The Effect of Audit Quality on Cash Incentive Compensation

8/8/2019 The Effect of Audit Quality on Cash Incentive Compensation

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The Effect of Audit Quality on Cash

Incentive Compensation

1 Introduction

The effects of audit quality on various firm parameters have been a productive

area of research for some time. Auditing is one of the main ways in which the

agency problem of a firm can be mitigated. Previous research has extensively

studied the effects of audit quality on earnings attributes of firms. However,

the effects of audit quality on the agency aspects of a firm have not been

studied in detail. Accordingly, in this paper we investigate the effect that

audit quality would have on managerial incentives.

Motivating an agent to take actions in the best interest of the princi-

pal is an important area of research in several academic disciplines includ-

ing finance, accounting, economics, management etc. It is widely accepted

that performance linked compensation is the most effective way of obtaining

such congruence, when the managerial effort is unobservable by the principal

(Jensen and Meckling, 1976; Murphy, 1999). Accounting measures of perfor-

mance, most notably earnings are widely used in compensation contracts to

link pay to performance (Lambert, 1993).

But accounting earnings can be easily manipulated by the management

(Dechow et al, 1995). In fact Healy (1985) finds that managers engage in

earnings management specifically to impact their incentive payments. How-

ever, if there is an external auditor, who can assure the quality of earnings,

the principal will have more faith in earnings and place greater weightage in

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that measure in designing the managers compensation. Prior research shows

that specialist auditors perform a higher quality audit1 than non specialist

auditors (Krishnan, 2003; Balsam et al, 2003). It also shows that higher

quality auditors ensure a lower level of earnings management resulting in

higher quality earnings (Becker et al, 1998). According to agency theory.

the improvement in earnings quality makes the earnings measure more at-

tractive for contracting purposes. Hence, we argue that specialist auditors

will cause the principal to place a greater weight on earnings in the managers

compensation compared to non-specialist auditors.

DeAngelo (1981) posits that the BigX2 auditors provide better audit qual-

ity than non-BigX firms. Audit quality is defined as the joint probability that

the auditor will (1) detect breaches in the clients accounting system, and

(2) will report them. It is widely accepted in the literature that Big X audi-

tors provide, or are perceived to provide, higher levels of audit quality (e.g.,

Teoh and Wong 1993, Becker et al. 1998, Francis et al. 1999). After a series

of mergers among the Big X and the recent collapse of Andersen, the Big 4

1DeAngelo (1981) defines audit quality as the ability to detect and the willingness toreport breaches in the clients accounting system. Hence, audit quality becomes a functionof the technical expertise of the auditor, which will enable him to detect breaches and theauditors political power vis-a-vis the managers of the firm that will enable him to reportthe breaches to the shareholders.

2BigX firms refer to the large public accounting firms that perform most of the audits

for publicly traded firms. The BigX were called the Big8 audit firms in the 1970s and the1980s. The Big8 consisted of Arthur Andersen, Arthur Young, Coopers & Lybrand, Ernst& Whinney, Deloitte, Haskins & Sells, Peat Marwick International, Price Waterhouse &Touche Ross. In 1989, Ernst and Whinney merged with Arthur Young to become Ernstand Young and Deloitte, Haskins & Sells merged with Touche Ross to become Deloitte& Touche. From 1989, the BigX were referred to as the Big6. Price Waterhouse mergedwith Coopers & Lybrand in 1998 to become PricewaterhouseCoopers and the Big6 becamethe Big5. Arthur Andersen collapsed after the firms indictment for obstruction of justiceinvolving Enron in 2002, and the Big5 became the Big4. Peat Marwick Internationalbecame KPMG. Hence the Big4 now consists of PricewaterhouseCoopers, KPMG, Ernst& Young and Deloitte & Touche.

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audit the majority of publicly traded companies in the US.

This study contributes to the literature in the following ways. First, we

show that the weight placed on accounting earnings in determining executive

cash compensation increases with audit quality. We also show that this

effect is valid for both CEOs and non-CEO executives. Finally, we examine

the impact of industry specialization as an audit quality attribute in a new

setting, namely executive compensation.

The paper is structured as follows. Section 2 contains the literature re-

view. Section 3 describes the analytical model and hypothesis development.

Section 4 describes the data and the research methodology. Section 5 ana-

lyzes the empirical data. Section 6 contains sensitivity analysis and alterna-

tive explanations while Section 7 discusses the results and concludes.

2 Literature Review

CEOs

Most of the literature in compensation looks at CEO compensation. Both

CEO compensation and academic research on CEO compensation has grown

rapidly in recent years.3 The financial press too is actively looking into the

perceived excesses in CEO compensation.4 Hence, CEO pay is a matter

of utmost interest to academics, practitioners, regulators and the public.

We attempt to provide additional insight to this evolving field of executive

compensation.

3Murphy (1999) says that CEO pay research has grown even faster than CEO pay-checks.

4A search on Lexis-Nexis for CEO pay for May 2006 shows 22 hits.

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In the modern business environment, the functions of ownership and man-

agement are separate. In such circumstances, classic agency theory states

that the owner (principal) can get the manager (agent) to maximize the

principals utility through monitoring and incentive alignment (Jensen and

Meckling, 1976). We investigate the effect of audit quality on the monitoring

aspect and incentive alignment aspect of managerial performance.

Theoretical research into agency theory shows that to motivate risk averse

managers into exerting effort so as to maximize the utility to the principal,

the managers incentives should be aligned to the performance of the firm.

Holmstrom (1979) shows that when the ultimate outcome alone is observable

(and not managers effort), the optimal contract will be the second best

contract, meaning the contract will be based on the observable outcome.

Banker and Datar (1989) show that different signals of firm performance

can be aggregated into an optimal measure of performance on which to base

the compensation contract. Jensen and Murphy (1990: p242) summarize the

essence of agency theory as Agency theory predicts that an optimal contract

will tie the managers expected utility to principals wealth; therefore agency

theory predicts that CEO compensation policies will depend on changes in

shareholder wealth.

There is an extensive stream of literature that empirically analyzes the

sensitivity of CEO pay to firm performance. Smith and Watts (1982) state

that incentive plans explicitly tie CEOs performance to changes in firm

value. They also state that cash incentives are tied ex ante to some

measure of firm profit. Lambert and Larcker (1987) analyze a sample of

firms from the Forbes annual compensation survey for the period 1970-1984

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and show that profits are strongly related to cash compensation. Jensen

and Murphy (1990) show that CEO compensation is directly proportional to

increases in shareholder wealth, and that the pay increases by $3.50 for every

$1000 increase in shareholder wealth. Sloan (1993) investigates the relative

weights placed on earnings and stock returns in CEO cash compensation,

and show that the relative weights depend on the noise of the two signals.

More recently, Core et al (2003) document the positive relationship between

earnings and CEO pay.

CEO compensation broadly consists of three attributes (Murphy, 1999;

Conyon, 2006). One is the base salary, which is one of two cash based

components. The base salary is generally a fixed amount negotiated by the

CEO beforehand. The other cash component is the bonus, which is generally

based on the ex-post performance. Finally there is a component of equity and

long term stock awards. In addition to the stock options awarded annually,

the CEO will also benefit from the increase in value of his existing equity

and options of the firm. Furthermore, a category not identified above is other

perks such as the use of the corporate jet, which presents another avenue of

implicit perks for the CEO.

According to Lambert & Larcker (1987) the cash component comprised

around 80%-90% of the total CEO compensation in the mid 1980s. However,

with the explosive growth in CEO options, this proportion has been declining.

