Statistics (logarithmic transformation) for Manufacturing Companies



ROA 0.05 0.06 0.66 -8.63 24.09

TotalExComp_log 7.29 7.23 1.07 -6.91 12.84

RDExp_log 4.21 4.07 1.86 -3.47 9.41

SaleTurn_log 7.60 7.60 1.81 -3.12 12.98

Panel B: Descriptive Statistics (logarithmic transformation) for Services Companies



ROA 0.02 0.05 0.21 -2.00 0.84

TotalExComp_log 7.19 7.14 1.23 -6.91 11.70

RDExp_log 4.30 4.14 1.74 -2.30 9.19

SaleTurn_log 6.61 6.63 1.63 0.55 11.58The examined samples consists of 22,442 manufacturing and 4,828 services firm-year observations from 2000

to 2014 respectively. Data about executive compensation, research and development expenses and sales

turnover for the examined period are collected from Compustat Execucomp and Compustat Annual

Fundamentals. ROA was constructed using data obtained from Compustat Annual Fundamental database.

Total compensation (TDC1 data item on Execucomp) includes salary, bonus, other annual, total value of

restricted stock granted, total value of stock options granted (using Black-Scholes), long-term incentive

payouts and all other total and referred to the total compensation of the top executives of manufacturing firms.

28


4.2 Pearson Correlation MatrixTable 6 presents the Pearson correlation matrix for the main explanatory variables used in the

regression analysis. These are total executive compensation (TotalExComp_log), research and

development expenses (RDExp_log) and firm size in terms of sales turnover (SaleTurn_log).

Panel A reports the results for manufacturing firms, whereas Panel B shows he results for

services companies. In both Panels, there are high correlations among all explanatory

variables.

More specifically, for the manufacturing firms the correlation between TotalExComp_log and

RDExp_log is 0.58 and for the services companies the same correlation is 0.48. The fact that

the coefficient for manufacturing firms is greater than the one for services companies denotes

that this impact is a bit larger in companies of the manufacture sector.

Continuously, the positive and significant coefficients between TotalExComp_log and

SaleTurn_log for both manufacturing (0.61) and services (0.50) companies demonstrate that

larger firms have the economic capacity to pay more their executives.

Finally, high correlations are documented also between RDExp_log and SaleTurn_log for

both manufacturing (0.70) and services (0.82) companies. The positive and significant

coefficients show that larger firms have greater economic capacity to invest in research and

development.

TABLE 6 - Pearson Correlation Matrices

Panel A: Manufacturing Companies

TotalExComp_log RDExp_log SaleTurn_log

TotalExComp_log 1.00

RDExp_log 0.58*** 1.00

SaleTurn_log 0.61*** 0.70*** 1.00

Panel B: Services Companies

TotalExComp_log RDExp_log SaleTurn_log

TotalExComp_log) 1.00

RDExp_log 0.48*** 1.00

SaleTurn_log 0.50*** 0.82*** 1.00Note: *,**,*** indicate significance at the 10%, 5% and 1% level respectively.

29


4.3 Main FindingsThis study focuses on determining whether executive compensation and research and

development expenses affect differently manufacturing and services companies in the United

States. Table 7 reports the results of the regression analysis based on a log-log Ordinary Least

Squares (OLS) model. In general the results show that executive compensation and R&D

expenses affect firms’ performance.

To begin with, in manufacturing firms-sample the R-sq (R square) shows that 0.2% of the

variance of the response variable ROA is explained by this regression model. The coefficient

on TotalExComp_log is positive and significant (5% level) in this regression. Hence, I have

enough evidence to accept the alternative hypothesis H1a1: Executives’ compensation has a

significant positive association with the performance of a manufacturing enterprise in the

United States. Particularly, when TotalExComp_log increases the level of agreement with the

statement that I am using, ROA also increases. There is also a numerical interpretation for

this; that is, for an increase in the total compensation of executives by one unit there is on

average an increase in the value of the variable ROA of 0.0129 units.

Next, the coefficient on RDExp_log is significant but negative and positive when firm

performance is measured in terms of return on assets (ROA). This positive and significant

coefficient means that an increase in R&D expenses by one unit will lead to an increase in the

value of ROA by 0.0162 units. Based on these, I have enough evidence to accept the

alternative hypothesis H1a2: Research and development expenses have a significant positive

association with the performance of a manufacturing enterprise in the United States.

