CLAREMONT McKENNA COLLEGE

THE IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON EXECUTIVE COMPENSATION

SUBMITTED TO

PROFESSOR GEORGE BATTA

AND

DEAN GREGORY HESS

BY

ANDREW JARMON

FOR SENIOR THESIS

SPRING 2010

APRIL 19, 2010

TABLE OF CONTENTS

INTRODUCTION 1

SOCIALLY RESPONSIBLE INVESTING 3

EXECUTIVE COMPENSATION LITERATURE REVIEW 5

HYPOTHESIS 8

DATA 9

METHODOLOGY 14

RESULTS 19

DISCUSSION 20

REFERENCES 23

1

INTRODUCTION

A topic often addressed in colloquial

understandings of the working world, yet

sparsely covered in the realm of executive

compensation, is the role of non-monetary

payoffs in contracting and compensation.

This is not without good reason. Unlike

monetary compensation, which can be

quantified, alternative goods such as “work

environment” and “quality of living” provide

a quagmire of definitions and measurement

that make studies of their existence and

impact much less feasible.

Of primary interest in this paper is

what a CEO would say if asked whether he or

she would prefer to work for a socially

responsible firm or a neutral firm (in regards

to social responsibility). This would be

presuming all things being equal in the firms,

the only difference being their social

responsibility track record. Even if one were

able to garner a “yes” out of the majority of

executives questioned, the difficulty in

studying the impact of this effect on

compensation could be found in the variety of

working definitions of “socially responsible”

that would likely be supplied. There might be

some commonalities, however the variance in

definition would add additional variance to

the study of its real-world effect.

At the heart of predicting the answer

of the executive would be understanding the

control that the executive might have on the

firm’s level of social responsibility. For

example, if one were to presume that there

was one factor that all persons asked would

point to as the unique component of social

responsibility, there might be firms for which

their very industry was classified as socially

irresponsible and for which the executive

team and board of directors would have

absolutely no control over the level of social

responsibility. This might also leave firms in

industries for which the decisions of

management did have a very significant

impact on the level of social responsibility in

the firm.

If the latter were the case, and the

CEO individually were to have a very high

degree of control over the level of social

responsibility at the firm (except for the cost

of effort), he or she might be completely

indifferent as to the prior level of social

responsibility in the firm prior to his or her

employment. This would be the case because

regardless of the firm, as long as the industry

did not dictate that the company by necessity

be socially irresponsible, the CEO could

implement his or her own version of social

responsibility at any company to which he or

she was hired.

To begin this study, it is first

necessary, however, to prove that there are

instances where qualitative goods such as

social responsibility are identified as having

an impact on compensation and job choice.

This will be followed by an analysis of the

work having been done on the impact of non-

monetary job benefits and costs at the

executive level. After this theoretical

framework under which job choice and

compensation has been shown to be impacted

by items such as the level of corporate social

responsibility, the goal of this study will be to

analyze whether it is possible to quantify the

effect of something such as corporate social

responsibility on executive compensation.

Like the examination of all things

which are not readily observable, the

necessity for proxies and natural simulations

of the good or behavior to be studied are

paramount. The overlapping of issues such as

social responsibility and worker and executive

compensation has produced a variety of

individuals from varied backgrounds

attempting to understand the topic. While this

2

particular study clearly identifies itself in the

field of finance, a variety of sources will be

examined.

The effect of items such as job

prestige and non-observable goods on job

choice has been examined in and outside of

financial literature. Stephen Marks (2008)

cites the work of Professor Scott Baker, who

attempts to determine whether lower relative

compensation for judges versus lawyers has

lead to a dearth in judge capabilities. The

initial hypothesis to be examined centered on

the fundamental economic idea that with

higher compensation comes more people

interested in the job and with this higher

interest comes better applicants. Marks notes

that this is also dependent upon the elasticity

of demand for the job based on compensation

and how sharply demand will fall off for

judgeship positions once salary begins to drop.

Although the study is controversial in

that Baker had to posit some qualifications in

terms of determining judge performance, a

position for which it is difficult to observe

this, his primary findings suggest that judge

salaries need not increase to ensure better

judge applicants. As Baker notes, and Marks

agrees, the non-observable aspects of the job

(e.g. an intellectually stimulating work

environment, tenure, prestige, generous

retirement benefits, the sense of public service,

etc.) seem to compensate for the difference in

monetary compensation. Thus, while the

monetary difference remains, the non-

monetary compensation received through the

judgeship position cancels out this difference.

Working in the same avenue of non-

finance research, Judge and Bretz (1991)

found that “work values” were a significant

factor in job choice. While the term “work

values” is vague, Judge and Bretz indicate

that it encompasses the value systems of

individuals. They further suggest that a

person’s job selection is based on said value

systems and is in search of a firm that mimics

the values that they hold. However, the

authors merely suggest that “work values”, as

they relate to company culture, play a

significant role in job choice. The study is

somewhat limited in its applicability largely

because of the uncertainty over what the term

“work values” actually means. Other than

suggesting that it encompasses non-

compensatory items, the authors do not

elaborate.

The most definitive work on the

effects of social responsibility or moral values

on compensation and job selection comes

from Frank (1996). In his study, the author

looked at several surveys to try and determine

the effect that social values had on job

desirability and compensation. He looked at

the difference in compensation for lawyers

working in the public versus private spaces, a

survey of Cornell students and their job

preferences and a study looking at the

required level of compensation for a person to

choose the same job at a relatively less

socially responsible firm. Frank’s primary

findings suggest that individuals are willing to

sacrifice pay for working in a more ethical

environment. Furthermore, when asked

directly workers would prefer lower pay at a

more ethical firm if they were performing the

same task that they would be performing at a

less socially responsible firm.

Within the sphere of financial research,

several papers have hinted at the importance

of non-monetary rewards in terms of firm

selection and the attractiveness of firms to

executives, however they typically did not

incorporate variables associated with these

factors in their studies. Jensen and Murphy

(1990), in looking at compensation and its

effects on trying to solve the agency theory

dilemma, posit that elements such as visibility,

prestige and power have an effect on the level

of required monetary compensation that an

executive needs in order to take the position.

3

They do not include such variables in their

study because from an ex ante perspective

they do not vary positively with company

value and thus do not serve as an effective

tool in trying to get agents to act in the

interest of principals.

In a later paper, Murphy (1995)

further identifies non-monetary items as

significant towards the total compensation of

a CEO. Murphy writes:

[t]he value an executive receives from

his position includes his monetary

compensation and also includes

important non-monetary elements

such as power, prestige, and

community standing (6).

Murphy’s main argument in this paper is that

actual monetary compensation has to be high

enough to make it such that the CEO is still

willing to take actions such as plant closings

or layoffs that will certainly hurt his or her

reputation but would be in the best interest of

shareholders. This argument would suggest

that Murphy believes there can be a tradeoff

between non-monetary and monetary

compensation, although he links it more to

CEO specific factors rather than associative

benefits within a corporation known for

prestige or community standing (i.e. social

responsibility).

