Gender differences in executive compensation:...

Journal of Economics and Business 63 (2011) 23–45

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Journal of Economics and Business

Gender differences in executive compensation:Variation with board gender composition and time

Susan Elkinawya,1, Mark Staterb,∗

a Loyola Marymount University, Department of Finance and Computer Information Systems, Hilton Center for Business,One LMU Drive, MS 8385, Los Angeles, CA 90045-2657, United Statesb Trinity College, Department of Economics, 300 Summit St., Hartford, CT 06106, United States

a r t i c l e i n f o

Article history:Received 4 September 2009Received in revised form 23 May 2010Accepted 28 May 2010

JEL classification:G34J16J33J44

Keywords:Executive compensationGender differencesWage decompositions

a b s t r a c t

This paper uses EXECUCOMP, COMPUSTAT and Investor’s Respon-sibility Resource Center data to examine gender differences inexecutive salaries and total compensation from 1996 to 2004. Wefind that the salaries of female executives are about 5 percent lowerthan those of male executives, controlling for executive, firm, andboard characteristics, and that the gap exists primarily in the lowerofficer ranks, where women are relatively highly concentrated.The gender difference in salary is larger in firms with more male-dominated boards; perhaps not coincidentally, such firms are alsofound to have fewer female executives in top managerial positionsas well as lower probabilities of having any top female execu-tives at all. The results of Oaxaca wage decompositions suggestthat, although the magnitude of the gender difference decreasesslightly over the sample period, the share of the gender differ-ence that is due to unobserved factors remains basically steady oreven increases. Thus, although women have become better repre-sented in top executive jobs in recent decades, their relative salariesremain below those of men, possibly due in part to governancestructures that remain male-dominated.

© 2010 Elsevier Inc. All rights reserved.

∗ Corresponding author. Tel.: +1 860 297 2462.E-mail addresses: [email protected] (S. Elkinawy), [email protected] (M. Stater).

1 Tel.: +1 310 338 2345.

0148-6195/$ – see front matter © 2010 Elsevier Inc. All rights reserved.doi:10.1016/j.jeconbus.2010.05.003

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24 S. Elkinawy, M. Stater / Journal of Economics and Business 63 (2011) 23–45

1. Introduction

Gender differences in compensation have been an important topic of public awareness dating backto at least the mid-20th century when the U.S. federal government began to legislate on matters relatedto the employment of women (National Committee on Pay Equity, 2009; Women’s InternationalCenter, 2009). Prior to the 1960s, employers could discriminate on the basis of gender, naturallyresulting in unequal pay, occupational segregation, glass ceilings, and a general economic disadvan-tage for women (Goldberg Dey & Hill, 2007). While women have made great strides in terms of payand representation in many professions in the decades following the passage of the Equal Pay Act of1963 and the Civil Rights Act of 1964, persistent unexplained differences in pay remain widespread(Blau & Kahn, 2000, 2006; O’Neill, 2003).

An important area of the labor market where women have historically been under-representedis corporate management (Oakley, 2000). This may be due, in part, to asymmetric information aboutproductivity-related attributes such as labor-force attachment which can systematically differ by gen-der and provide a basis for statistical discrimination against women. For example, employers may bereluctant to allow even women without families or children to advance through the professional ranksbecause such women are viewed as “potential mothers” whose careers will be interrupted in the futureby family responsibilities (Goldberg Dey & Hill, 2007). Although present in other professions as well,such attitudes may gain especially strong traction in the executive labor market, where the departureof a top executive with a large stock of firm-specific human capital can be particularly disruptive tothe firm.

Nevertheless, declines in fertility rates in western nations, increases in female educational attain-ment, increases in female labor-force participation, and time-saving innovations in home productiontechnologies arguably weaken the rationale for statistical discrimination on the basis of career inter-ruptions (Castles, 2003; OECD, 2004; Oropesa, 1993; Rathje, 2002). Gender differences that persistdespite these developments may indicate a source of discrimination that is not merely statistical (e.g.,a taste for discrimination among corporate owners), or that women have come to expect lower paydue to past discrimination (statistical and otherwise).

As corporate executives oversee some of the world’s largest and most influential companies, theycan affect working conditions for many other workers. Thus, female workers throughout the corporateranks could potentially be unduly disadvantaged by barriers to females seeking to enter top executivepositions. Unequal opportunities and compensation for women in the executive profession could alsodistort the career choices of women in ways that reduce economic efficiency, since some women withhigh managerial aptitude may elect to not enter the profession. Thus, it is important for research toprovide an ongoing assessment of the extent to which executive compensation varies by gender andthe possible reasons behind such variation.

This paper examines differences in base salaries and total compensation between top male andfemale corporate executives from 1996 to 2004 and to what extent these differences vary with financialand governance characteristics of the firm, most notably the gender composition of the board ofdirectors. As women have experienced large percentage gains in their representation in top executivepositions since the early 1990s (starting from 1.3% in 1992 and up to 6.7% in 2004, which is an increaseof over 400% during the period),2 it is possible to examine whether expanded access to executivecareers has been accompanied by greater gender equity in salary and compensation. Thus, we alsoexamine whether and how the gender difference (and the share of the difference that is attributableto unobserved factors) changes over the sample period.

The results of our analysis indicate that female executives have base salaries that are about 5percent lower than those of male executives when firm- and board-level controls and a detailed setof job title controls are included. The estimated gender difference in total direct compensation (abroader measure of pay) is more sensitive to specification, but when significant it is even larger thanthe gender difference in salary. The gender difference in salary is greater in firms with more male-dominated boards of directors, while the gender difference in total direct compensation is greater in

2 Sources: Bertrand and Hallock (2001) and authors’ own calculations.

S. Elkinawy, M. Stater / Journal of Economics and Business 63 (2011) 23–45 25

larger firms. Interestingly, larger firms and those with more male-dominated boards are also foundto have lower numbers of top female executives and lower probabilities of having any top femaleexecutives at all in a given year, whereas firms with more independent boards have higher numbersand probabilities of employing top female executives.

To investigate the extent to which gender differences in pay are attributable to unobserved ratherthan observed factors, we compute Oaxaca wage decompositions. The results suggest that, althoughthe magnitude of the gender difference decreases slightly over the sample period, the share of thegender difference that is due to unobserved factors remains basically steady or even increases. Thus,overall our findings indicate that greater female representation in the executive profession has notyet coincided with full gender equity in pay, and that male-dominated governance structures may bea factor in impeding this progress.

2. Literature review

The classic “taste for discrimination” model developed by Becker (1971) suggests that employ-ers with distaste for hiring certain groups will pay these workers a lower wage than equally skilledworkers in the more preferred group. Thus, if the owners of corporations (i.e., shareholders, whoseinterests are represented by the board of directors) have a taste for discrimination against femaleexecutives, women will be paid less than men with similar qualifications working in similar firms,and this difference will be more pronounced in firms where the taste for discrimination is larger (e.g.,perhaps in firms with more male-dominated ownership). However, human capital theory also sug-gests other reasons why women might receive lower wages than comparably qualified men. Theseinclude interrupted labor-force participation, selection into lower-paying occupations, and lower jobmobility. Thus, it is important to determine what part of the gender wage gap is potentially due todiscrimination and what part is merely due to gender differences in productivity-related attributes(Oaxaca, 1973).

Prior empirical work has examined male–female differences in executive compensation in the pri-vate, non-profit, and government sectors. Some studies find women are compensated less than men,while others do not detect a significant gender difference in pay. Bartlett and Miller (1988) track grad-uates of a liberal arts college who later became executives and find that gender is the most importantdeterminant of salary, with women earning less than men, holding constant human capital, networks,firm size, and organizational structure. Truman and Baroudi (1994) find that female executives in theinformation systems industry receive less compensation than men, holding constant job title, age,education, and experience. Gray and Benson (2003) find that female executives in the non-profit sec-tor are compensated significantly less than males, holding constant education, tenure, firm size, andfirm financial performance. Roth (2003) analyzes a sample of securities professionals from the year1997 and finds gender differences in earnings, holding constant background characteristics, humancapital, and area of specialization. Mohan and Ruggiero (2007) also find that women receive lowercompensation than men, holding constant firm financial performance.

As in our study, Bell (2005) uses EXECUCOMP data to examine executive compensation among topfive executives in corporations listed in the S&P 1500. She finds that the gender pay gap narrows fromthe mid 1990s to the early 2000s, yet women still earn 8–25 percent less than comparable males,holding constant age, firm size, job title, and industry. Like us, she finds that women become bettercompensated relative to men as the proportion of board members that are female increases. Illustratingthat top executives influence outcomes for other workers, she also finds that women at firms withfemale CEOs and/or female board chairs earn 10–20 percent more than women at firms where neitherthe CEO nor the board chair is female. Our paper differs from that of Bell (2005) primarily in thatwe have (1) a more recent sample (board data through 2004 rather than 2001), (2) industry-adjustedfirm performance data from COMPUSTAT, (3) data on board size and board independence, and (4)hand-collected data on the ages of sufficiently many executives that we are able to include both ageand board characteristics in the same empirical model.