Furthermore, in services firms-sample the R-sq (R square) shows that 5.9% of the variance of

the response variable ROA is explained by this regression model. In this sample the

coefficient on TotalExComp_log is positive but not significant. Consequently, I do not have

enough evidence to accept the alternative hypothesis H1b1: Executives’ compensation has a

significant positive association with the performance of a service company in the United

States. This might be explained by the fact that some companies belonging to the services

sector, such as banks and other financial institutions, are regulated to a greater extent than

manufacturing companies. Hence, the pay-performance sensitivity is expected to be much

lower.

30


Moreover, the coefficients of RDExp_log in the regression, is significant (1% level) but

negative. The negative coefficient means that as RDExp_log increases the level of agreement

with the statement that I am using, ROA decreases. The numerical interpretation for this is

that for an increase in R&D investments by one unit there is on average a decrease in the

value of the variable ROA of 0.0165 units. As a result, I cannot accept the alternative

hypothesis H1b2: Research and development expenses have a significant association with the

performance of a service company in the United States., despite the fact the coefficient is

significant.

Finally, based on all the aforementioned about executive compensation I have enough

evidence to accept H2: The significant positive association of executives’ compensation with

firm performance is bigger in manufacturing than in services companies in the United States.

As far as the second independent variable is concerned, the association between research and

development expenses and firm performance is also bigger in manufacturing than in services

companies. Based on these, I have enough evidence to accept H3: The significant positive

association of research and development expenses with firm performance is bigger in

manufacturing than in services companies in the United States.

TABLE 7 - Regression Analysis

Manufacturing Firms Services Firms

ROA ROA

SaleTurn_log -0.0263*** 0.0439***

(-7.12) (13.59)

RDExp_log 0.0162*** -0.0165***

(4.67) (-5.50)

TotalExComp_log 0.0129** 0.0002

(2.39) (0.07)

_cons 0.0880*** -0.203***

(2.71) (-10.84)

N 22,442 4,828

R-sq 0.002 0.059

Note: *,**,*** indicate significance at the 10%, 5% and 1% level respectively.

31


4.4 Interaction EffectsIn this chapter, I will present the results of the regression analysis focusing on the interaction

effects between executive compensation and firm size and R&D expenses and firm size.

Table 8 below shows the interaction effect between executive compensation and firm size.

The positive and significant (1% level) coefficient of the interaction effect depicted through

the variable ExComp_SaleTurn for manufacturing companies stress that H4a: The impact of

executives’ compensation on the performance of a manufacturing company in the United

States should be greater in larger firms is accepted. This means that larger firms have the

economic capacity to pay more their executives. The same coefficient for services companies

in not significant, hence I have to reject H4b: The impact of executives’ compensation on the

performance of a service company in the United States should be greater in larger firms.

TABLE 8 – Interaction Effect between Executive Compensation and Firm Size


ROA ROA

RDExp_log 0.0143*** -0.0166***

(4.10) (-5.52)

TotalExComp_log -0.1142*** -0.0046

(-6.76) (-0.46)

SaleTurn_log -0.1338*** 0.0392***

(-9.54) (3.89)

ExComp_SaleTurn 0.0154*** 0.0007

(7.95) (0.50)

_cons 0.9701*** -0.1689**

(8.39) (-2.39)

N 22,441 4,282

R-sq 0.005 0.060


Table 9 shows the interaction effect between R&D expenses and firm size. The positive and

significant coefficients of the interaction effect depicted through the variable

32


RDExp_SaleTurn for both manufacturing (0.0632) and services (0.0032) companies stress

that H5a and H5b (The impact of research and development expenses on the performance of a

manufacturing or service company respectively in the United States should be greater in

larger firms) stand.

TABLE 9 – Interaction Effect between R&D expenses and Firm Size


ROA ROA

TotalExComp_log 0.0134** 0.0000

(2.47) (0.01)

RDExp_log -0.0364*** -0.0399***

(-3.54) (-6.42)

SaleTurn_log -0.0515*** 0.0293***

(-8.70) (6.24)

RDExp_SaleTurn 0.0632*** 0.0032***

(5.45) (4.30)

_cons 0.2807*** -0.1041***

(5.85) (-3.52)

N 22,442 4,282

R-sq 0.004 0.063


The interaction results are quiet important for financial managers who are responsible for the

financial health of a company. Financial managers develop plans and strategies that set the

long-term financial goals of their companies. More specifically, the interaction effect between

executive compensation and firm size (for manufacturing companies) and the interaction

effect between R&D expenses and firm size (for both manufacturing and services companies)

are positive. Hence, these results provide financial managers with the opportunity to build

proper compensation and innovation (R&D) systems that will maximize firm performance

according to each company’s size.

