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Cornell University ILR SchoolDigitalCommons@ILR
Faculty Publications - Human Resource Studies Human Resource
Studies
10-1-2001
The Gender Gap in Top Corporate JobsMarianne BertrandUniversity
of Chicago
Kevin F. HallockCornell University, [email protected]
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The Gender Gap in Top Corporate Jobs
AbstractUsing the ExecuComp data set, which contains information
on the five highest-paid executives in each of alarge number of
U.S. firms for the years 199297, the authors examine the gender
compensation gap amonghigh-level executives. Women, who represented
about 2.5% of the sample, earned about 45% less than men.As much as
75% of this gap can be explained by the fact that women managed
smaller companies and wereless likely to be CEO, Chair, or company
President. The unexplained gap falls to less than 5% with
anallowance for the younger average age and lower average seniority
of the female executives. These results donot rule out the
possibility of discrimination via gender segregation or unequal
promotion. Between 1992 and1997, however, women nearly tripled
their participation in the top executive ranks and also strongly
improvedtheir relative compensation, mostly by gaining
representation in larger corporations.
Keywordsgender, gap, compensation, corporate, pay, women, data,
age, discrimination, executive, ceo
DisciplinesHuman Resources Management
CommentsSuggested CitationBertrand, M., & Hallock, K.
(2001). The gender gap in top corporate jobs [Electronic version].
Industrial andLabor Relations Review, 55,
3-21.http://digitalcommons.ilr.cornell.edu/hrpubs/14/
Required Publisher StatementPosted with permission from the
Industrial and Labor Relations Review.
This article is available at DigitalCommons@ILR:
http://digitalcommons.ilr.cornell.edu/hrpubs/14
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3Industrial and Labor Relations Review, Vol. 55, No. 1 (October
2001). by Cornell University.0019-7939/00/5501 $01.00
T
THE GENDER GAP IN TOP CORPORATE JOBS
MARIANNE BERTRAND and KEVIN F. HALLOCK*
Using the ExecuComp data set, which contains information on the
fivehighest-paid executives in each of a large number of U.S. firms
for the years199297, the authors examine the gender compensation
gap among high-levelexecutives. Women, who represented about 2.5%
of the sample, earned about45% less than men. As much as 75% of
this gap can be explained by the fact thatwomen managed smaller
companies and were less likely to be CEO, Chair, orcompany
President. The unexplained gap falls to less than 5% with an
allowancefor the younger average age and lower average seniority of
the female execu-tives. These results do not rule out the
possibility of discrimination via gendersegregation or unequal
promotion. Between 1992 and 1997, however, womennearly tripled
their participation in the top executive ranks and also
stronglyimproved their relative compensation, mostly by gaining
representation inlarger corporations.
*Marianne Bertrand is Assistant Professor of Eco-nomics at the
Graduate School of Business at theUniversity of Chicago, Faculty
Research Fellow at theNational Bureau of Economic Research, and an
Affili-ate of CEPR. Kevin Hallock is Associate Professor
ofEconomics and of Labor and Industrial Relations atthe University
of Illinois at Urbana-Champaign.
For helpful comments, the authors thank seminarparticipants at
the University of Illinois, StanfordUniversity, and the joint
meeting of the Society ofLabor Economists and European Society of
LabourEconomists in Milan, as well as Marianne Ferber,Peter
Feuille, Todd Fister, Wallace Hendricks, JohnJohnson, and Craig
Olson. Sherrilyn Billger pro-vided outstanding research
assistance.
1See, for example, Catalyst (1999), Morris (1998),Jones (1999),
and Meyer (1999).
Some of the data used in this study are fromStandard and Poors
ExecuComp data base and mustbe purchased from Standard and Poors.
Computerprograms used in the analysis are available from theauthors
upon request. The first drafts of this paperwere written while
Bertrand was on the faculty atPrinceton and Hallock was visiting
the IndustrialRelations Section at Princeton. E-mail:
[email protected]; [email protected].
his paper analyzes gender differencesamong top executives in a
large set of
U.S. public corporations. Our motivationfor undertaking this
study is twofold. Firstand foremost is the fact that,
notwithstand-ing the curiosity this topic raises both in themedia
and in policy circles,1 we know of no
systematic study to date of how well womenare doing in top
corporate jobs. We pro-vide the first detailed description of
therelative position of female top executives inthe 1990s.
Our second motivation is more academic.Problems plaguing many
past studies of thegender pay gapin particular,
unobservedcharacteristics of both workers and jobsare likely to be
less present in this specificoccupational group. Most of the
previouswork has indeed identified an unexplainedgender gap that
cannot be attributed to
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4 INDUSTRIAL AND LABOR RELATIONS REVIEW
observable differences between men andwomen.2 While this
unexplained gap couldbe due to labor market discrimination, itcould
also be attributable to differencesbetween men and women that are
unob-servable (at least to the econometrician),such as a relative
lack of long-term careercommitment among women.3 It is reason-able
to assume that such unobservable dif-ferences are minimized in the
group of topexecutives we propose to study. Men andwomen in this
sample are likely to be simi-lar in that both share a high level of
jobmotivation and high career ambitions.
Several authors have previously exam-ined gender pay differences
among thehighly paid. Examples include investiga-tions of lawyers
by Wood, Corcoran, andCourant (1993), and Biddle andHamermesh
(1998); of university faculty byBarbezat (1987), Barbezat and
Hughes(1990), Ferber and Greene (1982), Gander(1997), Hoffman
(1976), Johnson andStafford (1974), Katz (1973), and Ransomand
Megdal (1993); of engineers by Mor-gan (1998); of physicians by
Baker (1995);and of firm managers in the United King-dom by Gregg
and Machin (1993).4 No onebefore, however, has focused on
gendercompensation differentials among top ex-ecutives. There have
been two substantialbarriers to conducting an investigation
such
as ours. First, the required data simply didnot exist before.
Second, it has been widelybelieved that too few women were in
thesetop positions to carry out a formal analysisof their relative
pay.
We use Standard and Poors ExecuCompdata, which contain
information on com-pensation for the top five executives for
allfirms in the S&P 500, S&P Midcap 400, andS&P
SmallCap 600 for the years 199297.Included is information on base
salary,bonus, and the value of granted stock op-tions in the
current year.5 The ExecuCompdata set has three main advantages for
ourpurpose. First, it is very large. The samplewe use in most of
our analysis includesmore than 42,000 executive-year observa-tions.
All publicly traded firms are re-quired to disclose the names and
compen-sation of the top five highest-paid em-ployees annually.
This large sample size isespecially important for us because we
wantto estimate gender differences with suffi-cient statistical
precision in an economicsector where female representation is
small.A second advantage of the data set is that itcovers a variety
of occupational categoriesamong the top managerial jobs and notonly
Chief Executive Officers (CEOs). Weare thus able to investigate the
importanceof occupational differences at the top. Fi-nally, because
the data set covers a widecross-section of firms, the role of firm
sizeand industrial specialization in the gendercompensation gap can
be assessed.
The Gender Gap
The ExecuComp data set is unique formany reasons, including its
wide variety ofmeasures of compensation, details concern-ing firm
characteristics, and large samplesize. We can also arrange the data
as a
2An exception is Groshen (1991). Groshen showedthat most of the
gender gap can be attributed to sexsegregation rather than wage
differences by sex withinoccupation, industries, and
establishments. Using alarger sample but a similar empirical
methodology,Bayard, Hellerstein, Neumark, and Troske
(1999),however, found that a large part of the sex gap re-mains
unexplained after accounting for sex segrega-tion.
3Of course, it is also possible that lower pay leadsto lower
career commitment. In addition, it could bethat some compensating
differential exists wherebywomen on average select lower-paying
jobs than menand, at the same time, enjoy amenities on these
jobsthat the higher-paying jobs lack. We cannot explorethis issue
empirically in this paper.
4Gregg and Machin (1993) explored pay gaps fora much more
general class of managers than the classwe focus on. Another study
that focuses on suchlower-level managers for the United States is
Jacobs(1992).
