Monthly Labor Review March 2003 9

The Earnings Gap

How does gender play a role

in the earnings gap? an update

Although personal choices, occupational crowding,

and discrimination, contribute to the gender gap,

the higher share of women in an occupation

is still the largest contributor

Stephanie Boraas

and

William M. Rodgers III

Stephanie Boraas is

an economist in the

Office of

Employment and

Unemployment

Statistics, Bureau of

Labor Statistics.

William M. Rodgers

III is the Frances L.

and Edwin L.

Cummings

associate professor

of economics and

director of the

Center for the Study

of Equality at the

College of William

and Mary. The

views expressed in

this article are solely

the authors’ and

do not represent

the policy positions

of the Bureau of

Labor Statistics or

the U. S.

Department of

Labor. E-mail:

boraas_s@bls.gov;

wmrodg@wm.edu

A

ccording to the Bureau of Labor

Statistics, women earned approximately

77 percent as much as men did in 1999.1

Although the existence of the gender pay gap is

well documented, the factors that contribute to it

are still debated. One such factor is the difference

in the proportion of jobs held by women and

men. However, understanding how occupational

differences contribute to the gender pay gap is

made more complicated by the fact that both men

and women in predominately female occupations

earn less than men and women in male dominated

occupations.

An important question that has not been

resolved is, why do predominately female

occupations tend to pay less? A number of

possibilities including worker characteristics, job

characteristics, occupational crowding, devaluing by society of women, and discrimination have

been posited.2

This article sheds some light on reasons for

the gender earnings gap, focusing on the role

that the share of women in an occupation plays.

We utilize the methodology employed by George

Johnson and Gary Solon to identify the sources

of the relationship between wages and the share

of women in an occupation.3 Johnson and Solon

used Current Population Survey (CPS) data to

estimate the relationship between wages and the

concentration of females within occupations.

They found that the relationship was negative,

even after controlling for worker and job

characteristics. Industry was found to have the

largest effect on the relationship, primarily because

predominately male industries, such as

construction and manufacturing, pay higher

wages.4

The data used in this article are from the 1989,

1992, and 1999 Outgoing Rotation Group Files of

the CPS. The CPS has the advantage of including

detailed occupational data on a large national

sample of more than 50,000 households in addition

to the more commonly found earnings,

employment, human capital, and demographic

information.5

One of the primary drawbacks of using the CPS

for estimating the sources of the gender gap is

that it does not include a measure for actual work

experience. Because higher earnings are

associated with more experience, and women tend

to have less experience than men due to breaks in

their labor force participation for childrearing and

other reasons, this omission results in inaccurate

estimates of the contribution of each characteristic

to the wage gap.6

To approximate actual experience, researchers

construct potential experience.7 This method likely

overestimates the experience of women, as they

are more likely than men to take time out of the

labor force.8 However, it is well known that delayed

marriages and lower fertility rates have contributed

to a rise in women’s labor force participation,
The Earnings Gap

10 Monthly Labor Review March 2003

particularly among younger cohorts of women. Because of

this pattern, the use of potential experience probably provides

a better estimate of actual experience than it did in the past.

Prior to 1992, the Bureau of Labor Statistics asked CPS

respondents to provide the number of years of school

completed. However, in 1992, BLS changed its education

question by asking respondents to provide the highest

degree attained (for example, years of schooling for a

bachelor’s degree, and so forth), making it difficult to

construct potential experience.9 Instead of using a method

for imputing years of schooling from an independent CPS

file, which contains respondents’ answers to both education

questions, we chose to use age and age-squared to proxy for

experience.

Although the negative relationship between wages and

an occupation’s percentage of women has diminished since

1989, occupations that are predominately made up of females

are still found to have lower average wages for both men and

women. In 1999, men employed in predominately female

occupations earned 11 percent to 13 percent less than men

did in predominately male occupations. Much of this negative

impact on wages is due to the industry distribution, but the

positive effect of education and experience offsets this effect

somewhat. For women in 1999, working in a female dominated

occupation lowers wages by approximately 26 percent.

Accounting for education and age of women reduces this

effect to 21 percent.