33


5. ConclusionThis study, based on empirically data, tries to answer the following question:

RQ: Does executive compensation and R&D expenses have a stronger impact

on the output of services firms than on manufacturing companies in the United

States?

With regard to the theory, this study shows that the relation between executive compensation

and the performance of manufacturing firms is positive, whereas the relation between

executive compensation and the performance of services companies does not exist at all. To

begin with, prior and more recent empirical studies provide contradictory results on the

relationship between executive compensation and firm performance. However, the vast

majority of the already existing literature stresses the positive impact of executive

compensation on firm performance. Researchers like Chaubey and Kulkarni (1988), Zajac

(1990) and Wallsten (1999) pointed out the strong, unbreakable link between these two

variables. Furthermore, Mehran (1995), Hubbard and Palia (1995) and Canarella and

Gasparyan (2008) found that the aforementioned relationship applies for both manufacturing

and services companies. These studies lead me to hypothesize that the more executives are

paid the better firm performance will be. The empirical results support this hypothesis for

manufacturing companies but not for services companies for which this association is neither

negative nor positive.

In this part it should be interesting to compare my results with some contradictory results of

the prior literature. More specifically, there are researchers like Firth et al. (2006),

Doucouliagos et al. (2007) and Zhou et al. (2011) that found no association between payments

and performance. The basic reason that my results differ from theirs is the different firm-

samples that we used. As far as the manufacturing-firm-sample is concerned, Doucouliagos et

al. (2007) and Zhou et al. (2011) were focused on banks which are characterized by a higher

degree of regulation, whereas Firth et al. (2006) used state owned companies. On the other

hand, in service-firm-sample there is not an association between executive payments and firm

performance. This could be possibly because this sample contains also banks and other

financial services companies.

Furthermore, this study finds that the relation between R&D expenses and the performance of

manufacturing firms is positive, while the same relation is negative for services companies.

34


After a careful reviewing of the literature about the impact of R&D expenses on firm

performance, both prior and more recent literature show the positive impact that research and

development expense have on firm performance. Among the supporters of this positive

association are Hirschey and Weygandt (1985), Lev and Sougiannis (1999) and Cooper et al.

(2008), who are nevertheless emphasized on the future and not the present value of R&D

expenses on firm performance. In addition, according to Mansfield and Lee (1996) and Ehie

and Olibe (2010) both manufacturing and services companies use research and development

expenses to develop their products and services, hence their future returns. These studies lead

me to hypothesize that the more R&D expenses a company use the better firm performance

will be. The empirical results support this hypothesis for manufacturing companies but not for

services companies for which this association is negative.

Based on the results, executive compensation positively affects the performance of

manufacturing companies but it does not have an impact at all on the performance of services

companies. Additionally, R&D expenses have a positive impact on the performance of

manufacturing companies, but a negative one on the performance of services companies.

According to this research’s results, it is strongly supported that executive compensation

affect more the performance of manufacturing firms, as well as do R&D. Moreover, the

findings show that the impact of executive compensation on performance is going to be larger

in larger companies belonging in the manufacturing sector, while it seems that this association

does not apply for the services firms. Finally, the association between R&D expenses and

firm size is positive (for both manufacturing and services companies).

The results of this study are important to the large proportion of the American public that

owns shares in American manufacturing firms, as well as to foreign investors. Specifically, in

manufacturing companies the positive association between executive compensation and firm

performance on the one hand and R&D expenses and firm performance on the other hand

show that there is a chance for better management and higher profits. This indicates that these

associations implicate that policy-makers in manufacturing companies could create

compensation formulas to induce executives to attribute the best of their capabilities.

Moreover, they could create proper systems that would release funds for research and

development in order to obtain larger part in the economy. Unfortunately, for services

companies there is not an association between executive payments and firm performance.

35


Possibly, because of the greater degree of regulation of the financial firms that belong to the

greater services-firms sample.

