Are CEOs Paid Too Much?The role of disclosure, institutional

monitoring, incentives and sizeEconomic Society of Australia

Lunchtime Seminar, Wednesday 8 November 12.30–1.50

Presented by Peter Swan and based on joint work with a number of co-authorspeter.swan@unsw.edu.auUniversity of New South Wales

2

Outline of the talk

I begin by summarising many claims that CEO pay in both Australia and the US is both “excessive” and not performance related

The suggestion is that CEOs earn “rents” as a result of “board capture” by appointing “their mates” to the board and reflects poor corporate governance practices such as large boards with too many insiders

Disturbingly, statistically, the main explanation for CEO pay levels is company size with pay levels rising about 27% for each doubling in size

Towards the end of my talk I put forward two possible explanations for this robust finding

3

Outline of the talk

CEOs are (in theory) subject to a ‘participation constraint’- should not receive ‘rents’

If interests are aligned with shareholders, are risk averse, unable to diversity, then pay will increase when incentives are enhanced-compensation for added risks

CEO pay subject to two types of monitoring, in addition to board, With public disclosure, shareholders are in a better

position to monitor CEO pay Institutional investors are in a strong position to

monitor CEO pay levels

4

Outline of the talk

Monitoring, far from reducing pay, actually increases it

Both disclosure and institutional monitoring result in higher performance pay-executive option allocations and holdings

These raise pay-performance sensitivity and performance but raises total pay-additional undiversifiable risk

Explains why CEO pay has risen so dramatically No evidence that CEOs in general are overpaid

5

Outline of the talk

If rising CEO pay is due to incentives and risk aversion, why is pay so much higher in large firms? Explanation 1, cloning effect due to Rosen:

The CEO of the largest firm commands the most assets and clones himself multiple times via the hierarchy and subordinates.

Hence the largest firm must attract the most able manager with the highest opportunity cost by paying him the most

6

Outline of the talk

Explanation 2, careers effect due to Sung and Swan: randomly some agents work for small firms early in their career and others for large firms.

There is no difference in the abilities of the agents.

When agents evaluated, larger firms will prefer to employ those experienced in the large firm and will pay them more

7

Growth in executive pay and claims that it is excessive During the 1990s the real pay of Australian CEOs (882

CEOs of 772 largest firms- Merhebi, Pattenden, Swan and Zhou Accounting and Finance 46 (2006), 481-497, rose 85% (Mean) and 67% (Median). Graph is in 1990 dollars. The mean is influenced by high salaries for a number of large companies

Excludes severance payouts to retiring CEOs (e.g., Grasso’s departure from the NYSE)

In 1992, CEO of an S&P 500 firm earned $2.7 million. By 2000, increased 400 percent, to $14 million. In 1992, CEOs paid 82 times blue-collar workers; in 2004, paid 400 times – Robert Daines (Stanford)

Larry Ellison of Oracle cashed in $700 million worth of options in 2001

8

Growth in executive pay and claims that it is excessive "If you look at the numbers, it is accurate to say the

more you pay a CEO the worse the company performs and the less you pay the better it performs," - Dr John Shields ---If true pay for non-performance and penalise performers

CEO pay going up relative to average workers but this does not mean that it is excessive.

Are shareholders are getting value for the increasing amounts they have been paying CEOs?

9

Australian Real CEO Pay Growth (Salary Bonuses and Benefits), 1990-1999, in 1990 Dollars based on Merhebi et al (2006)CEO Pay: 1990s

0

100

200

300

400

500

600

700

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Mean

Median

10

Pay-size relationship puzzling-does not appear performance based For a 10% increase in size (firm revenue),

Australian CEO pay increases by 2.74% (Merhebi, Swan and Zhou findings)

Following table shows similar findings for most countries -Italy and Spain less pronounced

Pay-size relationship evident to researchers for decades but reasons are not clear

I put up two possible explanations

11

Merhebi et al (2006): Firm size determinant of pay with same pay size elasticities for most countries

Country Size Elasticity Study

Canada 0.247 Zhou (2000)

Italy 0.09 Brunello, Graziano and Parigi (2001)

Japan 0.247 Kaplan (1994)

Spain 0.131 Angel and Fumas (1997)

UK 0.261 Cosh (1975)

US 0.282 Joskow and Rose (1994)