SOCIALLY RESPONSIBLE INVESTING

This study derives its metric for

determining the social responsibility of a

given firm from the quickly growing field of

Socially Responsible Investing (SRI). It

would be foolish to develop a proprietary

metric for the evaluation of a given

company’s level of corporate social

responsibility (CSR) given that one would be

imposing personal bias in to the study and

ignoring all previous research done as to what

may or may not qualify as a valid metric in

evaluating CSR. It can also be presumed that

if the metrics accepted by the SRI industry are

capable of moving capital markets and

determining fund allocations they might also

be capable of swaying executive job choice

decisions and influencing compensation.

The theoretical backing for SRI can be

sourced from the intersection of capital

markets and human morality. The ideal

situation for it to come in to focus would be

an instance where the risk and payoff of two

companies was expected to be equal, however

the level of “social responsibility” was

deemed to be different for each. In this

instance, socially responsible investors would

select the more socially responsible company,

in essence adding a secondary payout to the

expected stock return: social payoff.

The difficulty in studying anything

associated with the SRI industry will always

be characterized by the usage of terms such as

“moral”, “socially responsible” and “ethical”,

which each cultivate ambiguities as to the

real-world examples for which these ideas can

best be witnessed. The definition of SRI

provided by Renneboog, Horst and Zhang

(2007) will be used for this study, whereby

SRI is:

…a set of investment screens to select

or exclude assets based on ecological,

social, corporate governance or ethical

criteria, and often engages in the local

communities and in shareholder

activism to further corporate strategies

towards the above aims (1723).

While this certainly is not the sole definition

that exists for SRI, for all intents and purposes

it is a comprehensive definition that will serve

for this study.

4

As far as the origins of SRI, many

academics peg the date of its start at various

points in history. Hill, Ainscough, Shank and

Manullang (2006) synthesize the history of

SRI by piecing together the citations of others

on the subject. In their account of it, SRI took

root in the 1940s when governments and labor

unions avoided investments in companies

with poor labor policies. This was further

expanded during the environmental

movement of the 1970s and concern over the

Vietnam war. The historical event that most

researchers, including Hill et. al. associate

with the development of SRI, is the apartheid

in South Africa during the 1980s. Renneboog,

Horst and Zhang (2007) even credit the SRI

movement with effectively ending apartheid.

In terms of actual SRI fund origins,

Muñoz-Torres, Fernández-Izquierdo and

Balaguer-Franch (2004) attribute European

SRIdevelopment with the movement

originating in the United States. Addressing

SRI in Spain, the authors note that the first

SRI fund in Europe started in 1965 in Sweden

and that the first SRI fund in the U.K. was

started in 1984. By point of comparison, the

first fund in the world ever employing social

screens was the Pioneer Fund, founded in

1928 in the United States. Renneboog et. al.

(2007) agree with Muñoz-Torres et. al. in

crediting the U.S. with development of the

SRI industry and furthermore credit the

Pioneer Fund with starting the movement on

the professional asset management side.

The modern day growth of SRI

investment products is associated by

Renneboog et. al. with the “ethical

consumerism” of today’s culture in which

consumers are willing to pay a price premium

for goods that are associated with being

ethically produced.

According to the Social Investment

Forum the SRI investment style currently

“encompasses an estimated $2.71 trillion out

of the $25.1 trillion in the U.S. investment

marketplace today”. The forum also notes

that there were 260 mutual funds performing

social screens as of 2007, with assets of

$201.8 billion1.

Although the typical profile of

individuals participating might be expected to

be dogmatic fundamentalists who allocate

their entire portfolio to assets deemed socially

responsible, Geczy, Stambaugh and Levin

(2003) indicate that the typical SRI investor

only allocates 25-35% of his or her total

wealth to such investments. Under this

paradigm it is easy to reject the notion that

those involved in SRI investing represent a

small subsection of the population and that

their views and values would likely never

overlap with those held by the CEOs of major

firms.

While the SRI industry does provide

an instance of individuals determining asset

allocations based on non-financial reasons,

the lack of definitive research demonstrating

an underperformance of such assets does not

lend itself to serving as another instance of

individuals sacrificing monetary gain for non-

monetary gain.

This is not to say that there has not

been research to document a financial cost

associated with investing behind an SRI

screen. Geczy, Stambaugh and Levin (2003)

find that:

[t]o an investor who believes strongly

in the CAPM and rules out managerial

skill, i.e. a market index investor, the

cost of the SRI constraint is typically

just a few basis points per month,

measured in certainly-equivalent

loss…The SRI constraint imposes

1 “Socially Responsible Investing Facts”, Social

Investment Forum.

http://www.socialinvest.org/resources/sriguide/srifacts.

cfm

5

large costs on investors whose beliefs

allow a substantial amount of fund-

manager skill (1).

These findings are mirrored to a certain extent

by Guerard (1997). Guerard notes:

…the difference between the average

return on socially-screened equity

mutual funds and the 2034 unscreened

equity mutual funds drops from -417

basis points over the past five years to

-105 basis points over the past ten

years, a less meaningful differential,

particularly given the very small

number of socially-screened equity

mutual funds with long-term track

records (2).

While Guerard arrives at similar values as

those suggested by Geczy et. al., he maintains

that this does not represent a substantial

difference in returns between SRI and non-

SRI funds.

To further confound results on the

effectiveness of investing in CSR firms, Hill,

Ainscough, Shank and Manhullang (2006)

argue that on a long enough time horizon,

CSR firms outperform others. According to

Hill et. al.:

…the long-term investment horizon of

10-years (1995-2005) produced alpha

coefficients for the U.S. and European

portfolios that are significant and

positive at the 95% level, revealing

superior long-term financial

performance by socially responsible

firms (171).

Their study used a U.S. portfolio comprising

the same securities as a study done by Shank,

Manullang and Hill (2005). Thus, the

different conclusions arrived at by academics

in the field suggests that the monetary cost of

non-monetary gain in the SRI field has not yet

been determined and may not exist.

Additionally, it might well be argued

that CSR is simply a proxy for strong

corporate governance, which as will be

addressed in the executive compensation

literature review has been shown to predict

financial underperformance and additional

excess compensation for executives.

EXECUTIVE COMPENSATION

LITERATURE REVIEW

Attempting to explain from an ex post

perspective the compensation levels that

executives in the United States receive has

been a very popular subject, especially since

the dramatic increase in executive pay during

the 1990s and the corporate scandals of 2001

and 2002. It will no doubt continue to be a

heavily researched and debated topic after the

fallout of the most recent financial crisis,

especially where CEOs were paid large sums

at firms after strong annual performance but

whose risk exposure during those boom times

likely helped foster the losses they booked in

later fiscal years. This has popularized the

notion of “claw-back” provisions on

previously paid bonuses and in issuing

compensation in the form of restricted stock.

Although interest in this subject area has

remained high, the degree of certainty with

which current models can understand past

compensation still leaves room for future

discovery.

Within the framework of trying to

understand the current factors affecting

executive compensation, one of the central

ideas behind the modern understanding is that

of agency theory, whereby a principal

(shareholder) hires an agent (executive) to

6

perform tasks in the principal’s best interest.