5Most studies of CEO pay do not include the valueof stock
options granted in a given year. Hall andLiebman (1998), however,
documented the growingimportance of granted options in the
compensationof CEOs since the early 1980s. ExecuComp reportsthis
information, and we have included it in our totalcompensation
measure.
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GENDER GAP IN TOP CORPORATE JOBS 5
panel, since we have multiple observationson a set of firms over
time. Most crucial forour work, however, is the identification
ofthe gender of each manager. Given thesubstantial discussion of a
dearth of womenin managerial positions in the United States(see,
for example, Catalyst 1999), we wereconcerned that examining the
question ofa compensation gap would be difficult.However, due to
ExecuComps substantialsize, we were able to identify more than1,134
female executive-year observations(449 unique individuals) on the
basis of thegender variable included in the data. Thisis roughly
2.4% of all observations in thesample.
Panel A of Table 1 summarizes meancompensation by gender for the
basicExecuComp sample. The table displaystotal compensation but
also decomposestotal compensation into its major elements:salary,
bonus, other annual compensation,and the value of options granted
in thecurrent year. Pooling all the ExecuCompyears together, total
compensation was, onaverage, 33% lower for women than formen.6 On
average, women earned a little
Table 1. Summary Statistics on Compensation, 199297.(Standard
Errors in Parentheses)
(1) (2) (3) (4)Characteristic All Managers Men Women
p-Valuea
Panel A: High-Level Managersb
Total Current Payc 1,323.0 1,333.7 894.1 0.000(13.8) (14.1)
(58.1)
Salary 336.3 338.6 246.9 0.000(1.0) (1.0) (6.1)
Bonus 257.6 260.7 136.1 0.000(3.8) (3.8) (8.4)
Other Annual Payd 20.6 20.8 12.7 0.006(0.7) (0.7) (2.8)
Value Granted Optionse 489.7 492.6 370.8 0.003(10.5) (10.7)
(39.5)
N 46,708 45,574 1,134
Panel B: Managers from CPSfAnnual Labor Earnings 45.6 52.4 36.0
0.000
(0.09) (0.13) (0.11)
N 73,411 43,011 30,400
Sources: The data on high-level managers in panel A are from
Standard and Poors ExecuComp database for19921997. The data on
managers from the CPS are from the 1992 to 1997 Merged Outgoing
Rotation Groupsof the Current Population Survey.
Notes: All data are reported in real 1997 thousands of dollars
adjusted using the consumer price index.aThis is the p-value for
the difference in sample means between men and women within each
row.bHigh-level managers include the top five highest-paid
executives in each firm in the ExecuComp database.cTotal current
pay is the sum of salary, bonus, other annual pay, and the value of
stock options granted in the
current year.dOther annual pay includes the dollar value of
annual compensation not categorized as salary or bonus.eValue of
granted options is the value of stock options granted in the
current period. This is not the value
of options cashed in in a given year.fThe managers from the CPS
are the set of full-time workers who report an occupation category
between 3
and 22 in the 1980 Census of Population Occupation
Classification. Annual income is constructed from averageweekly
earnings.
6When we later control for year effects, the gendergap in total
compensation reaches about 45%, thenumber reported in the
introductory paragraphs.
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6 INDUSTRIAL AND LABOR RELATIONS REVIEW
less than $900,000 (1997 dollars) in totalcompensation, compared
to more than $1.3million for the average male executive.
How does the gender gap among top-level managers compare to that
amonglower-level managers? Panel B addressesthis question. We use
the Merged Outgo-ing Rotation Groups of the Current Popu-lation
Survey (CPS) over the same period,199297. Given that the ExecuComp
dataare so specialized, we define a manager asanyone reporting that
he or she worked inan executive, administrative and manage-rial
occupation, excluding managementrelated occupations.7 We focus on
full-time workers only, that is, individuals whoworked at least 35
hours per week. Annualsalaries are constructed based on
averageweekly earnings. One can see that thegender earnings gap
among middle-levelmanagers is very similar to that among
topmanagers: about 46%.
We now consider gender differences inthe composition of the
compensation pack-age. Several features are worth noticing.First,
women seem to have received a largershare of their compensation in
the form ofstock options than did men. This patternmay reflect the
fact that the sample ofwomen was larger in the later years, whenthe
use of stock options was more com-mon.8 Also, compared to men,
womenreceived less compensation in the form ofbonuses and more in
the form of salary.
Decomposing the Gender Gap
In this section, we investigate how vari-ous characteristics of
female top executive
employment might account for the gendergap. We explore issues
such as firm size,industrial segregation, occupational
segre-gation, and individual demographic char-acteristics.
The Role of Firm Size
Women in top managerial positionstended to work for much smaller
corpora-tions than did men. Panel A of Table 2clearly illustrates
this fact. Female execu-tives firms were 3545% smaller, whethersize
is measured as the value of shareholderwealth, sales, total assets,
or number ofemployees.9 In an analysis not reportedhere, we found
that companies of extremesize were chiefly responsible for the
rela-tionship between firm size and gender. Wecomputed the fraction
of women by decilesof firm market value. Women constitutedabout
3.5% of top management employ-ment in the bottom two deciles and
only1% in the top decile. In all the otherdeciles, the fraction of
women fluctuatedbetween roughly 2% and 3%, and the de-cline was not
monotonic in size.
It is a well-known fact in the executivecompensation literature
that CEOs tend tobe paid more the larger the firms size(Murphy
1985; Kostiuk 1990; Rosen 1992).If this pay-size correlation also
holds forother top executives, it is reasonable to askhow much of
the gender gap can be attrib-uted to the under-representation of
womenin large firms. The first columns of Table 3answer this
question.
The dependent variable for all regres-sions in Table 3 is the
logarithm of real totalcompensation. All regressions in the
paper(Tables 3, 6, 8, and 9) include yearly timeindicators, and
standard errors are White-corrected standard errors. One can see
incolumn (1) that the gender gap is larger,44%, when one controls
for year effects
Controlling for year effects is crucial, because, as wewill show
below (in the section Trends in Participa-tion and Earnings), the
number of women was largerin the later years of the sample, when
compensationlevels were on average higher.
7This corresponds to categories 322 in the 1980Census of
Population Occupation Classification.Obviously, this definition
formally also includeshigher-level managers. But because this group
issmall, most of the individuals in the CPS sample arelower-level
managers.
8See the section Trends in Participation and Earn-ings.
9Shareholder wealth is the total number of sharestimes the
year-end share price. The correlation be-tween ln(shareholder
wealth) and ln(assets) is 0.8;between ln(shareholder wealth) and
ln(number ofemployees), 0.7; and between ln(assets)
andln(employees), 0.7.
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GENDER GAP IN TOP CORPORATE JOBS 7
than the gap implied by Table 1, whichdoes not control for any
covariates. Thiscan be explained by the fact that there aremore
observations for women in the latersample years. Column (2) shows
that thegender gap is, as we had expected, substan-tially reduced
when we control for the valueof shareholder wealth. The elasticity
ofmanagerial compensation to the value ofshareholder wealth is
about 0.4.10 About athird of the gender compensation differen-tial,
or 15 percentage points, can be ac-counted for by the lower
participation ofwomen in large firms.
The Role of Industrial Segregation
The female executives in our sample werenot uniformly
represented in all industrial
sectors. This can be seen in Table 4. Womenwere more likely to
be managing compa-nies that specialized in health and
socialservices and in trade. These were alsosectors in which a
disproportionate shareof lower-level managers were women, as wecan
see from the CPS results in column 6.In contrast, very few women
held top-levelpositions in agriculture, construction, min-ing, and
heavy manufacturing industries.