Conyon (2006) shows that the cash component (base salary + bonus) of CEO

compensation was 52% in 2003. Due to this decline, there is a tendency to

ignore the importance of the cash component in compensation. However, it

is important to note that a significant proportion of compensation is in the

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form of cash, and there are still some parts of this puzzle that are not totally

understood.

The incentives for cash compensation and equity compensation are dif-

ferent. Cash compensation is generally linked to past performance. Bonuses

are mostly linked to the prior periods accounting numbers (Murphy, 1999).

Hence cash compensation shows a strong positive relationship to accounting

numbers (Lambert & Larcker, 1987; Sloan, 1993). This indicates that the

cash component of compensation is a reward for past performance.

On the other hand, much theoretical work hypothesizes that equity com-

pensation is intended to induce risk-averse managers to take risks on behalf

of shareholders (Jensen & Meckling, 1976; Copeland et al, 2005, pg 487;

Murphy, 1999). Equity compensation will provide incentives for the man-

agers to maximize shareholder wealth by tying the managers compensation

directly to increases in the stock price (Core et al, 2003; Hanlon et al, 2003).

Hence, it is apparent that equity compensation is a method to induce risk

taking by the managers. Smith & Watts (1992) show that firms in industries

with greater investment opportunities will be more likely to use stock options

in compensating their CEOs. Rajgopal and Shevlin (2002) analyze the risk

taking behavior of a sample of managers from the oil and gas industry and

show that equity compensation is related to risk taking. If so, it would mean

that equity compensation will not be related to past accounting performance.

Instead, it will be related to future performance of the firm. Hanlon et al

(2003) show that equity compensation is related to future earnings, while

Baber et al (1996) and Core et al (1999) find that there is no relationship be-

tween equity compensation and past accounting performance. Hence, prior

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research illustrates two distinct and sepatrate motives for cash and equity

compensation.

Lewellen et al (1987) explicitly states this dichotomy in the composition

of CEO compensation.

Consider first, the role of immediate forms of compensation - salary and cur-rent bonus5- in the pay package. By whatever formula these payments are es-tablished, the payoff to the executive/recipient necessarily reflects only the firmsrevealed performance up to the payment date; the amounts involved are fixedwhen awarded and as received are independent of the firms subsequent perfor-

mance. Moreover bonus awards are typically based on short-term performancemeasures such as current year profits or return on equity. The time horizon rel-evant to shareholders however is in principle unlimited since all future residualcashflows the firm is expected to generate should be impounded in share prices.Managers therefore may need to be given an explicit claim to those future cashflows in order to encourage proper attention to decisions that will favorably affectthem.

This can be accomplished either by conveying to managers an equity interest

in the firm through some type of restricted stock compensation or by deferring a

portion of cash compensation. - Lewellen et al (1987) pg 289.

This leads us to conclude that since past earnings will be important only

in determining cash compensation, earnings quality will be important to cash

compensation, but not to equity compensation. Audit quality will impact

past accounting returns. And since we are investigating the effect of audit

quality on compensation, we posit that audit quality will have greater impact

on the cash component of CEO compensation than on the equity component.

Non-CEO Executives

Agency theory posits that the effect predicted for managers would apply

to non-CEO executives as well. However, the non-CEO managers can not

directly control the accounting profits of a firm, since each may be responsi-

ble only for a single functional or geographical area of performance. On the

5salary and bonus is the cash component of compensation

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other hand, when all non-CEO executives are considered in the aggregate,

the common feature of their performance will be the impact on firm prof-

itability. Therefore, the question arises whether it is fair to let the overall firm

performance (calculated in terms of ROA or ROE) impact the compensation

of non-CEO executives?

There has been relatively little research on non-CEO executive compen-

sation, with most of it appearing in management literature. When it comes

to determining the compensation of non-CEO executives, Balkin & Gomez-

Mejia (1990) find that in a study of compensation of business units within

firms, corporate profitability is linked to the pay levels. Fisher & Govindara-

jan (1992) analyze the compensation of profit center managers and find that

Return on Equity of the firm is significantly and positively related to the

compensation level of profit center managers.

Lambert et al (1993) conduct a study on the compensation of 4 distinct

organizational levels (ranging from plant manager to CEO) on a sample of

303 publicly traded firms during the years 1982 - 1984. They use the overall

firm ROA as a control variable in the analysis and find that ROA is positive

and significant. More recently, Carpenter & Wade (2002) find that prior ROA

is also significantly related to the compensation level of non-CEO executives.

Several studies derive their hypothesis for executive compensation using

agency theory, and assume that firm profitability is a component of exec-

utive compensation. But the studies do not limit the executive to CEOs.

Antle and Smith (1986) use the 3 most highly paid executives of the firms

in their sample. Lewellen & Huntsman (1970) derive their hypothesis that

the executives pay is related in a linear fashion to both profits and sales of

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the firm without making any distinction between CEOs and non-CEO exec-

utives. Although they only use the single highest paid executives pay for

each firm, they explain this by stating

.... As a matter of both convenience and efficiency, the compensation of

the single highest paid executive in each firm in each year is taken into account

here as the dependent variable measuring the size of the firms managerial pay

package. While it may seem more appropriate that the remuneration of all the

senior policy-making individuals in a corporation be tested for a relationship to

company performance, it happens that the pay of a firms top man is a suitable

surrogate for the pay of his closest subordinates in terms of their relative standingvis-a-vis corresponding officials in other firms. - Lewellen & Huntsman (1970), pg

714

The above review shows that even non-CEO executive compensation is

affected by the overall firm performance. In this study, we attempt to answer

the question; if the quality of the performance measure improves, will there

be a greater weight placed on that performance measure in determining the

compensation of non-CEO executives as well?

Measurement of audit quality

DeAngelo (1981) argues that BigX auditors provide better quality au-

dits than non-BigX auditors, which is supported by extensive subsequent

empirical research. Teoh and Wong (1993) find higher earnings response

coefficients for clients audited by BigX firms compared to those audited by

Non-BigX firms. Becker et al. (1998) and Francis et al. (1999) demonstrate

that BigX auditors are better at constraining earnings management in their

clients compared to non-BigX auditors by showing that the latter have higher

levels of discretionary accruals. Elder et al. (2004) show the same results in

the context of commercial banks.

In addition to the auditor size (or BigX non-BigX dichotomy) proxy for

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audit quality, another proxy that has been extensively studied in recent times

is auditor industry specialization. Casterella et al. (2004) describe auditor

industry specialization as A differentiation strategy whose purpose is to pro-

vide auditors with a sustainable competitive advantage over nonspecialists.

Craswell et al (1995) show that specialist auditors enjoy a fee premium.

Krishnan (2003) and Balsam et al (2003) find that there is less earnings

management in clients of specialist BigX auditors compared to non-specialist

BigX auditors by analyzing the discretionary accruals of client firms. Dunn &

Mayhew (2004) find that clients of specialist BigX auditors have significantly

better AIMR (Association for Investment Management and Research) rank-

ings than clients of non-specialist BigX auditors, signifying that the former

have better quality financial reports.

There is also empirical evidence from the governmental sector that sup-

ports the argument that industry specialization is an important audit qual-

ity attribute. Deis and Giroux (1992) document a negative relationship be-

tween auditor specialization and quality control review outcomes. In a similar

study, OKeefe et al. (1994) find a negative relation between auditor special-

ization and Generally Accepted Auditing Standards (GAAS) violations.

