EXECUTIVE COMPENSATION AND GENDER: WHAT WE CAN LEARN FROM US FIRMS?

João Paulo Vieitoa * and Walayet Khanb *

a Associate Dean and Professor of Finance- Polytechnic Institute of Viana do Castelo, Portugal b Professor of Finance- Evansville University, Indiana, USA

ABSTRACT

We examine if gender gap exists in total executive compensation for S&P1500 listed firms from 1992 to 2004. We also investigate if this difference exists in the case of new technology firms, where high scholarship is required for executive positions both for men and women. Additionally, we analyze weather the factors that explain executive compensation for men versus women are the same for S&P listed firms during the sample period.

Our results reveal that women represent only 1.12% of total executive sample in 1992 but it increases to 6.15% in 2004. We also find that the gap between men and women compensation exists but is reducing in later years, the forms of executive compensation for men versus women are different, and in the case of new technology firms the differences in total compensation are not statistically significant.

Although women have been considered more risk averse than men, but shareholders continue to pay women with a similar percentage of risk compensation components, like stock options and restricted stocks, than men. It seems that the shareholders are ignoring to take this factor into account when developing compensation packages for women and men.

Finally we also find that the factors that explain women and men total compensation are not the same for S&P1500 listed firms.

JEL CODE: J16; J79; G39 KEY WORDS: executive compensation; gender; new technology; performances; determinants of compensation *Correspondence to: João Paulo Vieito, Department of Finance and Accounting, School of Business Sciences- Polytechnic Institute of Viana do Castelo. Avenida Miguel Dantas - 4930 Valença – Portugal. Telephone number: 00351-251-800840. E-mail: joaovieito@esce.ipvc.pt

We would like to thank Prof. Kevin Murphy from the University of Southern California for his input to start this investigation. We also thank to Prof. Winnie Qian Peng, from Hong Kong University of Science & Technology, paper discussant at 2007 Financial Management Association International Conference (Barcelona), and to anonymous discussants at Finsia – Melbourne Centre for Financial Studies Banking & Finance Conference in 2008 (Melbourne- Australia).

mailto:pengq@ust.hkmailto:pengq@ust.hkmailto:pengq@ust.hkmailto:pengq@ust.hk

1. INTRODUCTION

In this paper we investigate presence of gender differences, if any, in terms of

executive compensation for the S&P 1500 listed firms during from 1992 to 2004. We

focus on the following research questions: (1): Is there a significant difference in total

compensation value between male versus female executives? (2) Are male executives

paid with different forms of compensation than the female executives? (3) Are the

determinants of the compensation same for male versus female executives? (4) Are

there differences in terms of compensation between male versus female executives in

new versus old technology firms?

Most gender based wage differential studies have essentially been developed in

psychology. Interest in studying wage differential is quite new among finance

researchers. Rapport (1995), Mohanand and Ruggiero (2003), Loucopoulos, Pavur and

Gutierrez (2002), and Ostroff and Atwater, (2003), among others, describe the existence

of a gender gap in terms of compensation between the male versus female employes.

However, these studies are limited in terms of scope and nature of their inquiry. They

do not examine whether or not the forms and the determinants of compensation between

male versus female executives are same. Also they do not evaluate the executive

compensation in new technology firms where the compensation differential is expected

to be lower given the higher qualifications requirements for all the executives. We

extend the research in executive compensation area by focusing on the stated questions

for S&P listed firms. Moreover our study is timely as it gives an insight to the executive

compensation area which now days is receiving global attention. Recent global financial

crisis of 2008 has brought the subject of executive compensation on the lime light.

Many believe that the unrestricted, discriminatory, and lucrative executive

compensations at the expenses of shareholders’ interests were a contributing factor in

generating the global financial crisis. In the environment of bail out packages offered by

various nations to fix the global financial problem it is highly likely that the executive

compensation in future will be subjected to strict scrutiny and reforms.

Our results show that women represent only 1.12% percentage of the total

S&P1500 executives in 1992 but this percentage increased to 6.15% in 2004. On

average, women receive more cash compensation (salary) than men but similar bonuses

after year 2000. There are significant differences in the forms and the relative weights

of compensations between male versus female executives. For example, women receive

fewer stock options in value as a fraction of the total compensation compared to men.

Women are remunerated with less risky compensation components, salary and bonuses,

at the sacrifice of more risky compensation of stock options and restricted stocks. Our

results also reveal that the factors that explain executive compensation for men versus

women are generally different. However, there are no significant differences in male

versus female executive compensation in the subset of new technology firms.

The rest of the paper is organized as follows. Section II presents the related

literature and the research questions. Section III describes the data, sample and

statistics. Section IV presents the research design. Section V presents the empirical

results and section VI the conclusions.

2. LITERATURE REVIEW AND RESEARCH QUESTIONS

Rapport (1995), Mohanand Ruggiero (2003), Loucopoulos, Pavur and Gutierrez

(2002), Ostroff and Atwater, (2003), among others, document that women are paid less

than men. Bell (2005) finds that this gap can range from 8% to 25% when firm size and

industry effects are controlled. Juradjda and Paligorova (2006) show evidence that

women in Czech firms receive compensations about 20% lower than men. Baker and

Fortin (1999) also find that the gap between men and women pay in Canada is smaller

than USA.

Rives, Renner and Bowlin (2002) also demonstrate that in the case of senior

executives from S&P500 listed firms, gender variable only explains their annual

compensation, based on salary and bonus, and not the total compensation that can also

incorporate stock options, restricted stocks, etc.

The differences between gender and executive compensation are also found in a

number of other situations such as American accounting profession (Hardin, Rending

and Stocks, 2002 and Moyes, Williams and Koch, 2006), British Universities (McNabb

and Wass, 1997), academic economists from the National Science Foundation (Broder,

1993), nursing administrators in Indiana and Michigan states, essentially not married

(Singh, Fujita and Norton, 2004). Also Santerre and Thomas (1993) find that gender is

one of the factors that explain differences in terms of compensation in public and

private hospitals.

Despite extensive exploration of gender bias in executive compensation no study

has focused on the S&P1500 listed firms and on the differences and changes in forms of

compensation between male versus female compensation packages. Moreover, there has

been no study which focuses on new technology firms.

We first empirical examine whether or not there is a statistically significant

deference in total value of compensation packages for male versus female over time.

