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Sign in
Progress toward gender equality in the United States
has slowed or stalled
Paula Englanda,1, Andrew Levinea, and Emma Mishela
a
Department of Sociology, New York University, New York, NY 10012
This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2018.
Contributed by Paula England, February 12, 2020 (sent for review October 30, 2019; reviewed by Francine Blau and Reeve Vanneman)
We examine change in multiple indicators of gender inequality for
the period of 1970 to 2018. The percentage of women (age 25 to 54)
who are employed rose continuously until ∼2000 when it reached its
highest point to date of 75%; it was slightly lower at 73% in 2018.
Women have surpassed men in receipt of baccalaureate and doctoral
degrees. The degree of segregation of fields of study declined dramatically in the 1970s and 1980s, but little since then. The desegregation of occupations continues but has slowed its pace. Examining
the hourly pay of those aged 25 to 54 who are employed full-time,
we found that the ratio of women’s to men’s pay increased from
0.61 to 0.83 between 1970 and 2018, rising especially fast in the
1980s, but much slower since 1990. In sum, there has been dramatic
progress in movement toward gender equality, but, in recent decades, change has slowed and on some indicators stalled entirely.
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gender inequality gender pay gap
gender education
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| occupational gender segregation |
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S
ocial scientists have documented a dramatic change in gender
inequality in the last half century, sometimes called a “gender
revolution.” Women’s employment increased and became the
norm, even for mothers of young children (1). Birth control became available to most (2, 3). The proportion of women receiving
baccalaureate or doctoral degrees increased dramatically (4, 5).
Women rose as a proportion of those getting degrees in fields of
study that have traditionally been dominated by men, such as
management, accounting, and science, technology, engineering,
and mathematics fields (6). A new women’s movement emerged.
Equal opportunity in employment, which had been the law since
the 1960s, became somewhat institutionalized in the personnel
policies of organizations (7). Because of new opportunities and
aspirations, many women entered professional and managerial
jobs filled previously almost exclusively by men, thus lessening the
segregation of occupations (8–10). The gender gap in pay fell
significantly after 1980 (11, 12). Attitudes about the proper roles
for men and women became more gender egalitarian (13, 14).
However, much of this research, as well as additional recent research (e.g., refs. 15 and 16), also shows a slowdown or even stall
in movement toward gender equality.
Our contribution is to update and broaden the scope of past
descriptions of trends in US gender inequality. To do this, we
document changes between 1970 and 2018 on multiple quantitative indicators: employment, educational attainment, segregation
of fields of study, segregation of occupations, and pay. Our
updated and broadened analysis strongly reinforces a conclusion a
number of scholars have reached recently: that progress toward
gender inequality has slowed in recent decades, and on some indicators has stalled completely. We end by suggesting what may be
necessary for further reductions in gender inequality to occur.
Results
Employment. Fig. 1 shows trends in employment for men and women,
giving the percentage of those aged 25 to 54 who were employed in
the week of the survey in each year from 1970 to 2018. Women’s
employment rose almost steadily from 1970 to 2000, moving from
6990–6997 | PNAS | March 31, 2020 | vol. 117 | no. 13
48% employed in 1970 to 75% employed in 2000. It then declined,
plateaued, and declined more in the Great Recession, reaching a
bottom of 69% and rebounding to 73% in 2018. Despite the rebound
after the recession, in 2018 it was no higher than its level in 1996.
Men have a higher level of employment than women at each
year, and their employment has gone up and down more than
women’s with business cycles, including the Great Recession.
Unlike for women, the long-term trend for men has been slowly
downward, from 91% in 1970 to 84% in 2018 (Fig. 1). The percent
employed fell more dramatically for men than for women in the
Great Recession, from 84 to 79% between 2008 and 2009, with a
larger rebound as well, back to 84% after 2010.
To assess the trend in the gender gap in employment, Fig. 2
shows the ratio of women’s percent employed to men’s percent
employed. The ratio rises continuously from 0.53 in 1970 to
0.85 in 1995. The progress toward equality was steepest from 1970
to 1995 as women’s employment went up dramatically and men’s
employment went down some. Thereafter, the ratio was quite flat
except for a rise and then decline of several points, reflecting, as
discussed, that the recession and recovery both affected men more
than women. The ratio was 0.86 in 2018.