Our nonlinear specifications show that in 1999, women

employed in occupations that are 90 percent female earn 29

percent less than women do in predominately male

occupations. Accounting for characteristics such as

education and age of women reduces the disadvantage to 19

percent. Women employed in occupations that are 50 percent

female earn 21 percent less than comparable women in

predominately male occupations. The disadvantage falls to

13 percent when personal characteristics (such as education,

age, and region of residence) are added to the model. The

disadvantage falls to 14 percent for women in occupations

that are 30 percent female. Personal characteristics explain

one-half of the lower earnings.

The human capital and occupational crowding theories

have been put forth as possible explanations for the

relationship between wages and the gender concentration of

occupations, as have job attributes, preferences, and

discrimination.10

How do these theories attempt to explain the relationship?

Human capital theory suggests that women tend to select

occupations that require small investments in human capital

and those in which the necessary skills do not atrophy with

disuse because they better suit the daily schedules or longterm labor force participation intentions of women. The

occupational-crowding hypothesis suggests that, from

childhood, individuals are socialized to view some occupations as “women’s work” and others as “men’s work”

and that they must choose one appropriate to their sex. This

socialization influences human capital development and

results in the crowding of women into the relatively small

number of appropriate occupations. The ready supply of

workers to fill these “female occupations” works to depress

the wages of individuals employed in these occupations.

Data

This analysis is based on data from the Outgoing Rotation

Group Files of the 1989, 1992, and 1999 Current Population

Survey (CPS). This is a slightly different data set than the

May 1978 CPS data used by Johnson and Solon, but it is

similar enough to allow for a close replication and update of

their results. The sample includes male and female

nonagricultural wage and salary workers, age 16 and older,

who responded to supplementary questions about “usual

weekly hours” and “usual weekly earnings.”11

Other explanatory variables included were educational

attainment (measured as years of schooling through 1991

and as the highest grade attained thereafter), potential

experience in 1989, age in 1992 and 1999, dummy variables for

region of residence, size of the metropolitan area, Black or

other minority status, voluntary and involuntary part- time

work status, marital status, membership in or coverage by a

union, public-sector employment, major industries (with

private household service being the omitted category), and

the percent of an individual’s occupation made up of females.

The percent of each three-digit occupation that is female was

created from the Outgoing Rotation Group files of the Current

Population Survey.

Multicollinearity between the individual level industry

data and the aggregate occupation data is not a problem for

our analysis. The percent of female variable is based on 500

occupation categories. As a formal check for multicollinearity,

we constructed pair-wise correlations between the percent

of female variable and the 22-industry dummy variables. In

1989, the absolute values of the correlations range from 0.009

to 0.18. In 1992, they range from 0.005 to 0.17. In 1999, they

range from 0.01 to 0.19. The values are far from the level at

which we should be concerned about multicollinearity.

Sources of the gender wage gap

Decomposing wage differentials has become a popular and

useful way to identify the sources of the wage gap and their

contributions. The Oaxaca-Blinder decomposition separates

the portion of the gap resulting from differing characteristics

of men and women from the portion that is not explained by

these personal characteristics. The unexplained portion of
Monthly Labor Review March 2003 11

the gap may result from discrimination. Men receive higher

returns to investments in their human capital and other

characteristics than women, or men with the same

characteristics as women are simply paid more.

A basic decomposition that uses the mean characteristics

of women and the coefficients of men as weights can be

written as:

( ) ( M F ) Gap = b M − bF X F + b M X − X ,

where Gap denotes the difference in wages of men and

women, the first term on the right hand side of the equation

measures the unexplained differential, and the second term

measures the gap due to differences in characteristics of men

and women. The unexplained differential has several

interpretations. It may be due to differences between the

characteristics of men and women that might not have been

included in the model. For example, some argue that the

quality of education has not been included and can play a

large role in the ability and productivity of an employee. The

remainder of the unexplained gap also may be due to

discrimination. We also construct the decomposition with

the mean characteristics of men and the coefficients of women

as the weights. Doing this provides a lower and upper bound

on the contribution of each characteristic.