Based on past studies we expect to observe this difference in our S&P listed firms as

well. We then test whether or not the forms of executive compensation between male

versus female executives are same over time. We expect a significant difference in

forms of compensation based on literature review as discussed below.

Charness and Gneezy (2004), Byrnes, Miller and Schafer (1999), Peng and Wei

(2007),Jianakoplos and Bernasek, (1998), Bliss and Potter (2002), and Balkin( 2000),

among others, document that women are more risk averse than men and this attitude is

reflected in their financial and investment decisions. For example, women fund

managers hold less risky positions than men. Niessen and Ruenzi (2006) demonstrate

that whereas women in mutual funds seem to take less unsystematic risk and opt for

more stable investment, male trade more frequently, reflecting a significantly higher

turnover ratio comparing to female managers. According to the authors, if the investors

want stable investment they should invest in mutual funds managed by women, and

investors that want riskier mutual funds should invest in founds that are managed by

men.

Based on described differences between men and women in terms of attitudes

towards risk, we develop the idea that women and men may have different forms of

compensation. In other words, the percentage that each compensation component

represents in the total compensation may well be different because women have been

reported as more risk averse. In our point of view shareholders must take into account

these differences and award more compensation based on cash to women and higher

risk compensation components like stock options and restricted stock to men.

We learn from the above discussion that men seem to be more willing to take

financial risks than women; men invest more than women (Charness and Gneezy, 2004)

and men are more overconfident than women. Peng and Wei (2007) among others

maintain that these differential risk attitudes and characteristics affect corporate

financial investment decisions. We therefore expect that the factors explaining

executive compensation may be different. In other words, we expect that the factors that

can explain variations in women versus men total compensation can not be all the same.

This leads us to our third empirical research question of whether or not the factors that

explain executive compensation foe male versus female are same.

Based on the findings by Becker (1957) on the economics of discrimination, we

develop the idea that part of the difference of executive compensation between men and

women can be associated to differences in terms of scholarship; nevertheless, this

difference must be significantly minimal in new technology firms which require a high

level of qualifications, both for male and female, for hiring purposes. New technology

firms usually hire employees that have a university degree.

Women have made a remarkable progress in receiving education. Women, in 2004,

outnumbered men as students in degree-granting institutions of higher education by 33

percent. Women are now increasingly taking graduation fields, such as business, law,

and medicine. The numbers of women that are in graduate schools in USA increased 66

percent between 1994 and 2004 and in the case of men the increase was just 25 percent,

and this difference between men and women is also significant between Blacks and

Latinos.

The companies, public or private, are demanding more education, both from male

and female, in order to compete at global level. The globalization process encourages

companies to use high skilled labor particularly in sectors where education is essential

like finance and professional services.

Based on the evidence that women are increasingly becoming more educated, and

given the increasing need of new technology firms to hire skilled and educated labor,

we believe that the differences between total compensation of men and women must not

exist. This leads us to our fourth and final empirical question of whether or not there

exists a gender bias in executive compensation in new technology firms.

3. DATA, SAMPLE SELECTION AND STATISTICS

A. DATA AND SAMPLE SELECTION

We use an unbalanced panel data from the Standard ExecuComp database,

which includes information about executive compensation in public U.S. companies

from 1992 to 2004 (1). This database contains information about the five most well paid

executives from these firms. In terms of compensation components, the database is

categorized by salary, bonus, ex-ante value of options, restricted stock award, long-term

incentive plan (LTIP), other annual compensation and all other compensations.

The sample has 79,650 observations of compensation related to the 5 most well -

paid executives from these 1500 firms between the years 1992 and 2004. We exclude

the entire executive observations whose sum of salary and bonus, and also total

compensation, was equal to zero. We also exclude the executives that don’t have

information about job title in ExecuComp database.

Using the Consumer Price Index (CPI), compiled by the Bureau of Labor

Statistics, and 1982 as a base of 100, we adjust the monetary variables to the inflation

level of the year 2004.

B. STATISTICS

In table 1 we present the time series of number of male versus female executives

for S&P1500 listed firms. Table 1: Number of executives by gender and year for S&P1500 listed firms

Men Women Total

Year Number of executives % Total Number of executives % Total

1992 2601 91.30% 32 1,12% 2849 1993 3917 89.23% 50 1,14% 4390 1994 4220 89.27% 79 1,67% 4727 1995 4506 88.82% 112 2,21% 5073 1996 4863 87.23% 143 2,57% 5575 1997 5366 86.26% 186 2,99% 6221 1998 5838 85.71% 211 3,10% 6811 1999 6076 84.80% 273 3,81% 7165 2000 6293 85.51% 333 4,53% 7359 2001 6394 86.45% 383 5,18% 7396 2002 6707 87.17% 402 5,22% 7694 2003 6943 87.47% 455 5,73% 7938 2004 6823 87.93% 477 6,15% 7760

The number of female executives increased from 1992 to 2004, starting from 32

in 1992 to 477 in 2004 - certainly an improvement but female still represents only

6.15% of the total executives.

In table 2 we test for the gender difference in executive total compensation for

each year during the period of 1992 to 2004. In table 3 we have described the relative

weights, fractions or proportions of each compensation component in relation to the

total compensation. In each of these tables, we also describe the results of the

Independent-Samples T-test to compare the means of executive compensation

components and Levene's test for equality of variances between the two sub-samples of

men and women executives.

Table 2: Total executive compensation of S&P 1500 listed firms by gender (thousands of dollars)

Year Women Men T test for Equality of Means Number of

Executives Mean Number of

Executives Mean Difference Significance

1992 32 1399,149 2601 1414,474 15,325

1993 50 880,904 3917 1390,666 509,762

1994 79 1005,568 4220 1526,06 520,492 **

1995 112 861,359 4506 1612,394 751,035 ****

1996 143 1094,382 4863 1929,823 835,441 **

1997 186 1583,233 5366 2382,496 799,263

1998 211 1405,499 5838 2740,54 1335,041

1999 273 2151,23 6076 3079,317 928,087 *

2000 333 2489,854 6293 3715,558 1225,704 **

2001 383 1889,292 6394 3326,989 1437,697 ***

2002 402 1787,889 6707 2713,794 925,905 ***

2003 455 1745,936 6943 2458,794 712,858 ***

2004 477 2005,371 6823 2755,854 750,483 ***

(***) Statistically significant at 1% level; (**), 5% level (*) or 10% level.