Effect of audit quality on compensation

Agency theory states that performance based incentives can reduce the

agency problem. However accounting based performance measures, which are

extensively used in compensation based contracts can be easily manipulated

by the managers. In fact, research shows that managers manipulate earnings

for the specific purpose of influencing their bonus payments. Healy (1985) in

a seminal study finds that managers will manage earnings around bonus caps.

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Hence, if the earnings go above the cap, the managers will manage earnings

downwards6. These results have been validated by subsequent research such

as Gaver et al (1995), Holthausen et al (1995) and Guidry et al (1998).

In the context of the principal agent model, the effect of information

asymetry between the principal and the agent could be reduced through the

use of high quality auditors. The main effect this would have will be to place

a greater weight on the incentives based on observable performance mea-

sures. Better monitoring effect could have two opposite effects on the level of

agent compensation. On the one hand, better monitoring could reduce rent

extraction by the agent, thus reducing the compensation level. On the other

hand, better monitoring might assure the principal that unseen rent extrac-

tion (i.e: use of corporate jets) has been eliminated or minimized and will

enable higher levels of managers compensation Furthermore, improved audit

quality will lead to more good effort expended by the managers, leading to

increased desired outcome for the firm. If the firm decided to share some of

it with the managers, the compensation level of the managers will increase.

Hence, it is difficult to make an a priori prediction.

In the next section, we derive an analytical model that enables us to

develop the hypothesis for empirical tests.

6The managers are putting the excess earnings away in the cookie jar to be used inthe lean years. Healy interprets this result as strong association between accruals (Healysmeasure of earnings management) and managers income-reporting incentives under theirbonus plan (p. 106).

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3 Analytical Model and Hypothesis Develop-

ment

3.1 Model

We use the LEN framework to study the effect of audit quality on compensa-

tion within the context of a principal agent model and a perfectly competitive

market for capital and labor. LEN stands for Linear compensation contract

to the agent, Exponential utility for both the agent and the principal, and

Normally distributed outcomes.

A firm is defined as ...contracting intermediaries. They facilitate ex-

changes among resource owners who voluntarily contract amongst themselves

to benefit each party (Zimmerman, 2006; p 157). We model the scenario

where investors are looking to invest funds and managers are seeking to em-

ploy their talents. The firm is trying to attract funds and hire managers to

manage those funds. To attract funds, the expected utility of the return from

the firm has to be greater than the investors reservation utility. Similarly, to

attract management talent, the expected utility of the compensation offered

to the managers has to be greater than their reservation utility. We assume

that both the investors and the managers are risk averse.

The managers will expend both productive (good) effort a and unpro-

ductive (bad) effort b.7 Managers have a shorter horizon than the investors.

Accordingly, the firm contracts with managers based on accounting returns

7Unproductive effort is effort such as earnings manipulation which will increase y1 butwill not have any effect on the true outcome to the firm.

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y1.

y1 = a + b + 1 (1)

However, Sloan (1996) shows that earnings manipulation in one period will

generally reverse in the subsequent periods. Therefore, model the accounting

returns after the contracting period as:

y2 = b + 2 (2)

Given that aggregate earnings have to equal aggregate cashflow, the true

outcome to investors is y1 + y2.

The following timeline summarizes the sequence of events.

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3.2 Results

The managers are paid a linear compensation [C(y1)] based on the observable

accounting returns y1. This will take the form,

C(y1) = S+ By1 (3)

where S is the (fixed) salary, and By1 is the (variable) bonus.

The managers have a cost of effort, C(a, b) which is convex. As the

managers expend more effort, the cost of effort increases in a convex fashion.

The cost applies to both the bad effort and good effort. However, we posit

that audit quality will increase the cost of bad effort. Given that auditors seek

to detect earnings manipulation, the difficulty or cost of achieving a given

level of earnings manipulation will naturally increase with audit quality. We

define audit quality as q and model the said effect as follows.

C(a, b) =1

2

a2 +

b2

1 q

(4)

When audit quality is non existent (i.e: q = 0), bad effort costs the same

to the investor as good effort. As audit quality increases, the cost of bad

effort increases comparative to the cost of good effort. When audit quality

is perfect (i.e: q = 1), the cost of bad effort is infinite.

The investor invests A amount of capital in the firm. A is a fixed quantity.

The residual left to the investors, or investors outcome (IO) will be,

IO = A(y1 + y2) (S+ By1) (5)

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Traditional principal agent theory posits that the managers will receive their

reservation wage, and the principal selects B so as to maximize his returns.

However, the management power view of compensation literature (Bebchuk

& Fried, 2004) mentions the close relationship between the managers and

their compensation committees to posit that firms maximize the managers

utility rather than the investors utility. To reconcile both viewpoints, we

formulate the firms utility maximization problem as follows.

Max ()EUI[A(y1 + y2) (S+ By1)] + (1 )EUM[(S+ By1)1

2(a2 +

b2

1 q)] (6)

subject to

EUI UI(Ay0).....IRinvestor

EUM UA(W0).....IRmanager

(a, b) argmaxba,bb

EUM....ICmanagers

where is a constant that captures the bargaining power of the investor

relative to the manager. EUI is the expected utility to the investors and EUM

is the expected utility to the managers. IR refers to the individuals ratio-

nality constraints and IC refers to the individuals compatibility constraint

of the manager. The investors IR constraint implies that the investors will

not invest in the firm unless the utility of returns to the investors is greater

than the utility of returns from an alternative investment. The managers

IR constraint implies that the managers will not accept the job unless the

utility of the compensation to the investors is greater than their reservation

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utility. The IC constraint implies that the managers, after accepting the job,

will decide on the amount of good effort a and bad effort b to maximize their

utility.

Solving the ICmanagers gives the following expressions for a and b.

a = B (7)

b = B(1 q) (8)

The productive (good) effort expended by the managers, increase with

the incentive payments. The unproductive (bad) effort also increases with

incentive compensation, but decreases with audit quality. The intuition for

this is as follows. Both the good effort and the bad effort equally contribute

to y1. Hence, when incentives are based on y1, both efforts are likely to

increase.

Given that we assume both the labor and capital markets are perfectly

competitive, in equilibrium, neither the managers nor the investors will be

able to obtain more than their reservation utilities. Equating their utilities to

their reservation utilities and solving the IRInvestor and IRmanager constraints

results in

ERInvestor = E[Aa (S+ By1)]

= Ay0 +rI2

[(AB)221 + A222] (9)

ECManager = W0 +1

2(a2 +

b2

1 q) +

rM2

(B221) (10)

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When = 1, (6) simplifies to the traditional principal agent utility max-

imization. When = 0, we get an inverted principal agent model, where the

firm maximizes the managers utility. It can be shown that the value of B

that maximizes expression (6) is independent of (see Appendix A).

The value of B which maximizes expression (6) can be written as8

B =A(1 + rI

21)

1 + (rI + rM)21 + (1 q)(11)

As audit quality (q) increases, the denominator of B decreases, and hence

B increases. Since B is the weight placed on the observable performance

measure, this gives us our first proposition.

Proposition 1

As audit quality improves, the weight placed on the accounting

performance measure increases

The intuition is as follows. As audit quality improves, the manipulation of

earnings reduces, and hence the earnings quality improves. The improvement

in earnings quality improves the informativeness of earnings with respect to

the productive effort. The increased informativeness allows the firm to place

a higher weight on earnings.

The model also provides the following corollaries.