The banking sector is an interesting case.While it had the
largest share of women inlower-level management among all
sectors,the share of women at the top was lowerthan in most other
sectors.11 Does the
Table 2. Firm and Manager Characteristics.(Standard Errors in
Parentheses)
(1) (2) (3) (4)Characteristic All Managers Men Women
p-Valuea
Panel A: Firms
Market Value (millions) 3,768.7 3,799.3 2,538.3 0.000(45.7)
(46.4) (245.4)
Salesb (millions) 3,423.6 3,470.9 1,893.5 0.000(42.1) (43.0)
(117.9)
Assetsc (millions) 7,473.0 7,525 5,379.6 0.000(117.2) (119.1)
(644.6)
Employeesd (thousands) 16.9 17.1 9.2 0.001(0.2) (0.2) (0.5)
Ne 46,708 45,574 1,134
Panel B: Managers
Age 52.6 52.6 47.5 0.000(0.5) (0.06) (0.5)
N 17,236 16,960 276
Seniority 13.2 13.3 7.7 0.000(0.1) (0.1) (0.5)
N 14,189 13,845 344
Source: The data are from Standard and Poors ExecuComp database
for 199297.aP-value for difference in sample means by gender for
each variable.bSample sizes for sales are 46,665, 45,533, and
1,132.cSample sizes for assets are 46,703, 45,569, and
1,134.dSample sizes for employees are 45,581, 44,482, and
1,099.eSample size for Market Value. This size variable is used in
most of the analysis below.
10This elasticity is slightly higher than that docu-mented in
Rosen (1992) for CEOs only.
11Bird (1990) previously documented substantialgrowth of female
employment in bank managementstarting in the 1970s, partly as a
result of pressures bythe Equal Employment Opportunities
Commission(EEOC). She further noticed that these women
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8 INDUSTRIAL AND LABOR RELATIONS REVIEW
apparent industrial segregation of femaleexecutives account for
some of the gendergap in compensation? The data reportedin Table 4
show no obvious pattern of aconcentration of women in low-wage
indus-tries. While managers in health and socialservices as well as
in trade were paid slightlyless than the average manager in the
sample,managers in the industries where womenwere very scarce were
paid below average,too.
Columns (3)(5) in Table 3 confirm thisobservation in a more
rigorous statisticalway. Columns (3) and (4) show that thefemale
dummy variable stays unchangedwhether we add 8 broad industry
dummiesor 115 finer dummies. Moreover, none ofthe gender gap
remaining after controllingfor firm size (column 2) can be
accountedfor by the differential representation ofwomen in
different industries (column 5).In summary, there is no evidence of
a sys-tematic allocation of women in low-payingindustries.
The Role of Occupational Segregation
Table 5 presents the share of women invarious occupations. We
constructed occu-pational categories based on the title vari-able
in ExecuComp. There are more than5,100 unique occupation tit les
inExecuComp. Some of these titles clearlyrepresent similar
occupations. For example,ex. vp and exec. vp are just differentways
of representing executive vice presi-dent. But many are more
complicated andcannot naturally be merged together. Webroke the
occupation categories into 31unique groupings, including Chair
andCEO, Vice-Chair, President, Chief Finan-cial Officer (CFO),
Chief Operating Of-ficer (COO), and so on. Because some ofthe
executives in the sample reported morethan one occupation in their
job title, weconstructed two different occupational cat-egories for
the first and second occupationreported for each manager in
ExecuComp.
The occupational breakdown reportedin Table 5 is a further
consolidation of our31 categories into only 11 based on the
firstoccupation reported, except for the Chairand CEO category.
Indeed, as most of theCEOs in the sample are also Chairs of
their
Table 3. Gender Pay Gap for High-Level Executives.(Dependent
Variable is the Log of Total Compensation;
White-Corrected Standard Errors in Parentheses)
Variable (1) (2) (3) (4) (5)
Female 0.44*** 0.28*** 0.44** 0.43*** 0.27***(0.05) (0.04)
(0.05) (0.04) (0.03)
Market Value 0.37*** 0.39***(0.004) (0.005)
Stock Return/1,000 0.04 0.02(0.03) (0.03)
8 Industries no no yes no no
115 Industries no no no yes yes
Constant 6.48*** 3.86*** 6.48*** 6.48*** 3.89***(0.01) (0.03)
(0.01) (0.01) (0.02)
R2 0.030 0.345 0.056 0.177 0.410
N 46,670 46,670 46,670 46,670 46,670
Source: The data are from Standard and Poors ExecuComp database
for 199297.Notes: All regressions control for time indicator
variables.*Statistically significant at the .10 level; **at the .05
level; ***at the .01 level.
tended to be mostly employed in retail banking andespecially
branch management, where chances ofadvancement were very low.
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GENDER GAP IN TOP CORPORATE JOBS 9
companies and sometimes reported theirtitle as CEO and Chairman
and some-times as Chairman and CEO, all respon-dents who reported
at least one of theseoccupations in their title were put in
theCEO/Chair category. Finally, we rankedthese occupations based on
our intuitiveassessment of their relative prestige. Col-umn (3) of
Table 5 reports the mean com-pensation for each occupation relative
tothe overall mean compensation in thesample. This confirms that
our intuitionwas roughly correct except with respect toCFOs, whose
relatively low compensationon average in our sample came as a
surpriseto us.
The most important fact in Table 5 is theunder-representation of
women in the topthree occupational categories and top
fouroccupations (Chair, CEO, Vice-Chair, andPresident).12 Women who
had made it intothe top managerial level (that is, they were
in the ExecuComp sample) were less likelyto be at the very top
than were men.13 Thefraction of women among CEOs, Chairs,and
Vice-Chairs was much less than 1%.There were also fewer female
presidentsthan there would have been if female topexecutives were
randomly distributed acrossoccupations.14 Once we look beyond
these
Table 4. Relative Pay, Percent Female, andFemale/Male Wage Gaps
by Broad Industry Categories.
High-Level Managers Managers from the CPS
(1) (2) (3) (4) (5) (6) (7) (8)% Industry Female/ % Industry
Female/
Number Female Wage/ Male Number Female Wage/ Malein in Market
Wage Gap in in Market Wage Gap
Industry Industry Industry Wage in Industry Industry Industry
Wage in Industry
Agriculture 222 0.00 0.60 312 42.63 0.77 .75***Mining, Oil,
Construction 3,197 1.38 0.93 0.47*** 4,338 18.21 1.01 .72***Food,
Tobacco, Textile 6,361 2.63 0.99 0.81 3,953 31.77 1.08
.76***Chemical, Concrete, Autos 10,252 1.11 0.93 0.49*** 10,486
22.20 1.23 .76***Transport, Communication 6,112 2.45 0.77 0.49***
6,495 31.25 1.10 .82***Wholesale / Retail Goods 5,484 3.61 0.81
0.65*** 10,671 40.72 0.76 .71***Banking 5,593 2.34 1.49 0.61***
8,975 50.14 1.05 .69***Personal & Business Serv. 5,378 3.18
1.20 0.89 9,296 39.46 0.88 .77***Health and Social Services 4,109
3.87 0.95 0.62*** 18,958 60.07 0.99 .74***
Source: The data on high-level managers are from Standard and
Poors ExecuComp database for 199297.The data on managers from the
CPS are from the 1992 to 1997 Merged Outgoing Rotation Groups of
the CurrentPopulation Survey.
***Significant at the 0.001 level or better.
12This is an example of what is known as verticalsegregation
(see Blau, Ferber, and Winkler 1998).Also see Ferber and Loeb
(1997) for a related ex-ample in higher education.
13Note, however, that in column 4 we report theratio of the
average pay of women to the average payof men within occupations.
For the CEO/Chaircategory, this ratio is positive and marginally
signifi-cant (p-value 0.08). It appears that although very fewwomen
made it to this top spot, once they got there,their average
compensation (without consideringcontrol variables) was quite
high.
14This finding is consistent with a vast prior litera-ture that
has shown that a substantial part of thedifference in pay between
men and women is attribut-able to the fact that women are less
likely to hold thehigher-paying jobs. See, among others, Goldin
(1990)and Blau and Ferber (1987). While sex segregationby
occupation can be reconciled with some form oftaste discrimination
by employers, employees, or cus-tomers (Becker 1957; Arrow 1973),
many authorshave preferred to rely on human capital models
toexplain this fact. See Lazear and Rosen (1990) forone such model.