Examining men’s and women’s employment within groups defined by educational attainment, shown in SI Appendix, Fig. S1,
reveals that men with a baccalaureate degree or more have seen
almost no reduction in their employment, from 92 to 91% between
1970 and 2018. The drop has been much greater among those who
are high school graduates but do not have a college degree, from 93
Significance
Social scientists have documented dramatic change in gender inequality in the last half century, sometimes called a “gender revolution.” We show dramatic progress in movement toward gender
equality between 1970 and 2018, but also that in recent decades,
change has slowed or stalled. The slowdown on some indicators
and stall on others suggests that further progress requires substantial institutional and cultural change. Progress may require
increases in men’s participation in household and care work,
governmental provision of child care, and adoption by employers
of policies that reduce gender discrimination and help both men
and women combine jobs with family care responsibilities.
Author contributions: P.E. designed research; A.L. and E.M. performed research; A.L. and
E.M. analyzed data; and P.E. wrote the paper.
Reviewers: F.B., Cornell University; and R.V., University of Maryland.
The authors declare no competing interest.
This open access article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND).
Data deposition: Code and links to data (which are publicly available) have been deposited at Open Science Framework (https://osf.io/kx94e/).
See QnAs on page 6963.
1
To whom correspondence may be addressed. Email: pengland@nyu.edu.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.1918891117/-/DCSupplemental.
First published March 30, 2020.
www.pnas.org/cgi/doi/10.1073/pnas.1918891117
INAUGURAL ARTICLE
100
0.90
0.85
90
0.80
Gender
Men
70
Women
Proportion
% Employed
80
60
0.75
0.70
0.65
0.60
50
0.55
40
0.50
1980
1990
2000
2010
2020
1970
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Year
1980
1990
2000
2010
2020
Year
Fig. 1. Percentage of women and men, age 25 to 54, employed in the last
week, 1970 to 2018. Source: Authors’ computations from IPUMS CPS ASEC
samples for 1970 to 2018.
Fig. 2. Ratio of percentage of women to men employed in the last week,
age 25 to 54, 1970 to 2018. Source: Authors’ computations from IPUMS CPS
ASEC samples for 1970 to 2018.
to 82%, and even steeper among men who did not complete high
school, from 89 to 74%.* SI Appendix, Fig. S1 further shows that
while women’s employment is always much higher for women with
more education, it rose in all education groups until ∼2000, and
thereafter stalled or went down. In SI Appendix, Fig. S2, the ratio of
women’s to men’s employment is disaggregated by education. We
see that the ratio of women’s employment to men’s goes up in all
education groups until the mid-1990s but has not risen much since
then at any education level. Thus, the stall in progress in gender
equality on employment is seen at all educational levels.
one-third of adult Americans who have a baccalaureate degree or
more, occupation and earnings are strongly affected, although by
no means entirely determined, by their field of study (17) (Fig. 6).†
Educational Attainment. Because educational attainment strongly
affects earnings, it is important for gender equality in earnings. As
Fig. 3 shows, in 1970, fewer women than men obtained baccalaureate
degrees. The next 20 y, men’s numbers remained relatively flat while
women’s rose, with the number of women recipients passing men in
the mid-1980s. Since 2000, the number getting degrees has risen for
both women and men, but more steeply for women.
Fig. 4 shows the analogous numbers receiving doctoral degrees, in which we include PhDs, MDs, DDSs, JDs (law degrees),
and other doctoral degrees. Men’s numbers were relatively flat
between 1970 and 2000, then rose; by contrast, the number of
women getting doctoral degrees started way below men’s numbers but rose steeply and continuously, passing men in the early
2000s. Fig. 5 shows the ratio of women’s to men’s baccalaureate
and doctoral degrees; both ratios began below 1 and now exceed
1. The ratios have stabilized at above 1 recently.
Segregation of Fields of Study. We turn now to gender differences
in the fields of study in which women and men get degrees. To
examine how segregation changed among those being awarded
baccalaureate and doctoral degrees, Fig. 4 presents trends in D,
the index of dissimilarity, which can take on values between 0 (no
segregation) and 1 (total segregation). For baccalaureate degrees,
D was 0.47 in 1970 and a much lower 0.33 in 2015. (For brevity, we
refer to the academic year 1970–1971 as 1970, to 2015–2016 as
2015, and so forth.) However, the drop was not continuous; segregation declined until it reached 0.28 in 1998, and has come up
again slightly since. For doctoral degrees, D moved from 0.35 in
1970 to a low of 0.18 in 1987 and has not gone lower since, but has
risen slightly. Thus, desegregation of both levels of degrees has
been substantial but has stalled for 20 or more years. The
remaining segregation is important because, for the approximately
*Men’s falling employment rebounded slightly after 2010 in all education groups, due
probably to the postrecession recovery.
England et al.