Australia 0.274* This study

12

Merhebi et al (2006) show Australian PPS similar to US and other countries 20.1 cent cash pay increase per $1,000

increase in Shareholder Wealth $1.82 per $1,000 including shareholding 1.16% increase for a 10% increase in

shareholder wealth (semi-log specification) Shows that claims that incentives low or

negative in Australia are false as these results are comparable with other markets

13

Working paper by Gallagher, Smith and Swan (2006) Study covering approx 100,000 exec years over

2550 firms (1992-2002 for the US) shows option incentives add to market value (Tobin Q)

Pay Performance Sensitivity of Option Holdings measured by the equivalent proportional equity share holding of executive

Conditional on options, higher total pay has negative impact on performance, as do conglomerates

Due to added total pay -compensate undiversified executive for added risks

14

Independent Variable (1) (2) (3)

0.0169*** 0.0041*** 0.7009***

(16.83) (8.75) (19.59)

0.0017*** 0.0004*** 7.42E-05

(10.99) (3.09) (0.08)

-0.1457*** -0.0334*** -6.0641***

(-14.17) (-7.87) (-18.52)

0.3995*** 0.9681*** 4.5965***

(49.95) (138.53) (19.68)

-0.2145*** 0.01** -0.1725***

(-41.95) (2.44) (-21.26)

Observations 101306 101306 23048

R-Sq 0.1736 0.0387 0.2223

Table VIFirm Performance as a Function of Executive Compensation

PPS of Option Holdings

PPS of Stock Holdings

Number of Business Segments

Log Total Compensation

Log Market Capitalization

This table shows the coefficients from regressions of Tobin's Q as functions of lagged measures ofexecutive compensation. Model 1 employs a OLS methodology. Model 2 employs a firm fixed effectsmethodology. Model 3 employs a 2SLS methodology, where a variable indicating whether the executiveis founder, executive age and the natural logarithm of stock turnover are used as instruments for thecompensation variables. The first stage results from model 3 are not presented. Controls for size effectsand the number of business segments are included in all models. Dummy variables for industry and yeareffects are also included in model 1 and 3. T-statistics are presented in parentheses and have beencomputed from robust standard errors. One, two and three asterisks denote significance at 10, 5 and 1percent levels.

15

What could be the cause of high and rising executive pay levels? Optimal incentive contract must satisfy:

Executive’s participation constraint -not worse off relative to outside opportunities

Incentive compatibility constraint - minimises cost to the executive of providing costly ‘effort’

Undiversified risk averse agent requires higher pay if forced to bear idiosyncratic risk borne by an undiversified shareholder

Would expect pay levels to go up as a higher fraction of his pay is tied to shareholder performance

16

Evidence from Swan and Zhou (2006) Disclosure of pay of Canadian CEOs in October 1993 -

substantial pay rise over next four years (even relative to US)

If CEOs already overpaid in Canada would expect shareholder scrutiny to result in fall, not a rise

Pay levels rose -value of incentives-option grants-rose substantially

Performance sensitivity rose from negligible to almost US levels

Increased sensitivity and risk averse CEOs explains increase in pay

Performance pay increased substantially -fixed pay did not vary

17

Impact of pay disclosure in Canada on pay levels and matched US firmsPre-disclosure Post-disclosure Percentage change

Median Mean Median Mean Median Mean

Salary + bonus:

Canadian ($thousand) 377 476 519 656 +38% +38%

US ($thousand) 534 654 645 835 +21% +28%

Canadian as of US 71% 73% 80% 79%

Total pay:

Canadian ($thousand) 394 507 577 761 +46% +50%

US ($thousand) 588 826 740 1,138 +26% +38%

Canadian as of US 67% 61% 78% 67%

Total pay + capital gains:

Canadian ($thousand) 446 660 620 1,112 +39% +68%

US ($thousand) 697 1,256 831 1,633 +19% +30%

Canadian as of US 64% 53% 75% 68%

18

Explanation for differences in pay levels between Canadian and US CEOs First two columns of next table shows no

significant difference between Canada and US after controlling for performance but semi-elasticity model shows a rise in total pay after disclosure in Canada

No significant change in intercept (fixed pay). Pay increase in Canada due to rise in performance pay

19

Explanation for pay levels- Canadian incentives improved due to disclosure

Pay-performance relation models

Independent variables Arithmetic sensitivity Elasticity

Semi-elasticity (stock return)

Semi-elasticity (return on assets)