From table 2 we can see that on average each year men receive more than

women and this difference is highest between 1998 and 2001. After 2001 however the

average difference reduces but is still significant. In table 3 we analyze weather or not

the relative weights (proportions or fractions of total compensation) of each

compensation component same over time between male versus female sub-samples.

Table 3: Percentage that each component represents in terms of total compensation by year and gender Data is from ExecuComp database. SALARY is the executive salary for the year. BONUS is the dollar value of bonus (cash and non-cash) earned by the executive officer during the fiscal years. STOCK OPTIONS is the aggregate value of stock options granted to the executive during the fiscal year as valued using S&P`s Black-Scholes methodology. RESTRICTED STOCKS is the value of restricted stock granted during the year (determined as of the date of the grant), LTIP is the amount paid out to the executive under the company’s long-term incentive plan.

Year Salary Bonus Stock Options Restricted Stock LTIP

Female Male Female Male Female Male Female Male Female Male

1992 47.62% 49.01% 18.73% 19.17% 21.94% 19.23% 2.24% 4.20% 2.94% 3.61%

1993 45.37% 47.01% 18.66% 21.23% 24.81% 18.92%*** 4.65% 3.99% 1.85% 3.42%

1994 47.71% 44.45% 19.64% 21.54% 24.71% 22.71% 2.32% 3.87% 1.73% 3.29%

1995 49.99% 43.82%** 19.23% 22.13% 19.54% 20.74% 4.37% 4.09% 2.68% 3.75%

1996 43.67% 40.82% 18.39% 21.57%** 24.42% 24.66% 5.24% 4.34% 2.54% 3.66%

1997 39.11% 38.18% 18.98% 20.98%* 26.67% 26.62% 4.26% 4.70% 2.62% 3.72%

1998 40.36% 37.31%** 18.24% 18.81% 28.53% 30.95% 6.30% 4.56% 2.84% 3.32%

1999 36.97% 35.04% 18.56% 18.89% 34.03% 34.60% 4.96% 3.97% 1.99% 2.71%

2000 35.74% 33.32%** 16.92% 18.98% 36.01% 35.63% 4.48% 4.60% 2.25% 2.67%

2001 37.15% 34.27%** 16.39% 15.65% 36.01% 38.27% 4.17% 5.00% 2.05% 1.94%

2002 39.70% 35.56%*** 18.26% 18.04% 30.42% 34.00%** 5.03% 5.78% 2.07% 2.16%

2003 37.78% 35.91%* 18.52% 19.75% 29.30% 28.28% 7.28% 7.92% 2.52% 2.67%

2004 34.34% 31.92%*** 21.69% 22.03% 25.29% 27.71%** 10.22% 10.16% 3.59% 3.03%

Note: in the column for male, we also describe if the difference between male and female values are statistically different at (***) 1% level; (**) 5% level or (*)10% level. .

We can see from Table 3 that women receive a little more salary than men and

the mean differences are in most cases statistically significant.

In the case of stock options the results are not always congruent with Lyness

and Thompson (1997) findings, who state that women receive fewer stock options than

men. In some years stock options have a higher weight for men and in some other years

for women. But the mean differences in relative weights for stock options are

statistically significant only in three years (1993, 2002 and 2004). From these results we

can extract two main conclusions. The first conclusion is that probably shareholders are

not considering the gender based differences in degree of risk aversion when developing

compensation packages for men versus women. The second conclusion is that probably

the findings of differences in risk aversion can not be generalized to all the population

and they are specific to samples examined by previous studies, as discussed earlier.

Another important observation from the above table is that in 2003 and 2004 the

use of restricted stocks increased but the use of stock options decreased. This is

probably due to the corporate governance changes introduced by Sarbanes Oxley Act of

2002.

In order to test whether or not gender based differences in executive

compensation exist in new economy firms we identify executives from these firms

using Murphy (2003) methodology that considers firms from new economy with the

following SIC codes: 3570, 3571, 3572, 3576, 3577, 3661, 3674, 4812, 4813, 5045,

5961, 7370, 7371, 7372 and 7373.

Table 4 describes the average total compensation values of these executives

from new economy firms listed in S&P1500 indexes. We also describe the results of the

Independent-Samples T-test to compare the means of executive compensation

components and Levene's test for equality of variances between the two sub-samples of

men and women.

Table 4: Total executive compensation in new economy firms by gender (thousands of dollars)

Year Women Men T test for Equality of Means

Number of Executives

% of Total Mean Number of Executives

% of Total Mean Difference Significant

1992 - 35 100% 2.923 - -

1993 3 1.17% 954 254 98.83% 1.723 769 No

1994 5 1.54% 1.163 319 98.46% 2.254 1.092 No

1995 6 1.70% 893 347 98.30% 2.402 1.508 No

1996 7 1.82% 2.399 378 98.18% 2.999 600 No

1997 10 2,19% 2.473 446 97.81% 4.288 1.815 No

1998 17 3.04% 2.090 542 96.96% 6.416 4.326 No

1999 27 3.84% 6.856 677 96.16% 6.069 -787 No

2000 37 4.80% 5.923 734 95.20% 6.971 1.048 No

2001 41 5.36% 4.015 724 94.64% 6.082 2.067 No

2002 41 5.30% 4.266 733 94.70% 3.730 -536 No

2003 52 6.44% 2.895 756 93.56% 2.801 -94 No

2004 49 5.61% 3.710 824 94.39% 3.197 -513 No

From table 4 we can see that the number of women increases in new economy

firms in a similar proportion as in the entire sample between 1992 and 2004. Still, they

represent a small fraction of the total executives. However, unlike the full sample the

compensation gap between male versus female executives is not statistically significant

in new technology firms. Also it is interesting to observe that beginning the year 2002

that average compensation values are higher for female executives relative to male

executives.

4. FIXED EFFECT MULTIPLE REGRESSION RESEARCH DESIGN

In order to test the determinants of the executive total compensation we control

for time effect with the introduction of one dummy variable for each year, between

1992 and 2004, and also for job title effect inserting ten difference categories, following

Bertrand and Hallock (2001). Time can be an important factor to explain the changes in

executive compensation, and job title has also been reported like a factor that can

explain executive compensation differences between men and women.