Occupational Segregation. Next, we examine trends in how segregated occupations are, using D, the same measure used above
to assess the segregation of fields of study. As Fig. 7 shows,
segregation of occupations has fallen steadily since 1970, with D
moving from 0.60 to 0.42. However, it moved much faster in the
1970s and 1980s than it has since 1990; segregation dropped by
0.12 in the 20-y period after 1970, but by a much smaller 0.05 in
the longer 26-y period after 1990.‡ Occupational segregation is
important in part because it is a large contributor to the gender
gap in pay (ref. 8, p. 21, and refs. 12 and 19).
Earnings. A key indicator of gender inequality is the pay gap. We
focus here on the hourly earnings of full-time workers. First, we
show trends separately for men’s and women’s median hourly
earnings in Fig. 8. Earnings are in constant 2018 dollars, adjusted
by the Consumer Price Index (CPI). Men’s median hourly earnings were approximately $27 to $28/h in the 1970s, then fell to
below $23/h by the mid-1990s, rising again in the late 1990s boom,
declining in the Great Recession, and rising some since; despite
the fluctuations, the median has always been between $22 and
$25/h in 2018 dollars since the mid-1990s.§,{ Women’s median
earnings have always been lower than men’s. During the 1970s,
they were approximately $17/h. They began to rise in the early
1980s for the rest of the decade, flattened, then rose again in the
†
For the percentage of American adults with a baccalaureate degree, see ref. 18.
‡
Blau et al. (10) use a different, gender-specific, method for harmonizing occupational categories across decades, and do it so as to preserve hundreds of detailed occupational categories, whereas we collapse all occupations to just 77 categories. Our use of
less detailed categories yields a lower D in any one year. For example, if we use the 320
detailed Census categories in our data for 2017, D is 0.48, compared to the 0.42 we
report here when collapsing those 320–77 broader categories. However, despite differences in categorization method, our conclusion of a slowdown in desegregation across
decades is consistent with what Blau et al. (10) report for 1970 to 2009.
§
Thus, Fig. 8 implies that the man at the middle of the distribution in 2018 earned less,
after adjusting for inflation, than the man at the middle in 1970. However, some argue
that the CPI overstates inflation, and suggest alternative indices (37, 38). Fortunately for
our conclusions about gender inequality in pay, how inflation is (or is not) adjusted for
has no effect on the trend in the ratio of women’s to men’s pay.
{
While we showed that the proportion of men who are not employed has gone up,
especially among the less educated, most scholars believe that this is not primarily a
result of more couples deciding that men would become stay-at-home dads, but rather
reflects the deterioration of the pay of jobs available to men without a college degree
(39); for a dissenting view, but one that also does not see acceptance of stay-at-home
dads as the source, see ref. 38.
PNAS | March 31, 2020 | vol. 117 | no. 13 | 6991
SOCIAL SCIENCES
1970
1.4
900,000
1.2
800,000
1.0
Gender
700,000
Women
600,000
Men
Proportion
Number
1,000,000
Degree
0.8
BA
0.6
500,000
0.4
400,000
0.2
Doctoral
0.0
300,000
1970
1980
1990
2000
2010
1970
2020
1980
1990
2010
2020
Fig. 3. Number of men and women receiving baccalaureate degrees, 1970
to 2015. Source: Authors’ calculation using data from NCES.
Fig. 5. Ratio of women to men receiving baccalaureate and doctoral degrees, 1970 to 2015. Source: Authors’ calculation using data from NCES.
late 1990s and early 2000s, and since have been relatively flat.
Women’s earnings fell less than men’s in the Great Recession.
To examine gender inequality in pay, Fig. 9 shows the trend in
the ratio of women’s to men’s median hourly earnings among
full-time workers, which was fairly stable at slightly above 0.60 in
the 1970s, then rose dramatically to 0.74 by 1990. The ratio has
shown a net rise in each decade since 1990 but at a much slower
rate than was observed in the 1980s. By 2018, women earned
83% what men did at the median. In percentage point increase,
the rise was less in the 28 y of 1990 to 2018 than it was in the
single decade of the 1980s.
The analyses of median hourly earnings above include only
those working full-time, defined as at least 35 h/wk. If instead we
examine all workers, full- and part-time, as shown in SI Appendix,
Fig. S3, the results are very similar, mainly because a large majority of US women and men work full-time. If we further limit
the sample, not just to full-time workers, but to those who are
full-time and worked all of the last year, and examine annual
rather than hourly earnings, we also see a slowdown in earnings
convergence, as SI Appendix, Fig. S4 shows. This measure too
went up much more in the 1980s than any other subsequent
decade, moving from 0.58 to 0.70 in the one decade of the 1980s,
a larger percentage point increase than the increase from 0.70 to
0.81 that occurred across the almost three decades between 1990
and 2018. Thus, the conclusion of continuing but slowed progress
is reached across measures and across samples.