In other contexts, the gender difference in compensation has been found to be small or even neg-ligible. Lausten (2001) studies executive compensation among Danish private-sector firms and findsthat the gender difference completely disappears when holding constant age, education, experience,


rank, and industry. Bertrand and Hallock (2001) use EXECUCOMP data from 1992 to 1997 and find thatwomen earn 45 percent less than men, but 3/4 of this gap is explained by differences in firm size andthe rank of the executive. The gender gap narrows even further holding constant executive age andexperience. Bowlin, Renner and Rives (2003) analyze firms in the S&P 500 and find no gender differ-ences in top executive pay, holding constant firm size and industry, CEO pay, and executive rank. Mostrecently, Khan and Vieito (2008) find that the gap between male and female executive compensationhas narrowed over time and that male and female executives in technology firms earn similar pay.

Despite the large existing literature in management and economics concerning male and femaleexecutive compensation, this study offers a number of contributions. We use a sample that is larger andmore recent and that includes more detailed age, tenure, and board of director data than some recentstudies that are similar in design and intent, such as Bertrand and Hallock (2001), Lausten (2001), Bell(2005), and Khan and Vieito (2008). Our job title controls are those of Bertrand and Hallock (2001),which capture the key employment titles in the executive ranks.

3. Data and descriptive statistics

3.1. Data

Our main sources of data are EXECUCOMP, the Investor Responsibility Resource Center (IRRC), andCOMPUSTAT. EXECUCOMP provides data on the top five executives in S&P 1500 firms for the years1992–2004, although due to limitations on the board data available from the IRRC we only use datafrom 1996 and beyond. EXECUCOMP reports each executive’s salary, total direct compensation (dataitem TDC1, which includes salary, bonuses, the total value of restricted stock granted, the total value ofstock options granted, long-term incentive payouts, and all other total annual compensation), gender,job title, and tenure with the firm.3 The IRRC provides data on the boards of the firms where theseexecutives work, such as the size of the board, the numbers of male and female directors, and the num-bers of inside (independent) and outside directors. The IRRC data begin with the year 1996, which isthe reason for starting the EXECUCOMP data with 1996 instead of 1992. Since a number of executiveages are missing in the EXECUCOMP files, we hand-collect additional age data from corporate proxystatements, annual reports, and news stories found on Lexis-Nexis. COMPUSTAT provides accountingand financial data on public companies that can be used to construct financial performance measures.From COMPUSTAT, we extract the firm’s total assets (a measure of its size) and construct a modifiedTobin’s Q to measure firm performance.4,5 The IRRC and COMPUSTAT data are then merged with theEXECUCOMP data using firm identifiers. The final sample contains 60,040 executive-year observationson 18,742 unique executives. However, there are only 31,594 executive-years (8122 unique execu-tives) for which age data are available along with all other variables. Thus, we perform the subsequentempirical analyses with and without age and tenure.

3.2. Descriptive statistics

Descriptive statistics for our sample are reported in Table 1. The sample reflects the male-dominance of the profession, as only 4.7 percent of the sample is female. The mean real annual totalcompensation of executives in our sample is roughly $1.4 million dollars per year, while the meanreal base salary is about $367,000. The much lower salary figure indicates that top U.S. executives

3 All salary and compensation figures are deflated by the average annual urban Consumer Price Index (CPI-U) of the year inwhich they are reported (source: ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt), relative to a base year of 1996. Specifically,we divide the compensation value by the ratio of the current year’s CPI to the CPI for 1996. It follows that all compensationfigures are expressed in constant 1996 dollars.

4 Like compensation and salary, total assets are also measured in real 1996 dollars.5 We calculate a modified Tobin’s Q as (market cap − book value of equity + book value of assets)/total assets, where for “book

value of equity” we use “stockholders’ equity” and for “book value of assets” we simply use “total assets.” As a robustness check,we also construct an alternative measure of Tobin’s Q: (market cap + short-term debt + long-term debt)/total assets, whichproduces similar results when included in our regressions.

ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt


Table 1Descriptive statistics.

Variables Full sample Men Women

Executive characteristicsFemale 0.047 0.000 1.000***

(0.212) (0.000) (0.000)Total direct compensation ($ thousands)a 1357.9 1337.6 960.4***

(3962.4) (4029.8) (2159.7)Base salary ($ thousands) 367.1 370.2 304.9***

(236.9) (238.6) (189.4)Ageb 53.26 53.41 48.21***

(7.97) (7.96) (6.29)Tenurec 7.52 7.65 4.88***

(11.67) (11.78) (8.66)CEO/chair 0.246 0.254 0.074***

(0.431) (0.435) (0.263)Vice chair 0.033 0.033 0.032

(0.179) (0.179) (0.175)President 0.128 0.129 0.101***

(0.334) (0.335) (0.301)Chief Financial Officer 0.134 0.132 0.170***

(0.341) (0.339) (0.375)Chief Operating Officer 0.024 0.025 0.019**

(0.154) (0.155) (0.135)Other “Chief” Officer 0.048 0.046 0.082***

(0.213) (0.209) (0.274)Executive Vice President 0.134 0.132 0.170***

(0.341) (0.339) (0.375)Senior Vice President 0.128 0.125 0.177***

(0.334) (0.331) (0.382)Group Vice President 0.008 0.008 0.003***

(0.088) (0.089) (0.056)Vice president 0.110 0.107 0.161***

(0.313) (0.310) (0.368)Other 0.007 0.007 0.012***

(0.085) (0.084) (0.110)

Firm characteristicsTotal assets ($ billions)d 6.06 6.06 6.16

(25.14) (24.95) (28.74)Tobin’s Qd,e 1.43 1.43 1.45

(1.14) (1.14) (1.00)Number of directors on board 9.73 9.74 9.44***

(3.02) (3.03) (2.80)Fraction of male directors on board 0.919 0.921 0.876***

(0.086) (0.085) (0.107)Fraction of independent directors on board 0.642 0.641 0.653***

(0.179) (0.179) (0.178)

Observations 60,040 57,206 2834

Standard deviations in parentheses.*** Mean is significantly different from corresponding mean for men at 1% level.** Mean is significantly different from corresponding mean for men at 5% level.* Mean is significantly different from corresponding mean for women at 1% level.Compensation, Salary, and Total Assets figures deflated by the annual Consumer Price Index (CPI) for each year.

a Total Direct Compensation includes base salary, bonuses, stock grants, stock options, long-term incentive pay, and all “other”forms of total annual compensation.

b Number of observations for age is 31,661.c Tenure represents the number of years credited for executive’s retirement (N = 59,843).d Variables lagged one year.e Tobin’s Q calculated as (market cap – stockholders’ equity + total assets)/total assets.


obtain much of their compensation in the form of performance-based incentives such as bonuses,stock grants, and stock options. Although this is true for both male and female executives, womenreceive a significantly greater proportion of their pay in the form of salary than men (63.5% versus60.6%, respectively, with the difference significant at the 1% level).6 On average, female executives areapproximately 5 years younger than male executives (48 versus 53 years, respectively), and womenalso have lower job tenures (4.9 versus 7.7 years). Women also work for firms with smaller, moreindependent, and less male-dominated boards.

The executives in our sample are grouped into 11 job title categories, which consist of the high-est levels of management (CEO/Chair, Vice Chair, President), various forms of “Chief” Officers (ChiefFinancial Officer (CFO), Chief Operating Officer (COO) and other “Chief” Officers such as Chief Infor-mation Officer), and various forms of Vice Presidents (Executive VP, Senior VP, Group VP, and VP). The“Other” category includes general managers, general counsels, secretaries, and so forth. These titlesare hand-coded from EXECUCOMP based on the descriptions provided in the database (an explanationof the coding system is provided in the Appendix) and coincide with the final set of titles presentedin Bertrand and Hallock (2001).7

The means for the job title categories indicate that a significantly lower proportion of women thanmen occupy the positions of CEO/Chair, President, COO, and Group VP. A significantly higher proportionof women than men are in the positions of CFO, other “Chief” Officer, Executive VP, Senior VP, VP, and“Other.” Thus, overall, men appear to be more concentrated in the higher ranks and women in thelower ranks of top management.

We also classify the executives into 10 industry groupings based on the industry classificationsdeveloped by Fama and French (1997). Table 2 lists and defines the industry groups and showsthe percent female, average salary, and average compensation by industry group. The industrieswith the highest numbers of executives are consumer/retail and textiles/construction/manufacturing.The industries in which the highest percentages of the executives are female are consumer/retail,entertainment/leisure, and finance/insurance/real estate. However, salaries and compensation are notparticularly low in these industries, especially finance/insurance/real estate, as it ranks 2nd in averagesalary among the 10 industry groupings and 1st in average compensation. Indeed, across all the indus-tries, the percent of executives in the industry who are female is actually positively correlated withthe average salary and average compensation in the industry (the simple correlation coefficients are0.32 and 0.12, respectively). Thus, women do not appear to be concentrated in particularly low-payingindustries. Nor do they seem to be concentrated in industries with especially low ratios of averagefemale-to-male salaries or compensation (the simple correlation between the percent of industryexecutives who are female and each of these items is positive).

4. Methodology and hypotheses

We estimate an executive’s annual compensation using the following regression model:

log Yit = X itˇ + ıFi + εit (1)

where Yit denotes either the real base salary or the real total direct compensation of executive i in yeart � {1996,1997, . . ., 2004}, X is a vector of characteristics describing executive i and the firm for whichhe or she works in year t, F is a binary variable that equals 1 for females and 0 for males, and ε is a

6 These figures are obtained by calculating the means of (salary/total compensation) for women and men, and then performinga t-test on the difference in the means. Note that one cannot correctly calculate the mean of the salary-compensation ratio bytaking the ratio of the means of these two variables, but must summarize the ratio directly.