5.1 Limitations and Future ResearchThere are two limitations to this study. First, the classification of companies in only two

major industries: the manufacturing and the services companies. The problem here is that

companies that belong to one of the two categories may differ among them a lot. For example,

financial services companies are characterized from a higher degree of regulation, which lead

to a lower pay-performance sensitivity. This could alter significantly the results of this study

for other companies that belong also to the broader services industry. Another limitation is

that this study examines the impact of executive compensation and R&D expenses on

performance for a period of time. Hence the impact may not be quiet visible. By this I mean

that the examined impact may be more obvious if there was a comparison to the same impact

in another period of time.

These limitations may be overcome with further research in the future. More specifically, it

would be beneficiary to examine the same impact in industries with different classification.

For example, future research may focus on the impact of executive compensation on

automotive industry versus financial services industry. Moreover, a comparison of the

examined impact during different decades may reveal how much this impact increased.

Another interesting extension would be to examine the same impact but controlling for

different variables, such as leverage, tenure, etc.

36


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44


Appendix

TABLE 1 - Definition and Measurement of Variables

Variable Name Definition Category Measurement

ROA Return on Assets Dependent Variable The return on assets of each firm

TotalExComp_log Total Executive Compensation

Independent Variable (control variable)

The natural logarithm of total executive compensation

RDExp_logResearch and development

expenses

Independent Variable (control variable)

The natural logarithm of research and development

expenses of each firm

SaleTurn_log Sales Turnover Control Variable (Moderator Variable)