We next examine earnings at various percentiles of each gender’s distribution. Fig. 10 shows that men’s earnings at the 10th
and 20th all declined a few dollars an hour, and after the CPI
adjustment, at the 50th percentile there was a slight decline. Men
at the 80th percentile gained about $5/h in net across the nearly
50-y period. However, men at the 90th percentile gained nearly
$12/h across the same period. The rise at the top and decline at
the bottom created substantially increased inequality among men,
a focus of much recent research (20, 21). Fig. 11 shows increasing
inequality among women as well, as past research has shown (20,
22). By comparing men and women at the same percentile of their
respective distributions, Figs. 10 and 11 show that, unlike men at
the bottom percentiles, women at the 10th and 20th percentiles of
their distribution gained slightly. Women at the median gained
more, about $4/h across the almost 50-y period. Women at the
80th percentile gained approximately $11/h, while women at the
90th percentile gained $18/h. As was true for men, women’s
earnings toward the top of the distribution got farther and farther
away from the earnings of those at the bottom, and even those at
the median. However, unlike men, women at the bottom did not
see a net decline.
We next examine whether gender inequality in pay is more
extreme toward the top, middle, or bottom of men’s and women’s respective distributions, showing the ratio of the earnings at
the 10th percentile of women’s distribution to the earnings of
men at their 10th percentile, and analogously for the median
(50th) and 90th percentile. As Fig. 12 shows, in 1970, women
earned ∼60% what men did regardless of the percentile of the
distribution examined. However, in all years since the middle
0.50
80,000
0.45
60,000
50,000
Gender
Women
40,000
Men
30,000
20,000
Dissimilarity Index
70,000
Number
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2000
Year
Year
0.40
Degree
0.35
BA
0.30
Doctoral
0.25
0.20
10,000
0.15
0
1970
1970
1980
1990
2000
2010
2020
Year
Fig. 4. Number of men and women receiving doctoral degrees, 1970 to
2015. Source: Authors’ calculation using data from NCES.
6992 | www.pnas.org/cgi/doi/10.1073/pnas.1918891117
1980
1990
2000
2010
2020
Year
Fig. 6. Segregation index (D) for baccalaureate and doctoral degree recipients’ fields of study, 1970 to 2015. Source: Authors’ calculation of D
(index of dissimilarity) using data from NCES.
England et al.
INAUGURAL ARTICLE
0.90
0.60
0.85
0.80
Proportion
Dissimilarity Index
0.55
0.50
0.75
0.70
0.45
0.65
0.40
0.60
1980
1990
2000
2010
2020
1970
1980
1990
Year
Fig. 7. Segregation index (D) for occupations, 1970 to 2017. Source: Authors’ calculation of D (index of dissimilarity) from IPUMS decennial Census
samples for 1970 to 2000 and ACS samples for 2001 to 2017. Uses three-digit
occupations collapsed to 77 categories.
1970s, the most gender equality (indicated by a higher ratio of
women’s pay to men’s pay) was found at the 10th percentile,
toward the bottom of the distribution.
As regards how trends in the ratio of women’s to men’s wages
differ between top, middle, and bottom of the distribution, Fig.
12 shows the highest increase in the ratio at the bottom, an intermediate amount at the middle, and the least at the top. This is
not because men at the top gained more in pay than women at
the top—computations from Figs. 10 and 11 actually show that
women’s wages at their 90th percentile have gone up more (in
either dollars or percentage change) than men’s wages at their
90th percentile went up. However, the extent to which women’s
wage increase exceeded men’s was even greater at each gender’s
respective 10th percentile than at the 90th percentile, and this
accounts for the increasing distance between the ratios for the
90th and 10th percentiles across most of the years in Fig. 12. In
sum, the female-to-male wage ratio, our measure of gender inequality, rose more for those at the bottom than the top (with
intermediate change for those in the middle). (See ref. 12 for a
similar finding using a different dataset.)
A final conclusion from Fig. 12 regards whether our conclusion that progress toward gender equality has slowed holds at the
top, middle, and bottom. The figure shows that it does; at each
percentile shown, the increase in the women’s to men’s earnings
ratio has not been as steep since 1990 as it was in the 1980s. The
2010
2020
Fig. 9. Ratio of women’s to men’s median hourly wage among full-time
workers employed in the last week, age 25 to 54, 1970 to 2018. Source:
Authors’ computations from IPUMS CPS ASEC samples for 1970 to 2018.
slowdown in progress occurred at the top, middle, and bottom.
In a related analysis, SI Appendix, Fig. S6 shows a slowdown or
stall in progress toward gender equality in earnings among fulltime workers at each education level.