7 It is possible based on the information provided in EXECUCOMP to classify the job titles at an even finer level of detail thanthe Bertrand and Hallock (2001) categories. For instance, one could attempt to distinguish between Vice Presidents of Finance,Administration, and Human Resources. However, we have opted to use the broader job title categories so as to maintain sufficientnumbers of female executives in each category to draw meaningful statistical comparisons. For instance, an inspection of theExecutive VP title, which is the largest of the VP titles but still has <500 total women as shown in Table 5, yields over 20 sub-titlesamong these executives, yet the vast majority of these executives are simply classified generically as “Executive VP.” Thus, itseems doubtful that tests conducted on more detailed sub-titles of the VP categories would have sufficient power to detectgender differences.

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Table 2Gender breakdown and average salary and compensation by industry.

Industry N % of N thatare fem.

% of fems.in ind.

Avg. salarya Avg. salaryfemalea

Avg. salarymalea

Avg. totalcomp.a

Avg. totalcomp. femalea

Avg. totalcomp. malea

Food/agricultureb 1798 3.62 2.29 453.40 411.38 454.97 1516.04 1233.43 1526.64Entertainment/leisurec 2902 6.89 7.08 406.38 325.02 412.40 1211.09 904.89 1233.76Consumer/retaild 13,917 7.54 37.01 368.47 336.80 371.05 1367.32 1038.77 1394.10Health caree 806 3.72 1.06 363.17 236.63 368.06 1340.54 570.13 1370.32Textiles/constr./manuf.f 13,748 2.57 12.49 343.66 237.49 346.47 1010.01 543.33 1022.34Drugs/chemicalsg 3910 4.22 5.82 379.49 310.26 382.54 1284.07 932.51 1299.56Mining/energyh 2701 2.26 2.15 361.79 253.74 364.29 1288.29 626.39 1303.58Utilities/telecom/transi 7354 4.46 11.57 354.88 259.58 359.33 1216.32 611.75 1244.55Electronicsj 5406 3.31 6.32 311.62 308.32 311.73 1726.86 1979.55 1718.20Finance/Ins./real estatek 7498 5.37 14.22 419.89 300.04 426.70 1935.61 1028.10 1987.16

a Salaries and total compensation measured in thousands of 1996 dollars.b Food/agriculture includes the following industries: Agriculture (Fama-French industry category 1), Food Products (FF category 2), Candy and Soda (FF 3), Alcoholic Beverages (4), and

Tobacco Products (5).c Entertainment/Leisure includes the following industries: Recreational Products (6), Entertainment (7), Printing and Publishing (8), and Restaurants, Hotels, and Motels (44).d Consumer/Retail includes: Consumer Goods (9), Apparel (10), Personal Services (34), Business Services (35), Wholesale (42), and Retail (43).e Health Care includes: Healthcare (11).f Textiles/Construction/Manufacturing includes: Medical Equipment (12), Rubber and Plastic Products (15), Textiles (16), Construction Materials (17), Construction (18), Steel Works

(19), Fabricated Products (20), Machinery (21), Electrical Equipment (22), Miscellaneous (23), Automobiles and Trucks (24), Aircraft (25), Shipbuilding and Railroad Equipment (26),Defense (27), Measuring and Control Equipment (38), Business Supplies (39), and Shipping Containers (40).

g Drugs/Chemicals includes: Pharmaceutical Products (13) and Chemicals (14).h Mining/Energy includes: Precious Metals (28), Nonmetallic Mining (29), Coal (30), and Petroleum and Natural Gas (31).i Utilities/Telecommunications/Transportation includes: Utilities (32), Telecommunications (33), and Transportation (41).j Electronics includes: Computers (36) and Electronic Equipment (37).k Finance/Insurance/Real Estate includes: Banking (45), Insurance (46), Real Estate (47), Trading (48).


zero-mean error term that is assumed to be uncorrelated with X and F. The error cannot be assumed tobe independent across observations because in many cases the same executive appears multiple timesin the data. We correct for this by clustering the standard errors by executive; this adjustment alsomakes the standard errors robust to heteroskedasticity across executives. With these modifications,the assumptions imply that the OLS estimators of ˇ and ı are consistent and have asymptoticallycorrect test statistics. The coefficient ı measures the wage gap between females and otherwise similarmales working in similar firms.

One rationale for estimating separate regression models for salary and total direct compensationis that prior studies such as Barber and Odeon (2001) and Graham, Harvey, and Puri (2009) find thatwomen tend to be more risk averse than men. Salary is the fixed component of compensation, whilebonuses and stock options are performance-based and are subject to greater uncertainty. Graham etal. (2009) find that male CEOs are more likely to prefer performance-based compensation over salarywhen negotiating their pay package, suggesting that the gender difference might be wider for thebroader measure of compensation. Indeed, the descriptive statistics support this finding, as men havemuch higher total direct compensation than women and have lower salaries in proportion to totaldirect compensation.

The variables in the vector Xit are: the executive’s age and age squared; the executive’s tenure withthe firm and tenure squared;8 dummy variables for 10 job titles (Vice Presidents are the excludedgroup); the log of real firm assets in year t − 1;9 the firm’s industry-adjusted Tobin’s Q in year t − 1;10

the total number of directors on the board; the percentage of directors who are male;11 and thepercentage of directors who are independent (outsiders) of firm management.

We make the standard predictions concerning the effects of the variables in Xit on base salary:positive but diminishing effects of age and tenure, larger coefficients on higher-ranked job title dum-mies, positive effects of lagged assets and Tobin’s Q, a negative effect of board independence, and apositive effect of board size (since larger and less independent boards have potentially greater agencyproblems which may result in inflated compensation awards). Finally, we expect the coefficient onthe female dummy to have a negative sign in light of studies such as those by Oakley (2000) andWanzenried (2008) which document that women have historically been under-represented and arestill under-compensated relative to men in this labor market. The hypotheses are the same for totaldirect compensation, except that we now expect board independence to have a positive effect asmore independent boards are better able to properly incentivize the manager with performance-basedcompensation (Hermalin & Weisbach, 2003).

We also estimate specifications that include interactions between the firm characteristics andthe gender dummy. We are mainly interested in whether the gender differences in salary and totalcompensation grow as the board becomes more male-dominated, since male-board dominance is agovernance structure that may work against females and has been the subject of prior scrutiny andregulation. In fact, Norway passed a law in 2006 requiring every public corporation to have a board ofdirectors that is at least 40 percent female (The Guardian, 2006). For completeness, however, we alsoinclude interactions of the gender dummy with the other firm characteristics.

8 We actually present estimates of models with and without the age and tenure controls, since age data are available for amuch smaller sample than the other variables in the model.

9 The 1-year lag on the firm size and financial performance measures is to avoid possible endogeneity with respect to thecompensation of top executives. For example, better-compensated top executives may engage in work practices that result ingrowth in the size of the firm as well as its market performance relative to other firms.

10 “Industry-adjusted” Tobin’s Q is calculated as the residual from an OLS regression of Tobin’s Q on a full set of industrydummies. This is equivalent to taking the deviation of the firm’s Tobin’s Q from the industry mean.

11 Since compensation is usually discussed within a subcommittee of the board, the gender composition of this subcommitteemight provide even sharper predictions of salary and compensation. Yet, the subcommittee’s composition should be stronglypositively correlated with that of the entire board, particularly in light of the high percentage of male board representation inour sample (91.9%) and the fact that Bilimoria and Piderit (1994) find that compensation subcommittees are disproportionatelymale. These considerations suggest that the full board’s gender composition could in many cases be a valid proxy for that of thesubcommittee. Indeed, it seems reasonable to speculate that board composition provides at least the correct directional impactof subcommittee composition.


We estimate Eq. (1) with Ordinary Least Squares (OLS).12,13 The most serious potential specificationproblem with Eq. (1), which would invalidate the assumptions required for consistent estimators andasymptotically valid test results, is the possibility that there are variables omitted from this equationthat are correlated with gender. An important example of such a variable may be education. Thisvariable has been omitted because it is difficult to locate data sources that provide education dataon a reasonably large and broad sample of the executives in EXECUCOMP. Thus, if male and femaleexecutives tend to have different educational credentials, then part of the estimated wage differentialmight just reflect gender differences in human capital. However, it is not obvious why this would be so,particularly since women might often need to obtain better credentials than men in order to be hiredand promoted in a market with a historical glass ceiling. Indeed, Bell (2005) finds that educationalcredentials are very similar between male and female executives in technology-intensive industries.