We use Unbalanced Panel Data with Fixed Effect Regression Model, also called

within estimator or Least of Square Dummy Variable (LSDV). Standard errors are

corrected using period Seemingly Unrelated Regression (SUR) Panel Corrected

Standard Errors (PCSE): correction for both period heteroskedasticity and general

correlation of observations within a given cross section (Beck and Katz, 1995)

The variable that we want to explain (dependent variable) is LN (Total

Compensation), which is the natural logarithm of total compensation. Stathopoulos,

Espenalub and Walker (2004), among others, also use this variable and it is the sum of

salary, bonus, stock options, restricted stocks, other annual compensations and all other

compensations. Aggarwal and Samwick (1999) use total compensation variable to

evaluate the contracts offered to executives in the context of strategic competition

between products and evaluation of relative performances; Fields and Fraser (1999) use

it to unmask the commercial banks when they attribute compensations to link

executives to the performances. The model is:

0 1 2

3 4 5

6 7 8

9...18 20...31

LN(Total Compensation)=β +β *LN(Firm Size)+β *Pension Plan Dummy+β *Interlocked Dummy+β *ROA(-1)+β *Growth 5Y+

+β *LN(BsVolatility)+β *LN(Ajex)+β *LN(Epsex)++β *Job Titles Dummys+ β *Years Dummys (199

+

3..2004)+f+ε

Where

FIRM SIZE can assume the variables LN (Market Value), LN(Assets) and

LN(Sales) and f is the fixed effect.

And

MenLN(Total Compensation)= or

Women

⎧⎪⎨⎪⎩

Financial Variables

One of the most important variables used to explain executive compensation is

firm’s size. Among many others, Ittner, Lambert and Larker (2003), Murphy (2003)

and Stathopoulos, Espenlaub and Walker (2004), Anderson, Banker and Ravidran,

(1998), and Chen and Hung (2006)) examine the impact of size on executive

compensation. To capture the influence of company size on total compensation previous

researchers have used variables like sales, assets and market value – one of the three

variables to test the impact of firm size on executive compensation without evaluating if

the results would be better if they used other variables. Because these variables are

highly correlated they can not be inserted at the same time in the fixed effect regressions

analysis. To solve this problem, and to make things clearer as to which of the three

variables will be the best to explain the effect of firm size on total compensation, we run

a separate fixed effect regression using each variable. We expect that firm size will have

a strong positive effect in total executive compensation in the case of women and men.

We also use the explanatory variable LN (Ajex) to examine the impact on total

executive compensation. The variable is the natural logarithm of the ratio used to adjust

per-share data for all stock splits that have occurred subsequent to the end of the

company’s fiscal year. We expect top executive to have some power to influence this

ratio and they will probably do so for having personal benefits. We expect a positive

relationship between LN (Ajex) and the dependent variable.

To measure the impact of the firm growth on total executive compensation, we

use the variable Growth5Y which is the 5-years least square annual growth rate of firm

sales. We expect a positive relationship between firm sales growth and total executive

compensation. If sales grow, shareholders probably will try to compensate their

executive for their efforts.

We also test the effect of the firm risk on executive compensation, following

Palia (2001), who finds a positive relationship between these two variables. We use the

variable LN (Bs-Volatility) which is calculated using the Black Scholes Options

Pricing Model using prior 60 months returns. We expect a positive relationship between

executive compensation and firm stock return volatility.

To test the impact of firms’ performances on executive compensation we use

the lagged variable ROA (-1). In our view, executive compensation can be a reward of

the managerial efforts developed in the previous years to increase firms’ performances.

We expect that higher firm performances will lead to a higher compensation.

In the fixed effect regressions we also control for time effect, following Barron

and Waddel (2003), and Grinstein and Hribar (2004), inserting a dummy variable each

year between 1993 and 2004. We expect that time will have a strong effect explaining

total executive compensation.

Finally, like Bertrand and Hallock (2001), we also fix the job functions because

we expect more men to on top jobs like CEO, Chair or President, than women. To fix

the effect of job title we insert the following dummy variables: CEO/Chair, Vice Chair,

President, CFO, COO, Other “Chief” Officer, Executive VP, Senior VP, Group VP and

other occupations. We expect a positive relationship between top job titles and total

compensation.

Governance variables

Like Hallock (1997) we expect men and women who are on two different boards

to extract higher compensations. If they are on two boards they can probably exert

pressure for the increase of their compensation. We measure the impact of interlocked

relationships on total compensation using the dummy variables Interlock that assume

the value of 1 when the executive are at the same time on two boards and zero when

they are not.

We also use the variable PensionPlan which is a dummy variable that assumes

the value equal to 1 if a firm pays for an executive pension plan and zero otherwise.

When a firm already pays for an executive pension plan, shareholders will have the

capacity to exert pressure on executives not to ask for more compensation. We expect a

negative relationship between the existence of firms’ pension plan and the total

compensation.

Table 5 summarizes all the variables.

Table 5 Summary of Dependent and Independent variables.

Dependent Variables Definition

LN(Total Compensation) Natural Logarithm of total compensation

Independent Variables Expected variation Definition

Firm size + LN (Market values), LN (Sales) and LN (Assets) are used separately to test the influence of firm size. This is the natural logarithm of firm market value, sales or assets.

Pension Plan - Dummy variable that assumes the value equal to 1 if the firm pays for an executive pension plan and zero otherwise.

Interlocked + Dummy variable that assumes the value equal to 1 if the executive is simultaneously on two different boards. ROA (lag) + Firm ROA of last year Growth5Y + Firm sales 5-year least of square annual growth rate.

LN( Ajex) +

Natural logarithm of the ratio used to adjust per-share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year.

LN ( Bs_Volatility) + Natural logarithm of a firm’s stock returns volatility.

Other Occupation + Dummy variable that assumes the value equal to 1 if the executive is classified in Other Occupation and zero otherwise.

Other Chief Officer + Dummy variable that assumes the value equal to 1 if the executive is CEO and zero otherwise.

COO + Dummy variable that assumes the value equal to 1 if the executive is COO and zero otherwise.

CFO + Dummy variable that assumes the value equal to 1 if the executive is CFO and zero otherwise.