Table 1 presents the wage gap decompositions for 1989,

1992, and 1999. The gender gap has narrowed from 30

percent in 1989, to 26 percent in 1992, and 24 percent in 1999.12

In 1989, using the male coefficients as weights, we find that

industry and the percentage of women in an occupation

account for 19.7 percent ((0.0600/0.304)*100) and 21.0 percent

((0.064/0.304)*100) of the wage gap, with percentage of

women being slightly larger. Using the female coefficients as

weights increases the role of percentage of women to 30.6

percent and reduces industry’s role to 12.5 percent. In 1992,

the role of industry ranges from 10.1 to 20.0 percent and the

contribution of percentage of women ranges from 20.0 to

32.0 percent. In 1999, the role of percentage of women in an

occupation ranges from 18.6 to 31.6 percent, with industry’s

role ranging from 7.6 to 14.3 percent. In all the years of this

analysis, education and age work to shrink the wage gap, but

are overshadowed by the percentage of women in an

occupation and industry. The explained effect meets or

exceeds the unexplained effect in every year except 1999.

To find the source of the relationship between wages and

the concentration of women within an occupation and its

decline, we use the decomposition framework from Johnson

and Solon.13 That study found that the largest contribution

to the relationship was from the link between wages and

industry of employment and the correlation between an

individual’s industry of employment and the occupation’s

concentration of women. To decompose the effect of the

various control variables on the relationship between the

concentration of women within an occupation and the wages

of individuals in that occupation, Johnson and Solon found

the difference between simple and multiple regression

estimates of the coefficient on the percentage of women in

each occupation. This can be expressed as:

$ ~ $

g − g = − b ,

=

∑ j

j

k

bjF

1

where gˆ is the wage-concentration of women within

occupations relationship in the full specification, g

~ is the

wage-concentration of women within occupations

relationship in the bivariate specification, the bj 's are the

estimated coefficients on the k control variables, and the

bjF's are the coefficients on the percent female from the

regression of each of the control variables on the percent

female in an occupation. This approach identifies the factors

that explain why the earnings of men and women are lower in

occupations that have larger shares of women.

Table 2 shows the coefficients on percent female from both

the bivariate and full regressions.14 For both men and women

in the bivariate specification, earnings in 1989, 1992, and 1999

were found to be lower in occupations with more women,

with the relationship between low earnings and “female

occupations” being stronger amongst women. After taking

account of worker characteristics, the coefficient for men was

reduced by about 30 percent in 1989 and 1992, and by

between 26 percent and 28 percent for women (the difference

divided by the bivariate). In 1999, taking account of worker

characteristics leads to no decline in the men’s coefficient,

largely due to the weakening in the simple bivariate

relationship during the 1990s. The decline in the coefficient

for women in 1999 is smaller than earlier years.

To illustrate the impact of accounting for worker

characteristics on the relationship between log wages and

percent of the occupation that is female, chart 1 (page 34)

illustrates the relationship at various points of the percent of

occupation distribution. These figures were created by using

predicted wages that were standardized to the earnings of a

woman in an all-male occupation. The top panel shows that

in 1989, the relationship for women was slightly nonlinear in

the bivariate regression and less so in the full regression. As

the concentration of females within an occupation increases,

the negative impact on wages grows more slowly. By 1999,

the bivariate regression is less nonlinear and the adverse

impact ranges from 10 percentage points to 12 percentage

points less than in 1989, as shown in the bottom panel. The

negative impact of the concentration of women within

occupations fell at all points of the percent female distribution, but the declines are largest in the upper one-half of

the distribution. As the concentration of women increases
The Earnings Gap

12 Monthly Labor Review March 2003

Table 1. Decomposition of the gender wage gap, 1989, 1992, and 1999

Weighting schemes

1989 1992 1999

Male Female Male Female Male Female

coefficient coefficient coefficient coefficient coefficient coefficient

Gender wage gap .................................... 0.304 0.304 0.256 0.256 0.237 0.237

Explained effect (in percent) .................... 58 57 53 55 39 42

Percent female ....................................... .064 .093 .051 .082 .044 .075

Schooling and experience ........................ .011 .009 .006 .005 –.001 –.001

Region .................................................. . 0005 .0005 .0003 .0002 .0002 .0002

SMSA size ............................................. .001 .002 .001 .001 .0001 .0001

Minority status ....................................... .003 .001 .004 .002 .004 .001

Part-time status ...................................... .019 .019 .012 .013 .000 .004

Marital status ......................................... .003 .000 .004 .001 .004 .002

Union membership .................................. . 011 .012 .008 .008 .005 .006

Government employment ......................... . 001 –.001 .001 .0003 .003 .001

Industry ................................................. .060 .038 .050 .026 .034 .018

Unexplained ........................................... .129 .130 .119 .116 .147 .133

Variable

beyond this point, the impact on wages becomes less

negative.