Another interpretation is offered byReskin and Ross (1990) and
Strober (1984).
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10 INDUSTRIAL AND LABOR RELATIONS REVIEW
four top occupations, there is also an ap-parent negative
correlation between thefraction of female executives in an
occupa-tion and relative compensation in that oc-cupation, but the
correlation is far fromstrong. For example, women were
over-represented among CFOs whose compen-sation was relatively
lower than we initiallyexpected, but CFOs were still paid morethan
most of the categories of Vice Presi-dents.
In Table 6, we turn to a regression analy-sis in order to more
precisely quantify theimpact of sex segregation by occupation onthe
gender earnings gap. The sample ofexecutives for which we can
construct occu-pation is about 9% smaller than the origi-nal
sample. The unconditional gender gapin this sample is 47% (column 1
in Table 6)and is not statistically different from the44% gap found
in Table 3 (which coversthe entire sample). The scarcity of
femaleCEOs and Chairs only explains as much as13% of the
compensation differentials (col-umn 2). Nearly half of the 47% gap
can beexplained by the scarcity of women in thetop four occupations
of Chair, CEO, ViceChair, and President (column 4). If onefurther
controls for firm size (column 5),the gender compensation
differential falls
to 12%. Interestingly, adding further occu-pational controls
only very weakly reducesthe remaining gender compensation
gap(columns 68 relative to column 4). Add-ing more than 60 detailed
controls for bothfirst and second occupations in the job
title(column 9) reduces the gender gap by an-other 7 percentage
points compared tocolumn (4).
Finally, columns (10) and (11) of Table6 examine the combined
effect of occupa-tional segregation, industrial segregation,and
firm size. As noted above, industryindicators do not reduce the
coefficient onthe female indicator at all (compare col-umn 8 to
column 10). Controlling for firmsize after controlling for
occupational cat-egories (column 11) still has a large effecton the
female dummy. The magnitude ofthe effect, however, is smaller than
in Table3. This very likely indicates that womenwere even less
likely to hold the top jobswhen they worked for larger
corporations.
By constructing occupational categoriesbased on the job title
variable, we were alsoable to extract information on broad fieldof
activity for a subsample of the observa-tions (see Appendix table).
Womens rep-resentation was highest in fields such ashuman
resources, utility services, and retail
Table 5. Relative Pay, Percent Female, andFemale/Male
Compensation Gaps by Broad Occupation Groups.
(1) (2) (3) (4)Number % Occupation Female/Male
in Female in Wage/ Wage GapPosition Occupation Occupation Market
Wage in Occupation
CEO / Chair 8,987 0.52 1.93 1.75*Vice Chair 2,000 0.85 1.53
0.50***President 5,840 1.71 1.30 0.58***CFO 326 6.44 0.61 0.67COO
164 1.83 1.16 0.61Other Chief Officer 2,155 1.58 1.48
0.47***Executive VP 8,581 2.66 0.83 1.10Senior VP 8,006 3.45 0.56
0.88**Group VP 493 0.81 0.44 0.91VP 7,468 4.27 0.37 0.79***Other
Occupations 695 2.88 0.55 0.40***
Source: The data are from Standard and Poors ExecuComp database
for 199297.***Means for men and women are significantly different
at the 0.01 level; **at the 0.05 level; *at the 0.10
level.
-
GENDER GAP IN TOP CORPORATE JOBS 11
Tab
le 6
. G
end
er P
ay G
ap f
or H
igh
-Lev
el E
xecu
tive
s w
hen
Det
aile
d M
anag
er O
ccu
pat
ion
Is
Con
sid
ered
.(D
epen
den
t V
aria
ble
is t
he
Log
of
Tot
al C
omp
ensa
tion
; Wh
ite-
Cor
rect
ed S
tan
dar
d E
rror
s in
Par
enth
eses
)
Inde
pend
ent V
aria
ble
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
Fem
ale
0.4
7***
0.3
4***
0.3
1***
0.2
5***
0.1
2***
0.2
6***
0.2
2***
0.2
2***
0.1
8***
0.1
9***
0.1
1***
0.1
3***
(0.0
5)(0
.05)
(0.0
4)(0
.04)
(0.0
4)(0
.04)
(0.0
4)(0
.05)
(0.0
4)(0
.04)
(0.0
3)(0
.02)
CE
O /
Ch
air
0.82
***
0.87
***
0.99
***
0.89
***
0.99
***
1.24
***
(0.0
2)(0
.02)
(0.0
2)(0
.02)
(0.0
2)(0
.06)
Vic
e C
hai
r0.
77**
*0.
89**
*0.
51**
*0.
50**
*0.
50**
*(0
.04)
(0.0
4)(0
.03)
(0.1
2)(0
.12)
Pre
sid
ent
0.64
***
0.66
***
0.65
***
0.89
***
(0.0
2)(0
.02)
(0.0
2)(0
.06)
CFO
0.0
70.
17(0
.09)
(0.1
1)
CO
O0.
48**
*0.
73**
*(0
.12)
(0.1
3)
CO
0.39
***
0.63
***
(0.1
1)(0
.13)
EV
P0.
57**
*(0
.06)
SVP
0.26
***
( 0.0
6)
GV
P0.
21**
*(0
.07)
VP
0.1
2**
(0.0
6)
31 O
ccs.
no
no
no
no
no
no
no
yes
no
yes
yes
yes
31 +
35
Occ
s.n
on
on
on
on
on
on
on
oye
sn
on
on
o11
5 In
ds.
no
no
no
no
no
no
no
no
no
yes
yes
no
Firm
Eff
ects
no
no
no
no
no
no
no
no
no
no
no
yes
Lo
g V
alu
e0.
36**
*0.
35**
*0.
27**
*(0
.004
)( 0
.004
)( 0
.009
)
Ret
urn
/1k
0.06
**0.
010
.04
(0.0
2)( 0
.03)
( 0.0
3)
Co
nst
ant
6.50
***
6.29
***
6.22
***
6.09
***
3.63
***
6.09
***
5.83
***
6.64
***
6.90
***
6.63
***
4.33
***
4.95
***
(0.0
1)( 0
.01)
( 0.0
1)( 0
.01)
( 0.0
3)( 0
.01)
( 0.0
6)( 0
.09)
( 0.1
0)( 0
.09)
( 0.0
8)( 0
.07)
R2
0.02
90.
143
0.17
00.
217
0.49
40.
219
0.26
60.
273
0.28
10.
382
0.57
50.
710
N42
,677
42,6
7742
,677
42,6
7742
,677
42,6
7742
,677
42,6
7742
,677
42,6
7742
,677
42,6
77
Sou
rce :
Th
e d
ata
are
fro
m S
tan
dar
d a
nd
Po
or
s E
xecu
Co
mp
dat
abas
e fo
r 19
929
7.N
otes
: A
ll r
egre
ssio
ns
con
tro
l fo
r ti
me
ind
icat
or
vari
able
s. T
he
om
itte
d o
ccu
pat
ion
cat
ego
ry i
n t
he
fin
al c
olu
mn
s is
all
oth
er o
ccu
pat
ion
s.*S
tati
stic
ally
sig
nif
ican
t at
th
e .1
0 le
vel;
**a
t th
e .0
5 le
vel;
***
at t
he
.01
leve
l.
-
12 INDUSTRIAL AND LABOR RELATIONS REVIEW
banking. Controlling for field in additionto occupation did not
affect the coefficienton the female indicator variable. We donot
report these results in the tables.