Corollary 1

As audit quality improves, the good effort expended by the managers increases.

As q increases, so does B. From (7) as B increases, a increases.

Corollary 2

8See Appendix A for proofs.

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As audit quality increases, the bad effort expended by the principal will de-

cline.

As q increases, the primary effect on b is an increase in the cost of earnings

manipulation, resulting in a decline in the bad effort. There will be a sec-

ondary effect caused by the increase in incentives (B), which will cause b to

increase and negate the primary effect somewhat, but it will not be sufficient

to dominate the primary effect. Hence, as q increases, b will decrease.

Corollary 3

As audit quality improves, the firms desired outcome also increases.

Since firms desired outcome is only a function of the managers good

effort, as the good effort increases with audit quality, so does the desired

outcome.

3.3 Hypothesis development

In this section, we test the predictions on compensation derived from the

propositions in the analytical model. From the literature survey, we conclude

that the predictions of the model will apply to payment made on observable

performance. The literature identifies this as the cash component of CEO

performance. Proposition 1 states that as audit quality increases, the weight

placed on the observable performance measure in determining the compen-

sation of managers will increase. The literature survey identified that BigX

auditors have a higher quality than non-BigX counterparts, and specialist

BigX auditors have a higher audit quality compared to non-specialist BigX

auditors. We identify the manager as CEO of the firm and state our first

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hypothesis as follows.

H1: There wil l be more weight placed on an internal performance measure,

in determining CEO cash compensation if the auditor is a specialist auditor.

The literature survey on non-CEO executive compensation shows that

firm profitability is a factor in non-CEO executive compensation as well.

Hence, we identify the managers from proposition 1 as non-CEO executives

and state our 2nd hypothesis.

H2: There will be more weight placed on an internal performance mea-

sure, in determining non-CEO executive cash compensation if the auditor is

a specialist auditor.

4 Data and Research Design

4.1 Data

We use the Execucomp database to obtain the CEO compensation data, and

the Compustat database to obtain the data for control variables. Execucomp

data is available from 1992. We extract the data using the variable PCEO

to obtain compensation data relevant to CEOs. The Execucomp database

contains information for 19,373 firm years of data for CEOs. Once this data

set is merged with the Compustat Database, and firm years with missing

data attributes are deleted, we end up with a total of 12,449 firm years,

representing 2,085 distinct firms for the period 1992-2004.

Of the 12,449 firm years of data, 12,136 firm years are for BigX clients,

while only 313 firm years, representing 69 distinct firms, are for non-BigX

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clients. This means that out of the total sample, only 2.51% of the data is

for non-BigX clients. Out of the total of 11,142 firm years of BigX clients,

6,091 firm years are for clients of non-specialist auditors, and 6,045 are for

clients of specialist auditors.

We also obtain compensation data for non-CEO executives from the Ex-

ecuComp database. We obtain a total of 68,158 firm-executive-years of data

for non-CEO executives, for whom data is also available on Compustat.

These are all firms that are audited by BigX auditors. In that sample, we

have 2,148 distinct firms and 19,666 non-CEO executives.

4.2 Research Design

Firm performance

In accordance with previous studies (Lambert & Larcker, 1987; Murphy,

1999; Core et al, 2003) we use return on assets (ROA) as the measure of

accounting performance. Most studies also use stock returns as an indicator

of firm performance (Core et al 1999; Core et al, 2003). Hence we use stock

returns (RET) as another indicator of firm performance, and as such, a

determinant of CEO compensation. However, we do not expect audit quality

to impact RET, since the market should have already priced in the impact

of audit quality into the stock price. Therefore, we do not expect the weight

placed on stock returns, in determining CEO compensation to change with

audit quality.

Auditor specialization

We test whether audit quality affects CEO compensation. We use auditor

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specialization as a proxy for audit quality. specialization is calculated as the

percentage of total client sales an auditor audits in a particular industry

during a particular year (Krishnan 2003). Hence specialization is measured

as:

Specialization =

Jikj=1 ClientSalesijkIk

i=1

Jiki=1 ClientSalesij

where:

ClientSales - Client sales revenue

i - denotes audit firm

j - denotes client firms

k - denotes industry category

Jik - number of clients of the ith auditor in the kth industry.

Ik - number of firms in the kth industry.

We use a specialization cutoff level of 20% in our analysis. This means

that every auditor who has more than 20% market share in a particular

industry in a particular year, in terms of client sales revenue, is classified as

an industry specialist for that particular industry, in that particular year.

Hence, we define a dummy variable, SPX, which will proxy for audit quality,

as follows:

SPX=1 if Specialization 20%

SPX=0 if Specialization < 20%

Dependent variable - Compensation

We obtain executive compensation data from ExecuComp. We use 3

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types of compensation in our analysis, namely cash compensation, equity

compensation, and total compensation. We obtain cash compensation using

the variable TCC from ExecuComp database, which is the total of salary

and bonus for the executive. The total compensation is obtained using the

variable TDC1 from the ExecuComp database. The equity portion of com-

pensation is calculated as TDC1 less TCC. To eliminate non-linearities, we

take the log value of cash, equity and total compensation.

Control variables - Economic Determinants of CEO compensa-

tion

CEO compensation is mostly determined by the nature of the firm. Hence,

it is necessary to control for firm characteristics that will impact compensa-

tion. According to a Meta analytic review of empirical compensation litera-

ture, Tosi et al (2000) find that firm size accounts for more than 40% of the

variance in CEO compensation. They find that some of the proxies used for

size are market value of equity, sales, total assets and employees. As in Core

et al (1999) We use the log of sales to proxy for size. We do not use market

value of equity, since we use the percentage change in market value of equity

to compute stock returns, which is one of our control variables. However,

using Market Value of Equity to proxy for size does not significantly alter

the results. Furthermore, Core et al (1999) state that sales also proxy for

complexity of the firm (such as managing many subsidiaries).

Tosi et al (2000) identify performance measures as contributing to 5% of

the variance in CEO compensation. Standard agency theory also predicts

that firm performance will be a significant factor in the compensation of the

CEO and this has been empirically confirmed by Murphy (1990). Tosi et

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al (2000) and Core et al (1999) identify returns on assets (ROA) and stock

returns (RET) as proxies for performance. Hence, we include ROA and RET

to proxy for firm performance.

Smith & Watts (1992) and Gaver & Gaver (1993) show that growth oppor-

tunities impact CEO compensation. They show that the higher the growth

opportunities, the greater the incentives for providing equity based compen-

sation. However, Yermack (1995) does not find such a relationship. We use

the Tobins Q approximate used by Yermack (1995) to proxy for growth op-

portunities. Growth opportunities are a proxy for the risk level of the firm

as well. Growth opportunity is measured as follows.

Grw =total assets(book value) + common stock(market value book value)

total assets(book value)

Smith & Watts (1992) and Core et al (1999) control for risk in the CEO

compensation regression. Fama and French (1992) state that Market to Book

ratio (MTB) is a proxy for risk. Hence, we use Tobins Q as the proxy for

growth and risk, as used by Yermack (1995).

We also control for industry9 and year, since these factors also affect CEO

compensation.

Independent variables

The main independent variable we use in this study is the interaction of

ROA with the dummy variable for specialization (SP Xx ROA) which we

define as SPXintROA. The coefficient of this variable will show whether

9We control for industry using the Barth et al (1999) industry classifications

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firms audited by specialist auditors place a different weight on ROA in deter-

mining the managers incentive payments. Since the compensation literature

identifies stock returns, which is an external measure of firm performance,

as another determinant of compensation, we investigate the effect of au-

dit quality on the weight placed on stock returns. We include the variable

SPXintRET, which will show if the weight placed on stock returns in deter-

mining incentive compensation is different for clients of specialist auditors.