Discussion
Our analysis has shown substantial reductions in gender inequality on all indicators. However, on every indicator considered, women’s progress relative to men has slowed, and in some
cases progress has stalled entirely. In every case except educational
attainment, where women are now ahead of men, a slowdown
or stall has occurred at a time when there was still substantial
gender inequality favoring men. Here, we review our findings
and use past research on causes of gender inequality to speculate about what would need to change to hasten the reduction
of inequality.
Women’s employment has stalled out at 70 to 75% for decades.
The ratio of women’s to men’s employment rose dramatically
from 0.53 in 1970 to 0.85 in 1995 but has changed little since. The
long-term increase in the ratio reflects women’s increasing and
men’s declining employment, and the stall in the ratio mainly
reflects a stall in the growth of women’s employment.
Women’s attainment of college and advanced degrees has
increased absolutely and relative to men’s. The ratio of women
to men getting degrees went from 0.76 to 1.34 for baccalaureate
degrees and from 0.13 to 1.18 in for doctoral degrees between
60
30.0
55
25.0
Gender
Men
22.5
Women
20.0
Hourly Wage (2018 USD)
50
27.5
Hourly Wage (2018 USD)
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2000
Year
45
Percentile
40
90
80
35
50
30
20
25
10
20
15
17.5
10
15.0
1970
1970
1980
1990
2000
2010
2020
1980
1990
2000
2010
2020
Year
Year
Fig. 8. Median hourly wage of full-time working women and men, age 25
to 54, employed in the last week, 1970 to 2018. Source: Authors’ computations from IPUMS CPS ASEC samples for 1970 to 2018.
England et al.
Fig. 10. Hourly wage of full-time working men, age 25 to 54, employed in
the last week, at 10th, 20th, 50th, 80th, and 90th percentile of distribution,
1970 to 2018. Source: Authors’ computations from IPUMS CPS ASEC samples
for 1970 to 2018.
PNAS | March 31, 2020 | vol. 117 | no. 13 | 6993
SOCIAL SCIENCES
1970
Hourly Wage (2018 USD)
45
40
Percentile
35
90
30
80
50
25
20
20
10
15
10
5
1970
1980
1990
2000
2010
2020
Year
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Fig. 11. Hourly wage of full-time working women, age 25 to 54, employed
in the last week, at 10th, 20th, 50th, 80th, and 90th percentile of distribution, 1970 to 2018. Source: Authors’ computations from IPUMS CPS ASEC
samples for 1970 to 2018.
1970 and 2015. The ratios have been approximately flat since
2000 for baccalaureate and since 2008 for doctoral degrees, but
women had already achieved more than equality before the
ratios stabilized.
While women have surpassed men in amount of education
attained, there has been nothing like convergence in the fields of
study in which men and women get degrees. For baccalaureate
degrees, D, the measure of segregation, which ranges from
0 (complete integration) to 1 (complete segregation), fell from
0.47 in 1970 to 0.28 in 1998, and has not gone down since, but
rather, segregation has risen slightly. For doctoral degrees, segregation went from 0.35 in 1970 to a low of 0.18 in 1987 and has
hovered slightly higher since. In neither case has there been any
net reduction in segregation for over 20 y.
Segregated fields of study contribute to occupational gender
segregation. The segregation of occupations has fallen substantially since 1970, moving from 0.60 to 0.42. However, it
moved much faster in the 1970s and 1980s than it has since 1990.
Thus, there has been a slowdown, but not a complete stall of
occupational desegregation.
All of the trends we have considered affect the gender gap in
pay; individuals’ pay is affected by their amount of education,
field of study, occupation, and years of employment experience.
The gap is also affected by various forms of gender discrimination
by employers—in hiring, pay differences within jobs, and the relative pay levels set in predominantly female versus predominantly
male jobs. Reflecting changes in all these factors, our main measure of gender earnings inequality, the ratio of women’s to men’s
median hourly earnings among full-time workers, went up strongly
from 0.61 in 1980 to 0.83 in 2018, with much faster progress in the
1980s than in decades since 1990. We compared results using
other measures (hourly earnings for all workers, or annual
earnings for those working full-time year-round). Whatever the
measure, the closing of the gender gap in pay, assessed by upward movement in the ratio of women’s to men’s pay at the
median, has slowed since 1990, although progress continues.
Moreover, the slowdown of progress toward equality in earnings
was also seen toward the bottom and top of the earnings distribution and at all educational levels.