In column (12) of Table 6, we allow forfirm-specific effects in
pay and add indi-vidual firm fixed effects to the regression.15The
chi-squared value of the Hausman testof fixed-effects versus random
effects ishighly significant (p-value less than 0.001)and indicates
that inferences based on thefirm fixed effects specification in
column(12) of Table 6 are most appropriate. Inany event, the
coefficient estimate on fe-male when controlling for individual
firmfixed effects (0.13) is nearly identical tothat in the previous
specification with in-dustry fixed effects (0.11 in column
11).16
Oaxaca Decomposition
Another way to consider wage gaps be-tween groups is described
in Oaxaca (1973).This method decomposes the overall gapinto a
portion that is due to differences inobservable skills between
groups and a partthat is still unexplained. This is easily doneby
running separate regressions for menand women and then rewriting
the overallwage gap in various ways as described be-low. First,
define f and f (a vector) ascoefficient estimates from a regression
oflog compensation on a constant and a set ofcovariates for women
only and X
f (a vector)
as the mean characteristics of women. m,m, and X
m are similarly defined for men.The overall gap between men and
women is
(1) w = m + mX
m f fX
f
There are two popular ways to re-write thisequation. The first
is based on adding andsubtracting mX
f, which yields
(2) w = (m f) +(m f)X
f + m (X
m X
f).
In this case we are assuming that the re-turns to male
characteristics, m, are thebaseline. The second common
decomposi-tion is found by adding and subtractingfX
m to equation (1), which yields
(3) w = (m f) +(m f)X
m + f(X
m X
f).
In this case we are assuming that the re-turns to female
characteristics, f , are thebaseline.17 In both equations (2) and
(3),the first two terms are the part of the totalgap left
unexplained and the third term isthe part of the gap due to
explained differ-ences in skills.
We present results for a simple Oaxaca(1973) decomposition in
Table 7. In thiscase, we use the covariates used in column(5) of
Table 6: year indicators, indicatorsfor the top three occupations,
log stockmarket value, and stock return in the previ-ous year. We
chose this parsimonious speci-fication because, as indicated by
Table 6,these covariates alone account for nearlyall of the
explained variation in compensa-tion. As stated above, we decompose
thetotal gap assuming that the male wage struc-ture is the true
wage structure (as in equa-tion 2) and then assuming that the
femalewage structure is the true wage structure(as in equation 3).
The results in Table 7confirm our previous findings. Most of
thetotal gap in compensation by gender forthese top managers
(between 71% = 0.30/0.42 and 88% = 0.37/0.42) was due to
ob-servable differences between men andwomen.18
The Role of Age and Tenure
A major drawback of the ExecuCompdata set is that it does not
report age andtenure consistently for all observations.
15In this case, we cannot also control for industry,since
industry does not vary within firms (for the mostpart).
16We also re-computed this specification with anindividual
person random effect model. In this case,the coefficient on female
is nearly identical, 0.12.
17Of course, these are just extreme cases, and anycombination of
m and f could also be a possibility(see Ransom and Oaxaca
1994).
18Further details of the decomposition, includingthe separate
regressions by gender, are available fromthe authors on
request.
-
GENDER GAP IN TOP CORPORATE JOBS 13
Table 7. Basic Oaxaca Decomposition.
Unexplained Gap Due toDecomposition Total Gap Gap Skill
Differences
Oaxaca Decomposition #1 (returns to male are baseline) 0.42 0.12
0.30
Oaxaca Decomposition #2 (returns to female are baseline) 0.42
0.05 0.37
Source: The data are from Standard and Poors ExecuComp database
for 199297.Note: Separate regressions are run for men and women
(see text). All regressions control for time indicator
variables, status as CEO, Chair, Vice Chair, President,
log(market value), and shareholder return.
These two variables are available for only asubset of the
observations in the sample.19Focusing on that subsample of the
data,Panel B of Table 2 shows that women inthese top managerial
jobs were very similarto men with respect to their labor
forceattachment and career commitment, butdiffered considerably
from their male coun-terparts with respect to age and seniority
intheir corporations. Women in thesubsample for which age and
tenure areavailable were about 5 years younger thanthe men, on
average (47.5 versus 52.6 yearsold), and had 5.6 fewer years of
seniority intheir company (7.7 versus 13.3 years). (It
isinteresting to note that the gaps in age andtenure are about the
same.) Because re-turns to age and experience are large in
themarket for executives, we expect that therelative youth and low
seniority of the fe-male executives is another important
de-terminant of the gender gap. This is for-mally shown in Table
8.
Because the sample used in this sectionis much smaller, we
re-estimate the uncon-ditional gender compensation gap for
thisgroup. As seen in column (1) of Table 8,the point estimate on
the female indicator,0.61, is substantially larger than in the
previous larger samples. Yet, standard er-rors are large. Once
we control for firmsize (as in column 2), the remaining gen-der gap
is again much smaller. If we furtheradd the three top occupation
dummies (col-umn 3), the gap falls to 8%. However,standard errors
are again large, and wecannot reject the possibility that the
coeffi-cient on the female indicator variable iseither 0 or the
same as the female dummyin the larger sample for the same set
ofcontrols (column 5 of Table 6). If wecontrol for occupation
effects (column 4),the point estimate for the female dummydrops to
0.05, and if we control for bothoccupation and industry effects
(column5), it drops to 0.09. Again, because stan-dard errors are
large, we cannot reject thepossibility that the coefficients on the
fe-male dummies are the same as the corre-sponding ones in Table
6.
The womens relative youth cannot initself fully explain the
gender gap. Column(6) of Table 8 shows that a 33% differencein
compensation still exists between menand women after we account for
age andseniority. However, this is not preciselyestimated. There is
also a clear but imper-fect correlation between executives ageand
the size of the companies they man-aged. When age and tenure are
included(last five columns of Table 8), the additionof a firm size
control does not improve theR2 as much, nor does it decrease (in
abso-lute value) the coefficient on the femaledummy as much (column
7 versus column2). Whether or not we control for age andseniority,
adding the three top occupationdummies (column 8) leads to about
thesame improvement in R2 as in column (3),
19We are not aware of any reason why age andtenure are only
reported for a subset of the data. Weinvestigated how individual
and firm characteristicsdiffer between our basic sample and the
sample inwhich age and tenure are available. We found
thatindividuals in the subsample were slightly more likelyto be
female (2.4% of the overall sample was female,compared to 2.6% of
the subsample) and worked forsmaller firms.
-
14 INDUSTRIAL AND LABOR RELATIONS REVIEW
Tab
le 8
. G
end
er P
ay G
ap f
or H
igh
-Lev
el M
anag
eria
l P
ay w
hen
Age
an
d T
enu
re o
f M
anag
er A
re C
onsi
der
ed.
(Dep
end
ent
Var
iabl
e is
th
e L
og o
f T
otal
Com
pen
sati
on; W
hit
e-C
orre
cted
Sta
nd
ard
Err
ors
in P
aren
thes
es)
Not
Con
trol
lin
g fo
r A
ge a
nd T
enur
eC
ontr
olli
ng f
or A
ge a
nd T
enur
e
Inde
pend
ent
Var
iabl
e(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)(1
0)
Fem
ale
0.6
1***
0.2
5***
0.0
80
.05
0.0
90
.33
0.1
40
.04
0.0
060
.05
(0.2
0)(0
.17)
(0.1
5)(0
.14)
(0.1
3)(0
.20)
(0.1
7)(0
.14)
(0.1
4)(0
.13)
Age
0.20
***
0.08
***
0.06
***
0.06
***
0.08
***
(0.0
3)(0
.02)
(0.0
2)(0
.02)
(0.0
2)
Age
20
.002
***
0.0
06**
*0
.001
***
0.0
01**
*0
.000
7***
(0.0
003)
(0.0
002)
(0.0
002)
(0.0
002)
(0.0
002)
Ten
ure
0.01
7**
0.0
050
.02*
**0
.019
***
0.0
22**
*(0
.007
)(0
.005
)(0
.005
)(0
.005
)(0
.004
)
Ten
ure
20
.000
10.
0000
0.00
02*
0.00
02*
0.00
03**
*(0
.000
2)(0
.000
1)(0
.000
1)(0
.000
1)(0
.000
1)
Lo
g V
alu
e0.
45**
*0.
39**
*0.
38**
*0.
38**
*0.
424*
**0.
40**
*0.