Salary + bonus

Canadian –7.4313 0.0118 –0.1109 –0.2049 (0.26) (0.50) (0.81) (1.52) Canadian Disclosure 4.5458 0.0071 0.2073 0.1560 (0.13) (0.25) (2.29) (1.74) Total direct pay:

Canadian –23.2294 0.0068 0.0509 –0.0365 (0.42) (0.23) (0.32) (0.23) Canadian Disclosure –0.6056 0.0102 0.2860 0.2312

(0.01) (0.29) (2.47) (2.02)

20

Pay-performance elasticity

Independent variables ln(Salary + bonus) ln(Total pay)

Intercept 0.0471 0.0575 (3.03) (3.19) Disclosure 0.0186 0.0187 (1.02) (0.88) ln(Shareholder value) 0.0807 0.1300 (3.37) (4.69) ln(Shareholder value) Disclosure 0.1048 0.0556 (3.29) (1.50) Adjusted R2 0.0513 0.0459 Observations 1,592 1,592

Swan and Zhou (2006) show impact of disclosure on CEO pay in Canada

21

Pay-performance relation models

Independent variables Arithmetic sensitivity Elasticity

Semi-elasticity (stock return)

Semi-elasticity (return on assets)

Salary + bonus

Canadian –7.4313 0.0118 –0.1109 –0.2049 (0.26) (0.50) (0.81) (1.52) Canadian Disclosure 4.5458 0.0071 0.2073 0.1560 (0.13) (0.25) (2.29) (1.74) Total direct pay:

Canadian –23.2294 0.0068 0.0509 –0.0365 (0.42) (0.23) (0.32) (0.23) Canadian Disclosure –0.6056 0.0102 0.2860 0.2312

(0.01) (0.29) (2.47) (2.02)

Swan and Zhou summary of estimates of CEO incentive parameters

22

Swan and Zhou show option grants and stock holding rose in Canada due to disclosurePre-disclosure Post-disclosure Change

Median Mean Median Mean Median Mean

Current option grants:

Canadian 0.000 0.235 0.029 0.243 +0.029 +0.008

US 0.018 0.167 0.080 0.271 +0.062 +0.104

Canadian as of US 0% 141% 36% 90%

Unexercised options:

Canadian 0.000 0.401 0.227 0.839 +0.227 +0.438

US 0.325 0.852 0.547 1.051 +0.222 +0.199

Canadian as of US 0% 47% 41% 80%

Stock ownership:

Canadian 0.103 3.286 0.121 3.709 +0.018 +0.423

US 0.394 3.319 0.411 3.762 +0.017 +0.443

Canadian as of US 26% 99% 29% 99%

23

Role of institutional investors in monitoring executive pay Large investors, such as institutions, can play

an important role in controlling the errant behaviour of executives.

Institutions focus on compensation to control executive behaviour.

Institutional influence designed to increase incentives- rewards for maximizing firm value.

Actions by institutions to better align interests of executives with shareholders desirable.

24

Findings of Gallagher, Swan and Smith (GSS) (2006)on monitoring by institutional investors Hartzell and Starks (2003), hereafter H&S, examine the

issue of institutional monitoring of executive compensation.

Findings of H&S: Institutional investor influence increases option grant

pay-for-performance sensitivity. Institutional investor influence reduces compensation

level H&S conclude “institutions serve a monitoring role in

mitigating the agency problem between shareholders and executives”.

25

GSS: Invalid measure of institutional influence H&S measure institutional influence as institutional

concentration. Defined as shares held by the five largest institutions

divided by shares held by all institutions. Firm A – top 5 institutions own 30 percent of shares

outstanding while all institutions own 60 percent of shares outstanding. Institutional concentration is 0.3/0.6=0.5Firm B – top 5 institutions own 10 percent of shares outstanding while all institutions own 20 percent of shares outstanding. Institutional concentration is 0.1/0.2=0.5.

Institutional concentration does not reflect ownership structure or influence of institutional investors.

26

GSS: Questionable use of control variables Institutional concentration is highly negatively

correlated with firm size. H&S use the level of market capitalization as a

control for firm size. They do not use the natural logarithm! H&S may not be reliably controlling for size effects. Any relation between executive compensation

measures and institutional concentration may be reflecting a size effect rather than institutional influence.

27

GSS: What do we find?

Institutions do still play an important role in increasing sensitivity of pay to performance via higher option grants and holdings

Institutions, contrary to findings of H&S, raise salary levels but, by increasing incentive pay, reduce salary as a proportion of total compensation Salary rise consistent with compensation for higher

risk borne by executives when incentives raised Share ownership incentives are reduced!