The goal is to create a compensation program

that encourages the agent to act in the best

interest of the principal, since in the absence

of this the agent might pursue objectives more

related to his or her own personal enrichment.

Murphy, Jensen, Gibbons,

Zimmerman and Baker (1998) identify

agency theory as the prominent idea

governing current executive compensation

thought and provide the most comprehensive

overview of executive compensation study for

the years leading up to the publication of their

paper. Jensen et al. note the positive

correlation between company size and

executive pay but also note that this

connection has weakened over time. The

authors also note a “US premium” paid to

executives in the United States, where even

after adjusting for public benefits and

purchasing power parity executives in the

United States are still more highly paid than

their international counterparts.

Expanding beyond the aforementioned

correlation between firm size and

compensation, Aggarwal and Samwick (1999)

suggest the significance of accounting for

stock price volatility when trying to explain

executive compensation in an ex post manner.

The authors find that when some parameter

explaining risk (i.e. dollar return variance) is

not included in the model that the connection

between pay and performance is biased

towards zero.

This study was refuted, however, by

Core and Guay (2002) who determined that

the dollar return variance variable utilized by

Aggarwal and Samwick to stand in for firm

performance volatility was actually in fact a

noisy proxy for firm size. Core et. al. argue

that “because dollar return is nearly perfectly

correlated with firm size, this evidence is also

consistent with a richer agency model in

which firm size is a proxy for agent wealth

constraints and a number of additional factors

determine CEO incentives”(3). Because of

the findings of Core and Guay, dollar return

variance will not be examined in this study.

In terms of trying to classify executive

specific attributes, Belliveau, O’Reilly and

Wade (1996) looked at social capital and its

effects on executive compensation. Belliveau

et al. came to the conclusion that CEOs with

relatively more social capital (i.e. higher

quality of networking, prestige, and

“célébrité”) compared to their compensation

chairs were able to exact more compensation.

While their most explanatory regression

achieved an adjusted R2 of 0.64, the only

determinant variables used by the authors and

cited frequently in the literature on executive

compensation were executive tenure, sales

and return on equity (ROE) (the authors did

not specify whether this was book value ROE

or market value ROE). The authors did not

note from what period these explanatory

variables were from, which a priori would be

extremely important towards explaining

certain levels of executive compensation.

In addition, the authors did not include

explanatory variables for return on assets

(ROA), book to market or an S&P 500

dummy variable, which were all shown in the

research by Core, Guay and Larcker (2008) to

be extremely powerful in explaining

executive compensation. Thus, while

suggesting some interesting ideas, the study

by Belliveau et. al. would need to be

reworked with the inclusion of additional

explanatory variables to confirm the

legitimacy of their assertions.

Although controversial, Bebchuk and

Fried (2005) note that there is a significant

discrepancy between what executives are paid

now and what they would be paid in an arm’s-

length transaction whereby contracting

between the board and the executive is merely

the intersection of the personal interest of

7

both parties. The debate over whether

executive compensation is arrived at by

arm’s-length contracting or through what is

called the executive power model, whereby

executives are able to strong arm

compensation boards in to getting oversized

payouts, has yet to be settled. Much of the

research currently being done on executive

compensation in one way or another is geared

towards trying to determine which model best

explains executive compensation as it

currently exists.

There have also been many studies

that have examined alternative explanations

for varying levels of executive pay outside of

the above mentioned studies, which mainly

focus on straight forward and expected

determinants. Wade, Porac, Pollock and

Graffin (2006) look at CEO certification

contests and their impact on executive

compensation. Their findings principally

conclude that CEOs that have been certified

(what they refer to as “star CEOs”) from the

onset of this award receive higher

compensation. Their certification does not

lead to higher or lower one-year accounting

profits when compared to firms with non-

award winning CEOs. While the higher

compensation is true from the onset, star

CEOs that underperform are typically

compensated less than those non-certified

CEOs performing at a comparable level. This

leads to what Wade et al. refer to as a “double

edged sword”.

Work has also been done trying to

examine the effect of corporate governance

on executive compensation.

Bebchuk and Fried (2003) identify the

following as indications of poor corporate

governance and factors leading to higher

excess compensation:

i) The board is relatively weak or

ineffectual;

ii) there is no large outside

shareholder;

iii) there are fewer institutional

shareholders;

- or -

iv) managers are protected by anti-

takeover arrangements (78)

While Bebchuk and Fried tend to write

controversial pieces regarding executive

compensation, their findings on the positive

correlation between weak corporate

governance and excess compensation (i.e. that

not explained by conventional performance or

company identifier variables) is echoed by

others.

In a paper addressing this same topic,

Core, Holthausen and Larcker (1999) find

similar results as those suggested by Bebchuk

and Fried (2003) in that CEO compensation is

influenced by board-of-director characteristics

and ownership structure, even after

accounting for the typical determinants of

executive pay. Indicators of poor governance

include the CEO also serving as the board

chair, the board being larger, board members

who are older and serving on multiple boards,

the board being made up by a larger

percentage of outside directors and when

these outside directors are appointed by the

CEO. Based on their study, Core et. al. state:

…our results suggest that firms with

weaker governance structures have

greater agency problems; that CEOs at

firms with greater agency problems

extract greater compensation; and that

firms with greater agency problems

perform worse (372-373).

This study unequivocally demonstrates that

CEOs operating at firms designated as having

poor corporate governance receive higher

excess compensation than those working at

firms which are not.

8

Core (2000) builds on studies

suggesting poor corporate governance

influences excess CEO compensation

positively by looking at Director and Officer

(D & O) insurance premiums in Canada as a

proxy for poor corporate governance. Core

finds this to be the case, noting:

D & O premiums are significantly

higher when inside control of share

votes is greater, when inside

ownership is lower, when the board is

comprised of fewer outside directors,

when the CEO has appointed more of

the outside directors, and when inside

officers have employment contracts

(451)

Furthermore, Core notes that excess CEO

compensation is notably higher in firms with

high D & O premiums, further suggesting that

D & O insurance premiums can serve as an

effective proxy for poor corporate governance.

Identifying in the literature on

executive compensation a common regression

model from which one could begin to add in

additional explanatory variables was an

important component of this study. This

model would ideally generate what one would

expect an executive to be paid given

explanatory performance and identification

variables for the firm and executive. Getting

to this model that for all intents and purposes

explained the most variance in executive

compensation would make determining the

causes for firm specific deviations easier.

For all practical purposes, Core, Guay

and Larcker (2008) have provided such a

combination of variables in identifying their

formula for calculating the expected level of

compensation for executives. Core et al.

suggest that the natural logarithm of executive

compensation should follow the following

formula:

Log(Compensationit) = α + xitβ + uit

With factors that hold as proxies for the

economic determinants serving as

determinant variables. These determinant

variables, as identified by the authors, include

the logarithm of sales from the year prior, the

logarithm of executive tenure from the current

year, whether or not the firm is in the S&P

500 in the current year, book value to market

from the prior period, market return for the

given year, market return from the prior year

and return on assets for the given year and

prior year.