389*
**0.
390*
**(0
.01)
(0.0
1)(0
.01)
(0.0
1)(0
.01)
(0.0
1)(0
.011
)(0
.013
)
Ret
urn
/10
000
.02
0.03
0.04
0.0
50
.01
0.02
0.02
0.0
6(0
.04)
(0.0
3)(0
.03)
(0.0
3)(0
.03)
(0.0
3)(0
.03)
(0.0
3)
CE
O /
Ch
air
0.90
***
0.92
***
(0.0
4)( 0
.04)
Vic
e C
hai
r0.
43**
*0.
44**
*(0
.08)
( 0.0
8)
Pre
sid
ent
0.66
***
0.66
***
(0.0
4)( 0
.04)
31 O
ccs.
no
no
no
yes
yes
no
no
no
yes
yes
115
Ind
s.n
on
on
on
oye
sn
on
on
on
oye
s
Co
nst
ant
6.92
***
3.69
***
3.49
***
4.26
***
4.25
***
0.75
1.26
***
1.86
***
2.70
***
2.18
***
(0.0
4)( 0
.08)
( 0.0
7)( 0
.12)
( 0.1
3)( 0
.68)
( 0.4
8)( 0
.44)
( 0.4
5)( 0
.42)
R2
0.01
60.
427
0.53
30.
544
0.60
90.
136
0.44
90.
544
0.55
60.
612
N7,
379
7,37
97,
379
7,37
97,
379
7,37
97,
379
7,37
97,
379
7,37
9
Sou
rce:
Th
e d
ata
are
fro
m S
tan
dar
d a
nd
Po
or
s E
xecu
Co
mp
dat
abas
e fo
r 19
929
7.N
otes
: A
ll r
egre
ssio
ns
con
tro
l fo
r ti
me
ind
icat
or
vari
able
s.*S
tati
stic
ally
sig
nif
ican
t at
th
e .1
0 le
vel;
**a
t th
e .0
5 le
vel;
***
at t
he
.01
leve
l.
-
GENDER GAP IN TOP CORPORATE JOBS 15
where we do not control for age and tenureat all. The female
dummy decreases (inabsolute value), from 0.14 to 0.04.
Thesefindings, while imprecise, indicate that thegender
compensation gap could be lessthan 5% after all observables are
controlledfor.20
Trends in Participation and Earnings:Is the Glass Ceiling
Cracking?
One of the major labor market trends inthe United States in the
past two decadeshas been the convergence in outcomes be-tween men
and women. Focusing on differ-ences in earnings, Blau and Kahn
(1997)showed that womens relative position con-siderably improved,
especially during the1980s, when men experienced a real de-cline in
earnings while female real wagesgrew very rapidly. They showed that
part ofthis shrinking gender pay gap could beexplained by an
improvement in femalehuman capital, especially in the form oflabor
market experience, and by a smallerunexplained gender gap, that
could re-flect either a reduction in labor marketdiscrimination or
an improvement inwomens unmeasured characteristics. Yet,another
important factor in explaining thedecline in the gender gap has
been animportant shift in occupational categoriesfor women. More
specifically, the repre-sentation of women in managerial and
pro-fessional jobs has been growing while theshare of women in
low-paying clerical andrelated jobs has not.
In this section, we address the questionof whether these trends
also exist amongtop executives. In other words, we ask
whether there is any evidence that the glassceiling is cracking
little by little in U.S.corporations. We ask whether the
relativeparticipation of women in these top mana-gerial jobs has
increased over time, andalso study trends in relative
compensation.It is important to note that because ourdata set only
covers the period 199297, weare unable to investigate relative
gains inthe 1980s, the period during which most ofthe catch-up by
women occurred, at least inthe other segments of the labor
market.
Table 9 reports trends over time in thefraction of women in
top-level management.While the fraction of women in
lower-levelmanagement only went from 40% to 43%over the sample
period (column 9), thefraction of women in top-level
managementnearly tripled, going from 1.29% in 1992 to3.39% (column
1) in 1997.21 The fractionof firms with at least one woman in the
topexecutive ranks (one of the top 5 mosthighly paid) grew from
5.4% in 1992 to15.03% in 1997. Although the fraction offirms with
strictly more than one woman inthese top positions was much
smaller, italso grew a great deal over the period, from0.17% in
1992 to 1.95% in 1997.
Also note that the fraction of firms withno women in one year
and at least onewoman in the next year grew steadily overtime, from
2.19% in 1993 to 3.85% in 1997(column 4). Female top executives
alsoseem to have improved their relative earn-ings quite
substantially. While the ratio ofaverage female to average male
compensa-tion in 1992 is a puzzle to us, it appears thatfemale
relative compensation rose ex-tremely quickly between 1993 (52%)
and1997 (73%).22 During these same years, wefind that the ratio of
average female toaverage male annual earnings in our CPS
20If we examine relatively new entrants to the topmanagerial
jobsthat is, men and women with onlyfive or fewer years of labor
market experiencewefind that the conditional wage gap is also
statisticallyinsignificant and the point estimate is not
muchdifferent from our estimate on the complete set ofdata. If we
estimate a specification like that in Table6, column (11), for the
5,113 executives with 5 yearsof experience or less, the coefficient
on the femaleindicator is 0.093 with a standard error of 0.134.
21See Catalyst (1999) for descriptive evidence thatis consistent
with this finding.
22Because we were concerned by possible changesin the set of
companies covered by ExecuComp overtime, we investigated the
robustness of these findingsby limiting the sample to the companies
that werepresent in 1992. The findings were unaffected.
-
16 INDUSTRIAL AND LABOR RELATIONS REVIEW
Tab
le 9
. I
nfo
rmat
ion
ove
r T
ime
(199
219
97)
for
Tw
o Se
ts o
f M
anag
ers.
(Wh
ite-
Cor
rect
ed S
tan
dar
d E
rror
s in
Par
enth
eses
)
Hig
h-L
evel
Man
ager
s fr
om E
xecu
Com
p (c
olum
ns 1
8)
CPS
Sam
ple
(col
umns
91
1)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
Frac
. Fir
ms
with
No
Wom
enFr
ac.
Frac
.in
One
F/M
F/M
F/M
Firm
sFi
rms
Yea
r an
dC
ompe
n-R
egre
ssio
nF/
MSh
are
Jobs
Com
pen-
Reg
ress
ion
Frac
tion
with
1
with
> 1
The
n
1 in
satio
nA
djus
ted
Firm
in
Top
Fr
actio
nsa
tion
Adj
uste
dYe
arFe
mal
eW
oman
Wom
anN
ext
Gap
aD
iffer
entia
lbSi
ze G
apG
roup
cFe
mal
eG
apd
Diff
eren
tiale
1992
1.29
5.44
0.17
0.
67*
0.2
21**
0.42
***
0.64
***
40.4
10.
67**
*0
.271
***
(0.0
91)
(0.0
09)
1993
1.52
7.25
0.39
2.19
0.52
***
0.1
37**
*0.
52**
*0.
48**
*40
.25
0.69
***
0.2
54**
*(0
.053
)(0
.009
)
1994
2.22
10.2
51.
133.
440.
56**
*0
.163
***
0.52
***
0.36
***
41.7
90.
68**
*0
.247
***
(0.0
53)
(0.0
09)
1995
2.60
11.7
61.
583.
660.
57**
*0
.144
***
0.56
***
0.32
***
41.2
00.
68**
*0
.260
***
(0.0
43)
(0.0
09)
1996
3.01
13.4
21.
723.
920.
61**
*0
.085
**0.
61**
*0.
40**
*42
.07
0.70
***
0.2
49**
*( 0
.043
)( 0
.009
)
1997
3.39
15.0
31.
953.
840.
73**
*0
.013
0.77
***
0.37
***
42.8
60.