Group Vice President + Dummy variable that assumes the value equal to 1 if the executive is GROUP VICE PRESIDENT and zero otherwise.

Vice Chair + Dummy variable that assumes the value equal to 1 if the executive is VICE CHAIR and zero otherwise.

President + Dummy variable that assumes the value equal to 1 if the executive is PRESIDENT and zero otherwise.

Vice President + Dummy variable that assumes the value equal to 1 if the executive is VICE PRESIDENT and zero otherwise.

Executive + Dummy variable that assumes the value equal to 1 if the executive is EXECUTIVE and zero otherwise.

CEO/Chair + Dummy variable that assumes the value equal to 1 if the executive is CEO or CHAIR and zero otherwise. Year1992…2004 + Dummy for each year during the analyzed period (1992 to 2004)

5. EMPIRICIAL RESULTS

A. SUMMARY STATISTICS

Table 6 describes the summary statistics of key variables classified into male

versus female subsamples.

Table 6 Descriptive Statistics

Data for S&P1500 listed firms is collected from ExecuComp database. Mk Value is the firm’s market value; Sales is the firm’s sales value; Assets is the firm’s asset value and. Pension Plan is a dummy variable that assumes the value one if company pays for an executive pension plan and zero otherwise. Interlocked is a dummy variable that assumes the value equal to 1 when it is true that an executive is at the same time in two different boards and zero if not. ROA is the return on Assets from the last year. Growth5Y is the firm sales 5-year least of square annual growth rate sales. Ajex is the ratio used to adjust per-share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year. Bs_Volatility is the firm stock return volatility and Employees is the number of firm employees. PANEL A: MEN

N Mean Median Maximum Minimum Std. Deviation Mk Value 60830 8900.332 1996.368 594626.8 38.20007 27757.05

Assets 60990 12737.18 1739.331 1484101 16.5890 56184.02 Sales 60980 5191.544 1397.220 286103.0 3.124000 13962.04 PensionPlan 60927 0.134637 0,00000 1 0 0,341338 Interlock 61009 0.026947 0.00000 1 0 0,161929 ROA 60986 5.815218 5.480534 161.5661 -505.4146 12.02004 Growth5Y 60735 15.56275 10.08000 1600.455 -48.64200 33.62584 Ajex 61010 1.810611 1.00000 128.0000 0.100000 2.290511 Bs Volatility 58531 0.394636 0.347000 3.513000 0.107000 0.200996 Employees 60050 22.41070 6.60000 1700.000 0.015000 60.09118 PANEL B: WOMEN

N Mean Median Maximum Minimum Std. Deviation Mk Value 2666 7813.116 1844.550 329154.8 39.24322 22747.45

Assets 2680 13320.69 1619.357 1264032 16.58900 69327.86 Sales 2680 4419.783 1276.275 142897.0 13.34100 9863.216 PensionPlan 2680 0.059858 0.000000 1 0 0.237268 Interlock 2680 0.015672 0.000000 1 0 0.124225 ROA 2680 6.091151 5.602247 161.5661 -505.4146 14.61464 Growth5Y 2664 15.90981 10.59550 1028.230 -33,20300 35.13982 Ajex 2680 1.677746 1 128.0000 0.100000 3.046095 Bs Volatility 2564 0.425933 0.388000 2.419000 0.116000 0.186695 Employees 2627 21.32617 6.468000 779.1000 0.015000 56.92773

We can see from the above table that for female sub-sample, on average, firms

have a lower market value and sales volume than the male sub-sample. Firms in female

sub-sample also fewer employees with pension plans and fewer executives who are on

two boards (interlocked). However, female sub-sample has, on average, higher ROA

and also higher asset base and stock return volatility.

Determinants of executive compensation by gender in S&P1500 listed firms We now examine whether women and men total compensation can be explained

by different factors since, as we already documented, women and men have been

reported as having differences in terms of risk aversion.

Tables 7 and 8 describe the values of the fixed effect regression analysis for men

and women.

16

Table 7 Fixed Effect Regression: Least Square Dummy Variables - Executives Men - S&P1500 Listed Firms We have used Unbalanced Panel Data Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and using 1982 base of 100, we have adjusted monetary variables for inflation reporting the values to the year 2004. The dependent variable is LN (Total Compensation) which is the natural logarithm of total executive compensation. LN (Sales) is the natural logarithm of firm sales, LN (Assets) is the firm Assets’ value and LN (Market value) is the natural logarithm of firm market value. These three variables are used separately in order to see which one is the best to explain firm size influence in total compensation. We have also used the variables Pension Plan, which is a dummy variable that assumes the value one when if the company pays for an executive pension plan and zero otherwise; Interlocked is a dummy variable that assumes the value equal to 1 if an executive is on two different boards and zero otherwise; ROA (lag) is the ROA from the last year; LN (Ajex) is the natural logarithm of the ratio used to adjust per-share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; LN(Bs Volatility) is the firm’s stock return volatility. We also control for Job titles inserting a dummy variable that assume the value of 1 when it is true that an executive is CEO/Chair; Vice Chair; President; CFO; COO; Other “Chief” Officer, Executive VP; Senior VP; Group VP and other occupations. Finally we also control for the time effect inserting a dummy variable for each year between 1992 and 2004.

LN( Total Compensation)