Clearly, there are other omitted variables that could differ between women and men and giverise to an apparent gender difference in wages. These fall under the broad heading of “unobservedproductivity” and include such things as commitment to the labor force, motivation to succeed inthe job, innate ability, and family responsibilities (United States General Accounting Office, 2003).For example, if female executives have lower unobserved productivity than men, it would result in agender compensation gap even if firms do not discriminate on any dimension whatsoever aside froman executive’s skill. While it is again not immediately apparent why this would be the case, giventhe skill and fortitude women must have in order to succeed in such a historically male-dominatedprofession, fully definitive conclusions on the underlying sources of gender differences must awaitthe arrival of more detailed information on factors such as education, family status, and even innateintelligence/ability.14

5. Empirical results

5.1. Results without gender–firm interactions

In Table 3 we report the estimates of our basic regression models. The first three models examinethe determinants of real base salary. When female is the only covariate in the model (first column),the estimates suggest that men earn 16 percent more than women in real terms. This can be regardedas the “uncontrolled gender difference” because no executive or firm characteristics are held constant.When executive and firm characteristics are added to the model, the estimated base salary advantagefor men falls greatly, to about 4.5 percent, but is still significant. When age and tenure controls areadded, the sample size drops substantially, but the estimated gender difference rises slightly, to about5.6 percent.15

12 Estimating (1) with OLS, even with clustered standard errors, ignores the (unbalanced) panel structure of the data. However,estimates obtained by explicit panel data methods are very similar to those obtained by OLS. In particular, under random effectsthe female and (female × pct. males on board) variables both have negative and significant coefficients, while under fixed effectsthe (female × pct. males on board) variable has a negative and significant coefficient in the salary regressions, but an insignificantcoefficient in the compensation regressions. Note that the female variable cannot be included in the fixed effects models becauseit is part of the time-invariant heterogeneity.

13 Salary is effectively right-censored at $1 million, as only 1.7% of the executives in our sample have salaries exceeding $1million. Treating this as an upper limit for salary and re-estimating the salary regressions as right-censored tobit models hasessentially no impact on the results.

14 Note that estimation by fixed effects, which provides results similar to OLS as described above, provides a control fortime-invariant unobserved factors, which may include aspects of unobserved productivity.

15 We also estimate alternative specifications that include a dummy for whether the executive appears, based on the data, tohave been hired internally, as opposed to externally, for his or her position. “Internal” hires are regarded as executives observedto move between different job titles or with long tenures with the firm (10 years or more) when first appearing in the sample.“External” hires are regarded as those observed to move between different firms or with short tenures with the firm (3 years orless) when first appearing in the sample. In these regressions, there are no significant gender differences. Furthermore, internalhires are found to have higher salaries but lower total compensation than external hires. As we regard this evidence as quitepreliminary (due to the difficulty in firmly establishing the nature of the hire without a detailed case-by-case examination), theresults are not presented.


Table 3Basic regression results.

Independent variables Y = log base salary Y = log total direct compensation

(1) (2) (3) (4) (5) (6)

Executive characteristicsFemale −0.162*** −0.045*** −0.056*** −0.245*** −0.026 −0.097***

(0.020) (0.013) (0.021) (0.035) (0.024) (0.037)Age 0.030*** 0.017

(0.009) (0.011)Age squared/100 −0.023*** −0.025**

(0.009) (0.011)Tenurea 0.008*** −0.011***

(0.002) (0.003)Tenure squared/100 −0.013** 0.021***

(0.005) (0.008)CEO or Chairman 0.708*** 0.635*** 1.104*** 1.069***

(0.015) (0.020) (0.019) (0.034)Vice Chair 0.400*** 0.325*** 0.628*** 0.562***

(0.018) (0.025) (0.034) (0.047)President 0.349*** 0.375*** 0.608*** 0.662***

(0.011) (0.019) (0.019) (0.035)Chief Financial Officer 0.147*** 0.129*** 0.321*** 0.262***

(0.009) (0.018) (0.017) (0.035)Chief Operating Officer 0.299*** 0.284*** 0.593*** 0.566***

(0.015) (0.026) (0.031) (0.052)Other “Chief” Officer 0.048*** 0.074*** 0.117*** 0.103**

(0.013) (0.025) (0.025) (0.050)Executive Vice President 0.191*** 0.163*** 0.342*** 0.292***

(0.009) (0.019) (0.017) (0.036)Senior Vice President 0.086*** 0.060*** 0.153*** 0.102***

(0.009) (0.020) (0.016) (0.037)Group Vice President 0.088*** 0.020 0.077* 0.132*

(0.020) (0.048) (0.040) (0.070)Other title 0.042 0.114** 0.213*** 0.304**

(0.029) (0.051) (0.066) (0.131)

Firm characteristicsLog of total assetsb 0.172*** 0.165*** 0.379*** 0.400***

(0.003) (0.006) (0.005) (0.008)Industry-adjusted Tobin’s Qb,c 0.009*** 0.006 0.135*** 0.132***

(0.003) (0.005) (0.008) (0.011)Number of directors on board 0.007*** 0.004 −0.017*** −0.011***

(0.002) (0.003) (0.003) (0.004)Fraction of male directors on board −0.442*** −0.521*** −0.066 −0.261**

(0.045) (0.076) (0.075) (0.120)Fraction of independent directors on board −0.076*** −0.018 0.042 0.170***

(0.021) (0.032) (0.034) (0.052)

Constant 5.746*** 5.921*** 5.058*** 6.506*** 6.189*** 6.223***

(0.006) (0.054) (0.232) (0.009) (0.091) (0.344)Year and industry controls? No Yes Yes No Yes YesObservations (executive-years) 60,040 60,040 31,594 60,040 60,040 31,594Unique executives 18,742 18,742 8122 18,742 18,742 8122R-squared 0.0028 0.377 0.288 0.0023 0.441 0.402F-statistic for overall significance 66.8*** 421.7*** 200.9*** 49.2*** 508.7*** 214.1***

Notes: Compensation and Salary figures deflated by the annual Consumer Price Index (CPI) for each year.Total Direct Compensation includes base salary, bonuses, stock grants, stock options, long-term incentive pay, and all “other” forms oftotal annual compensation.Excluded job title is Vice President.Industry groupings are food/agriculture (excluded), entertainment/leisure, consumer/retail, health care, textiles/construction/manufacturing, drugs/chemicals, mining/energy, utilities/telecommunications/transportation, and finance/insurance/real estate.Standard errors in parentheses (clustered by executive and therefore robust to heteroskedasticity across executives).

a The number of years credited toward the executive’s retirement with the firm.b Variables lagged by one year.c Tobin’s Q calculated as (market cap – stockholders’ equity + total assets)/total assets. Industry-adjusted value is obtained as the residual

from the OLS regression of Tobin’s Q on the full set of Fama-French industry dummies.* Estimated coefficient or F-statistic is significantly different from zero at 10% level.

** Estimated coefficient or F-statistic is significantly different from zero at 5% level.*** Estimated coefficient or F-statistic is significantly different from zero at 1% level.


The coefficients on the job title dummies indicate salary differentials that roughly correspond to thepresumed rank-ordering of these titles within the firm. Excluding age and tenure controls, CEOs andBoard Chairs earn 70.8% more than Vice Presidents (the excluded group), Vice Chairs earn 40% morethan Vice Presidents, Presidents earn 34.9% more than Vice Presidents, etc. In fact, all the job titlesincluded in the model earn significantly more than Vice Presidents, except “Other,” which includesgeneral managers, general counsels, secretaries, etc.

Salaries are also affected by firm and board characteristics. Executives in larger firms earn morethan those in smaller firms, as a one-percent increase in total assets is predicted to increase realbase salary by about 0.17 percent. Firms with higher value as measured by Tobin’s Q also pay theirexecutives more: a one-unit increase in industry-adjusted Tobin’s Q is expected to result in a 0.9percentage-point increase in salary.16 Interestingly, larger boards tend to pay their executives morein base salary which could suggest a monitoring or a signal-extraction problem, but this effect onlyappears when executive age and tenure are excluded. More male-dominated and independent boardspay their executives less, but the effect of board independence becomes insignificant with the inclusionof age and tenure controls.

In the fourth through sixth columns of Table 3 we estimate real total direct compensation, whichincludes bonuses, fringe benefits, and equity-based compensation. Many of the findings are similar insign and significance to the base salary model, although there are several notable differences. Femalesdo not earn significantly less total direct compensation than males when executive and firm controls(other than age and tenure) are included, but earn 9.7% less than men when age and tenure are included.Thus, the gender difference in total compensation is more sensitive to specification and sample thanthe gender difference in salary.

The estimates for the job titles indicate that CEOs and Chairs earn 110% more total compensationthan VPs, while Vice Chairs and Presidents earn 62.8% and 60.8% more than VPs, respectively, excludingage and tenure controls. In fact, most of the coefficients on the job title dummies are larger in thecompensation than in the salary model, suggesting that performance-based compensation rises inrelation to salary as job rank increases.17

Increases in total assets and industry-adjusted Tobin’s Q increase total compensation, and theseeffects are considerably larger than the corresponding ones for salary. A one-percent increase in totalassets increases total compensation by 0.38 percent, and a one-unit increase in industry-adjustedTobin’s Q increases total compensation by 13.5 percent, excluding age and tenure. It follows thatincentive pay is much more sensitive to firm size and value than is base salary. Interestingly, largerboards offer executives less in total direct compensation even though they offer more in base salary,whereas more independent boards offer executives more in total compensation and less in base salary.These findings could reflect agency problems associated with larger and less independent boards, suchthat managers are not as well incentivized by these types of boards. Finally, more male-dominatedboards offer less total compensation, but this effect is only significant when age and tenure controlsare included.18

Age and tenure have the expected positive but diminishing effects on salary.19 Age likewisehas a positive but diminishing effect on total compensation, although the linear term is not sig-

16 We experimented with several alternative measures of firm performance. Accounting measures such as (lagged industry-adjusted) return on assets and return on sales had positive effects on both salary and compensation. The firm’s lagged annualcumulative abnormal stock returns (CARs) had positive effects on salary and compensation as long as age and tenure wereexcluded. Finally, the industry-adjusted (lagged) percentage change in Tobin’s Q from one year to the next had a positive effecton salary but a surprising negative effect on total compensation. Ultimately, since the results were largely insensitive to whichperformance measure was used, it was determined that Tobin’s Q was the best available option, since it captures both firmvalue and performance.