We have shown a slowing of progress toward equality and for
some indicators a complete stall of progress in women’s relative
status. Discovering why progress has slowed or stalled is beyond
the scope of our analysis, but we offer a few speculations, based on
past research on causes of gender equality, about what would be
necessary for further progress. Further change will require deeper
changes in both cultural attitudes and institutional practices.
6994 | www.pnas.org/cgi/doi/10.1073/pnas.1918891117
Change in the gender system has been deeply asymmetric;
women’s entrance to careers came more readily than changes in
men’s roles at home (15). This can be seen in our analysis that
shows a much larger increase in women’s employment than decrease in men’s employment, implying that there was nothing
close to an increase of one stay-at-home husband for every one
increased woman employed. The asymmetry is also seen in other
research showing a much larger increase in women’s paid
work hours than increase in men’s family work (housework, child
care, and shopping) (ref. 23, tables 5A.1 and 5A.2, data on
married mothers and fathers).
This asymmetry in behavioral change by women and men is
reflected in cultural attitudes as well. There is still a strong norm
eschewing anything but full-time paid work for husbands (24); this
creates pressure on women to do more family work than men, and
adjust their careers accordingly. Given that most women form
families with men, it may be difficult to close the remaining gender
gap in pay without increases in men’s domestic work or public
provision of child care. Public provision of child care could strongly
impact the employment of working-class women, as their jobs often pay less than or little more than the costs of child care for their
children. Institutional change in employer policies that eased both
men’s and women’s ability to combine family with work would also
help close the gender gap in pay, provided that such policies are
not used only by women, perpetuating the expectation that women
will carry most of the responsibility of care. Although an increasing
proportion of marriages feature a woman earning more than her
husband, there is substantial evidence that many couples try to
avoid this (25); gender inequality within couples would be eased by
cultural change that led people to accept change in men’s as well
as women’s roles, and to accept marriages in which women earn
more than their husbands as unremarkable.
Cultural change may also be required to tackle the strong level
of sex segregation in fields of study and in occupations. For several
decades, girls’ high school math coursework and scores have been
as good as boys’ so they are unlikely to explain gender differences
in baccalaureate majors (26). Nor does women’s anticipation of
more family work explain gender differences in choice of major
(ref. 4, chapter 8). However, this does not mean the explanation
lies entirely with policies of universities; indeed, most universities
allow any student to declare a major in any field (sometimes with
grade point average requirements, which if anything advantages
women). Gender differences in fields of study may arise from
lingering essentialist beliefs about differences in men and women’s
natures (27). These beliefs create external social pressures on men
and women to choose gender-typical fields of studies and careers
0.95
0.90
0.85
Proportion
50
Percentile
0.80
10
0.75
50
90
0.70
0.65
0.60
0.55
1970
1980
1990
2000
2010
2020
Year
Fig. 12. Ratio of women’s to men’s hourly wage at the 10th, 50th, and 90th
percentile of their distributions, for full-time workers employed in the last
week, age 25 to 54, 1970 to 2018. Source: Authors’ computations from
IPUMS CPS ASEC samples for 1970 to 2018.
England et al.
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Materials and Methods
We provide trend data from 1970 to circa 2018 on several important indicators with which social scientists have gauged gender inequality: employment, pay, educational attainment, gender segregation of fields of study for
baccalaureate and doctoral degrees, occupational gender segregation, and
hourly and annual earnings.
For employment and earnings, we use data for each year from 1970 to
2018 from the Current Population Surveys (CPSs). For occupational sex segregation, where a large sample is crucial, we use Census data for 1970, 1980,
1990, 2000, and thereafter every year through 2017 from the American
Community Study (ACS).# For the number of women and men (and ratio of
number of women to men) getting baccalaureate and doctoral degrees in all
fields combined, and the extent of segregation by gender in the fields in
which these baccalaureate and doctoral degrees were awarded, we use data
from the National Center for Education Statistics (NCES), for every
academic year from 1970–1971 to 2015–2016, the latest year available.
jj
We follow Autor et al. (20) in using the convention of multiplying by 1.5, which approximates what is achieved by assuming a Pareto distribution for values in high percentiles.
**See https://data.bls.gov/cgi-bin/cpicalc.pl. For a discussion of limitations of the CPI, and
how it may overstate inflation and thus lead to understating wage growth, see refs. 37
and 40.
††
A small number of CPS weights are negative. We recoded any negative weights to 0.
#
Specifically, we use the IPUMS 1% state form 1 sample for 1970, 5% state samples for
1980 and 1990, 5% sample for 2000, and 1% samples for 2001 through 2017.
England et al.
‡‡
Anyone with 12, 13, 14, or 15 y of education is classified as “high school,” so some of
these individuals have attended some college.