The natural logarithm of sales turnover of each firm

ExComp_SaleTurnTotal Executive Compensation * Sales Turnover

Interaction EffectThe interaction effect

between executive payments and firm size

RDExp_SaleTurn R&D expenses * Sales Turnover Interaction Effect

The interaction effect between R&D expenses and

firm size

TABLE 2

SIC codes of manufacturing companies and their description

2000 Food and Kindred Products

2011 Meat Packing Plants

2013 Sausages & Other Prepared Meat Products

2015 Poultry Slaughtering and Processing

2020 Dairy Products

2024 Ice Cream & Frozen Desserts

2030 Canned, Frozen & Preserved Fruit, Veg & Food Specialties

2033 Canned, Fruits, Veg, Preserves, Jams & Jellies

2040 Grain Mill Products

2050 Bakery Products

2052 Cookies & Crackers

2060 Sugar & Confectionery Products

2070 Fats & Oils

2080 Beverages

2082 Malt Beverages

45


2086 Bottled & Canned Soft Drinks & Carbonated Waters

2090 Miscellaneous Food Preparations & Kindred Products

2092 Prepared Fresh or Frozen Fish & Seafood

2100 Tobacco Products

2111 Cigarettes

2200 Textile Mill Products

2211 Broadwoven Fabric Mills, Cotton

2221 Broadwoven Fabric Mills, Man Made Fiber & Silk

2250 Knitting Mills

2253 Knit Outerwear Mills

2273 Carpets & Rugs

2300 Apparel & Other Finished Prods of Fabrics & Similar Matl

2320 Men's & Boys' Furnishings, Work Clothing, & Allied Garments

2330 Women's, Misses', and Juniors Outerwear

2340 Women's, Misses', Children's & Infant's Undergarments

2390 Miscellaneous Fabricated Textile Products

2400 Lumber & Wood Products (No Furniture)

2421 Sawmills & Planing Mills, General

2430 Millwood, Veneer, Plywood, & Structural Wood Members

2451 Mobile Homes

2452 Prefabricated Wood Bldgs & Components

2510 Household Furniture

2511 Wood Household Furniture, (No Upholstered)

2520 Office Furniture

2522 Office Furniture (No Wood)

2531 Public Bldg & Related Furniture

2540 Partitions, Shelvg, Lockers, & office & Store Fixtures

2590 Miscellaneous Furniture & Fixtures

2600 Papers & Allied Products

2611 Pulp Mills

46


2621 Paper Mills

2631 Paperboard Mills

2650 Paperboard Containers & Boxes

2670 Converted Paper & Paperboard Prods (No Containers/Boxes)

2673 Plastics, Foil & Coated Paper Bags

2711 Newspapers: Publishing or Publishing & Printing

2721 Periodicals: Publishing or Publishing & Printing

2731 Books: Publishing or Publishing & Printing

2732 Book Printing

2741 Miscellaneous Publishing

2750 Commercial Printing

2761 Manifold Business Forms

2771 Greeting Cards

2780 Blankbooks, Looseleaf Binders & Bookbinding & Related Work

2790 Service Industries For The Printing Trade

2800 Chemicals & Allied Products

2810 Industrial Inorganic Chemicals

2820 Plastic Material, Synth Resin/Rubber, Cellulos (No Glass)

2821 Plastic Materials, Synth Resins & Nonvulcan Elastomers

2833 Medicinal Chemicals & Botanical Products

2834 Pharmaceutical Preparations

2835 In Vitro & In Vivo Diagnostic Substances

2836 Biological Products, (No Diagnostic Substances)

2840 Soap, Detergents, Cleaning Preparations, Perfumes, Cosmetics

2842 Specialty Cleaning, Polishing and Sanitation Preparations

2844 Perfumes, Cosmetics & Other Toilet Preparations

2851 Paints, Varnishes, Lacquers, Enamels & Allied Prods

2860 Industrial Organic Chemicals

2870 Agricultural Chemicals

2890 Miscellaneous Chemical Products

47


2891 Adhesives & Sealants

2911 Petroleum Refining

2950 Asphalt Paving & Roofing Materials

2990 Miscellaneous Products of Petroleum & Coal

3011 Tires & Inner Tubes

3021 Rubber & Plastics Footwear

3050 Gaskets, Packg & Sealg Devices & Rubber & Plastics Hose

3060 Fabricated Rubber Products, NEC

3080 Miscellaneous Plastics Products

3081 Unsupported Plastics Film & Sheet

3086 Plastics Foam Products

3089 Plastics Products, NEC

3100 Leather & Leather Products

3140 Footwear, (No Rubber)

3211 Flat Glass

3220 Glass & Glassware, Pressed or Blown

3221 Glass Containers

3231 Glass Products, Made of Purchased Glass

3241 Cement, Hydraulic

3250 Structural Clay Products

3260 Pottery & Related Products

3270 Concrete, Gypsum & Plaster Products

3272 Concrete Products, Except Block & Brick

3281 Cut Stone & Stone Products

3290 Abrasive, Asbestos & Misc Nonmetallic Mineral Prods

3310 Steel Works, Blast Furnaces & Rolling & Finishing Mills

3312 Steel Works, Blast Furnaces & Rolling Mills (Coke Ovens)

3317 Steel Pipe & Tubes

3320 Iron & Steel Foundries

3330 Primary Smelting & Refining of Nonferrous Metals

48


3334 Primary Production of Aluminum

3341 Secondary Smelting & Refining of Nonferrous Metals

3350 Rolling Drawing & Extruding of Nonferrous Metals