Regression Analysis

To test if audit quality affects CEO compensation, we run the following

2 regressions.

(CEOComp) = 0 + 1lnSales + 2ROA + 3RET + 4Grw

+5SP X+ 6SPXintROA + 7SPXintRET+

+Dummy Control variables for Y ear and Industry

(12)


+5SP X+ 6SPXintROA + 7SPXintRET+ 8BigX

+9BigXintROA + 10BigXintRET

+Dummy Control variables f or Y ear and Industry (13)

where

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Variable Definition

CEOComp Log of CEO compensation.lnSales Log of Net Sales Revenue for the year

(Compustat #12)ROA Return on Assets

(Compustat #178/ Average Total Assets for the year)RET Market Returns + Div YieldMarket Returns (M V Et M V Et1)/MVEt1Div Yield Dividends for the year (Compustat #21)/MV Et1Grw total assets (book value) + common stock (market value book value)

total assets (book value)

Growth is calculated as follows.

Grw = Compustat #6 + (M V E Compustat #60)Compustat #6SPX Dummy variable equal to one if auditor is a specialist,

zero otherwiseSPXintROA SPX*ROASPXintRET SPX*RETBigX Dummy variable equal to one if auditor is a BigX auditor,

zero otherwiseBigXintROA BigX*ROABigXintRET BigX*RET

When CEOComp is the compensation paid to CEOs, we use regression

equations (12) and (13) to test hypothesis 1. When CEOComp is the com-

pensation paid to non-CEO executives, we use regression equation (13) to

test hypothesis 2.

In regression equation (12), when CEOComp=CEO cash compensation,

in accordance with H1, we expect 6 to be positive and significant. Since we

consider the accuracy of stock returns to be independent of audit quality, wedo not expect audit quality to impact the weight placed on stock returns.

Therefore we expect 7 to be insignificant. As discussed in the literature

review, we do not expect audit quality to have an impact on CEO equity

compensation, which is granted to induce CEOs to take risk on behalf of the

investors in the future. Hence, when CEOComp=CEO equity compensation,

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we would expect 5, 6 and 7 to be insignificant.

In regression equation (13), in addition to auditor specialization, we in-

troduce auditor size (BigX vs non-BigX) as an additional proxy for audit

quality. As a result, we expect the same results as for (12). In accordance

with H1, we expect 6 and 9 to be positive and significant. we would expect

7 and 10 to be insignificant. When CEOComp= CEO equity compensation,

we would expect 5 - 10 to be insignificant. One caveat to the results for

(13) is the effect of multicollinearity. Since BigX is a categorical variable, and

BigX firms account for 97.5% of the sample, BigXintROA and BigXintRET

might be affected by multicollinearity.

5 Results and Analysis

5.1 Descriptive Statistics

Insert Table 1

CEO compensation

Table 1 contains the descriptive statistics. Panel A shows a distinct dif-

ference in size and compensation between clients of BigX auditors and clients

of non-BigX auditors. Clients of BigX auditors are significantly bigger in size(proxied by sales and MVE), compared to clients of non-BigX auditors, in

accordance with the findings of Becker et al (1998) and Krishnan (2003).

BigX clients also pay their CEOs more, both in cash and in equity. Overall,

CEOs of non-BigX clients receive only 51% of the total pay received by their

BigX client counterparts. However, the proportionate differences change de-

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pending on whether the payment is in cash or equity. CEOs of non-BigX

clients receive on average, 76% of the cash pay of their BigX client counter-

parts, but they receive only 42% of the equity pay. CEOs of non-BigX clients

receive 42% of their compensation in the form of cash, while the CEOs of

BigX clients receive 29% of their compensation in cash.

Panel B shows the descriptive statistics for clients of specialist BigX au-

ditors vs clients of non-specialist BigX auditors. The clients of specialist au-

ditors are significantly bigger in size (proxied by sales and MVE) than clients

of non-specialist BigX auditors, in accordance with Abbott & Parker (2000)

and Dunn & Mayhew (2004). The CEOs of Specialist clients on average,

receive more compensation, both in terms of cash and equity compensation,

compared to their non specialist BigX client counterparts. The CEOs of

non specialist BigX clients 77% of the cash compensation of their specialist

counterparts, and receive almost the same proportion (74%) of equity pay

too. CEOs of non-specialist BigX clients receive 29% of their compensation

in cash, while CEOs of specialist BigX clients receive 28% of their compen-

sation in cash.

non-CEO Executive Compensation

We only consider the clients of BigX auditors in our analysis of non-

Executive CEOs. Untabulated results show that for non-CEO executives,

the mean cash compensation over the period was $ 501,235, the mean equity

compensation was $ 872,817 and the overall compensation was $ 1,392,320.

This means that non-Executive CEOs receive 36% of their pay in the form

of cash compensation and the balance in the form of equity compensation.

Executives of specialist BigX audit clients receive $ 549,657, $ 984,635 and $

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1,555,480 as cash, equity and total compensation respectively. Executives of

non specialist BigX audit clients receive 454,809, $ 765,607 and $ 1,235,890

as cash, equity and total compensation respectively. The average non-CEO

executive pay for specialist BigX clients is 38% of the equivalent CEO pay.

However, the proportions of equity and cash pay differ. Non-CEO executives

receive 47% of the cash pay of the CEOs but only 34% of the equity pay. For

clients of non-specialist BigX firms, the proportions are 51%, 36% and 40%

of the cash, equity and total pay received by the CEOs. This indicates that

cash compensation is more important to non-Executive CEOs.

Correlation statistics

Insert Table 2

Table 2 presents the Pearson and Spearman correlation coefficients. Pear-

son correlation coefficients are shown above the diagonal, and Spearman cor-

relation coefficients are shown below the diagonal. all variables are correlated

to each other at the 1% significance level except for the following. Sales are

not correlated with MTB both according to Spearman and Pearson corre-

lation coefficients. Interestingly, the Pearson correlation coefficients show

that cash compensation is not correlated with Grw, but that equity com-

pensation is. This indicates that cash compensation is not related to future

growth opportunities. The overall correlations between the variables show

that the cash and equity components of compensation are closely related to

the control variables.

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5.2 Control Variables

Insert Table 3

First of all, we test if the control variables we use have the desired ex-

planatory power. We do this by regressing CEO pay against only the 4

control variables. As shown in table 3, panel A, for CEO cash compensation,

lnSales, RET and ROA are positive and significant, indicating that the CEO

is rewarded for the size and complexity of the firm, the profit she generates,

and the value she creates for the shareholders. Grw is positive but not signif-

icant. This confirms our position that cash compensation rewards the CEO

for past performance. Grw proxies for future growth opportunities and risk.

Hence the better mechanism for rewarding the manager for Grw is through

equity compensation. Accordingly, in table 3, panel B, we find that Grw

is positively and significantly correlated with equity compensation. LnSales

and stock returns have the expected positive significant correlations. ROA

has a negative significant relationship with equity compensation (Coefficient

= -1.34; t-stat = -3.98), which is unexpected. However, this relationship is

driven by financially distressed firms. We posit that financially distressed

firms increase the equity compensation to their CEOs in order to increase

the future value of the firm. Untabulated results show that when firm yearswith negative ROA are omitted from the sample, ROA has a marginally sig-

nificant negative relationship. (Coefficient = -0.849; t-stat = -1.67). This

relationship is in line with the findings of Gilson and Vetsuypens (1993) who

show that in a study of firms that have filed for bankruptcy, firms generally

increase subsequent equity compensation for senior management in order to

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increase the future value of the firm.