Consistent with encouraging management to take higher risks

28

GSS: Data

Data sources: Executive Compensation – S&P’s ExecuComp. Institutional Holdings – Thomson Financial

CDA/Spectrum. Sample:

Data is collected for fiscal years corresponding to 1992-2002.

The firms included in the sample are those in the S&P1500. This represents a total of 2559 firms.

Our matched sample covers 97,679 executive years.

29

GSS: Institutional influence variables Three Measures of Institutional Influence

1. Institutional Concentration- Shares owned by top 5 institutions divided by shares

held by all institutions-misleading H&S measure2. Top 5 Ownership-

Shares owned by top 5 institutions divided by total shares outstanding-size related control used H&S

3. Ownership Measures derived from factor analysis- Bushee (1998) employs factor analysis. We capture

institutional propensity to have large holdings and short term holdings

Ten variables reduced to two factors that are not size related

30

GSS: Institutional influence variables capture highlighted types of institutional investor

Partitions of institutions based on factor scores.

Institutions with small long term holdings.

Institutions with small short term holdings.

Institutions with large long term holdings.

Institutions with large short term holdings.

31

GSS: Pay-for-Performance sensitivity computation Our study uses the Yermack (1995) approach

to measure option and stock PPS. PPS = Delta * (No. shares divided by total

shares outstanding in firm). For option grants the option delta computed

using the Black-Scholes formula adjusted for dividend. For stock, the delta is 1.

PPS gives a dollar change in executive wealth for a $1000 change in shareholder wealth.

32

GSS: PPS of option grants as a function of institutional influence Model 1 replicates the HS methodology- yields

same conclusions as the HS study. Model 4 replicates HS except using log market

capitalization- institutional concentration is now insignificant.

Top 5 Ownership and Large Short Term Institutional Ownership both have a positive relation with PPS of option grants.

Changing transformation of market capitalization affects Large Long Term holders in model 3 and 6.

33

GSS: Pay-for-Performance sensitivity of option grants as a function of institutional influence

Independent Variable (1) (2) (3) (4) (5) (6) -2.63E-06*** -3.06E-06*** -3.43E-06*** -2.21E-06*** -2.10E-06*** -3.02E-06*** Change Shareholder Wealth

(-3.78) (-4.37) (-4.6) (-3.2) (-3.06) (-4.18) 1.36E-06* 1.80E-06** 1.53E-06* 1.29E-06* 9.19E-07 -6.82E-08

Lag1 Change Shareholder Wealth (1.69) (2.23) (1.8) (1.74) (1.25) (-0.09)

0.467*** -0.0388 Lag1 Institutional Concentration

(18.61) (-1.4) 0.237*** 0.3313***

Lag1 Total Institutional Ownership (13.91) (20.03)

0.3925*** 0.365*** Lag1 Top5 Institutional Ownership

(11.86) (11.41) -0.0589** 0.1989***

Lag1 LHLT Institutional Ownership (-2.17) (7.48) 0.2486*** 0.3896***

Lag1 LHST Institutional Ownership (10.64) (17.12)

0.0867*** 0.0854*** 0.0744*** 0.1116*** 0.1093*** 0.1124*** Lag1 Tobin’s Q

(10.57) (10.33) (8.41) (13.82) (13.52) (12.99) -7.26E-06*** -8.44E-06*** -9.28E-06***

Lag1 Market Capitalization (-33.35) (-39.47) (-42.18)

-0.1167*** -0.0981*** -0.1094*** Lag1 Log Market Capitalization

(-48.13) (-47.57) (-50.69) 0.5745*** 0.5739*** 0.5653*** 0.5729*** 0.5738*** 0.5647***

CEO Dummy (66.6) (66.36) (60.94) (67.38) (67.27) (61.9)

Number of Observations 97679 97679 79640 97679 97679 79640

34

GSS: Impact of monitoring on salary level HS Specification (Model 1) shows significant

fall in salary level Correcting for mis-specified size control

(Model 4) reverses HS findings Models 2 and 3 with better measures of

influence show that salary increases but not by much

With appropriate size control, Models 5 and 6 still show positive and significant impact on salary

35

GSS: Salary as a function of institutional influence

Independent Variable (1) (2) (3) (4) (5) (6) 1.20E-05*** 1.33E-05*** 1.17E-05*** 1.17E-05*** 1.16E-05*** 1.12E-05*** Change Shareholder Wealth