17 Indeed, when we estimate (unreported) regressions where the salary/compensation ratio is the dependent variable, thecoefficients on higher-ranked titles are generally smaller (i.e., more negative) than those on lower-ranked titles.

18 In the unreported regressions where the salary/compensation ratio is the dependent variable, larger boards are found tooffer higher salaries in relation to total compensation, while boards with higher fractions of male and independent directorsare found to offer lower salaries in relation to total compensation.

19 To address the possibility that the relationship between seniority and executive pay is due to existing stock holdings, wealso estimate alternative specifications that include the log of the number of shares owned by the executive. When included,this variable generally has a positive and highly significant effect, but it does not affect the estimates of the gender difference


nificant. In contrast, tenure has a negative effect on total compensation that is minimized after0.011/(2 × 0.00021) = 26.2 years and then starts to increase. The diminishing effect may be becausethere is less of a monitoring problem with executives who have been with the firm longer, such thatthe firm has to rely less on performance pay to incentivize the manager. Indeed, Graham et al. (2009)find that younger CEOs receive proportionately more of their pay in the form of performance-basedcompensation. The finding is also consistent with greater risk aversion on the part of longer-tenuredexecutives, which may cause them to more strongly prefer fixed compensation (Graham et al., 2009).

5.2. Results of interactions between gender and firm characteristics

In Table 4, we present estimates of models that include interactions between the gender dummyand the firm characteristics. We start by examining the coefficient on male board representation(which speaks to how this variable affects salary and compensation for men) and the interaction ofgender with male board representation (which addresses whether this variable has different effects formen and women). The results indicate that an increase in male representation on the board decreasesreal base salary for men, and decreases base salary even more for women. Changing the male compo-sition of the board by ten percentage points (e.g., from 90 percent male to 100 percent male), whilekeeping the size of the board the same, reduces salary by about 4 percent for men, but by about 8percent for women. The percentage of males on the board also reduces the total compensation ofwomen more than it does for men, but the gender difference in the effect is significant only when ageand tenure are excluded. These findings suggest that male directors may harbor a taste for discrim-ination against female executives that is exerted particularly when setting salaries. It also raises thepossibility that firms make up the gender difference in salary to some extent with higher incentivepay for women, for example because firm owners are less certain about the productivity of women,and so give them a chance to prove themselves by earning incentive pay. (In unreported regressionswhere the salary/compensation ratio is the dependent variable, the coefficient on female is negative,but is not significant (p-value = 0.215)). Yet another possible explanation is that female executiveswith higher unobserved productivity are less attracted to male-dominated firms.

The coefficients on the other firm characteristics indicate that firm size and value increase boththe base salary and total compensation of male executives, while larger boards offer male executiveshigher base salaries but lower total compensation, consistent with the existence of agency problemsamong bigger boards. The coefficient on the (female × log assets) interaction indicates that an increasein firm size increases the salaries and compensation of females by significantly less than it increases thesalaries and compensation of males, suggesting that larger firms pay women less relative to men thando smaller firms. This may be because larger firms are more appealing to women, perhaps becausethey can offer greater employment stability and opportunities for advancement, or non-pecuniarybenefits such as flexible scheduling and on-site child care. Alternatively, women may be relativelyhighly concentrated in non-line, lower-paying positions such as VP of Administration or VP of HumanResources that are more prevalent in larger firms and represent a finer level of disaggregation thaneven our detailed job title controls. The estimates also suggest that firms with more independentboards pay women lower salaries and compensation relative to men, although this difference is onlysignificant if age and tenure are excluded from the model.20

Overall, the results reported in Tables 3 and 4 suggest that firm characteristics contribute to genderdifferences in both salary and compensation. More male-dominated boards are consistently found topay women relatively less in base salary, which could indicate that male board dominance is a gov-

or the coefficients on age and tenure. Nevertheless, we have opted not to include this variable in our reported results due toconcerns about endogeneity. The value of stock holdings is part of total direct compensation, so when stock holdings go uptotal direct compensation must necessarily go up as well.

20 Since the regressions reported in Table 4 allow for slope differences by gender, the positive coefficient on the female dummyin some of the models merely indicates that females have a higher intercept than males. Hence, the model predicts that femaleswould be paid more than males if all of the covariates that are interacted with female were set equal to zero. When evaluated ata more plausible set of coordinates, such as the sample means of the firm characteristics, the female–male differences in salaryand compensation are negative and significant.


Table 4Regression results with interactions between gender and firm characteristics.

Independent variables Y = log base salary Y = log total direct compensation

(1) (2) (3) (4) (5) (6)

Executive characteristicsFemale 0.236** 0.445*** 0.363 0.136 0.558* 0.237

(0.102) (0.159) (0.228) (0.212) (0.286) (0.390)Age 0.030*** 0.017

(0.009) (0.011)Age squared/100 −0.023*** −0.025**

(0.009) (0.011)Tenurea 0.008*** −0.011***

(0.002) (0.003)Tenure squared/100 −0.013** 0.020***

(0.005) (0.008)

Firm characteristicsLog of total assetsb 0.173*** 0.174*** 0.166*** 0.379*** 0.382*** 0.404***

(0.003) (0.004) (0.006) (0.005) (0.005) (0.009)Industry-adjusted Tobin’s Qb,c 0.009*** 0.009*** 0.006 0.135*** 0.135*** 0.131***

(0.003) (0.003) (0.005) (0.008) (0.008) (0.011)Number of directors on board 0.007*** 0.007*** 0.004 −0.017*** −0.017*** −0.011**

(0.002) (0.002) (0.003) (0.003) (0.003) (0.004)Fraction of male directors on board −0.415*** −0.404*** −0.486*** −0.051 −0.027 −0.228*

(0.047) (0.047) (0.081) (0.077) (0.077) (0.125)Fraction of indep. directors on board −0.074*** −0.066*** −0.012 0.042 0.057 0.183***

(0.021) (0.022) (0.033) (0.034) (0.035) (0.053)

Interaction termsFemale × log total assets −0.025*** −0.010 −0.050*** −0.078***

(0.008) (0.011) (0.017) (0.024)Female × industry-adjusted Tobin’s Q 0.016 0.022 0.011 0.018

(0.011) (0.016) (0.024) (0.028)Female × number of directors −0.002 −0.010 −0.004 0.002

(0.005) (0.008) (0.011) (0.014)Female × fraction of male directors −0.320*** −0.418*** −0.301* −0.184 −0.389* −0.176

(0.113) (0.116) (0.164) (0.239) (0.233) (0.314)Female × fraction of indep. directors −0.160* −0.093 −0.304** −0.275

(0.089) (0.138) (0.141) (0.214)Constant 5.894*** 5.878*** 5.018*** 6.173*** 6.141*** 6.190***

(0.055) (0.056) (0.234) (0.092) (0.093) (0.347)Industry, year, and job title controls? Yes Yes Yes Yes Yes YesObservations (executive-Years) 60,040 60,040 31,594 60,040 60,040 31,594Unique executives 18,742 18,742 8122 18,742 18,742 8122R-squared 0.377 0.377 0.288 0.441 0.442 0.403F-statistic for overall significance 410.5*** 376.6*** 185.3*** 493.6*** 444.6*** 190.5***

Notes: Compensation and Salary figures deflated by the annual Consumer Price Index (CPI) for each year.Total Direct Compensation includes base salary, bonuses, stock grants, stock options, long-term incentive pay, and all “other”forms of total annual compensation.Industry groupings are food/agriculture (excluded), entertainment/leisure, consumer/retail, health care, textiles/construction/manufacturing, drugs/chemicals, mining/energy, utilities/telecommunications/transportation, and finance/insurance/realestate.Standard errors in parentheses (clustered by executive and therefore robust to heteroskedasticity across executives).

a The number of years credited toward the executive’s retirement with the firm.b Variables lagged by one year.c Tobin’s Q calculated as (market cap – stockholders’ equity + total assets)/total assets. Industry-adjusted value is obtained

as the residual from the OLS regression of Tobin’s Q on the full set of Fama-French industry dummies.* Estimated coefficient or F-statistic is significantly different from zero at 10% level.

** Estimated coefficient or F-statistic is significantly different from zero at 5% level.*** Estimated coefficient or F-statistic is significantly different from zero at 1% level.


ernance structure that tends to work against the advancement of women in the profession. Moreindependent boards also pay women relatively less, except in models where age and tenure areincluded, and since these boards presumably govern better, this raises the possibility that the genderdifferences we find are not due to an explicit preference for males but to legitimate differences in thelabor supply behavior, risk preferences, or unobserved productivity of men and women that shape theprofitability associated with hiring male and female executives. Thus, the labor market outcome maybe efficient if not entirely equitable.

5.3. Gender differences within specific job titles

To more closely investigate whether there are gender differences in salary and compensation whenexecutives are matched by job title, we estimate separate regressions for each of the 11 job titlesincluded in our data. The coefficient on the female dummy, as well as the mean and median salariesfor men and women in each title, are summarized in Table 5.