Table 3, panel C shows the results for overall compensation, which follows

positive significant relationships for lnSales, RET, and Grw, and a negative

relationship for ROA. Again, we posit that this negative relationship is driven

by financially distressed firms. Untabulated results show that when finan-

cially distressed firms are omitted from the sample, the coefficient for ROA

is positive and insignificant (Coefficient = 0.196; t-stat = 0.85).

5.3 Analysis with Specialist auditors

Insert Table 4

We test hypothesis 1 by regressing CEO compensation on audit quality as

given in equation (12) on a sample containing only clients of BigX auditors.

The results are given in table 4. Panel A of table 3 shows that for CEO

cash compensation, the coefficient for SPX is -0.07 with a t-statistic of -

2.70. This indicates that in the presence of higher audit quality, the fixed

component of cash compensation declines. Panel B of table 4 shows that the

coefficient of SPX is insignificant (with a t-statistic of -0.21). As discussed in

the literature review, auditor specialization does not have any impact on the

weightages placed on accounting earnings or stock returns in determining the

equity compensation of the CEO. Panel C of table 3 evaluates the impact

of specialization on total compensation. The SPX variable has the expected

direction, but is not significant.

To test hypothesis 1, the variable of interest is SPXintROA, which is the

interaction of the specialization dummy variable with ROA. Panel A, which

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contains the results for the regression where the dependent variable is cash

compensation, the coefficient of SPXintROA is 0.66 with a t-statistic of 3.21.

This shows that as predicted in hypothesis 1, SPXintROA is positive and

significant. Hence, we conclude that when audit quality is high, there is more

weight placed on earnings, which is an accounting measure of performance10.

Panel B of table 3 shows the results for equity compensation. As discussed in

the literature review, we do not expect audit quality to have any impact on

equity compensation. In confirmation of this, the coefficient of SPXintROA is

insignificant. This indicates that, audit quality does not have any impact on

the weightage placed on accounting, or internal, measures of performance, in

determining CEOs equity compensation. SPXintROA has the expected sign

for total compensation (shown in Panel C) and the coefficient is marginally

significant. However this result is driven by the effect of cash compensation.

SPXintRET is the interaction of the specialization dummy variable with

equity returns for the past year. SPXintRET is insignificant for both CEO

cash compensation and CEO equity compensation. This indicates that the

weight placed on external measures of performance (such as stock returns)

is not sensitive to audit quality. Under the perfect information hypothesis,

audit quality should not have any impact on stock returns, since the market is

aware of all the true information about a firm, and has already incorporated

this information in the stock price. So, audit quality which will prevent

accounting manipulations will not have any impact on stock returns, nor on

the weight placed on stock returns in determining the CEOs compensation.

10Accounting measures of performance are internal and observable measures of perfor-mance.

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This is confirmed by the insignificant coefficient on SPXintRET. 11

The size variable proxied by the log of sales has the expected sign. In

accordance with previous literature, we find that both cash and equity com-

pensation are significantly and positively related to the size proxies. ROA

does not have the expected sign because the ROA and returns effects are cap-

tured by SPXintROA and SPXintRET respectively. Table 3 results confirm

that without the interaction variables, ROA and RET remain significant in

determining cash compensation.

5.4 Analysis with both Specialist and BigX variables

Insert Table 5

Audit literature shows that both the BigX auditors as well as specialist

auditors are a good proxy for audit quality (DeAngelo, 1981; Becker et al,

1998; Balsam et al, 2003; Krishnan, 2003). However, using BigX as a proxy

for audit quality could intorduce multicollinearity since out of the total sam-

ple of 12,449 firm years, only 313 firm years (2.65%) are for non-BigX firms.

Even though the presence of multicollinearity would tend to bias against

the results, we run the regression shown in equation (13). The results are

11Untabultated results show that when SPX, SPXintROA and SPXintRET in equation

(12) are replaced with BigX (a dummy variable equal to 1 if the auditor is a BigX auditor,and zero otherwise), BigXintROA (the interaction of BigX with ROA) and BigXintRET(the interaction of BigX with RET) respectively, for cash compensation, the BigX vari-able, although negative is insignificant. BigXintROA is positive and significant; howeverROA is insignificant. This is due to multicollinearity. Since BigX clients comprise 97%of the sample, BigXintROA and ROA are very highly correlated. Untabulated resultsalso show that the variance inflation factors for ROA, RET, BigXintROA and BigXin-tRET are all greater than 10, indicating the presence of multicollinearity. However, sincemulticollinearity would bias against the results, the fact that BigXintROA is marginallysignificant would indicate support for hypotheses 1.

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shown in Table 5. Untabulated results show that the variance inflation fac-

tors for the variables ROA, RET, BigXintROA and BigXintRET are affected

by multicollinearity. The BigX variables are all insignificant. However, the

SPX variables have the expected signs and significance. We interpret these

results as further confirmation of hypothesis 1.

5.5 Audit quality and non-CEO Executive compensa-

tion

To test hypothesis 2, we run (12) with the dependent variable being non-CEO

Executive compensation. The sample is limited to clients of BigX auditors.

We use only auditor specialization to proxy to audit quality in order to avoid

the multicollinearity issues associated with using BigX as a audit quality

proxy. The results are given in Table 6.

Insert Table 6

Table 6, Panel A shows that all control variables have the expected signs.

Sales are significantly and positively related to cash compensation levels, as

are stock returns (RET) and growth (Grw). ROA is not significant, but we

posit that this is due to the ROA effect being subsumed by SPXintROA.12

SPX is negative and significant and SPXintROA is positive and significant.

Hence, this proves hypothesis 2, that greater weight is placed on accounting

earnings when audit quality improves. SPX being negative and significant

also seems to indicate that in the presences of higher audit quality overall

12When the SPX, SPXintROA and SPXintRET variables are omitted from the regres-sion equation (12), ROA becomes positive and significant.

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pay decreases. As is the case for CEO compensation, higher audit quality

does not have any effect on the weight on stock returns. Panel B shows that,

unlike CEO compensation, non-CEO executive equity compensation behaves

in a similar way to cash compensation. In the face of higher audit quality,

the weight placed on accounting earnings increase and the level of equity

compensation decline. However, the results are not as strong as for cash

compensation.

6 Sensitivity Analysis & Alternative Expla-

nations

6.1 Sensitivity Analysis

To verify the results obtained in Table 4, we run the following regression.


+5SP X+ 6SPXintROA + 7SPXintRET+ 8BigX

+9BigXintROA + 10BigXintRET

+Dummy Control variables for Y ear and Industry (14)

CEOComp=CEOCompt-CEOCompt1Other variable definitions are the same as for (12)

This regression is the same as in equation (11), but the dependent variable

is the change in CEO compensation. Using the change in compensation will

get rid of any heterogeneous exogeneities that may bias the results. If these

exogenous factors are time invariant, they will be differenced away. The

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results are shown in Table 7.