(22.4) (23.12) (18.01) (23.87) (23.78) (20.19) -6.34E-06*** -5.30E-06*** -8.27E-06*** -2.24E-06*** -2.11E-06*** -3.24E-06***

Lag1 Change Shareholder Wealth (-9.58) (-7.48) (-10.46) (-4.25) (-4.01) (-5.53)

-0.6691*** 0.1192*** Lag1 Institutional Concentration

(-55.76) (9.16) 0.3175*** 0.1403***

Lag1 Total Institutional Ownership (36.27) (17.33)

0.1797*** 0.2166*** Lag1 Top5 Institutional Ownership

(10.75) (14.47) 0.5397*** 0.0747***

Lag1 LHLT Institutional Ownership (37.13) (5.6) 0.3721*** 0.1168***

Lag1 LHST Institutional Ownership (31.03) (10.56)

-0.2124*** -0.2007*** -0.1917*** -0.25*** -0.2492*** -0.2581*** Lag1 Tobin’s Q

(-52.89) (-48.09) (-42.28) (-64.68) (-64.39) (-60.76) 1.45E-05*** 1.75E-05*** 1.76E-05***

Lag1 Market Capitalization (74.7) (85.17) (76.51)

0.1943*** 0.1942*** 0.1932*** Lag1 Log Market Capitalization

(149.46) (191.61) (170.08) 0.7166*** 0.7197*** 0.7147*** 0.7172*** 0.7172*** 0.7132***

CEO Dummy (183.03) (173.6) (159.34) (199.1) (198.91) (179.53)

Number of Observations 97333 97333 79372 97333 97333 79372

36

GSS: Impact of institutions on Salary Proportion Ratio Model 1 with H&S Specification suggests that

SPR increases in institutional concentration (but not ownership)

Model 4, correcting size control measure, reverses finding on institutional ownership as it is now positive.

Better constructed measures (Models 5 and 6) all show a strong negative relationship

37

GSS: Salary Proportion Ratio (SPR) as a function of institutional influence Independent Variable (1) (2) (3) (4) (5) (6)

-3.80E-06*** -4.31E-06*** -3.24E-06*** -3.64E-06*** -3.68E-06*** -3.04E-06*** Change Shareholder Wealth (-15.43) (-16.79) (-11.1) (-15.45) (-15.47) (-11.18)

1.32E-06*** 7.87E-07** 1.96E-06*** 1.05E-06*** 1.18E-06*** 1.68E-06*** Lag1 Change Shareholder Wealth

(4.4) (2.52) (5.63) (4.02) (4.5) (5.73) 0.2275*** 0.0121

Lag1 Institutional Concentration (34.73) (1.61)

-0.1778*** -0.1353*** Lag1 Total Institutional Ownership

(-37.63) (-29.2) -0.1437*** -0.1704***

Lag1 Top5 Institutional Ownership (-15.77) (-19.19) -0.2149*** -0.0836***

Lag1 LHLT Institutional Ownership (-27.54) (-10.83) -0.2085*** -0.1385***

Lag1 LHST Institutional Ownership (-30.74) (-21.03)

-0.0566*** -0.0615*** -0.0683*** -0.0458*** -0.0452*** -0.0472*** Lag1 Tobin’s Q

(-24.14) (-25.83) (-26.44) (-19.77) (-19.5) (-18.59) -3.27E-06*** -4.41E-06*** -4.28E-06***

Lag1 Market Capitalization (-40.14) (-52.33) (-45.85)

-0.0504*** -0.0579*** -0.0545*** Lag1 Log Market Capitalization

(-71.33) (-101.83) (-87.07) -0.0892*** -0.0904*** -0.0891*** -0.0894*** -0.0897*** -0.0887***

CEO Dummy (-41.59) (-40.98) (-37.44) (-42.37) (-42.32) (-38.52)

Number of Observations 97333 97333 79372 97333 97333 79372

38

GSS: Salary and Salary Proportion Ratio of Total Compensation as a function of institutional influence

Using the Log of Market Capitalization changes the H&S results dramatically- the institutional concentration coefficient changes from negative and significant to positive and significant.

Top 5 Ownership and our Factor Analysis derived measures are less sensitive to changes in the transformation of market capitalization- they consistently yield a positive and significant relation with salary and total compensation.