HYPOTHESIS

This study posits that classification as

a socially responsible firm will have a

statistically significant, negative effect on

total compensation. As a secondary

hypothesis, this study posits that being

classified as a socially irresponsible firm will

have a statistically significant, positive effect

on total compensation.

The reasoning behind the statistical

significance of the first and second hypothesis

finds its roots in the literature cited in the

introduction of this paper, demonstrating that

non-monetary rewards can have an impact on

job choice and on monetary compensation.

Within the introduction of this paper,

it was theorized that a researcher might not

observe significance in the level of social

responsibility of a firm in terms of affecting

executive compensation if the executive were

to have such control over the firm’s culture

and practices so as to implement his or her

own desired level of social responsibility. If

this were to be the case, an executive would

demand no less amount of compensation to

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work at a historically or currently socially

responsible or irresponsible firm because he

or she could change this. The argument

suggested with the two hypotheses for this

study is that CEOs do not have full control

over the level of social responsibility at their

firms. Rather, that they see the socially

responsible environment in which they get to

work at those companies that have been

included in an index such as the KLD 400 as

a non-monetary compensatory item.

The negative impact suggested by the

first hypothesis of social responsibility on

executive compensation can be explained

using either executive power or arms-length

contracting models of executive

compensation arrangement. This allows the

findings of this paper to stand true regardless

of the direction of the debate on executive

contracting, presuming that the hypotheses

are found to be correct.

Under an executive power model, the

negative sign on the social responsibility

variable can be explained by the executive

simply demanding less compensation because

of the outside non-monetary pay she receives

in the pride or social recognition from being

associated with a socially responsible firm.

Under an arms-length contracting model, both

the compensation committee and the

executive will agree upon a lower

compensation package because they will both

recognize the positive value of being

associated with a socially responsible firm.

The positive effect on executive

compensation expected from being a socially

irresponsible from the second hypothesis was

expected for the same reasons as social

responsibility was expected to have a negative

impact. This would be that managers

working at firms classified as socially

irresponsible would be incurring a cost by

being associated with the irresponsibility of

the firms they work for. Thus, their monetary

compensation would have to be higher to

compensate for the non-monetary cost they

incurred to work for such firms.

The “sin” or social irresponsibility

variable is also added in to verify that the

social responsibility dummy variable is not

simply a noisy proxy for lack of involvement

in questionable industries. In other words, to

verify that the value to executives of social

responsibility is not about the absence of “bad”

practices but rather the presence of “good”

practices. If the sin variable is found to take

away statistical significance from the social

responsibility variable or serves as a better

explanatory variable of executive

compensation, it can be concluded that social

responsibility is merely a proxy for the

absence of “sin” or social irresponsibility in a

company.

DATA

The goal of this study is to provide

insight in to the effect of company

characteristics, such as social responsibility,

on CEO compensation. The proxy for the

social responsibility indicator is inclusion in

the FTSE KLD 400 Social Index (previously

known as KLD’s Domini 400 Social Index).

Started in 1990, the index attempts to provide

a benchmark by taking in to account

environmental, social and governance (ESG)

factors. The index seeks “90% large cap

companies, 9% mid cap companies chosen for

sector diversification, and 1% small cap

companies with exemplary social and

environmental records”2. More specifically

beyond ESG factors, the index breaks down

2 “FTSE KLD 400 SOCIAL INDEX Fact Sheet”

http://www.kld.com/indexes/data/fact_sheet/DS400_Fa

ct_Sheet.pdf

10

their qualitative criteria for companies in to

the topics and criteria3 identified in Table I.

Beyond the company size factors

identified earlier, the qualitative criteria in the

form of ESG factors is scrutinized on a more

subjective basis by analysts working for the

index, with there being an index committee

whose approval is required before any

company may be added or subtracted from the

index. There are also several areas of

involvement for which companies are

immediately removed from consideration for

the index if they have engagement beyond a

specified threshold. These questionable areas,

as identified by KLD, include: abortion (i.e.

the service of abortions), adult entertainment,

alcohol, contraceptives (i.e. the distribution of

contraceptives), firearms, gambling, military,

nuclear power and tobacco4.

An employee at RiskMetrics, which

runs KLD, was kind enough to provide me

with the index constituents as of every

December for 2002 to 2008. This data, which

includes company name and stock ticker,

serves as the foundation for the socially

responsible dummy variable to be utilized in

the regression analysis.

3 “KLD ESG Ratings & Involvement Criteria” KLD

Research & Analytics, Inc., 2009 4 “KLD ESG Ratings & Involvement Criteria” KLD

Research & Analytics, Inc., 2009

The next step is to identify the sample

space from which KLD is likely drawing their

index constituents. After testing the KLD

index for inclusion in various forms of the

S&P 500, the most effective group was

determined to be the combined constituents of

the S&P 500 Large-Cap and S&P 400 Mid-

Cap indices, from here on referred to as the

S&P 900. The S&P 600 Small-Cap was not

used due to the exceptionally small amount

(1%) of the KLD index that could be drawn

from this group.

The yearly constituents for the S&P

900 for the time period considered, 2002 to

2008, is drawn from the Index Constituents

section of the COMPUSTAT database. Since

this information is organized by date added to

a particular index and the date removed from

a particular index, a cutoff is needed to

determine the fiscal year for which a company

was a member of a particular index. If a

company is added to the S&P 500 prior to

December of any given year (i.e. November

31st or earlier), the company is counted as

Environment Community &

Society Customers

Employees &

Supply Chain

Governance &

Ethics

1. Climate Change 1. Philanthropy 1. Marketing &

Advertising

1. Labor-

Management

Relations

1. Reporting &

Engagement

2. Non-Carbon

Releases

2. Impact on

Community

2. Product/Service

Quality & Safety 2. Employee Safety

2. Governance

Structures

3. Impact of

Products & Services

3. Civil Rights: Civil

& Political

3. Anti-Competitive

Practices

3. Workforce

Diversity 3. Business Ethics

4. Resource

Management & Use

4. Customer

Relations

4. Supply Chain

Labor

4. Political

Accountability

Table I

11

being a member of the index for that entire

fiscal year including any subsequent years. If

a company is delisted from an exchange after

November 31st of any given year (i.e.

December 1st and onward), it is considered to

have been a member of the index for that full

fiscal year. In both instances if this case is

not met for a company changing indices

during a particular year, the year is not

counted for either the index being left or the

index being entered.

Explanatory variables, including

revenue, stockholders equity, market value of

equity, total assets and total compensation is

drawn from the COMPUSTAT database for

Executive Compensation and Annual

Fundamentals. Total monthly returns (to

calculate market return) is obtained from the

CRSP/COMPUSTAT merged database. For

all of these variables, data is matched with the

respective company based on year and gvkey

or the matched PermNo associated with the

gvkey. Regrettably, since the ticker symbol is

the only matching data provided by KLD, a

combination of this and the year is used to

determine KLD index membership in the

sample space of companies.