Table 3 shows the results of the decomposition of the

relationship between wages and percentage of women.

Although diminishing, industry of employment always

played the largest role for men (except in 1992 when marital

status was stronger). Multicollinearity is not a problem. The

coefficients on the percent female from the regression of each

industry dummy variable on the percent female in an

occupation, the range from -0.182 to 0.249. The R2’s

have a maximum of 0.045.

Years of schooling and experience help to offset

industry’s negative impact. Since 1989, the share of men, in

female dominated jobs, who have more education and are

older than the men in less female-dominated jobs has

increased. For all women, industry of employment plays very

little role in explaining the relationship between percentage

of women and wages. Years of schooling and experience are

the other key factors that explain the relationship. However,

in 1999, schooling and age are the only factors that have a

significant ability to explain the relationship between

percentage of women and wages. Industry of employment

explains slightly more than 10 percent of the relationship.

bjF's

Table 2. Coefficients on the percent female in an occupation, for men and women from the bivariate and full regressions,

 1989, 1992, and 1999

Men Women

Bivariate Full Difference Bivariate Full Difference

1989 .................................. –0.231 –0.155 –0.076 –0.315 –0.227 –0.088

1992 .................................. –.184 –.129 –.055 –.284 –.208 –.076

1999 .................................. –.125 –.123 –.002 –.259 –.209 –.050

NOTE: The only control variable in the bivariate specification is the

percent of an occupation that is female. The full specification contains all

of the control variables (educational attainment, potential experience in

1989 and age in 1992 and 1999, dummy variables for region of residence,

size of metropolitan area, black or other minority status, voluntary or

involuntary part-time work status, marital status, membership in or coverage

by a union contract, public sector employment, major industries (with

private household service being the omitted category), and the percent of

an individual’s occupation that is made up of women.

Table 3. Decomposition on the influence of control

 variables on the estimation of the relationship

 between wages and percentage of female (y),

 1989, 1992, and 1999

 Men

1989 1992 1999

Total effect .......................... –0.076 –0.055 –0.002

Schooling and age ............... .140 .116 .153

Region ................................ .030 .028 .005

Minority status .................... –.006 –.007 –.007

Part-time status ................... –.017 –.009 –.001

Marital status ...................... –.042 –.348 –.028

Union status ........................ –.020 –.016 –.014

Government employment ....... –.005 –.004 –.010

Industry .............................. –.156 –.129 –.101

 Variable Women

Total effect .......................... –.088 –.076 –.050

Schooling and age ............... –.045 –.057 –.057

Region ................................ –.005 .000 .001

Minority status .................... .000 –.001 –.001

Part-time status ................... –.022 –.015 –.007

Marital status ...................... .002 .001 .002

Union status ........................ –.003 –.001 .003

Government employment ....... .001 .000 –.002

Industry .............................. –.016 –.003 .010

NOTE: Potential experience is used in 1989 and age is used in 1992

and 1999.

 Variable

Year
Monthly Labor Review March 2003 13

0 10 20 30 40 50 60 70 80 90 100

-0.45

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.45

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

Bivariate regression

Full regression

0 10 20 30 40 50 60 70 80 90 100

-0.45

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.45

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

Bivariate regression

Full regression

0 10 20 30 40 50 60 70 80 90 100

-0.45

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

-0.45

-0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

Bivariate regression

Full regression

Chart 1. Relationship between wages and the percent of an occupation that is predominated by women, 

 1989,1992, and 1999

1989

1992

1999

NOTE: The only control variable in the bivariate specification is the percent of an occupation that is female. The full specification contains 

all of the control variables (educational attainment, potential experience in 1989 and age in 1992 and 1999, dummy variables for region of 

residence, size of metropolitan area, black or other minority status, voluntary or involuntary part-time work status, marital status, 

membership in or coverage by a union contract, public sector employment, major industries (with private household service being the 

omitted category), and the percent of an individual's occupation that is made up of females.