PNAS | March 31, 2020 | vol. 117 | no. 13 | 6995
INAUGURAL ARTICLE
Analyses of Employment and Earnings.
Data source and sample. These analyses use data from Integrated Public Use
Microdata Series (IPUMS) samples of the Annual Social and Economic Supplement (ASEC) of the CPS, 1970 to 2018.
For our analyses of employment and earnings, we included individuals
between 25 and 54 y old (n = 3,371,391); these are ages when few individuals
are still in school and few have retired yet. Thus, most people at these ages
are employed, unless they cannot find a job (whether they are still looking
or have given up) or are not seeking paid work because they are taking care
of children or elders.
When presenting median or other percentiles of hourly earnings, we limit
the sample to individuals employed full-time in the past week and further
exclude individuals who are self-employed, in the armed forces, or who
reported 0 wage and salary earnings (resulting n = 1,979,268). Medians are
preferred to means because the latter are strongly affected by the strong
and increasing rightward skew of earnings.
SI Appendix compares results from the analyses on earnings just described
to those from a more comprehensive sample of all those employed least
week, whether full-time or not. SI Appendix also compares these analyses to
trends for median annual earnings among full-time year-round workers; the
analysis of annual earnings limits the sample to individuals between 25 and
54 y old who are full-time, year-round workers (i.e., working 35 or
more hours per week, and 50 or more weeks per year) and excludes individuals who are self-employed, in the armed forces, or reported 0 wage and
salary earnings (n = 1,763,891).
Measures of employment and earnings. For employment, we use the CPS variable
providing information on whether the individual reported being employed in
the last week (whether full- or part-time).
For all of our measures of earnings, we begin from annual earnings from
wages or salaries reported by respondents. The Census Bureau top-codes
annual earnings to provide confidentiality for extremely high earners. Because CPS top-coding procedures vary from 1970 to 2018, we recoded any
earnings above a given year’s top-code threshold to the appropriate topcode threshold value. We then multiplied these top-coded income values for
each sample by 1.5.jj We converted the resulting measure of annual earnings
to constant 2018 dollars using the CPI.**
Our main analyses of the pay gap use hourly wage among full-time
workers, constructed from the annual earnings measure described above
and information on weeks and hours of employment. Due to data availability,
our construction of hourly wage is different for 1970 to 1975 and 1976
onward. For 1970 to 1975, we construct hourly wage by dividing annual
earnings by the number of weeks worked in the last year and dividing the
product by the total hours worked in the last week; since hours worked was
presented in intervals, we used the midpoint of the interval. For 1976 onward,
the data on hours worked were in number of hours, not intervals, so we did
not have to estimate with a midpoint, and, more important, the question
on hours worked asked about usual hours worked per week in the last year,
rather than hours worked last week. Thus, changes in the earnings or the
gender ratio of earnings around 1975 should be interpreted with caution as
they may be an artifact of this change in measurement.
Descriptive analyses. For each year, we show the percentage of men and
women (age 25 to 54) employed for pay the week before the survey, as well as
the ratio of women’s to men’s percent employed. For hourly wage, we
present the median, as well as the 10th, 20th, 80th, and 90th percentiles of
the wage distribution, separately for each gender at each year among those
25 to 54. We then examine the ratio of women’s to men’s earnings at
each year for the 10th, 50th, and 90th percentiles. All descriptive analyses
employ CPS sample weights.††
SI Appendix shows supplementary analyses examining trends in employment and wages when dividing respondents by education. For these, we used
the CPS measure of educational attainment, assuming completion of grade 12
implies a high school degree and completion of 4 y of higher education implies a baccalaureate degree. We thus use three categories: less than a high
school degree, a high school degree,‡‡ and a baccalaureate degree or more.
SOCIAL SCIENCES
that may also be internalized as norms. Whether the force is external or internal, the result is a different (although overlapping)
distribution of choices by men and women. Gender differences in
job choices may also reflect differences in preferences (finding
gender-typical activities more interesting and meaningful) that
originate in gendered socialization. Such beliefs and preferences
also incline men and women who do not complete college degrees
in favor of gender-typical jobs. Changes in these beliefs or preferences would enhance equality from the supply-side of labor
markets. However, culture does not only affect the supply side of
labor markets. Such beliefs about what men and women should do
or, are better at, or prefer, when held by employers and managers,
lead to discriminatory hiring, placement, and promotion. Thus,
changing these beliefs would lessen discrimination by gender as
well as reduce supply-side forces promoting segregation.
Institutional change is necessary as well. Policy changes that
reduce gender bias by employers would also help further reduce
occupational segregation and the gender gap in pay. At the time
that gender discrimination in hiring and pay was outlawed in the
1960s, many firms had explicit policies of not hiring women in
certain jobs, and sex preferences were stated in advertisements.