In terms of cleaning the data, the

primary concern is the identifier for whether

or not the company was part of the KLD

index for a given year. Stock tickers are at

best unreliable since tickers can be reassigned

to other companies or an individual company

may change their stock ticker. This is why

the company specific gvkey used in

COMPUSTAT, which is unique to each

individual company and is never reassigned,

is used for matching all other data outside of

the KLD index variable.

Because the social responsibility

effect is the primary issue to be studied, it is

paramount to ensure that there are no false

negatives (false positives are less of a concern,

because it is inconceivable that in a particular

year if the COMPUSTAT database has a

particular stock ticker for a company and the

KLD index data has that same ticker that they

are not indeed referring to the same company).

Out of the 3,200 entries within the KLD index

data provided, spanning from 2002-2009, 250

companies comprising 757 entries could not

be located within the sample space of the

S&P 900 from the same time period.

For any company not found in the

sample space of companies studied, there are

two possible explanations. The first is that

the company simply is not in the S&P 900

studied, which is not altogether unlikely. This

is because FTSE mentions in their

methodology of assembling the KLD index

that certain small caps are selected and some

of the midcaps that are selected possibly do

not fall in the S&P 400 midcap index

considered for this study. The second

possibility is that either the ticker entered in

the S&P 900 data is incorrect or the ticker in

the KLD 400 data is incorrect.

Before beginning any substantial data

processing, there was one ticker that was

obviously a mistake from the KLD index in

2002. This was AOL Time Warner, which

had been ascribed the “AOL” ticker rather

than the acquirer, Time Warner’s, “TWX”.

This was obviously simply a data entry

oversight, easily explained by the acquisition

of AOL by Time Warner in 2000.

Beyond this easy catch, the first step is

to identify out of the “not found” companies

within the KLD data those that indeed have

correct tickers, but simply do not appear in

the sample space of companies because they

are not included in the S&P 900 for the year

examined. To determine this list, the now

249 (minus AOL Time Warner) companies is

pulled through the CRSP/COMPUSTAT

Merged Linking Table, which would also

ascribe gvkeys to those companies that are

included.

12

Of the companies pulled through the

table, there are a total of 73 companies

comprising 104 entries that did in fact have

correct tickers but are simply not found in the

sample space of the S&P 900. The next step

is to see if there are indeed entries in the S&P

900 sample that match the gvkey and year of

any of the 104 entries, but simply have

“wrong” tickers in place. For five companies

comprising 26 entries this is the case,

subsequently the tickers in the S&P 900 space

are changed to agree.

This leaves 68 companies comprising

78 entries that are simply not in the sample

space of companies. The remaining 176

companies comprising 661 entries needs to be

checked by hand based on the full company

name. After performing this check, 105

entries are found to actually be within the

sample space, whereas the remaining 556

entries are confirmed to have not been

contained in S&P 900 for the specified year.

Additionally, it is necessary to

associate the company SIC codes with the

written industry description such that I could

associate the companies involved in industries

that are automatically disqualified from KLD

analysis. The list of paired SIC codes and

written industry description is obtained from

the SEC website5

, however the actual

assignment of SIC codes to each particular

company is obtained from the COMPUSTAT

database.

The dataset is then checked to assure

that for each company their SIC code

corresponds with a written description

provided by the SEC. There are found to be

413 entries whose SIC code does not match

with a subsequent description from the SEC.

The vast majority of these entries are regional

or national banks (coded: “6021” instead of

the SEC’s “6020”), followed by several

5 http://www.sec.gov/info/edgar/siccodes.htm

conglomerates and at least two alcohol

manufacturers. In the instance of Leucadia

National (a conglomerate with no matchup for

SIC), the company is assigned the tag of an

alcohol distributor for its holding of winerys6,

which according to the KLD Ratings and

Involvement Criteria for 2009 disqualified the

company from inclusion7

. While this

certainly does not represent a majority of the

company’s business, given the emphasis of

this study and Leucadia’s absence of a

previously assigned code, it is felt to be

prudent to match the KLD standards of

screening and ascribe Leucadia the alcohol

marker.

In terms of the robustness of using

SIC codes to code for industry, as is

witnessed above there is no perfect overlap

for standard usage of any given SIC code. At

times assignment can be subjective and

different providers of classifications will at

times be in disagreement. Several studies

have been done regarding the use of SIC

codes, including that by Guenther and

Rosman (1994). As cited by the authors,

differences between different SIC code

providers has been witnessed in the 20%

range. In addition, the SEC, as cited by the

authors, acknowledges that when one single

product line or business for a given company

is difficult to determine, the resulting SIC

assignment will appear subjective.

Because the SIC codes utilized are

those provided by COMPUSTAT, it is of the

utmost importance to identify the

methodology for SIC assignment and whether

or not this is in fact robust. As identified by

Guenther and Rosman, the methodology for

SIC code assignment (as of the time of their

paper) includes the following:

6 Leucadia National Corp. Form 10-K for FY 2009.

http://www.sec.gov/Archives/edgar/data/96223/000009

622310000004/leucadia200910k.htm 7 “KLD ESG Ratings & Involvement Criteria” KLD

Research & Analytics, Inc., 2009

13

1. Group SIC codes together by

major groups (e.g., all codes from

2801 to 2899) based on the business

segment breakout given by the

company, or the principal products for

a single segment company.

2. Compare related SICs within major

groups to see if one specific SIC

within a major group accounts for 50

percent or more of group sales; if so,

choose that specific SIC.

3. Choose a more general code if a

more specific is not applicable or

available (e.g., Office of Management

and Budget guidelines require there to

be at least six companies in an

industry) ( 117-118).

The SIC codes used to identify

companies in industries who would receive

immediate disqualification from further

consideration can be seen in Table II.

This yielded 101 entries that would be

assigned the “Socially Irresponsible” binary

variable.

Because the values for total

compensation that are found in the

COMPUSTAT database are determined to be

in thousands of dollars while the entries for

revenue, book value of shareholder equity,

market value of shareholder equity, net assets

and net income are determined to be in

millions of dollars, a change was made to put

total compensation in millions of dollars.

Entries are dropped from

consideration when data is missing for one of

the statistically significant explanatory

variables as identified by the model of

expected executive compensation from Core

et. al. (i.e. logarithm of sales, book to market,

RETt, RETt-1, ROAt and ROAt-1). The only

explanatory variable in their expected

executive compensation model for which

entries are not dropped in their entirety for

missing values is for executive tenure. This is

because there are several missing entries for

“date became” CEO as obtained from

COMPUSTAT, and given executive tenure’s

relatively low significance in the predicted

compensation model, it is determined that

executive tenure can simply be omitted for

entries for which it cannot be correctly

calculated. This is done in the interest of

protecting a large number of otherwise

complete entries.