Insert Table 7

The results shown in Table 7 confirm the results obtained in Tables 4 and

5. Panel A shows that the SPX variable is negative and significant, the SPX-

intROA is negative and significant and the SPXintRET is insignificant. This

shows that the change in CEO pay from one year to the next is dependent

on the audit quality. SPX being negative and significant shows that having

a higher quality auditor reduces fixed component of cash compensation in-

creases. SPXintROA being positive and significant indicates that a greater

weight is placed on accounting earnings, in determining the increase in the

incentive component of cash compensation in the presence of higher audit

quality. SPXintRET being insignificant indicates that audit quality does not

increase the weight placed on stock returns in determining the increases in

cash compensation. Panel B shows that audit quality does not have any

impact in determining the increases in equity compensation.

Untabulated results show that when the specialist variables are replaced

in equation (14) by BigX variables, the BigX variables show the same ten-

dencies as the specialist variables, indicating that BigX auditors attenuate

the cash compensation increments, and BigX auditors result in more weight

placed on accounting earnings in determining the increase in cash compen-

sation. These results hold even in the presence of multicollinearity, which

would tend to bias against the results.

Fixed effects regression

Since our analysis is performed on a panel data set, comprising of many

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different firms over many years, there is always the possibility that the differ-

ent intercepts of the individual firms would distort the results. To eliminate

that possibility, we run a fixed effects regression using the following equation

with year and firm fixed.

Insert Table 8

Table 8, Panel A shows that the SPXintROA variable is positive and sig-

nificant, confirming our earlier findings that in the presence of higher audit

quality, firms place a higher weightage on accounting earnings in determin-

ing the CEOs cash compensation. SPXintRET is positive and significant.

Although SPX is positive and significant, we discount this result because in

a fixed effects model, the intercepts of individual firms are fixed. Since SPX

is a categorical variable that partitions the total sample, the SPX variable

only shows the residual effect.

Untabulated results show that fixed effects regression for changes in CEO

compensation yield similar results. SPXintROA is positive and significant.

SPXintRET is positive and marginally significant. These results confirm the

findings of Table 6, and provide additional confirmation of hypothesis 1.

We also conduct sensitivity tests on the cut-off for auditor specialization.

We run the same regressions using specialization cut-offs of 10%, 15%, and

25%. The results do not change qualitatively for the 15%, and 25% cut-offs,

but the SPX, SPXintROA and SPXintRET variables become insignificant at

a cutoff of 10%. We posit that the 10% cut-off is not relevant these days, since

there are only 4 BigX audit firms. At the 10% cut-off, using our measure of

specialization, most auditors would be specialists in all industries.

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6.2 Alternative Explanations

Governance

An alternative explanation for the results we obtained may be that audit

quality is proxying for corporate governance. Better monitoring by the board

would prevent rent extraction by the CEO. The effect of governance on

compensation has been widely documented by Lambert et al (1993), Herma-

lin and Weisbach (1998), Core et al (1999), Bebchuk et al (2002) etc. Hence,

does audit quality in some way proxy for the effect of corporate governance?

If the results that we obtained for compensation can be explained away

by corporate governance, then the effects of audit quality investigated ex-

haustively in audit literature may also be impacted by governance. However,

since literature treats the two effects independently, we would also expect

that audit quality would be independent of corporate governance.

We test the above assumption by running the following regression.


+5SP X+ 6SPXintROA + 7SPXintRET+ 8GOV

+9GOV intROA + 10GOV intRET


GOV is an index which proxies for corporate governance constructed of 5

separate elements as follows. GOV = CEO CH+ Board + N om + Comp +

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Audit where CEO CH = 1 if the CEO is not the board chair and zero

otherwise, Board = 1 if the board has a majority of independent members

and 0 otherwise, N om = 1 if the nominating committee has a majority of

independent members and 0 otherwise, Comp = 1 if the compensation com-

mittee has a majority of independent members and 0 otherwise and finally

Audit = 1 if the audit committee has a majority of independent members

and 0 otherwise. The first element is used in Core et al (1999) and the last

four elements are used in Chhaochharia & Grinstein (2006) in studying the

impact of corporate governance on compensation. The index can have a

minimum of 0 and a maximum of 5. As the quality of corporate governance

improves, the index will increase.

We obtain data for the analysis from the IRRC governance database.

The data is available from 1996 onwards. We merge this dataset with the

ExecuComp database and the Compustat database. The results for the re-

gression equation (15) are given in Table 9, Panel A. The results show that

audit quality as measured by specialist auditors is significant even after con-

trolling for corporate governance. Hence, we conclude that the impact of

audit quality on cash incentive compensation is independent of corporate

governance. Untabulated results show that this result holds even after we

decompose the corporate governance index into its components and include

all of the separate components in the regression equation.

Earnings Quality

Similarly, it may be argued that the results that we obtian may be ex-

plained by the fact that audit quality is proxying for earnings quality. Schip-

per & Vincent (2003) define earnings quality as ..the extent to which re-

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ported earnings faithfully represent Hicksian income... Hicksian income

corresponds to the change in net economic assets other than from transac-

tions with the owners (Schipper & Vincent, 2003). Hence higher quality

accounting earnings would more closely reflect the actual economic perfor-

mance of the firm while lower quality accounting earnings would diverge from

the actual economic performance of the firm.

Accounting performance will never perfectly measure the actual economic

performance of a firm since there are so many subjective estimates that must

be made when reporting accounting earnings. If the perfect earnings measure

that corresponds exactly to the economic performance of the firm may be

found, and every auditor, regardless of quality is able to ensure that firms

will report this measure, then audit quality will become redundant. In such

a scenario the results we show will not be observed. However, since such a

measure is not practical, audit quality will continue to be significant.

We test to see if the results we have observed for audit quality will dis-

appear when we control for measurable earnings quality. There are several

proxies for earnings quality (See Schipper & Vincent (2003) for a discussion

of the different proxies). One measure extensively used is accruals or discre-

tionary accruals (Schipper & Vincent, 2003; Frankel et al, 2002; Chan et al,

2006; Balsam et al, 2003). Accruals enable earnings to more closely repre-

sent economic performance of firms, but accruals are open to manipulation

by managers.

Hence, we use performance matched discretionary accruals to control for

measurable earnings quality, and run the following regression.

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+5SP X+ 6SPXintROA + 7SPXintRET+ 8ACC

+9ACCintROA + 10ACCintRET


ACC stands for performance matched discretionary accruals measured as

per Louis (2004). The results are shown in Table 9 panel B. The results shows

that even after controlling for measurable proxies of earnings quality, audit

quality still significantly impacts incentive compensation. Hence we con-

clude that the impact of audit quality on compensation we have documented

is independent of measurable proxies of earnings quality. The results hold

when we substitute total accruals in place of performance matched abnormal

accruals.13

13One concern about this analysis is that since higher audit quality is associated withlower abnormal accruals (Balsam et al, 2003) including both audit quality and abnormalaccruals in equation (16) would cause multicollinearity. However, the major undesirableconsequence of multicollinearity would be to increase the variances of the collinear pa-rameters (Kennedy, 2003). Hence multicollinearity would tend to bias against the results.The fact that we still obtain significant results for our desired independent variables wouldimply that multicollinearity is not an issue for this particular analysis

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7 Conclusions

We use an agency theoretic framework to model the effect of audit quality on

incentive compensation. The analytics shows that as audit quality increases,

the ability of managers to manipulate earnings becomes more costly. Hence,

the informativeness of earnings increases with respect to the productive effort

of the managers. The firm is therefore able to place a higher weightage on

accounting earnings in determining the managers incentive compensation.We investigate the analytically derived hypotheses using emperical data.