SOURCE: Authors' tabulations from the Outgoing Rotation Group Files of the Current Population Survey.

Percent of occupation that is female

Percent of occupation that is female

Percent of occupation that is female
The Earnings Gap

14 Monthly Labor Review March 2003

Summary and conclusions

This article revisits the work of Johnson and Solon and others

that estimate the portion of the gender gap that can be

explained by the share of women in an occupation. Using

microdata from the Outgoing Rotation Group files of the

Current Population Survey, we first show that the share of

women in an occupation is still one of the largest contributors

to the gender wage gap. This occurs because women have a

higher likelihood of working in female dominated jobs, which

typically have below average wages. In fact, in 1999, our

most recent data, the share of women is the largest contributor

to the gender pay gap.

Without controlling for worker characteristics, we then

estimate the size of the negative effect that employment in

predominately female occupations has on the wages of men

and women. We find that the relationship has weakened for

both men and women since 1989. In 1999, a woman working

in a predominately female occupation earned 25.9 percent

less than a woman working in a predominately male

occupation . The comparable figure for a man working in a

predominately female occupation is 12.5 percent less than a

man working in a predominately male occupation. One

interpretation of these relationships is that they are the

adverse consequences of occupational “crowding.” Limited

occupational choice creates crowding in particular

occupations, putting downward pressure on the wages of

men and women in these occupations. The limited

occupational choices could be due to discrimination.

However, others might argue that the crowding reflects the

personal choices of some men and many women who want or

need a job that fits their family obligations. The wage and

percent female relationship reflects the presence of a

compensating differential.

If true, then the estimates need to be adjusted for not only

labor supply factors that proxy for personal preferences, but

also for labor demand and institutional factors (for example,

government employment and unions) that are correlated with

both wages and the share of women in an occupation. To

remove the potential biases and identify their roles in

explaining the wage-percent female relationship, we utilize

the decomposition in Johnson and Solon that identifies the

observable sources of the relationship between wages and

the share of women in an occupation.15 In practice, this

amounts to decomposing the difference between the

relationship between wages and percentage of women that

excludes worker characteristics and the relationship between

wages and percentage of women that includes worker

characteristics.

We find that, for men, the most important measurable factor

in each year is the industry of employment as opposed to

personal characteristics such as education, age, and region

of residence. It is well known that particular industries pay

more than others, and our results show that these industries,

on average, have higher concentrations of men. The opposite

occurs for women. Education and age are the most important

factors for explaining the wage- and percent-female

relationship. If education and age capture personal

preferences that influence occupational choice, then solely

focusing on expanding occupational choice will result in a

small narrowing of the gender wage gap.16 This is because, as

shown in our Oaxaca-Blinder decompositions, even after we

add the concentration of women in an occupation to the

model, the overall gender wage gap is still largely unexplained.

Notes

ACKNOWLEDGMENT : We thank anonymous referees for their

comments and suggestions. This article was prepared for the National

Economic Association panel on “Racial Inequality in the U.S. Political

Economy,” Atlanta, Georgia, January 4, 2002.

1 Highlights of Women’s Earnings, Report no. 943 (Bureau of Labor

Statistics, May 2000).

2 See for example, Elaine Sorensen, “Measuring the Pay Disparity

Between Typically Female Occupations and Other Jobs: A Bivariate

Selectivity Approach,” Industrial and Labor Relations Review, July

1989, 624–39; George Borjas, Labor Economics (New York, McGrawHill, 1996); and Barbara Bergmann, “The Effect on White Incomes

of Discrimination in Employment,” Journal of Political Economy,

79, March-April 1971, pp. 294–313.

3

 George Johnson and Gary Solon, “Estimates of the Direct Effects

of Comparable Worth Policy,” The American Economic Review, Dec.