The means indicate that in the majority of the titles, men have a higher average salary than women,and have higher average compensation in all but one of the titles. However, in virtually all cases wherewomen have a higher average salary or compensation, they have a lower median value, which indicatesthat a few extremely highly paid females are substantially pulling up some of the title-specific averages.This is particularly true given that some of the categories have extremely small numbers of women(e.g., only 210 out of 14,761 CEOs are female).

The results of the job title-specific regressions indicate that women earn significantly lower salariesthan men only in the following jobs: Chief Financial Officer, other “Chief” Officer, and Vice President.Women actually earn higher salaries than men in the title of Group Vice President. In most othercases, the sign of the female coefficient is negative, but there may simply not be enough women inthe category for a significant difference to be detected. With regard to total compensation, womenonly earn less than men in the title of other “Chief” Officer but again earn more than men in the titleof Group Vice President. In the majority of categories, the sign of the female coefficient is negative(as with the salary regressions), but again there may be insufficient power in the tests to achievesignificance. Nevertheless, taking these results literally despite possible small sample issues, it appearsthat the gender differences noted above in the full-sample appear to be driven primarily by differencesamong a few specific titles, rather than across-the-board differences throughout the executive ranks.Notably, the significant differences in favor of males exist primarily among the middle or lower ranksof top management, where women appear to be relatively highly concentrated, and not in the highestcorporate positions such as CEO/Chair or President, where women are relatively scarce.

5.4. Results of gender–year interactions

To address the issue of whether gender differences in salary and compensation vary over the sampleperiod, we report in Table 6 the estimates of models that include interactions between the femaledummy and the year dummies. The coefficients on the female dummy indicate that in the initial year ofthe sample (1996), females earn significantly less than males in real base salary (as long as age controlsare not included), but not significantly less in total compensation (except in the model with only genderand year controls in column 4). The coefficients on the year dummies indicate that for males, real basesalary is generally decreasing up until 2001 or 2002, while real total direct compensation is generallyincreasing over the entire sample period. This is consistent with an increased use of performance-based incentives over time (Frydman & Saks, 2007). The coefficients on the gender–year interactionsshow that, in the model with only gender and year controls (column 1), there is a clear pattern of femalesalaries increasing relative to male salaries. But, when we add job title, firm, and industry controls,much of this apparent pattern disappears. The salaries of females rise relative to those of males onlyin 1997 and 2002, and in all other years remain the same relative to males as they were in 1996. Thus,these results provide little evidence of a strong improvement in the relative salaries of females overthe sample period. There is also scant evidence of improvement in relative female compensation, asmost of the female–year interactions are also insignificant in the compensation regressions, but theseregressions generally do not indicate a significant gender difference in the first place.

S.Elkinawy,M


icsand

Business63 (2011) 23–45

37Table 5Comparison of male–female salary and total compensation across job titles.

Job title N % female Avg. salary Avg. salary female Avg. salary male Median salary female Median salary male Coeff on female in regressb,d,e

Panel A: salarya

CEO/Chair 14,761 1.43 564.64 575.63 564.48 502.49 517.53 −0.080Vice Chair 1996 4.51 487.48 438.58 489.79 412.08 444.64 −0.023President 7672 3.71 384.64 393.77 384.28 342.13 342.13f 0.013CFO 8053 5.97 282.08 272.75 282.68 247.21 259.66 −0.066***

COO 1468 3.61 321.01 334.73 320.50 248.47 296.61 −0.101Chief 2861 8.11 260.50 246.91 261.70 234.24 232.57 −0.085**

Executive VP 8042 5.98 324.82 326.47 324.71 307.92 295.99 −0.014Senior VP 7680 6.52 264.06 251.46 264.94 228.08 245.34 −0.031Group VP 467 1.93 268.89 254.06 269.18 263.65 259.47 0.138**

VP 6602 6.91 214.62 199.64 215.73 189.81 195.19 −0.043*

Other 438 7.99 260.89 240.77 262.63 195.11 224.25 −0.091

Job title N % female Avg. total comp. Avg. comp. female Avg. comp. male Median comp. female Median comp. male Coeff on female in regressb,d,e

Panel B: total compensationa,c

CEO/Chair 14,761 1.43 2551.08 2925.23 2545.65 1008.65 1180.92 0.052Vice Chair 1996 4.51 2030.98 1460.23 2057.94 1051.68 1165.83 −0.057President 7672 3.71 1444.05 1221.83 1452.62 735.10 717.49 0.042CFO 8053 5.97 853.45 844.04 854.05 481.08 494.99 −0.030COO 1468 3.61 1232.04 1105.60 1236.78 565.59 603.51 −0.163Chief 2861 8.11 759.03 691.22 765.02 333.20 387.51 −0.147*

Executive VP 8042 5.98 1077.73 1058.47 1078.95 624.49 599.50 0.014Senior VP 7680 6.52 693.65 603.36 699.95 436.03 427.23 0.023Group VP 467 1.93 923.55 613.05 929.65 462.87 381.86 0.461***

VP 6602 6.91 454.11 354.66 461.49 271.01 284.75 −0.063Other 438 7.99 1058.34 605.54 1097.66 298.05 383.19 −0.246

a Salary and total compensation measured in thousands of 1996 dollars.b Dependent variable is the natural log of salary (or log of total direct compensation). Control variables include the log of firm assets, industry-adjusted Tobin’s Q, the number of directors on the

board, pct. of males on the board, pct. of independent directors on the board, industry controls, and year controls. Standard errors clustered by executive.c Total direct compensation includes base salary, bonuses, stock grants, stock options, long-term incentive pay, and all “other” forms of total annual compensation.d In the regressions, industry groupings include food/agriculture (excluded from the regressions), entertainment/leisure, consumer/retail, health care, textiles/construction/manufacturing,

drugs/chemicals, mining/energy, utilities/telecommunications/transportation, electronics, and finance/insurance/real estate.e Note that the female regression coefficient can be negative even when the female average is higher than the male average because the dependent variable in the regressions is log(salary) rather

than salary. Males can have a higher average for the logged variable even if they have a lower average for the unlogged variable, since the log is a non-linear function. Furthermore, the regressioncoefficient controls for male-female differences in factors that affect salary, whereas the simple difference in male and female means does not.

f Perhaps remarkably, the median salaries of male and female presidents are identical. This is merely a surprising coincidence rather than an error in data recording or programming, since theother deciles of the salary distribution are not the same for male and female presidents – it is only the 50th deciles that happen to be the same!.

* Regression coefficient is significantly different from zero at 10% level.** Regression coefficient is significantly different from zero at 5% level.

*** Regression coefficient is significantly different from zero at 1% level.


Table 6Regression results with gender–year interactions.

Independent variables Y = log base salary Y = log total direct comp.

(1) (2) (3) (4) (5) (6)

GenderFemale −0.266*** −0.090** −0.074 −0.357*** −0.071 −0.127

(0.047) (0.038) (0.050) (0.082) (0.067) (0.088)Year

1997 −0.060*** −0.044*** −0.057*** −0.011 0.068*** 0.064***

(0.007) (0.008) (0.012) (0.012) (0.012) (0.018)1998 −0.041*** −0.028*** −0.047*** 0.052*** 0.102*** 0.092***

(0.009) (0.009) (0.015) (0.014) (0.013) (0.019)1999 −0.028*** −0.027*** −0.039*** 0.127** 0.145*** 0.131***

(0.009) (0.008) (0.014) (0.015) (0.014) (0.021)2000 −0.035*** −0.034*** −0.040** 0.165*** 0.190*** 0.171***

(0.009) (0.009) (0.015) (0.017) (0.016) (0.024)2001 −0.042*** −0.035*** −0.049*** 0.082*** 0.129*** 0.096***

(0.010) (0.010) (0.015) (0.017) (0.016) (0.024)2002 −0.029*** −0.009 −0.034* 0.017 0.114*** 0.080***

(0.010) (0.011) (0.017) (0.017) (0.016) (0.025)2003 −0.014 0.001 −0.020 −0.044** 0.018 −0.008

(0.011) (0.011) (0.018) (0.017) (0.017) (0.026)2004 0.021* 0.015 −0.021 0.086*** 0.114*** 0.070**

(0.011) (0.012) (0.019) (0.018) (0.018) (0.028)

Gender–year interaction termsFemale × 1997 0.074* 0.067** 0.063* 0.098 0.121* 0.056

(0.039) (0.031) (0.037) (0.077) (0.065) (0.084)Female × 1998 0.087** 0.048 0.044 0.095 0.062 0.054

(0.044) (0.035) (0.044) (0.084) (0.069) (0.087)Female × 1999 0.074 0.024 0.001 0.133 0.098 0.094

(0.046) (0.037) (0.047) (0.090) (0.074) (0.091)Female × 2000 0.102** 0.040 0.005 0.133 0.045 0.028

(0.048) (0.039) (0.049) (0.092) (0.076) (0.100)Female × 2001 0.096* 0.042 0.019 0.087 0.026 0.065

(0.050) (0.040) (0.052) (0.094) (0.076) (0.102)Female × 2002 0.141*** 0.067* 0.039 0.117 0.025 0.037

(0.050) (0.040) (0.054) (0.091) (0.073) (0.099)Female × 2003 0.124** 0.060 0.010 0.139 0.067 0.040