This left 5,913 entries for the time

period 2002 to 2008 used in this study

(however when executive tenure is included

in regressions this drops to 5,751

observations). Table III provides a

correlation table between the social

responsibility dummy variable (social binary),

the social irresponsibility dummy variable

(Sin Binary) and the S&P 500 dummy

2082 MALT BEVERAGES

2100 TOBACCO PRODUCTS

2111 CIGARETTES

3760 GUIDED MISSILES & SPACE VEHICLES & PARTS

5180 WHOLESALE-BEER, WINE & DISTILLED ALCOHOLIC BEVERAGES

Table II

14

variable (S&P 500 binary), followed by the

mean value for each one of these variables in

the dataset. Table IV provides the mean value

for the Social, Sin and S&P 500 binary by

year.

Additionally, Table V provides

summary statistics for all of the explanatory

variables used in this study, excluding the

social, sin and S&P 500 binary variables.

Table VI lists the mean for the same

explanatory variables broken out by year.

Table VII breaks out the summary statistics

provided in Table V for the same explanatory

variables by whether the firm is socially

responsible or not. Finally, Table VIII

provides a correlation table between total

compensation, book to market, revenue from

the prior period, executive tenure, the S&P

500 binary variable, the social binary variable

and the sin binary variable. Of notable

interest in this correlation table is the strong

positive correlation between being an S&P

500 company and total compensation and

lagged revenues. In addition, there is also a

strong positive correlation between being an

S&P 500 company and being a socially

responsible company.

METHODOLOGY

The goal for this study is to imitate the

expected executive total compensation model

utilized by Core, Guay and Larcker (2008)

while adding a socially responsible and

irresponsible dummy variable. As identified

earlier, this model used the logarithm of sales,

the logarithm of executive tenure, whether or

not the firm was in the S&P 500, book value

to market from the prior period, market return

for the given year, market return from the

prior year and return on assets for the given

year and prior year.

In their 2008 study, Core et. Al. were

primarily concerned with excess

compensation, identified as:

Excess Compensation = (Total Received

Compensation) – (Expected Compensation)

Whereby expected compensation was simply

that which could be predicted utilizing

commonly accepted firm performance and

identifier variables and executive identifier

variables (i.e. logarithm of executive tenure).

This relatively simple regression

model would serve as the baseline test for the

explanatory power of a social or sin dummy

variable on compensation. Only after either

of these indicators was proven statistically

significant at this baseline level could one

begin to start adding in additional elements

for which the academic community studying

executive compensation has deemed to be

significant.

To run the expected compensation

regression model as identified by Core et. al.,

it is necessary to calculate values for market

return for the given year and prior year and

the same for return on assets.

Part of the concern when calculating

the market return (RET) values was up to

what point this should be calculated. For

example, while company performance

information can be found on an annual fiscal

year basis, because this information is not

released to the market until typically three

months after the end of the fiscal year, the

question would be whether the stock price

performance solely from the start to finish of

the fiscal year is an adequate snapshot of the

company’s market performance. This is

because if this is the only time period

considered, the market technically may not be

fully aware of the operating data from the

prior year.

15

Mean Standard

Deviation Minimum Maximum

25th

Percentile

50th

Percentile

75th

Percentile

Total

Compensationt 7.31 7.15 0.29 40.57 2.80 5.08 9.11

Book/Markett-1 0.43 0.26 -0.01 1.34 0.25 0.39 0.57

Revenuet-1 8493.70 14877.97 215.30 94713.00 1311.74 3055.42 8679.31

Executive Tenuret 7.15 6.71 0.50 35.02 2.56 5.00 9.22

RETt-1 0.13 0.35 -0.67 1.37 -0.07 0.10 0.30

RETt 0.06 0.36 -0.76 1.24 -0.15 0.06 0.26

ROAt 0.03 0.04 -0.14 0.14 0.01 0.03 0.05

ROAt-1 0.03 0.04 -0.13 0.14 0.01 0.03 0.05

Social

Binary

Sin

Binary S&P 500

Binary Correlation Table

Social Binary 1

Sin Binary -0.08 1

S&P 500 Binary 0.31 0.06 1

Variable Mean

Value 0.35 0.01 0.56

Mean Value By

Year

Social

Binary Sin Binary

S&P 500

Binary

2002 0.34 0.01 0.56

2003 0.33 0.01 0.56

2004 0.34 0.01 0.56

2005 0.35 0.01 0.55

2006 0.36 0.01 0.55

2007 0.35 0.01 0.56

2008 0.37 0.01 0.57

Total 0.35 0.01 0.56

Table III

Table IV

Table V

16

Mean

Value By

Year

Total

Compensationt Book/Markett-1 Revenuet-1

Executive

Tenuret RETt-1 RETt ROAt ROAt-1

2002 6.88 0.43 7609.76 6.96 0.06 -0.14 0.01 0.02

2003 6.49 0.53 7191.95 7.08 -0.12 0.37 0.02 0.02

2004 7.19 0.43 7733.56 7.31 0.40 0.17 0.03 0.02

2005 7.54 0.42 8259.48 7.17 0.19 0.12 0.03 0.03

2006 7.93 0.41 9199.70 7.37 0.13 0.14 0.03 0.03

2007 7.80 0.40 9813.80 7.02 0.15 0.05 0.03 0.04

2008 7.43 0.42 9894.75 7.15 0.10 -0.32 0.02 0.04

Total 7.31 0.43 8493.70 7.15 0.13 0.06 0.03 0.03

Not Socially

Responsible Mean

Standard

Deviation Minimum Maximum

25th

Percentile

50th

Percentile

75th

Percentile

Total Compensationt 6.97 7.14 0.29 40.57 2.62 4.67 8.42

Book/Markett-1 0.45 0.26 -0.01 1.34 0.26 0.41 0.59

Revenuet-1 7646.76 14946.80 215.30 94713.00 1079.80 2497.00 7316.22

Executive Tenuret 7.26 6.75 0.50 35.02 2.58 5.17 9.45

RETt-1 0.14 0.37 -0.67 1.37 -0.07 0.11 0.32

RETt 0.07 0.37 -0.76 1.24 -0.15 0.07 0.27

ROAt 0.02 0.04 -0.14 0.14 0.01 0.02 0.04

ROAt-1 0.02 0.04 -0.13 0.14 0.01 0.02 0.04

Socially Responsible

Total Compensationt 7.97 7.14 0.29 40.57 3.14 5.87 10.28

Book/Markett-1 0.40 0.24 -0.01 1.34 0.22 0.35 0.54

Revenuet-1 10082.53 14619.79 215.30 94713.00 1915.20 4577.23 10792.59

Executive Tenuret 6.95 6.62 0.50 35.02 2.50 4.77 8.93

RETt-1 0.11 0.31 -0.67 1.37 -0.08 0.09 0.26

RETt 0.05 0.33 -0.76 1.24 -0.16 0.05 0.24

ROAt 0.03 0.04 -0.14 0.14 0.01 0.03 0.05

ROAt-1 0.03 0.04 -0.13 0.14 0.01 0.03 0.05

Table VI

Table VII

17

This is also dependent upon

perceptions of efficient market hypothesis

(EMH). If one were presuming that markets

function in a very strong manner of EMH,

then there would be no need to calibrate for

the delay in release of the annual performance

of the company to the market, since market

prices would already reflect this.