1986, pp. 1117–25.

4

 Multicollinearity was not a problem because the concentration of

women within an occupation was documented at the three-digit level,

providing more than 490 occupation categories.

5

 The Outgoing Rotation Group Files represent one-fourth of the

sample.

6

 Further, the CPS does not include more detailed measures of skill

plus a wide range of unmeasurable human capital characteristics.

However, numerous studies have controlled for gender differences in

school choice, type of degree, choice to enter higher paying occupations, childcare, work history, school performance, and job-setting

measures. Collectively, they find that a large unexplained gap remains.

See, for example, Mary E. Corcoran, Paul N. Courant, and Robert G.

Wood, “Pay Differences among the Highly Paid: The Male-Female

Earnings Gap in Lawyers’ Salaries, “Journal of Labor Economics, July

1993, pp. 417–41 and Catherine J. Weinberger, “Mathematical College

Majors and the Gender Gap in Wages, “Industrial Relations, July 1999,

pp. 407–13.

8

 Others have used non-CPS data sets to control for gender

differences in school choice, type of degree, and choice to enter

higher paying occupations. For example, Corcoran, Courant, and

Wood, “Pay Differences,” 1993, examined pay differentials between

male and female lawyers, and found that even after controlling for

childcare, work history, school performance, and job-setting measures,
Monthly Labor Review March 2003 15

one-third to one-fourth of the gender gap was left unexplained.

Weinberger, “Mathematical College Majors,” 1999, estimates gender

gaps between men and women who acquired technical majors in college.

While she found that the lower mathematical content of the college

major of women explains a good deal of the gender gap, she found that

almost 9 percent of the gap was left unexplained.

9

 Further, instead of asking survey respondents their years of

schooling, the survey bracketed years of schooling below 12th grade and

asked respondents to report their highest degree of completion.

10 See, for example, Paula England, Comparable Worth: Theories

and Evidence (New York, Aldine De Gruyter, 1992); Paula England,

“The Failure of Human Capitol Theory to Explain Occupational Sex

Segregation, “Journal of Human Resources, vol. 17, 1982, pp. 358–

70; David Macpherson, and Barry Hirsch, “Wages and Gender

Composition: Why do Women’s Jobs Pay Less?” Journal of Labor

Economics, vol. 13, no. 3, 1995, pp. 426–71; Borjas, Labor

Economics, 1996; and Barbara Bergmann, In Defense of Affirmative

Action (New York, Basic Books, 1996).

11A benefit of using the CPS is that it contains data on a large nationwide

sample and includes detailed occupational data in addition to the more

commonly found earnings, employment, human capital, and demographic

information. One of the primary drawbacks of the CPS is that it does not

include a measure of actual experience. Potential experience must be

generated from age minus years of schooling minus six. This may be done

until 1990. After that time, changes in the education question in the CPS

make it impossible to accurately measure the number of years an individual

spent in school. Estimating potential experience is more likely to

overestimate the experience of women, as they are more likely to have

taken time out of the labor force than are men.

12 These estimates of the gender gap are created from the difference

of the logarithm of the mean earnings of men and women. Estimates

by the Bureau of Labor Statistics are created from a special method of

finding the median earnings of men and women using $50 increments

centered around units of $50. The results of these two methods agree in

1989 and 1992, but differ by 5 percentage points in 1999. When the

gender gap in 1999 is estimated using these data by taking the difference

of the median earnings of men and women, the result is similar to that

obtained by BLS.

13 Johnson and Solon, “Estimates of the Direct Effects of

Comparable Worth Policy,” 1986.

14 The full results of these regressions are available upon request

from the authors.

15 Johnson and Solon, “Estimates of the Direct Effects of

Comparable Worth Policy,” 1986.

16 To illustrate this point with published BLS data, we computed the

wage gap between male and female median weekly earnings in the threedigit occupations that pay more than the U.S. median weekly earnings

of $576 in 2000. The average wage gap for this sample is 33 percent,

with only one occupational gap below 10 percent. The significance

of these gaps is that they are for women who chose to go into the

highest paying U.S. occupations, yet they experience pay gaps that are

just as large and larger than the overall U.S. gender pay gap.