Coefficient t-Statistics Coefficient t-Statistics Coefficient t-Statistics Constant 4.181*** 58.249 5.065*** 60.590 4.950* 59.242 LN( Market Value) 0.345*** 51.535 LN(Assets) 0.225*** 25.556 LN(Sales) 0.249*** 27.409 PensionPlan -0.075*** -5.484 -0.086*** -6.065 -0.081*** -5.751 Interlocked 0.090*** 3.863 0.090*** 3.702 0.088*** 3.657 ROA (lag) 0.002*** 5.608 0.006*** 19.022 0.005*** 16.268 Grwoth5Y 0.002*** 11.691 0.002*** 1.546 0.002*** 13.750 LN (Ajex) 0.035*** 3.446 -0.078*** -7.567 -0.088*** -8.733 LN (BS Volatility) 0.241*** 12.864 0.075*** 3.952 0.082*** 4.346 OTHER Occupations -0.195 -0.867 -0.376 -1.621 -0.324 -1.402 Other Chief Officer 0.162 1.004 0.149 0.908 0.138 0.846 COO 0.189 0.626 0.211 0.673 0.286 0.914 CFO -0.029 -0.146 -0.072 -0.352 -0.066 -0.320 Group Vice President -0.018 -0.094 -0.011 -0.056 -0.056 -0.277 Vice Chair 0.188* 1.666 0.087 0.733 0.143 1.216 President 0.117* 1.680 0.113 1.556 0.145** 2.005 Vice President -0.098 -1.116 -0.236*** -2.581 -0.206** -2.251 Executive -0.063 -1.112 -0.096 -1.619 -0.078 -1.316 CEO/Chair 0.525*** 8.943 0.418*** 6.803 0.451*** 7.339 Year1993 0.014 0.574 0.020 0.763 0.023 0.881 Year1994 0.168*** 6.780 0.123*** 4.827 0.125*** 4.873 Year1995 0.173*** 6.888 0.144*** 5.567 0.141*** 5.407 Year1996 0.255*** 9.961 0.223*** 8.448 0.217*** 8.189 Year1997 0.323*** 12.433 0.341*** 12.667 0.335*** 1.240 Year1998 0.440*** 16.660 0.428*** 1.560 0.422*** 1.530 Year1999 0.540*** 20.257 0.522*** 1.873 0.521*** 1.863 Year2000 0.618*** 22.474 0.602*** 20.783 0.591*** 2.037 Year2001 0.614*** 22.125 0.603*** 20.544 0.598*** 2.036 Year2002 0.618*** 22.293 0.529*** 17.869 0.538*** 1.825 Year2003 0.522*** 18.741 0.498*** 16.745 0.507*** 1.715 Year2004 0.623*** 22.325 0.600*** 19.985 0.601*** 2.011 Nº 58381 58381 58381 Adjusted R- Squared 76.97% 75.76% 75.82% Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995). Note 2: (***) Statistically significant at 1% level; (**), 5% level (*) or 10% level.

17

Table 8

Fixed Effect Regression: Least Square Dummy Variables - Executives Women - S&P1500 Listed Firms We have used Unbalanced Panel Data Fixed Effect Regression Analysis. Using the Consumer Price Index (CPI), compiled by the Bureau of Labor Statistics, and using 1982 base of 100, we have adjusted monetary variables for inflation reporting the values to the year 2004. The dependent variable is LN (Total Compensation) which is the natural logarithm of total executive compensation. LN (Sales) is the natural logarithm of firm sales, LN (Assets) is the firm Assets’ value and LN (Market value) is the natural logarithm of firm market value. These three variables are used separately in order to see which one is the best to explain firm size influence in total compensation. We have also used the variables Pension Plan, which is a dummy variable that assumes the value one when if the company pays for an executive pension plan and zero otherwise; Interlocked is a dummy variable that assumes the value equal to 1 if an executive is on two different boards and zero otherwise; ROA (lag) is the ROA from the last year; LN (Ajex) is the natural logarithm of the ratio used to adjust per-share data for all stock splits that have occurred subsequent to the end of the company’s fiscal year; LN(Bs Volatility) is the firm’s stock return volatility. We also control for Job titles inserting a dummy variable that assume the value of 1 when it is true that an executive is CEO/Chair; Vice Chair; President; CFO; COO; Other “Chief” Officer, Executive VP; Senior VP; Group VP and other occupations. Finally we also control for the time effect inserting a dummy variable for each year between 1992 and 2004. LN( Total Compensation) Coefficient t_Statistics Coefficient t_Statistics Coefficient t_StatisticsConstant 3.397*** 7.224 4.329*** 7.386 4.405*** 7.690 LN( Market Value) 0.391*** 10.844 LN(Assets) 0.273*** 5.100 LN(Sales) 0.258*** 4.791 PensionPlan -0.090 -1.026 -0.059 -0.648 -0.060 -0.655 Interlocked -0.353*** -2.650 -0.284** -2.027 -0.291** -2.073 ROA (lag) 0.002* 1.879 0.005*** 4.464 0.005*** 4.445 Growth5Y -0.004*** -5.426 -0.003*** -3.717 -0.003*** -3.626 LN (Ajex) 0.025 0.495 -0.067 -1.278 -0.082 -1.598 LN (BS Volatility) 0.406*** 4.103 0.218** 2.164 0.238** 2.356 OTHER Occupations Other Chief Officer -0.587** -2.077 -0.429 -1.397 -0.147 -0.486 COO CFO Group Vice President -0.429 -1.089 -0.152 -0.367 -0.082 -0.202 Vice Chair President -0.378* -1.867 -0.386* -1.755 -0.354 -1.597 Vice President Executive CEO/Chair Year1993 0.647* 1.648 0.560 1.190 0.632 1.407 Year1994 0.794** 1.998 0.601 1.260 0.675 1.476 Year1995 0.762* 1.950 0.575 1.223 0.664 1.478 Year1996 0.872** 2.234 0.690 1.466 0.787* 1.754 Year1997 0.957** 2.446 0.832* 1.763 0.937** 2.082 Year1998 0.978** 2.504 0.850* 1.802 0.956** 2.127 Year1999 1.172*** 3.000 0.984** 2.084 1.093** 2.426 Year2000 1.212*** 3.103 1.060** 2.240 1.164*** 2.581 Year2001 1.204*** 3.074 1.048** 2.209 1.162** 2.568 Year2002 1.166*** 2.977 0.933** 1.962 1.069** 2.362 Year2003 1.141*** 2.911 0.977** 2.052 1.121** 2.475 Year2004 1.194*** 3.039 1.027** 2.152 1.173*** 2.584 Nº 2547 2555 2555 Adjusted R- Squared 77.37% 75.90% 75.84 Note 1: Standard errors are corrected using period Seemingly Unrelated Regression (SUR) Panel Corrected Standard Errors (PCSE): correction for both period heteroskedasticity and general correlation of observations within a given cross section (Beck and Katz, 1995). Note 2: (***) Statistically significant at 1% level; (**), 5% level (*) or 10% level

18

Tables 7 and 8 present the results of multivariate regression models which

explain more than 75% of the variation in total compensation for men and women

respectively. As we have expected, women and men compensation are not explained by

all the same variables.

Firm size continues to be the most important variable to explain executive

compensation for both men and women and is statistically significant in all the cases.