(0.051) (0.041) (0.058) (0.090) (0.072) (0.096)Female × 2004 0.116** 0.036 −0.006 0.108 −0.010 −0.097

(0.052) (0.042) (0.055) (0.093) (0.073) (0.096)

Constant 5.772*** 5.921*** 5.056*** 6.453*** 6.188*** 6.219***

(0.008) (0.054) (0.233) (0.013) (0.091) (0.345)Job title, firm, and industry controls? No Yes Yes No Yes YesAge controls? No No Yes No No YesObservations (executive-years) 60,040 60,040 31,594 60,040 60,040 31,594Unique executives 18,742 18,742 8122 18,742 18,742 8122R-squared 0.004 0.377 0.288 0.006 0.441 0.402F-statistic for overall significance 11.9*** 341.1*** 166.9*** 25.1*** 409.1*** 176.7***

Notes: Compensation and Salary figures deflated by the annual Consumer Price Index (CPI) for each year.Total direct compensation includes base salary, bonuses, stock grants, stock options, long-term incentive pay, and all “other”forms of total annual compensation.Industry groupings are food/agriculture (excluded), entertainment/leisure, consumer/retail, health care, textiles/construction/manufacturing, drugs/chemicals, mining/energy, utilities/telecommunications/transportation, and finance/insurance/realestate.Firm controls in columns 2, 3, 5, and 6 include: lagged total assets, lagged industry-adjusted Tobin’s Q, number of directors,fraction of male directors, and fraction of independent directors.Standard errors in parentheses (clustered by executive and therefore robust to heteroskedasticity across executives).

* Estimated coefficient or F-statistic is significantly different from zero at the 10% level.** Estimated coefficient or F-statistic is significantly different from zero at the 5% level.

*** Estimated coefficient or F-statistic is significantly different from zero at the 1% level.


5.5. Oaxaca decompositions

The absolute gender differences in executive pay computed thus far are intercept differences inmale and female regression lines derived under the assumption that the slope coefficients for malesand females are identical. We have already seen that this is not the case, since the effects of somefirm and board characteristics differ for men and women. Thus, it is desirable to have a method forcomputing gender differences that incorporates the difference in slope coefficients. The traditionalmethod for doing so is the “Oaxaca decomposition” (Oaxaca, 1973), which decomposes the genderdifference in wages into a part that is due to males and females having different characteristics anda part that is due to males and females having different returns to a given set of characteristics. Toperform the Oaxaca decomposition, we estimate Eq. (1) by OLS separately for men and women with thegender dummy excluded. Then, letting y = log(Y), the Oaxaca decomposition of the gender differencein average wages is:

yM − yF = (XM − XF ) ˆM + XF ( ˆ

M − ˆF ) (2)

where yM and yF are the average predicted log wages for men and women, respectively, XM and XF

are the sample averages of the explanatory variables for men and women, respectively, and ˆM and

ˆF are the estimated coefficients from the male and female regressions, respectively.

The first term on the right side of (2) is the part of the gender difference in log real wages that is dueto men and women having different characteristics (assuming that the unobserved non-discriminatorywage structure is given by the set of male coefficients), while the second term is the part due to thelabor market offering men and women different real returns for the same (i.e., female) characteristics.Thus, the latter term can be regarded as the component of the gender wage gap that is due to unob-served factors (analogous to the intercept difference when the slope coefficients are constrained tobe identical). To develop a sense of how the share of the gender difference that is due to unobservedfactors varies over time, one can also estimate separate male and female wage regressions for eachyear in the sample period and then decompose each of the yearly gender differences.

Table 7 reports the results of the decomposition of (2) into its two components for the pooled sampleof all years and separately for each year of the sample period. (The estimates of the regression modelsused to form these decompositions are not reported for the sake of brevity). The results indicate thatover the entire 9-year sample period, the gender difference in salary is 22.6 percent and the genderdifference in compensation is 31.2 percent. (These are not the same as the differences recorded inTables 3 and 4 because none of those earlier models allow all of the slope coefficients to differ formen and women. It follows that allowing the full set of slope coefficients to differ by gender makesthe gender differences appear even larger). In addition, 24.7 percent of the gender difference in realbase salary and 31.4 percent of the gender difference in real total compensation is due to males andfemales receiving different returns to their characteristics rather than differences in the characteristicsthemselves. Although the standard interpretation of such results is that discrimination is at work,other omitted factors (such as education, family status, reservation wages, or risk aversion) could beresponsible.

Interestingly, the year by year results indicate that (with some fluctuations) the gender differenceshave generally dropped over time, but the share of the differences due to unobserved factors hasgenerally remained steady or even increased. For example, 21.9 percent of the salary gap is due todifferent coefficients in 1996, but 38.8 percent is due to coefficient differences in 2004. The comparablefigures for total compensation are 24.7 percent in 1996 and 51.6 percent in 2004. Thus, a sizeable andpersistent proportion of the gender pay gap appears not attributable to the observable characteristicsof male and female executives or the firms where they work.

5.6. Determinants of the number of female executives with the firm

This section extends the analysis of how firm and board characteristics shape work outcomes forfemale executives by considering how these characteristics affect firm’s propensity to employ femaleexecutives. We have already found some evidence that larger firms, firms with more independent

40S.Elkinaw

y,M.Stater

/JournalofEconomics

andBusiness

63 (2011) 23–45

Table 7Oaxaca decompositions of the male–female salary and compensation differences.

Oaxaca decompositions ALL YRS 1996 1997 1998 1999 2000 2001 2002 2003 2004

Decompositions of log salaryTotal gender difference: (Xmˇm − Xfˇf) 0.226 0.373 0.264 0.257 0.253 0.222 0.176 0.166 0.204 0.200Difference due to X’s: (Xmˇm − Xfˇm) 0.170 0.291 0.262 0.214 0.164 0.161 0.128 0.125 0.142 0.123Difference due to ˇ’s: (Xfˇm − Xfˇf) 0.056 0.082 0.002 0.043 0.089 0.061 0.048 0.041 0.063 0.078% of total difference due to different ˇ’s 24.7 21.9 0.8 16.6 35.2 27.3 27.2 24.4 30.6 38.8

Decompositions of log total direct comp.Total gender difference: (Xmˇm − Xfˇf) 0.312 0.464 0.392 0.376 0.283 0.268 0.209 0.254 0.271 0.383Difference due to X’s: (Xmˇm − Xfˇm) 0.214 0.349 0.315 0.272 0.214 0.158 0.142 0.198 0.185 0.184Difference due to ˇ’s: (Xfˇm − Xfˇf) 0.098 0.115 0.077 0.104 0.069 0.110 0.067 0.056 0.086 0.198% of total difference due to different ˇ’s 31.4 24.7 19.6 27.7 24.3 40.9 32.1 22.1 31.7 51.6

For each time period and for the full-sample period, separate male and female regressions are estimated, but the results of these regressions are not reported.Regression models for individual years lack year and gender dummies, but are otherwise identical to the models with age and tenure controls presented in.Xm and Xf are the vectors of average values of the regressors among the males and females, respectively, in each regression sample.ˇm and ˇf are the vectors of estimated regression coefficients for the male and female regressions, respectively.The numbers in the table measure differences in predicted average log wages (i.e., proportional differences in average wages) between women and men. The predicted log wages arecomputed using each of the estimated regression models.

S.Elkinawy,M


icsand

Business63 (2011) 23–45

41Table 8Determinants of presence and number of female top executives in firm.

Independent variables Poisson regressions Probit regressions

Total number of femaleexecutives

Number of femaleexecutives besides CEO

Prob. of any femaleexecutives

Prob. of any femaleexecutives besides CEO

Log total assets −0.057***(0.012)

−0.067***(0.013)

−0.053***(0.013)

−0.063***(0.014)

−0.020**(0.010)

−0.034***(0.010)

−0.018* (0.010) −0.031***(0.010)

Ind.-adj. Tobin’s Q −0.051***(0.017)

−0.021(0.015)

−0.032*(0.017)

−0.007(0.015)

−0.022*(0.011)

−0.011(0.011)

−0.014 (0.011) −0.003(0.011)

Num. of Directors −0.016**(0.007)

−0.016**(0.007)

−0.010(0.007)

−0.011(0.007)

−0.020***(0.005)

−0.021***(0.006)

−0.016***(0.005) −0.017***(0.006)

% Board Male −4.421***(0.153)

−3.670***(0.167)

−3.790***(0.171)

−3.043***(0.185)

−3.340***(0.149)

−2.855***(0.164)

−2.868***(0.152) −2.362***(0.166)

% Board indep. 0.027(0.093)

0.240**(0.097)

0.069(0.096)

0.283***(0.100)

0.073(0.072)

0.241***(0.076)

0.121* (0.072) 0.280***(0.076)

CEO is female 0.242*** 0.151* 0.395*** 0.339***(0.090) (0.090) (0.091) (0.092)

Entert./leisure 0.568*** 0.539*** 0.306*** 0.290***(0.120) (0.121) (0.092) (0.092)

Consumer/retail 0.604*** 0.533*** 0.298*** 0.258***(0.107) (0.108) (0.079) (0.079)

Health care 0.110 0.108 0.082 0.083(0.178) (0.180) (0.129) (0.130)

Text/const/Mfg. −0.216* −0.266** −0.216*** −0.232***(0.113) (0.114) (0.080) (0.080)

Drugs/chem. 0.219* 0.183 0.135 0.127(0.122) (0.123) (0.089) (0.089)