3357 Drawing & Insulating of Nonferrous Wire

3360 Nonferrous Foundries (Castings)

3390 Miscellaneous Primary Metal Products

3411 Metal Cans

3412 Metal Shipping Barrels, Drums, Kegs & Pails

3420 Cutlery, Handtools & General Hardware

3430 Heating Equip, Except Elec & Warm Air; & Plumbing Fixtures

3433 Heating Equipment, Except Electric & Warm Air Furnaces

3440 Fabricated Structural Metal Products

3442 Metal Doors, Sash, Frames, Moldings & Trim

3443 Fabricated Plate Work (Boiler Shops)

3444 Sheet Metal Work

3448 Prefabricated Metal Buildings & Components

3451 Screw Machine Products

3452 Bolts, Nuts, Screws, Rivets & Washers

3460 Metal Forgings & Stampings

3470 Coating, Engraving & Allied Services

3480 Ordnance & Accessories, (No Vehicles/Guided Missiles)

3490 Miscellaneous Fabricated Metal Products

3510 Engines & Turbines

3523 Farm Machinery & Equipment

3524 Lawn & Garden Tractors & Home Lawn & Gardens Equip

3530 Construction, Mining & Materials Handling Machinery & Equip

3531 Construction Machinery & Equip

3532 Mining Machinery & Equip (No Oil & Gas Field Mach & Equip)

3533 Oil & Gas Field Machinery & Equipment

3537 Industrial Trucks, Tractors, Trailers & Stackers

49


3540 Metalworkg Machinery & Equipment

3541 Machine Tools, Metal Cutting Types

3550 Special Industry Machinery (No Metalworking Machinery)

3555 Printing Trades Machinery & Equipment

3559 Special Industry Machinery, NEC

3560 General Industrial Machinery & Equipment

3561 Pumps & Pumping Equipment

3562 Ball & Roller Bearings

3564 Industrial & Commercial Fans & Blowers & Air Purifying Equip

3567 Industrial Process Furnaces & Ovens

3569 General Industrial Machinery & Equipment, NEC

3570 Computer & office Equipment

3571 Electronic Computers

3572 Computer Storage Devices

3575 Computer Terminals

3576 Computer Communications Equipment

3577 Computer Peripheral Equipment, NEC

3578 Calculating & Accounting Machines (No Electronic Computers)

3579 Office Machines, NEC

3580 Refrigeration & Service Industry Machinery

3585 Air-Cond & Warm Air Heatg Equip & Comm & Indl Refrig Equip

3590 Misc Industrial & Commercial Machinery & Equipment

3600 Electronic & Other Electrical Equipment (No Computer Equip)

3612 Power, Distribution & Specialty Transformers

3613 Switchgear & Switchboard Apparatus

3620 Electrical Industrial Apparatus

3621 Motors & Generators

3630 Household Appliances

3634 Electric Housewares & Fans

3640 Electric Lighting & Wiring Equipment

50


3651 Household Audio & Video Equipment

3652 Phonograph Records & Prerecorded Audio Tapes & Disks

3661 Telephone & Telegraph Apparatus

3663 Radio & TV Broadcasting & Communications Equipment

3669 Communications Equipment, NEC

3670 Electronic Components & Accessories

3672 Printed Circuit Boards

3674 Semiconductors & Related Devices

3677 Electronic Coils, Transformers & Other Inductors

3678 Electronic Connectors

3679 Electronic Components, NEC

3690 Miscellaneous Electrical Machinery, Equipment & Supplies

3695 Magnetic & Optical Recording Media

3711 Motor Vehicles & Passenger Car Bodies

3713 Truck & Bus Bodies

3714 Motor Vehicle Parts & Accessories

3715 Truck Trailers

3716 Motor Homes

3720 Aircraft & Parts

3721 Aircraft

3724 Aircraft Engines & Engine Parts

3728 Aircraft Parts & Auxiliary Equipment, NEC

3730 Ship & Boat Building & Repairing

3743 Railroad Equipment

3751 Motorcycles, Bicycles & Parts

3760 Guided Missiles & Space Vehicles & Parts

3790 Miscellaneous Transportation Equipment

3812 Search, Detection, Navigation, Guidance, Aeronautical Sys

3821 Laboratory Apparatus & Furniture

3822 Auto Controls For Regulating Residential & Comml Environments

51


3823 Industrial Instruments For Measurement, Display, and Control

3824 Totalizing Fluid Meters & Counting Devices

3825 Instruments For Meas & Testing of Electricity & Elec Signals

3826 Laboratory Analytical Instruments

3827 Optical Instruments & Lenses

3829 Measuring & Controlling Devices, NEC

3841 Surgical & Medical Instruments & Apparatus

3842 Orthopedic, Prosthetic & Surgical Appliances & Supplies

3843 Dental Equipment & Supplies

3844 X-Ray Apparatus & Tubes & Related Irradiation Apparatus

3845 Electromedical & Electrotherapeutic Apparatus

3851 Ophthalmic Goods

3861 Photographic Equipment & Supplies

3873 Watches, Clocks, Clockwork Operated Devices/Parts

3910 Jewelry, Silverware & Plated Ware

3911 Jewelry, Precious Metal

3931 Musical Instruments

3942 Dolls & Stuffed Toys

3944 Games, Toys & Children's Vehicles (No Dolls & Bicycles)

3949 Sporting & Athletic Goods, NEC

3950 Pens, Pencils & Other Artists' Materials

3960 Costume Jewelry & Novelties