39

GSS: Conclusion with respect to institutional monitoring

Institutions exert upward influence on option grant and option holding PPS

However, a negative relation exists between institutional influence and private share holding PPS by CEOs

Institutional influence is positively related to both salary and total compensation

Institutions prefer equity characterised by lower levels of PPS and higher total compensation

These findings are inconsistent with the belief that CEOs are overpaid

40

Oetomo and Swan -Rosen’s cloning hypothesis Examine 663 executive movements between

companies Mostly executives move from smaller to

bigger companies Tend to be paid more in larger company Receive more of their pay in incentives such

as options Performance of the initial (smaller) company

tend to be better than current company

41

Oetomo and Swan, Executive movements: firm in initial employment (Firm1), subsequent employment (Firm

2) Firm 1 Firm 2 t-Value

Mean Median Mean Median

Firm 2 >

Firm 1

Ttl Comp at grnt date; TC_fy ($000)

2,071.39 1,138.17 5,409.67 2,009.27 5.11***

Ttl Comp at fscl yr end; TC_gd ($000)

1,759.61 1,047.51 4,561.62 1,879.35 5.49***

Incent Prpn at fscl yr end; IncPropn_fy

0.61 0.62 0.70 0.75 9.17***

Mkr Del Ratio at grnt date; MDR_fy

0.37 0.35 0.47 0.47 7.29***

Mkr Del Ratio at fscl yr end; MDR_gd

0.35 0.32 0.46 0.46 8.61***

Salary(‘$000) 361.44 314.97 403.00 334.26 6.20***

Market Value of Equity; MV(‘$000)

4,757.15 1,942.80 8,551.57 2,384.55 5.78***

Firm’s Market Return; Marketrtn 0.17 0.13 0.15 0.09 -0.61

Return on Equity; ROE 0.023 0.08 -0.63 0.07 -1.56

Total Assets; AT(‘$000) 9,454.45 2,368.38 17,527.74 2,208.10 4.40***

Number of Business Segments 1.32 1 2.15 1.33 12.38***

42

The size difference between the new and old company is explained by: The better the performance of the old

company the greater the absolute and relative size increase

Existing CEOs move between similar sized companies

CEOs at small companies move to non-CEO position at large companies

Non-CEOs at larger companies move to CEO positions at smaller companies

43

Oetomo and Swan: Relationship between firm size, past performance and nature of executive move

AT %AT

Intercept 9,880.53 2.15(3.40)*** (4.29) ***

Firm 1 Return 16,772 2.4(2.46)* (2.05)*

CEO-Non_CEO 27,324 4.15(4.18)*** (3.68) ***

Non_CEO-CEO -13,777 -2.68(-2.61)** (-2.95)**

CEO-CEO -6,504.94 0.64(-0.82) -0.46

IndDumChange -10,424 -0.544(-2.59)** (-0.78)

R-Sqr adj 0.0568 0.0401F-Value 8.77 6.39

44

Sung and Swan career path agency model Agents have identical abilities but some spend early

career in a small firm, others in a large firm Agents are then evaluated on performance with

some hired with high pay in large firms and others at low pay in small firms-large firms mostly hire CEOs from other large firms, according to the model

Taking account of higher managerial ability in large firms significantly reduces but does not eliminate pay-size relationship-leaves room for Sung-Swan explanation

45

Oetomo and Swan: augmented pay-size relationship and conventional pay-size regression

Estimation of Full

Relationship

OLS Estimation of Crude

ModelColumns: 1 3

Dependent Variable Ln(TC_fy) Ln(TC_fy)

Intercept 5.37 5.6(23.90)*** (29.27)***

Market Return Firm 2 0.55 0.56(6.67)*** (6.65)***

Ln Observed Size Firm 2 0.23 0.268(7.79)*** (11.52)***

Ln Observed Size Firm 1 0.57(1.7)

Market Return Firm 1 0.66(4.20)***

R-Sqr adj 0.258 0.2177F-Value 52.6 86.87

-

-

46

Conclusion Pay-size relationship explained in part by the

movement of talented managers from smaller to larger companies

Accompanied by increases in performance pay When control for talents of managers at firms of

different sizes the true pay-size relationship smaller than estimated one

Pay levels rise as CEOs better monitored indicates that CEOs are not over-paid

Pay will rise further as incentives are relied on more heavily by boards with size mattering less

Outcomes preferable for shareholders as performance enhanced