We used three variables separately to measure the impact of firm size on the total

executive compensation in order to choose which one is the best. Market Value seams

to be the best to explain this relationship. Executive total compensation tends to

increase with market value of the firm but the impact is relatively less strong for

women.

There is a negative relationship between pension plan variable and executive

compensation, but only in the male subsample. The results indicate that when the firm is

already committed to pay for executive pension plans it can then let the compensation

increases go.

Another interesting result is that only men experience increase in total

compensation if they are on two boards simultaneously (interlocked). Women total

compensation is negatively related when the interlocked relationship exists.

The association between the firm growth in the last five years and the total

compensation is positively significant but only for male sub-sample. Also the ratio used

to adjust per-share data for all stock splits (that have occurred subsequent to the end of

the company fiscal year) affects male total compensation and not women’s.

Women’s compensation is lot more influenced by the firm stock return volatility

than men’s compensation. That is, women’s total compensation changes more than

men’s when stock return volatility changes.

As stated earlier we also control job positions, following Bertrand and Hallock

(2001), inserting for that purpose ten dummies.

Finally we test the effect of time to explain executive compensation and find that

time is an important factor to explain changes in total executive compensation during

the period from 1992 to 2004. This effect is strongly pronounced in women’s case

essentially after 1999.

19

6. CONCLUSION

We examine several gender based questions surrounding the total executive

compensation associated with S&P1500 listed firms from 1992 to 2004. More precisely,

we analyze if total compensation is different between men versus women executives for

S&P listed firms. We also examine if these differences exist when men and women

work in new technology firms, where both are expected to have the same skill set and

qualifications` levels. We analyze whether the factors which explain men’s and

women’s executive compensation are different.

Our results reveal that women are only 1.12% of the total sample in 1992 but

that percentage has increased to 6.15% in 2004. Women receive, on average, less than

men and these differences in most years are significant, but the difference is reducing

essentially after the year 2000.

Because women, as it has been argued in the literature, are more risks averse, we

thought that shareholders would offer them different forms of compensation - less stock

options and restricted stocks. We find that the difference, though small, in stock options,

as a percentage of total compensation, is significant only for three years. In some years

women get higher compensation in the form of stock options and in some other years

lower than men. The results can lead us to two conclusions. The first is that probably

shareholders don’t care, or don´t know about these differences in terms of risk aversion

and gender and thus they pay to women the same proportion of risk compensation

components as to men. The second explanation is that the past findings about the

differences between risk aversion and gender can not be generalized to all the

population and they appear to be sample specific.

Our results reveal that in new technology firms the differences between men and

women total compensation are not statistically significant. Finally, we also find that the

factors that explain executive compensation of women and men are not the same.

20

NOTES

1. The data is from 1992 to 2004 because 1992 is the first year of information in the

ExecuComp database and 2004 was the last completed year of information when we

started this work. In this database, there are executives with data for all of the years and

others with only a small number of years of information

REFERENCES

Aggarwall R, Samwick A. 1999. Executive compensation, strategic competition and

relative performance evaluation: theory and evidence. Journal of Finance 54 (6): 1999-2043.

Aggarwall R, Samwick A. 2003. Performance incentives within the firms. The effects

of managerial responsibility. Journal of Finance 58(4):1613-1649

Anderson M, Banker R., Ravidran S. 1998. Executive Compensation in the information

Technology. Working Paper University Texas Dallas.

Atkinson S, Baird S, Frye M. 2003. Do female mutual fund manage differently?.

Journal of Financial Research 26 (1): 1-18

Balkin D. 2000. Gender effects on employee participation and investment behaviour

with 401(k) retirement plans. Working Paper Colorado University

Becker G. 1957. The economics of discrimination. Chicago. University of Chicago

Press.

Barber B., Odean T. 2001. Boys will be boys: gender, overconfidence and common

stock investment. The Quarterly Journal of Economics 1: 261-292.

Barro J, Barro J. 1990. Pay, performance and turnover on bank CEOs. Journal of

Labour Economics 8: 448-481.

Barron J, Waddell G. 2003. Executive rank, pay and project selection. Journal of

Financial Economics, 67: 305-349

Bell L. 2005. Women-led firms and gender gap in top executive jobs. IZA Working

paper nº 1689, Germany

Bertrand M, Hallock K. 2001. The gender gap in top corporate jobs. Industrial &

Labour Relations Review 55(3): 1-21.

21

Bliss R, Potter M. 2002. Mutual fund managers: Does gender matter?. The Journal of

Business and Economic Studies 8(1): 1-91.

Bowlin W, Renner C, Rives J. 2003. A DEA study of gender equity in executive

compensation. Journal of the Operational Research Society 54:751-757.

Bowlin W, Renner C. 2008. Assessing gender and top-management-team pay in the

S&P Mid-Cap and Small-Cap companies using data envelopment analysis,

European Journal of Operational Research 185(1): 430-437

Buress J, Zucca L. 2004. The gender equity gap in top corporate executive positions.

Mid American Journal of Business 19(1): 55-63.

Brett J, Stroh L. 1997. Jumping Ship: Who benefits from an External Labour market

Career Strategy? Journal of Applied Psychology 82: 312-341.

Broder I. 1993. Professional achievements and gender differences among academic

economists. Economy Inquiry 31 (1): 116-127.

Byrnes J, Miller D, Schafer W. 1999. Gender differences in risk taking; a meta-analysis.

Psychological Bulletin 125(3):367-383.

Chance D, Kumar R, Todd R. 2000. The “repricing” of stock options. Journal of

Financial Economics 57:129-154.

Charness G, Gneezy U. 2004. Gender differences in financial risk-taking. Working

paper from University of California at Santa Barbara.

Chen Y, Hung C. 2006. Corporate governance and insider compensation. NASDAQ

Technology Firms, working paper presented at FMA Conference in Salt Lake

City.

Chidambaran N, Prabhala N. (2003). Executive stock repricing, internal governance and

management turnover. Journal of Financial Economics, 69:152-189.

Douglas S, Fujita F, Norton S. 2004. Determinants of satisfaction with pay among

nursing home administrators. Journal of American Academy of Business 5(1/2):

230-

Dreher G, Ash R. 1990. A comparative study of mentoring among men and women in

managerial, professional, and technical positions. Academy of Management

Journal 75(5): 539-546.