3990 Miscellaneous Manufacturing Industries

TABLE 3

SIC codes of services firms and their description

6012 Pay Day Lenders

6021 National Commercial Banks

6022 State Commercial Banks

6029 Commercial Banks, NEC

52


6035 Savings Institution, Federally Chartered

6036 Savings Institutions, Not Federally Chartered

6099 Functions Related To Depository Banking, NEC

6111 Federal & Federally Sponsored Credit Agencies

6141 Personal Credit Institutions

6153 Short-Term Business Credit Institutions

6159 Miscellaneous Business Credit Institution

6162 Mortgage Bankers & Loan Correspondents

6163 Loan Brokers

6172 Finance Lessors

6189 Asset-Backed Securities

6199 Finance Services

6200 Security & Commodity Brokers, Dealers, Exchanges & Services

6211 Security Brokers, Dealers & Flotation Companies

6221 Commodity Contracts Brokers & Dealers

6282 Investment Advice

6311 Life Insurance

6321 Accident & Health Insurance

6324 Hospital & Medical Service Plans

6331 Fire, Marine & Casualty Insurance

6351 Surety Insurance

6361 Title Insurance

6399 Insurance Carriers, NEC

6411 Insurance Agents, Brokers & Service

6500 Real Estate

6510 Real Estate Operators (No Developers) & Lessors

6512 Operators of Nonresidential Buildings

6513 Operators of Apartment Buildings

6519 Lessors of Real Property, NEC

6531 Real Estate Agents & Managers (For Others)

53


6532 Real Estate Dealers (For Their Own Account)

6552 Land Subdividers & Developers (No Cemeteries)

6770 Blank Checks

6792 Oil Royalty Traders

6794 Patent Owners & Lessors

6795 Mineral Royalty Traders

6798 Real Estate Investment Trusts

6799 Investors, NEC

7000 Hotels, Rooming Houses, Camps & Other Lodging Places

7011 Hotels & Motels

7200 Services-Personal Services

7310 Services-Advertising

7311 Services-Advertising Agencies

7320 Services-Consumer Credit Reporting, Collection Agencies

7330 Services-Mailing, Reproduction, Commercial Art & Photography

7331 Services-Direct Mail Advertising Services

7334 Services-Photocopying and Duplicating Services

7340 Services-To Dwellings & Other Buildings

7350 Services-Miscellaneous Equipment Rental & Leasing

7359 Services-Equipment Rental & Leasing, NEC

7361 Services-Employment Agencies

7363 Services-Help Supply Services

7370 Services-Computer Programming, Data Processing, Etc.

7371 Services-Computer Programming Services

7372 Services-Prepackaged Software

7373 Services-Computer Integrated Systems Design

7374 Services-Computer Processing & Data Preparation

7377 Services-Computer Rental & Leasing

7380 Services-Miscellaneous Business Services

7381 Services-Detective, Guard & Armored Car Services

54


7384 Services-Photofinishing Laboratories

7385 Services-Telephone Interconnect Systems

7389 Services-Business Services, NEC

7500 Services-Automotive Repair, Services & Parking

7510 Services-Auto Rental & Leasing (No Drivers)

7600 Services-Miscellaneous Repair Services

7812 Services-Motion Picture & Video Tape Production

7819 Services-Allied To Motion Picture Production

7822 Services-Motion Picture & Video Tape Distribution

7829 Services-Allied To Motion Picture Distribution

7830 Services-Motion Picture Theaters

7841 Services-Video Tape Rental

7900 Services-Amusement & Recreation Services

7948 Services-Racing, Including Track Operation

7990 Services-Miscellaneous Amusement & Recreation

7994 Services-Video Game Arcades

7995 Services-Gambling Transactions

7996 Services-Amusement Parks

7997 Services-Membership Sports & Recreation Clubs

8000 Services-Health Services

8011 Services-Offices & Clinics of Doctors of Medicine

8050 Services-Nursing & Personal Care Facilities

8051 Services-Skilled Nursing Care Facilities

8060 Services-Hospitals

8062 Services-General Medical & Surgical Hospitals, NEC

8071 Services-Medical Laboratories

8082 Services-Home Health Care Services

8090 Services-Misc Health & Allied Services, NEC

8093 Services-Specialty Outpatient Facilities, NEC

8111 Services-Legal Services

55


8200 Services-Educational Services

8300 Services-Social Services

8351 Services-Child Day Care Services

8600 Services-Membership organizations

8700 Services-Engineering, Accounting, Research, Management

8711 Services-Engineering Services

8731 Services-Commercial Physical & Biological Research

8734 Services-Testing Laboratories

8741 Services-Management Services

8742 Services-Management Consulting Services

8744 Services-Facilities Support Management Services

8748 Business Consulting Services, Not Elsewhere Classified

8880 American Depositary Receipts

8888 Foreign Governments

8900 Services-Services, NEC

56

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