If one were presuming that markets

operate in a very weak version of EMH,

however, this would suggest that RET need

be calculated for the year ended the date that

the company’s annual performance

information (i.e. 10-K) was released to the

market. Rather than speculate on the type of

EMH that is most realistic, RET for the

trailing twelve months (TTM) as of the fiscal

year end date and the fiscal year end date plus

three months is calculated.

Obtained through CRSP, the total

monthly return is used to calculated trailing

twelve month (TTM) RET in the normal and

plus three month scenario. This is done by

compounding the returns in the following

manner:

(1 + monthly RET1) * (1 + monthly RET2)

* … (1 + monthly RET12) – 1

Using fiscal year end date data obtained

through COMPUSTAT for any given

company on any given year in the S&P 900

data set, TTM RET was calculated for the

given year, prior year, given year starting

three months after fiscal year end date and

prior year starting three months after fiscal

year end date.

Although both versions of RET are

tested in the model, the simple TTM RET (i.e.

TTM from fiscal year end date) possessed

more explanatory power. Because of this, the

simple RET is the one utilized in the final

model.

Return on assets (ROA) was

calculated for the given and prior year for any

given entry in the S&P 900 database based on

the following formula:

Net Incomet / [(Total Assetst-1 + Total Assetst)

/ 2]

Where t equals the year for which ROA was

to be calculated. To assure that this was the

most explanatory methodology of calculating

ROA, an alternate form of ROA was also

Total

Compensationt Book/Markett-1 Revenuet-1

Executive

Tenuret

S&P

500

Binaryt

Social

Binaryt

Sin

Binaryt

Total

Compensationt 1

Book/Markett-1 -0.08 1

Revenuet-1 0.43 0.00 1

Executive Tenuret 0.02 -0.05 -0.06 1

S&P 500 Binaryt 0.37 -0.12 0.37 -0.09 1

Social Binaryt 0.06 -0.10 0.07 -0.02 0.31 1

Sin Binaryt 0.04 -0.03 0.05 -0.03 0.08 -0.07 1

Table VIII

18

tested:

Net Incomet / Total Assetst

Because the original (i.e. net income divided

by average assets of the period) was

determined to have more explanatory power,

this was the ROA used in the final model.

Within the model for expected

executive compensation as specified by Core

et. al. is a value for executive tenure. This is

calculated by number of years passed from

the date the executive became CEO to the end

of the current fiscal year being examined.

The date that the executive became CEO is

found through COMPUSTAT.

Prior to performing the regression

analysis, dummy variables for year and two

digit SIC code are created. These variables

are found to be significant and thus are

included in the final regression analysis. In

addition, because Core et. al. did not account

for firm fixed effects in their study, this will

not be done in this study.

In addition, although Core et. Al. did

not winsorize their data, this is done in this

study at the one percent level to eliminate

irregularities presented in the data. Several of

the companies in the company set analyzed

included compensation levels far outside of

the observed distribution, for which

winsorizing the data helped to correct.

Finally, the logarithm is taken of

revenue from the prior year, executive tenure

from the current year and total compensation

from the prior year to agree with the model as

VARIABLES Logarithm of Total Compensationt

Logarithm of Revenuet-1 0.32***

(0.02)

Logarithm of Executive Tenuret 0.02

(0.02)

Book/markett-1 -0.35***

(0.08)

RETt 0.29***

(0.05)

RETt-1 0.23***

(0.05)

ROAt -0.23

(0.47)

ROAt-1 -0.70*

(0.41)

S&P 500 Binary 0.30***

(0.04)

Constant -1.30***

(0.17)

Observations 5,751

R-squared 0.346

Robust standard errors in parentheses

Year and SIC dummies included in regression

but not shown above.

*** p<0.01, ** p<0.05, * p<0.1

Table IX

19

specified by Core et. al. Regression analysis

was also performed by accounting for

clustering around each firm specific gvkey.

While this was not performed in the Core et.

al. methodology, it is utilized in this test to

identify persistence in values connected to

each firm specific gvkey.

RESULTS

First, the Core et. al. expected

compensation regression is performed on the

dataset. This generates an R2 value of 0.346.

The results of this can be seen in Table IX.

When adding in the social binary

variable to the expected compensation model

outlined by Core et. al., the variable is found

to have a statistically negative effect at the 5%

level. This regression generated an R2 value

of 0.348. The results from this regression can

be seen in Table X.

The dollar effect of this variable can

be backed out of the current regression model

explaining the logarithm of total

compensation where:

Log(R) = a + bv1 + cv2 + … zvn

The formula can be rewritten as:

R = (10a)*(10

bv1)*(10

cv2)*…(10

zvn)

VARIABLES Logarithm of Total Compensationt

Logarithm of Revenuet-1 0.32***

(0.02)

Logarithm of Executive Tenuret 0.02

(0.02)

Book/markett-1 -0.35***

(0.08)

RETt 0.28***

(0.05)

RETt-1 0.22***

(0.05)

ROAt -0.16

(0.47)

ROAt-1 -0.64

(0.42)

S&P 500 Binary 0.33***

(0.05)

Social Binary -0.09**

(0.04)

Constant -1.30***

(0.16)

Observations 5,751

R-squared 0.348

Robust standard errors in parentheses

Year and SIC dummies included in regression

but not shown above.

*** p<0.01, ** p<0.05, * p<0.1

Table X

20

If we let b be the coefficient on the social

binary variable (denoted v1), then total

compensation R when v1 = 0 will be:

R(v1=0) = (10a)*(10

cv2)*…(10

zvn)

Thus for v1=1, the equation can be rewritten

as:

R(v1=1) = R(v1=0)*(10bv

1)

If we use the mean of total

compensation for all firms of $7.31 million

(Table V) and the coefficient on the social

binary variable of -0.09 (Table X), then this

would generate an expected value of total

compensation when v1=1 of $5.94 million, a

difference of $1.37 million. This is clearly a

very economically significant amount.

To check that the sign on the social

binary variable is correct, only the social

binary variable, year and industry dummy

variables are regressed on the logarithm of

total compensation. This is also done for the

social binary variable and S&P 500 binary

variable and the social, sin and S&P 500

binary variables (always including industry

and year dummy variables). These results can

be seen in Tables XI and XII.

When only the social binary is

regressed on the logarithm of total

compensation, the sign on the coefficient is

positive. This can however be explained by

the significant correlation between being

categorized as a socially responsible firm and

being in the S&P 500. When the S&P 500

binary variable is added in, the coefficient

once again returns to a negative value and

some statistical significance is stripped away.

As can be seen by the regression involving

the sin, social and S&P 500 binary variable,

the sin binary did not add any explanatory

power to the model. This persisted at the

level where all explanatory variables are

included, so it is dropped from the final

regression model.

Perhaps the lack of significance of the

sin binary can be explained by the

exceptionally small number of observations

recorded for this variable, for which even if it

were extremely significant would have

prevented it from having an impact in the

model. Perhaps this is why the test to

determine whether the social and sin binary

variables are statistically significantly

different fails to be true at any commonly

accepted level of significance.