72**
*0
.225
***
(0.0
49)
( 0.0
09)
Sou
rce:
Th
e d
ata
are
fro
m S
tan
dar
d a
nd
Po
or
s E
xecu
Co
mp
dat
abas
e fo
r 19
929
7.a *
**D
iffe
ren
ce i
n a
vera
ge f
emal
e/av
erag
e m
ale
com
pen
sati
on
by
year
is
sign
ific
ant
at t
he
0.01
lev
el; *
*at
the
0.05
lev
el; *
at t
he
0.10
lev
el.
bT
his
is th
e co
effi
cien
t on
the
fem
ale
ind
icat
or
fro
m a
n a
nn
ual
reg
ress
ion
of l
n (
pay
) o
n a
n in
dic
ato
r fo
r fe
mal
e, p
lus
the
oth
er c
ova
riat
es in
clu
ded
in T
able
6, c
olu
mn
(11
) . W
hit
e-co
rrec
ted
sta
nd
ard
err
ors
are
rep
ort
ed.
c By
to
p g
rou
p
we
mea
n t
ho
se jo
bs
clas
sifi
ed a
s C
EO
/ch
air,
vic
e ch
air,
or
pre
sid
ent.
dA
dju
stin
g fo
r h
ou
rs.
***D
iffe
ren
ce in
ave
rage
fem
ale/
aver
age
mal
e co
mp
ensa
tio
n b
y ye
ar is
sig
nif
ican
t at
th
e 0.
01 le
vel;
**a
t th
e 0.
05 le
vel;
*at
th
e 0.
10le
vel.
e Th
is is
the
coef
fici
ent o
n th
e fe
mal
e in
dic
ato
r fr
om
an
an
nu
al r
egre
ssio
n o
n ln
(p
ay)
on
an
ind
icat
or
for
fem
ale,
exp
erie
nce
, exp
erie
nce
-sq
uar
ed, a
lin
ear
term
fo
r ed
uca
tio
n, a
n i
nd
icat
or
for
bla
ck, a
nd
on
e d
igit
in
du
stry
in
dic
ato
rs.
-
GENDER GAP IN TOP CORPORATE JOBS 17
sample went from 67% to 72%. In addi-tion, in column (6) we have
reported coef-ficients on the female indicator from
ourspecification in column (11) of Table 6 byrunning separate
regressions by year. Thesubstantial and statistically significant
esti-mate of 0.221 from 1992 declined throughthe sample period to a
statistically insignifi-cant 0.013 in 1997.
Note, however, that the regressions-ad-justed gap for managers
in the CPS changedmuch less over time (column 11). Whatcaused such
a rapid decline in the gendergap for female managers at the top? In
theprevious section, we isolated two main fac-tors that strongly
hampered womens rela-tive earnings: under-representation in
largefirms, and under-representation in the topfour occupations.
Were female top execu-tives in 1997 doing better on either of
thesetwo fronts? Column (7) of Table 9, whichdisplays the ratio of
average female manag-ers company size to average male manag-ers
company size, clearly indicates thatfemale executives were steadily
gaining ac-cess to larger U.S. corporations.23 On theother hand,
column (8) shows no evidencethat womens representation in the
topoccupational group (CEOs, Chairs, ViceChairs, and Presidents)
was improving overtime. If anything, womens representationin this
group was declining, though notsteadily.
Summary and Concluding Remarks
We have shown that, contrary to whatsome commentators have
claimed, the glassceiling is somewhat porous and somewomen, even if
only a limited number, areinvolved in the top-level management
ofU.S. corporations. Over all years in thesample we examined, about
2.4% of theexecutives were women.24 Although this
number is small, it increased substantiallyin the later years of
the sample period.
Our results further indicate that the gen-der gap in
compensation among top execu-tives was at least 45%. An important
fact isthat female managers were under-repre-sented in large
corporations. Because thereturns to firm size are very high among
topexecutives, this explains up to 15 percent-age points of the
gender gap. Interestingly,while female managers do not seem to
havebeen distributed randomly across indus-tries, there is no
evidence that sex segrega-tion by industry explains any of the
ob-served gender gap. On the other hand, wefound that sex
segregation by occupationwas important. The scarcity of female
CEOs,Chairs, Vice-Chairs, and Presidents accountsfor as much as
half of the unconditionalgender compensation gap. Once we
lookbeyond these four top occupations, how-ever, there is no
significant evidence of aconcentration of women in the
lower-com-pensation occupations.
A last crucial factor is that women in thesample were much newer
than their malecounterparts in this top stratum of manage-rial
jobs. Women in the sample were muchyounger, and had much less
seniority intheir company, than men. Part of the effectof age and
seniority on the gender gapseems to be reflected in the size of
compa-nies women managed. All in all, we findthat the unexplained
gender compensa-tion gap for top executives was less than 5%after
one accounts for all observable differ-ences between men and
women.
Finally, we asked whether there is anyevidence of a growing
crack in the glassceiling over the period under study. Wefound that
the participation of women inthe top corporate jobs was growing
dra-matically in the years we looked at, from1.3% in 1992 to 3.4%
in 1997. Also, thegender compensation gap declined, verymuch like
in the other segments of thelabor market. Most of the decline
appearsto have been correlated with a decline insex segregation by
firm size. Female topexecutives were heading larger and
largercorporations.
Because top executives probably consti-
23This could be due to a lessening of employers orcustomers
tastes for discrimination or to changingtastes on the part of the
female executives themselves.
24This is still a small fraction relative to otherforms of
organization. For example, Hallock (1998)found that about 20% of
public charities are headedby women.
-
18 INDUSTRIAL AND LABOR RELATIONS REVIEW
tute a fairly homogeneous group with re-spect to job motivation,
career commit-ments, and human capital, a finding of anunexplained
gender compensation gap inthis sample could have reasonably
beeninterpreted as evidence for taste discrimi-nation against
women. In fact, we find thatthe conditional gender gap in this
sample isvery small. This obviously does not implythe absence of
discrimination. Low gen-eral participation, sex segregation by
firmsize, and sex segregation by occupationcould reflect some form
of taste discrimina-tion. The absence of a significant condi-tional
gender gap simply means that womenand men who held similar
functions infirms of similar size received fairly equaltreatment in
terms of compensation.
Additional caveats should be stated here.First, the latter
results should not be gener-alized to a claim that all female
executivesin the United States are paid like their malecounterparts
in the same occupation cat-egory and in firms of the same size.
Inves-tigating that issue would require a differentdata set that,
to our knowledge, does notexist. Second, one might argue that
thevery few women who made it into our sampleare truly exceptional
and should not in fact
be compared to the average man in thesample but rather to the
highest-ability menin the sample.25 Under that view, one mighthave
expected that these women shouldhave been paid more than the
average maleexecutives. The data clearly reject a posi-tive
female-male gender gap in earnings.
Future work might involve a more for-mal analysis of why some
companies decideto promote women to top jobs while othersdo not.26
For example, one might inquirewhether various characteristics of
the boardof directors, such as the sex and age distri-bution of
their members, are correlatedwith the selection of women into top
execu-tive positions. More fundamentally, onemight try to
understand what factors makesmall companies more likely to attract
fe-male top executives and why women arevirtually absent from the
very top of theU.S. corporate world.
25Our analysis has assumed that men and womenin our sample form
a homogeneous group and thatthe distribution of unobservables is
similar acrossgenders.
26One might also investigate gender differences inmobility.
-
GENDER GAP IN TOP CORPORATE JOBS 19
Appendix
Relative Pay, Percent Female, and Female/Male Wage Gaps by Broad
Field Groups
High-Level Managers Only
(1) (2) (3) (4)% Field Wage/ Female/Male
Number in Female Market Wage GapField Field in Field Wage in
Field
Human Resources 343 14.86 0.89 0.60***Finance/Accounting 1,682
2.02 1.01 1.41Legal/Regulatory Affairs 258 10.85 1.28 0.51***Sales
683 2.05 0.99 0.77Marketing/Merchandising, Advertising 721 6.52
1.00 0.89Product Devel.: R&D, Engin., Design 702 3.99 1.02
1.52U.S. Operations 1,132 0.88 0.90 1.48International Operations
332 1.20 1.25 1.81Corporate Affairs 907 4.96 1.05 1.63Customer
Service 120 12.5 0.89 1.36Product Management/ Manufacturing 268
4.10 0.96 0.78Real Estate/Construction 66 4.55 0.81 0.62Utility
Services 26 30.77 0.34 1.33Retail Banking, Credit 84 16.67 0.74
0.98Sourcing, Procurement 21 9.52 1.03 1.23Administration 299 5.69
0.88 0.46***Communication, Information 89 6.74 0.70 0.78Healthcare
59 1.69 1.36 0.53a
Mergers & Acquisitions 50 0.00 0.91 NAa
Computers 38 2.63 1.40 0.93a
Developed Markets 7 42.86 2.84 1.78Investment 35 2.86 1.99
0.13a
Distribution 30 3.33 0.70 0.48a
Missing 38,756 2.04 2.27 0.67***
aOnly one woman (or zero in the case of mergers) in these
casesno t test for differences in means.