We proxy for audit quality by using auditor specialization. We predict that,

for clients of specialist auditors, there will be more weightage placed on

accounting performance measures in determining the cash component of CEO

compensation.

We show that clients of specialist auditors place a larger weight on ROA

(an accounting performance measure) in determining the CEO cash compen-

sation, compared to clients of non-specialist auditors. The fixed component of

CEO cash compensation declines in the face of higher audit quality. We pro-

vide evidence that CEO equity compensation is not related to audit quality

as proxied by auditor specialization. We show the same effect for non-CEO

executives.

We investigate two alternative explanations for the results that we have

observed, namely that audit quality may be proxying for the effect of higher

earnings quality or that audit quality may be proxying for better corporate

governance. We show that the results we have obtained hold even after con-

trolling for measurable earnings quality and governance. Hence, we conclude

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that the impact of audit quality on cash incentive compensation is distinct

from the effects of earnings quality or corporate governance.

The contribution of this study to accounting literature is three-fold. First,

to the best of our knowledge, this is the first paper that looks at the effects

of audit quality on CEO compensation. We show that audit quality is ben-

eficial in mitigating the agency problem in firms, by both reducing the cash

component of managerial compensation, and by making it more responsive to

performance. Second, this study also investigates the different drivers of eq-

uity compensation and cash compensation, and provides additional evidence

that cash compensation is backward looking, while equity compensation is

forward looking. The final contribution is in the field of auditor specializa-

tion. This paper contributes to our understanding of the effects of auditor

specialization on the audit clients, and provides another reason as to why

the audit clients are willing to pay a specialist premium.

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9 Appendix A - Detailed model derivation

y1 = a + b + y1 y1 = N(0, 21) (17)

y2 = b + y2 y2 = N(0, 22) (18)

x = y1 + y2 = a + y1 + y2 (19)

where

y1 = Observable performance measure of a firm. a = Good effort expended by

the managers. b = Bad effort expended by the managers. y2 = Performance

measure which captures the reversal of bad effort which will take place in the

subsequent period. y1 + y2 = True performance of the firm during the period

prior to managers compensation. The managers is paid a linear compensation

based on the observable performance measure y1. This will take the form,

C(y1) = S+ By1 (20)

where S is the fixed component of compensation, and By1 is the variable

component, or bonus. B is the sensitivity of compensation to the observable

performance measure.

The managers have a cost of effort that is convex. As the managers

expend more effort, the cost of that effort increases in a convex fashion. We

define cost of effort as C(a, b). The cost applies to both the bad effort and

good effort. However, we posit that the auditor will increase the cost of bad

effort. If bad effort is expended to manipulate earnings, the auditor will be

detect such manipulations. The managers will then have to expend a lot

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more effort to achieve the same level of manipulation. Hence, as the quality

of the auditor increases, the cost of bad effort increases as well. We define

audit quality as q and model the said effect as follows.

C(a, b) =1

2[a2 +

b2

1 q] (21)

The investor invests A amount of capital in the firm. The residual out-

come left to the investor (IO) will be,

IO = A(y1 + y2) (SBy1) (22)

Traditional principal agent theory posits that the managers will receive their

reservation wage, and the principal selects B so as to maximize his returns.

However, the management power view of compensation literature (Bebchuk

& Fried, 2004) mentions the close relationship between the managers and

their compensation committees to posit that firms maximize the managers

utility rather than the investors utility. To reconcile both viewpoints, We

formulate the utility maximization problem as follows.

Max ()EUI + (1 )EUM

= Max ()EU [A(y1 + y2) (SBy1)] + (1 )EU(SBy1) 12

(a2 + b21 q

)(23)

subject to

EUI UI(y0).....IRinvestor

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EUM UA(W0).....IRmanagers

(a, b) argmaxb

a,bbEUA....ICmanagers (24)

where is a constant, EUI is the expected utility to the investor, and

EUM is the expected utility to the managers. IR refers to the individuals

rationality constraint and IC refers to the individuals compatibility con-

straint. The investors IR constraint implies that the investor will not invest

in the firm unless the utility of returns to the investor is greater than the util-

ity of returns from an alternative investment. The managers IR constraint

implies that the managers will not accept the job unless the utility of the

compensation to the manager is greater than his reservation utility. The IC

constraint implies that the managers, after accepting the job, will decide on

the level of good effort a and bad effort b to maximize their utility. Since

the utility function is exponential, We can express the certainty equivalent

of EUM as the expected value of the compensation less the variance.

EUM = E[C(y1) C(a, b)] V[C(y1)]

EU[S+ By1 1

2(a2 +

b2

1 q)] = S+ By1

1

2(a2 +

b2

1 q)

rM2

(B221 )

= S+ B(a + b)1

2

(a2 +b2

1 q

)rM

2

(B221)

(25)

Since the managers will select a and b to maximize their utility, taking

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the first order conditions of (28) with respect to a and b will yield

a = B (26)

b = B(1 q) (27)

We assume that both the capital markets and labor markets are perfectly

competitive. There is a market clearing return for each level of risk associated

with a firm for investors, and a market clearing wage associated with the risk

and degree of difficulty for managers. If the investors expect more than the

market clearing returns, the firm can obtain the necessary capital from other

investors who are willing to accept the market clearing returns. Similarly if

the managers want wages above the market clearing wage, then the firm can

find plenty of equally talented managers who will be willing to accept the

market clearing wage. We define the market clearing return as the reservation

return for the investor and the market clearing wage as the reservation wage

for the managers.

Since both the investors and the managers only receive their reservation

utilities, both IRinvestor and IRmanager are binding. Hence, We express the

managers IR constraint as

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EUM = EU[(S+ By1)1

2

(a2 +

b2

1 q)

]

= E[(S+ By1)rM2

(B221)1

2

(a2 +

b2

1 q)

]

= A(a y0)rI2

(AB)221 rI2

(A222)rM2

(B221)1

2

(a2 +

b2

1 q)

]

(32)

Substituting the results of equations (31) and (32) in equation (23), weobtain the following expression.

Max w.r.t. B (EUI) + (1 )(EUM)

=

AB W0

1

2

(a2 +

b2

1 q)

rM

2(B221)

rI

2(AB)221

rI

2A222

+(1 )

AB Ay0

rI

2(AB)221

rI

2(A222)

rM

2(B221)

1

2

(a2 +

b2

1 q)

]

= AB rI2

(AB)221 rI2(A222) rM2

(B221) 12(a2 + b2

1 q)]

(1 )Ay0 ()(W0)

= AB rI

2(AB)221

rI

2(A222)

rM

2(B221)

B2

2[1 + (1 q)]

(1 )Ay0 ()(W0)

F.O.C. w.r.t. B AB[rM21 + 1 + (1 q)] + rI(AB)

21 = 0

B = A(1 + rI21)1 + (rI + rM)21 + (1 q)

(33)

Proposition 1

As audit quality improves, the weight placed on the accounting performance

measure increases

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From (26), as B increases, a increases. And since B increases with q, so

will a.

Corollary 1

As audit quality improves, the good effort expended by the managers increases.

The desired outcome to the firm y1 +y2 = a+1 +2. Hence, E(y1 +y2) =

a. As q increases, a will increase, and so will E(x).

Corollary 2

As audit quality increases, the expected outcome to the firm will increase.

From (27) and (33)

b = B(1 q)

=A(1 + rI

21)(1 q)

21 (rI + rM) + 1 + (1 q)

= A(1 + rI21)

21

(rI+rM)+1

(1q)+ 1

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