DISCUSSION

This study determined that the

inclusion of a dummy variable corresponding

with the KLD 400 Social Index added

statistically significant explanatory power to

the expected executive compensation model

outlined by Core, Guay and Larcker (2008).

However, the social responsibility dummy

variable failed to be significantly different

from a dummy variable representing social

irresponsibility, composed of all companies

with SIC codes matching industries for which

the KLD 400 immediately excludes from

consideration.

The reason for the KLD 400 Index’s

explanatory power in executive compensation

is somewhat debatable. Besides potentially

being a proxy for some other element that all

socially responsible firms share but those that

are not in the list do not, the question would

also be whether the variable’s significance

comes by way of being a indicator or as a

defining element.

21

For example, if executives look to the

KLD 400 Index to extract the social

responsibility level of various firms, inclusion

on this list is going to be very important.

However, it might be that the KLD index

simply serves as an indicator for whether

others are also likely to view the firm as

socially responsible.

If the KLD index is merely a proxy for

some other variable or indicator that is a

better predictor of social responsibility, then

the variable or indicator would serve as a

better explanatory variable in understanding

executive compensation from an ex post

manner than the KLD index.

As identified in the literature review

of current work on executive compensation, it

has also been determined that poor corporate

governance has a statistically significant

positive impact on excess executive

compensation. It is possible that social

responsibility is simply a noisy proxy for

strong corporate governance. Although

including explanatory variables representing

corporate governance was outside the scope

of this study, interacting a variable such as

inclusion in the KLD index with a proxy for

corporate responsibility would be necessary to

conclude that social responsibility is

separately explanatory.

Presuming social responsibility was

not a proxy for strong corporate governance,

identifying causality would be the next step.

This could be done by finding a real-world

situation where the effect of social

responsibility on executive compensation was

observable. An example of such a situation

would be one where social moors changed

dramatically (i.e. companies were assigned

different social responsibility rankings very

quickly) and then studying what impact (if

any) this had on executive compensation.

In addition, other methodologies such

as a paired study (i.e. company X which has

been deemed socially responsible with

company Y of similar size/industry etc. which

has not been deemed socially responsible)

would be important in further documenting

the effect of social responsibility. This would

be an element that would need to be included

in additional study.

Moreover, attempting to develop a

more sophisticated portfolio of socially

irresponsible companies or “sin” companies

would be helpful. This study simply

Table XI

22

employed five SIC codes that unequivocally

can be associated with companies screened

out immediately by SRI indices such as the

KLD 400. However, this does not include

companies with “irresponsible” revenue

streams (such as ones supporting abortion,

involved in nuclear power, or the production

of firearms) since specific SIC code

designations do not exist for these industries.

In addition, companies that derive a

significant amount of their revenue from

socially irresponsible business lines will not

be captured in the simple “sin” portfolio

identified in this study.

Table XII

23

REFERENCES

Aggarwal, R.K., and A.A. Samwick:

1999, ‘The Other Side of the Trade-Off: The

Impact of Risk on Executive Compensation’,

Journal of Political Economy 107, 65-105.

Bebchuk, L.A., and J.M. Fried:

2003, ‘Executive Compensation as an Agency

Problem’, The Journal of Economic Perspectives

17(3), 71-92.

Bebchuk, L.A., and J.M. Fried:

2005, ‘Pay Without Performance: Overview of the

Issues’, Journal of Applied Corporate Finance

17(4), 8-23.

Belliveau, M.A., C. O’Reilly and J. Wade:

1996, ‘Social Capital at the Top: Effects of Social

Similarity and Status on CEO Compensation’, The

Academy of Management Journal 39(6), 1568-

1593.

Core, J.E., R.W. Holthausen and D.F. Larcker:

1999, ‘Corporate governance, chief executive

officer compensation, and firm performance’,

Journal of Financial Economics 51, 371-406.

Core, J.E.:

2000, ‘The Directors’ and Officers’ Insurance

Premium: An Outside Assessment of the Quality

of Corporate Governance’, The Journal of Law,

Economics, & Organization 16(2), 449-477

Core, J., and W. Guay:

2002, ‘The Other Side of the Trade-Off: The

Impact of Risk on Executive Compensation, A

Revised Comment’, The Wharton School,

University of Pennsylvania.

Core, J.E., W. Guay and D. F. Larcker:

2008, ‘The power of the pen and executive

compensation’, Journal of Financial Economics

88, 1-25.

Frank, R.H.:

1996, ‘What Price the Moral High Ground?’,

Southern Economic Journal 63(1), 1-17.

Geczy, C.C., R.F. Stambaugh and D. Levin:

2003, ‘Investing in Socially Responsible Mutual

Funds’, The Wharton School; University of

Pennsylvania.

Guenther, D.A. and A.J. Rosman:

1994, ‘Differences between COMPUSTAT and

CRSP SIC codes and related effects on research’,

Journal of Accounting and Economics 18, 115-128.

Guerard, J.B.:

1997, ‘Is There a Cost to Being Socially

Responsible in Investing?’.

Hill, R.P., T. Ainscough, T. Shank and D.

Manullang:

2006, ‘Corporate Social Responsibility and

Socially Responsible Investing: A Global

Perspective’, Journal of Business Ethics 70, 165-

174.

Jensen, M.C., and K.J. Murphy:

1990, ‘CEO Incentives—It’s Not How Much You

Pay, But How’, Harvard Business Review 3, 138-

153.

Judge, T.A., and R.D. Bretz:

1991, ‘The Effects of Work Values on Job Choice

Decisions’, Center for Advanced Human Resource

Studies.

Murphy, K. J.:

1995, ‘Executive Compensation and the Modern

Industrial Revolution’, University of Southern

California.

Muñoz-Torres, M.J., M.A. Fernández-Izquierdo

and M.R. Balaguer-Franch:

2004, ‘The social responsibility performance of

ethical and solidarity funds: an approach to the

case of Spain’, Business Ethics: A European

Review 13(2), 200-218.

Murphy, K.J., M. Jensen, R. Gibbons, J.

Zimmerman and G. Baker:

1998, ‘Executive Compensation’, Marshall School

of Business, University of Southern California.

Renneboog, L., J.T. Horst and C. Zhang:

2007, ‘Socially responsible investments:

Institutional aspects, performance, and investor

behavior’, Journal of Banking & Finance 32,

1723-1742

Shank, T. M., D. Manullang and R. P. Hill:

2005, ‘Doing Well While Doing Good’ Revisited:

A Study of Socially Responsible Firm’s Short-

term versus Long-term Performance’, Managerial

Finance 30(8), 33-46.

24

Marks, S.:

2008, ‘A Comment on the Relationship Between

Judicial Salary and Judicial Quality’, Boston

University Law Review 88(843), 843-853.

Wade, J.B., J.F. Porac, T.G. Pollock and S.D.

Graffin:

2006, ‘The Burden of Celebrity: The Impact of

CEO Certification Contests on CEO Pay and

Performance’, Academy of Management Journal

49(4), 643-660.