-
20 INDUSTRIAL AND LABOR RELATIONS REVIEW
REFERENCES
Arrow, Kenneth J. 1973. The Theory of Discrimina-tion. In Orley
Ashenfelter and Albert Rees, eds.,Discrimination in Labor Markets.
Princeton: PrincetonUniversity Press, pp. 333.
Baker, Lawrence. 1996. Differences in Earningsbetween Male and
Female Physicians. New EnglandJournal of Medicine, Vol. 334, pp.
96064.
Barbezat, Debra. 1987. Salary Differentials by Sex inthe
Academic Labor Market. Journal of HumanResources, Vol. 22, No. 3
(Summer), pp. 42228.
Barbezat, Debra A., and James W. Hughes. 1990.Sex Discrimination
in Labor Markets: The Role ofStatistical Evidence. Comment.
American EconomicReview, Vol. 80, No. 1 (March), pp. 27786.
Bayard, Kimberly, Judith Hellerstein, David Neumark,and Kenneth
Troske. 1999. New Evidence on SexSegregation and Sex Differences in
Wages fromMatched Employee-Employer Data. Working pa-per,
March.
Becker, Gary S. 1957. The Economics of Discrimination.Chicago:
University of Chicago Press.
Biddle, Jeff, and Daniel S. Hamermesh. 1998.
Beauty,Productivity, and Discrimination: Lawyers Looksand Lucre.
Journal of Labor Economics, Vol. 16, No.1 (January), pp.
172201.
Bird, Chloe. 1990. High Finance, Small Change:Womens Increased
Representation in Bank Man-agement. In Barbara Reskin and Patricia
Roos,eds., Job Queues, Gender Queues. Philadelphia:
TempleUniversity Press.
Blau, Francine, and Marianne Ferber. 1987. Dis-crimination:
Empirical Evidence from the UnitedStates. American Economic Review,
Vol. 77, No. 2, pp.31620.
Blau, Francine, Marianne Ferber, and Anne Winkler.1998. The
Economics of Women, Men, and Work, 3rdedition. Englewood Cliffs,
N.J.: Prentice Hall.
Blau, Francine, and Lawrence Kahn. 1997. Swim-ming Upstream:
Trends in the Gender Wage Differ-ential in the 1980s. Journal of
Labor Economics, Vol.15, No. 1 (January), pp. 142.
Catalyst. 1999. Catalyst Census of Women CorporateOfficers and
Top Earners. New York.
Ferber, Marianne A, and Carole A. Greene. 1982.Traditional or
Reverse Sex Discrimination? TheCase of a Large Public University.
Industrial andLabor Relations Review, Vol. 35, No. 4 (July), pp.
55064.
Ferber, Marianne, and Lane Loeb. 1997, AcademicCouples: Problems
and Promise. Urbana: University ofIllinois Press.
Goldin, Claudia. 1990. Understanding the Gender Gap:An Economic
History of American Women. New York:Oxford University Press.
Gander, James P. 1997. Gender-Based Faculty-PayDifferences in
Academe: A Reduced-Form Ap-proach. Journal of Labor Research, Vol.
18, No. 3(Summer), pp. 45161.
Gregg, Paul, and Stephen Machin. 1993. Is the GlassCeiling
Cracking? Gender Compensation Differen-tials and Access to
Promotion among UK Execu-
tives. University College London Discussion Paper9405, July.
Groshen, Erica. 1991. The Structure of the Fe-male/Male Wage
Differential: Is It Who You Are,What You Do, or Where You Work?
Journal ofHuman Resources, Vol. 26, No. 3 (Summer), pp.45772.
Hall, Brian, and Jeffrey Liebman. 1998. Are CEOsReally Paid Like
Bureaucrats? Quarterly Journal ofEconomics, Vol. 113, No. 3
(August), pp. 65391.
Hallock, Kevin F. 1998. Gender CompensationDifferences among
Executives in Nonprofits. Work-ing paper, University of Illinois,
August.
Hoffman, Emily. 1976. Faculty Salaries: Is ThereDiscrimination
by Sex, Race, and Discipline? Ameri-can Economic Review, Vol. 66,
No. 1 (March), pp.19698.
Jacobs, Jerry. 1992. Womens Entry into Manage-ment: Trends in
Earnings, Authority, and Valuesamong Salaried Managers.
Administrative ScienceQuarterly, Vol. 37, No. 2 (June), pp.
282301.
Johnson, George, and Frank Stafford. 1974. TheEarnings and
Promotion of Women Faculty. Ameri-can Economic Review, Vol. 64, No.
6 (December), pp.888903.
Jones, Terril Yue. 1999. Gender Motors. Forbes,May 17, p.
50.
Katz, David. 1973. Faculty Salaries, Promotions, andProductivity
at a Large University. American Eco-nomic Review, Vol. 63, No. 3
(June), pp. 46977.
Kostiuk, Peter. 1990. Firm Size and ExecutiveCompensation.
Journal of Human Resources, Vol. 25,No. 1 (Winter), pp. 90105.
Lazear, Edward, and Sherwin Rosen. 1990. Male-Female Wage
Differentials in Job Ladders. Journalof Labor Economics, Vol. 8,
No. 1, pp. 10623.
Meyer, Michael. 1999. In a League of Her Own.Newsweek, August 2,
p. 56.
Morgan, Laurie. 1998. Glass-Ceiling Effect or Co-hort Effect? A
Longitudinal Study of the GenderEarnings Gap for Engineers, 1982 to
1989. Ameri-can Sociological Review, Vol. 63, No. 4 (August),
47993.
Morris, Kathleen. 1998. The Rise of Jill Barad.Business Week,
May 25, pp. 11218.
Murphy, Kevin J. 1985. Corporate Performance andManagerial
Remuneration. Journal of Accountingand Economics, Vol. 7, Nos. 13,
pp. 1142.
Oaxaca, Ronald. 1973. Male-Female Wage Differen-tials in Urban
Labor Markets. International Eco-nomic Review, Vol. 14, No. 3
(October), pp. 693709.
Ransom, Michael R., and Sharon Bernstein Megdal.1993. Sex
Differences in the Academic LaborMarket in the Affirmative Action
Era. Economics ofEducation Review, Vol. 12, No. 1 (March), pp.
2143.
Ransom, Michael, and Ronald Oaxaca. 1994. OnDiscrimination and
the Decomposition of WageDifferentials. Journal of Econometrics,
Vol. 61(March), pp. 551.
Reskin, Barbara F., and Particia A. Roos, eds. 1990.Job Queues,
Gender Queues: Explaining Womens In-
-
GENDER GAP IN TOP CORPORATE JOBS 21
roads into Male Occupations. Philadelphia: TempleUniversity
Press.
Rosen, Sherwin. 1992. Contracts and the Market forExecutives. In
Lars Wein and Hans Wijkander,eds., Contract Economics. London:
Blackwell.
Strober, Myra H. 1984. Toward a General Theory ofOccupational
Segregation: The Case of PublicSchool Teaching. In Barbara F.
Reskin, ed., Segre-
gation in the Workplace: Trends, Explanations, Rem-edies.
Washington, D.C.: National Academy Press,pp. 14456.
Wood, Robert G., Mary E. Corcoran, and Paul N.Courant. 1993. Pay
Differences among the HighlyPaid: The Male-Female Earnings Gap in
LawyersSalaries. Journal of Labor Economics, Vol. 11, No. 3(July),
pp. 41741.
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