Mining/energy −0.291* −0.337** −0.217** −0.237**(0.152) (0.153) (0.100) (0.101)

Utilities/telecomm 0.313*** 0.249** 0.142* 0.103(0.113) (0.115) (0.083) (0.083)

Electronics 0.038 −0.035 −0.122 −0.126(0.122) (0.124) (0.088) (0.088)

Fin/insur/real est 0.568*** 0.506*** 0.315*** 0.283***(0.113) (0.114) (0.083) (0.083)

Constant 2.982*** 1.721*** 2.278*** 1.024*** 1.821*** 2.018*** 1.255***(0.168) (0.227) (0.187) (0.245) (0.206) (0.166) (0.208)

Year controls? No Yes No Yes No Yes No YesObservations 12,107 12,107 12,107 12,107 12,107 12,107 12,107 12,107Log-likelihood −8515.6 −8288.0 −8280.6 −8068.8 −6652.3 −6480.9 −6586.9 −6426.8Likelihood ratio �2 752.2*** 1207.3*** 547.4*** 970.9*** 552.9*** 895.7*** 471.6*** 791.9***Pseudo R2 0.042 0.068 0.032 0.057 0.040 0.065 0.035 0.058

*** Estimated coefficient or Chi-Square statistic is significantly different from zero at the 1% level.** Estimated coefficient or Chi-Square statistic is significantly different from zero at the 5% level.* Estimated coefficient or Chi-Square statistic is significantly different from zero at the 10% level.


boards, and firms with more male-dominated boards pay women lower salaries and total compensa-tion relative to men. The question now is whether such firms are also less willing to hire females intotop executive positions. Although the data do not tell us the total number of executives, or even thetotal number of top executives, that the firm has actually hired or promoted over a given period oftime, we can view the number of female top executives observed to be with the firm in a given year asan indicator of the firm’s willingness to hire and retain females in its executive ranks. So, for each firm,we sum up the total number of female executives (and the number below the rank of CEO) who areobserved to work for the firm in each year. Then we estimate Poisson regressions for the number offemale executives (and the number below the rank of CEO) with the firm in a given year as a functionof the characteristics of the firm measured in that year. Specifically, we estimate the ˇ’s in the model:

Pr[Nji = n] =exp(exp(x′

jiˇ))[exp(x′

jiˇ)]n

n!, n = 0, 1, 2, .... (3)

where Njt is the number of female executives observed at firm j in year t, xjt is a vector of characteristicsdescribing firm j in year t (the same characteristics used in the salary and compensation regressions),and ˇ is the vector of coefficients to be estimated. Poisson regression is used instead of OLS becausethe dependent variable (Njt) can only take on a discrete set of small positive values. We also estimatea probit model for the probability that the firm has any female executives at all in year t, with thesame control variables as in the Poisson model. In addition, we estimate Poisson and probit models forthe number and probability, respectively, of female executives below the rank of CEO/Chair. In theseregressions, we also include a dummy that equals 1 if the CEO is female.

The results are presented in Table 8. We find that firms with more total assets, larger boards, andmore male-dominated boards have a smaller number of female top executives and a lower probabil-ity of having any female executives at all. In some specifications we also find that firms with higherindustry-adjusted values of Tobin’s Q have lower numbers of female executives and lower probabilitiesof having any female executives. However, firms with more independent boards have higher numbersof female executives and a higher probability of having any female executives, as long as industry con-trols are included. We also find that firms with female CEOs have higher numbers of non-CEO femaleexecutives and higher probabilities of having any female executives below the rank of CEO. Theseresults support the idea that firm characteristics not only shape gender patterns in compensation, butalso the gender composition of the firm’s top management. (But given that the latter is true, perhapsit is not altogether surprising that the former is also true.) In particular, in contrast to board indepen-dence, male dominance on the board not only leads to lower relative salaries for female executives,but also to lower numbers of female executives working for the firm. This provides further evidencethat male-dominated governance structures may not offer environments that are most conducive tothe ascent of female executives in the executive profession.

6. Conclusion

Gender differences in wages have been documented in many professions through several decadesof economic research. Corporate management is no exception, despite the high level of education andcareer dedication demonstrated by women who enter this demanding field. Since large corporationsmay serve as models for the design of human resource policies at a broad set of firms and corporatemanagers possess the power to affect the working conditions of many other workers, gender equityin the executive profession is an important labor market issue.

This study examines gender differences in executive salaries and total compensation and how thesedifferences vary with firm characteristics and over time. We find that the base salaries received byfemale executives are in the range of 4.5–5.5 percent lower than those of male executives, holding con-stant executive, firm, and board characteristics. The gender difference in total direct compensation ismore sensitive to specification, but is even greater than the salary difference when significant. We alsofind that the relative salaries and total compensation of females decrease as the firm becomes largerand as the board of directors becomes more male-dominated or more independent. Also, larger firmsand those with more male-dominated boards have fewer top female executives and lower probabilitiesof having any top female executives.


The estimates provide little evidence that the salaries or total compensation of female executiveshave significantly increased relative to men over time, even though female representation in theprofession has steadily expanded. In fact, Oaxaca decomposition results suggest that the share of thegender wage difference that is due to unobserved factors may even be increasing. While these resultssuggest that gender inequities may persist in top-level corporate jobs, particularly in firms with male-dominated governance structures, limitations on human capital, family status, and labor supply dataprevent us from definitively pinpointing the source of these disparities. Along these lines it is alsoworth noting that including the set of executive, firm, and job title controls that we do have access toeliminates about 75 percent of the estimated raw gender difference in salary (from 16.2% down to 4.5%).

The relative compensation of women has implications for the efficiency of the executive labormarket and perhaps even for the economy as a whole. Inferior compensation could deter femaleprofessionals from making the sacrifices necessary to pursue top executive careers, even when suchpursuits would increase the profitability of corporations and the productivity and incomes of otherworkers. Therefore, further research should examine increasingly detailed personal data on a broadsample of executives to determine whether and why women are under-compensated in this profes-sion. Only then can we be confident that policy measures to promote gender equity will rectify ratherthan exacerbate any shortcomings that may exist in the market.

Acknowledgments

The authors would like to thank John Becker-Blease for generously sharing data and providing use-ful comments at the formative stages of the paper. Elkinawy also gratefully acknowledges the supportof a summer research grant from Loyola Marymount University. Conference participants at the West-ern Economic association provided helpful comments and suggestions, as did seminar participants atAuckland University of Technology in New Zealand.

Appendix A. Classification of EXECUCOMP job descriptions into job titles

A.1. Titles

Our first step is to group titles into 11 different categories per Bertrand and Hallock (2001): (1)CEO/Chair; (2) Vice Chair; (3) President; (4) Chief Financial Officer (CFO); (5) Chief Operating Officer(COO); (6) Other “Chief” Officer (e.g., Chief Information Officer); (7) Executive Vice President; (8) SeniorVice President; (9) Group Vice President; (10) Vice President; (11) “Other” occupations (e.g., GeneralManager, General Counsel, Secretary)

A.2. Coding method

Our basic approach is to choose the highest occupation listed for the executive in each year andcode that occupation into the appropriate one of the above 11 categories. In determining which titleis the highest, we take the above list to be a complete rank-ordering of all the titles (e.g., CEO > ViceChair > CFO > Group VP, etc.). In some cases, a given executive is associated with titles within the maincompany as well as titles within subsidiaries or divisions of the firm. We operate under the premisethat the executive’s title with the full company should supersede his or her title with a subsidiary ordivision. Thus, for example, someone whose listed titles are “Executive Vice President; CEO-subsidiary”is classified as an Executive VP rather than a CEO. In cases where only titles within subsidiaries ordivisions are listed, and none with the main company, we classify the executive into the highestamong those titles.

A.3. Classification of particular titles

The titles of Controller, Treasurer, Director, and Senior Director are categorized as “Other ChiefOfficer.” Likewise, titles of the form “Head of {research, equities, investment banking, etc.}” are clas-sified as “Other Chief Officer.” The exceptions to this are the titles “Head of Finance,” and “Head of


Operations,” which are classified as CFO and COO, respectively. Titles such as “President of Finance”and “Principal Financial Officer” are also classified as CFO. Occupations such as Secretary, GeneralCounsel, General Manager, and Managing Director are all classified as “Other Occupations.” Chairsof various subcommittees of the board of directors, as well as the titles of Regional Chairman andDeputy CEO/Chairman are classified as “Vice Chair.” The title of Group President is classified simplyas “President.”

A.4. Alternative coding schemes

We tried some alternative approaches to coding the titles in order to determine whether the resultsare sensitive to the method utilized. One of the alternatives was similar to the above method exceptthat: (i) when the executive was listed as both CFO and COO, we adopted whichever of these titles waslisted first, and (ii) when titles were given for both the main company and subsidiaries, we simply tookthe first title listed. This approach yielded results essentially identical to those reported in the paper.The second alternative was to simply take the first title listed in all cases. The results of this approachyielded even stronger gender differences than those reported in the paper. In particular, the genderdifference in compensation was always significant, and females had significantly lower salaries in alarger number of jobs: CFO, Chief, VP, Executive VP, and Senior VP, rather than just CFO, Chief, andVP. However, we believe that the original coding scheme described above best represents the trueprimary occupation of the executive, while at the same time yielding more conservative estimates ofgender differences.

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