Fields L, Fraser D. 1999. On the compensation implications of commercial bank entry

into investment banking. Journal of banking & Finance 23: 1261-1276.

Fortin N, Baker M. 1999. Occupational Gender Composition and wages in Canada:

1987-1988. Nber Working paper No W7371

22

Giddings L. 2002. Changes in gender earnings differentials in Bulgaria’s transition to a

mixed-market economy. Eastern Economic Journal 28 (4): 481-498.

Hardin J, Rending K, Stocks M. 2002. The effect of gender on the recruitment of entry-

level accountants. Journal of Managerial Issues 12 (2), 251-266.

Hartzell J, Ofek E, Yermack D. 2004. What´s in it for me? CEOs whose firms are

acquired. The Review of Financial Studies, 17(1): 37-61.

Heilman M, Block C, Martell R, Simon M. 1989. Has anything changed? Current

characterizations of men, women and managers, Journal of Applied Psychology

74(6), 935-942.

Hersch J. 1998. Compensating differentials for gender-specific job injury risk. The

American Economic Review 88 (3): 589-608.

Ittner C; Lambert R, Larker D. 2003. The structure and performance consequences of

equity grants to employees of new economy firms. Journal of Accounting and

Economics 34: 89-127.

Lippert R, Nixon T, Pilotte E. 2000. Incentive compensation and stock price response to

dividend increase announcements. The Financial Review 35(4):69-94.

Jianakoplos N, and Bernasek A. 1998. Are women more risk averse?. Economic

Enquiry 36 (4): 620-630.

Jordan C, Clark S, Waldron M. 2007. Gender bias and compensation in the executive

suite of the fortune 100. Journal of Organizational Culture, Communication and

Conflict 11 (1): 19-29.

Jurajda S, Paligorova T. 2006. Female managers and wages in central Europe. IZA

Discussions paper No. 2303.

Kahan D, Braman D, Gasti J, Slovic P, Mertz C. 2005. Gender, race and risk

perception: the influence of cultural status anxiety,

http:77ssrn.com/abstract=723762

Keaveny T, Inderrieden E. 2000. Gender differences in pay satisfaction and pay

expectations. Journal of Managerial Issues 12 (3): 363-379.

Kesner I. 1988. Director characteristics and committee membership: An investigation of

type, occupation, tenure and gender. Academy of Management Journal 31(1):66-

84.

Lam S, Dreher G. 2004. Gender, extra-firm mobility and compensation attainment in

the United States and Hong Kong. Journal of Organizational Behavior, 25: 791-

805.

23

Lyness K, Thompson D. 1997. Above the glass ceiling?. Journal of Psychology.

82(3):359-375.

Loucopoulos C, Pavur R. 2002. Insights into gender discrimination in employment

compensation thought the use of classification models. Journal of Management

Issues 14(3): 375-387.

McNabb R, Wass V. 1997. Male-female salary differentials in British Universities.

Oxford Economic Papers 49(3) :328-343.

Milbourn T. 2003. CEO reputation and stock-based compensation. Journal of Financial

Economics 68: 233-262.

Mohan N, Ruggiero J. 2003. Compensation differences between male and female CEOs

for publicly traded firms: a nonparametric analysis. Journal of the Operational

Research Society 54: 1242-1248.

Mohan N, Ruggiero J. 2007. Influence of firm performance and gender on CEO

compensation. Applied economics 39(9), 1107-1113.

Moyes G, Williams P, Koch B. 2006. The effects of age and gender upon the

perceptions of accounting professional concerning their job satisfaction and

work-related attributes. Managerial Accounting Journal 21(5), 536-561.

Murphy K. 1986. Incentives, learning and compensation: a theorical and empirical

investigation of managerial labour contracts. Rand Journal of Economics, 17:59-

76.

Murphy K. 2003. Stock –Based pay in new economy firms. Journal of Accounting and

Economics 34: 129-147.

Niessen A, Ruenzi S. 2006. Sex matter: gender and Mutual Funds. FMA Conference –

Stockholm (Sweeden).

Orlitzky M, Swanson D, Quatermaine L. 2006. Normative myopia, executives´

personality, and preference for pay dispersion: toward implications for corporate

social performances. Business and Society 45(2), 149-177.

Ostroff C, Atwater L. 2003. Does whom you work with matter? Effects of referent

group gender and age compensation on manager’s compensation. Journal of

Applied Psychology 4:725-740.

Palia D. 2001. The endogeneity of managerial compensation in firm valuation: a

solution. Review of Financial Studies 14(3): 735-764.

24

Peng W, Wei K. 2007. Women executives and corporate investement. Evidences from

S&P 1500. Financial Management Annual Conference Working Paper, Orlando,

Florida (USA).

Perelman A. 2000. Women entrepreneurs: A comparative analysis of decision style in

high-tech. California Scholl of Professional Psychology, Los Angeles, Phd

Dissertation

Perry, T. and Zenner, M. (2001). Pay for performances? Governance regulation and the

structure of compensation contracts. Journal of Financial Economics, 62:453-

488

Perry, T. and Peyer, U. (2005). Board Seat Accumulation by Executives: A

Shareholder’s Perspective. Journal of Finance 60(4):2083-2123

Rapaport, C. (1995). Apparent wage discrimination when wages are determined by non-

discriminatory contracts, The American Economic Review 85 (5): 1263-1277

Renner C, Rives J, Bowlin W. 2002. The significance of gender in explaining senior

executive pay variations: an exploratory study. Journal of Managerial Issues. 14

(3): 331-346.

Santerre R, Thomas J. 1993. The determinants of hospital CEO compensation. Health

care Management Review 18 (3): 31-41.

Solberg E, Laughlin T. 1995. The gender pay gap, fringe benefits, and occupational

crowding. Industrial & Labour Relations Review 48(4): 692-708.

Stathopoulos K, Espenlaub S, Walker M. 2004. U.k. executive compensation practices:

new versus old economy 16: 57-92.

Sunden A, Surette B. 1998. Gender differences in the allocation of assets in retirement

savings plans. The American Economic Review 88(2): 207-211.

Young M, Buchholtz A. 2002. Firm performance and CEO pay: Relational demography

as a moderator. Journal of Managerial Issues 14 (3): 296-309

25