Such overt policies are largely gone. However, more subtle hiring
discrimination probably persists, although it is hard to measure.
Also, policies that reduced employer discrimination against mothers would help shrink the gender pay gap; research has documented
hiring discrimination against mothers (but not fathers) (28). Gender
biases may affect pay differences within jobs as well despite how
simple “equal pay for equal work” sounds; one study showed
men getting higher raises than women in the same company even
when they received the same quantitative evaluations by their
supervisors (29).
A substantial part of the gender gap in pay is between occupations (9, 12, 19). This portion could be reduced by supply or
demand-side changes that reduced segregation. This betweenoccupation portion of the pay gap could also be reduced by policies that successfully remove gender bias from decisions about the
relative pay levels of predominantly male and predominantly female jobs. There is strong suggestive evidence that employers take
the sex composition of jobs into account when setting their pay
levels; studies find lower relative pay in predominantly female
occupations than can be explained by their skill requirements or
working conditions (30, 31). This issue, called “comparable worth”
or “pay equity” in the 1990s, never led to legislation, so it is a type
of discrimination that is generally not illegal in the United States.
In sum, without deliberate efforts to promote both cultural
and institutional change along the lines we have discussed,
progress toward gender equality may remain slow or stalled.
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Regression adjustment for demographic composition. Analyses in SI Appendix
supplement our descriptive analyses by performing adjustments that remove
effects of compositional change across the years examined. With separate
analyses for men and women, we regression-adjust for changes in age
(treated as continuous), age squared, race (white, black, Asian, Native
American, other), and Hispanic ethnicity. To produce adjusted versions of the
percentages or percentiles described above, we used logistic regressions
predicting employment, and quantile regressions predicting wages at the
various percentiles. These regressions were pooled across years and contained
indicator variables for each year, as well as the factors for which we were
adjusting, listed above. Regression analyses were weighted by CPS sample
weights. Using parameters from these regressions, and (via the margins
command in STATA) an average-marginal-effects approach, we produced
predicted, compositionally adjusted values for each of the dependent variables for each year and each gender. We then computed female-to-male
ratios of these adjusted estimates to assess gender gaps. SI Appendix, Figs.
S7 and S8 show adjusted and unadjusted employment trends and the trend in
the ratio of percentage of women to men employed; adjusted and unadjusted trends are virtually identical. SI Appendix, Figs. S9 and S10 show how
the demographic adjustment affects estimated trends in median wages for
full-time men and women, and estimated trends in the gender pay gap at the
median. The regression-adjusted results also show a slowdown in the convergence of women’s and men’s wages. SI Appendix, Figs. S11 and S12 show
the adjusted results for trends in men’s and women’s wages at the 10th, 20th,
50th, 80th, and 90th percentiles of the distributions, and SI Appendix, Fig. S13
shows the gender gap at the 10th, 50th, and 90th. (These are analogous to
the unadjusted Figs. 10 and 11 above.) They do not change our basic conclusion of a slowdown in convergence of women’s and men’s pay.
Analysis of Educational Attainment and Segregation of Fields of Study. We use
data from the NCES on the number of men and women getting baccalaureate
and doctoral degrees in each academic year from 1970–1971 to 2015–2016
(32) Doctoral degrees include PhD, MD, DDS, JD, and a few other doctorallevel professionally oriented degrees. We show the number of men and
women getting such degrees. Then, to show the trend in women’s and
men’s relative educational attainment, we calculate the ratio of the number
of women to men among those receiving baccalaureate and doctoral degrees in all fields combined for each year.
To assess the level of segregation by gender in fields of study, we use data
from NCES that report the total number of men and women who received
each of baccalaureate and doctoral degrees in each academic year from
1970–1971 to 2015–2016 in the following 17 fields: agriculture and natural
resources; architecture and related services; biological and biomedical sciences; business; communication, journalism, and related programs and in
communications technologies; computer and information sciences; education; engineering technologies; English language and literature/letters;
foreign languages and literatures; health professions and related programs;
mathematics and statistics; physical sciences and science technologies; psychology; public administration and social services; social sciences and history;
and visual and performing arts (ref. 33, tables 325.10 to 325.95). While more
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6996 | www.pnas.org/cgi/doi/10.1073/pnas.1918891117
detailed fields would be preferred, this is the most detailed categorization
of fields for which NCES provides data on consistent categories for years
1970 to 2016.
For both baccalaureate and doctoral degrees, we compute the index of
dissimilarity (D) (34) for each year. It is the mostly commonly used measure of
segregation of two groups across multiple units (here fields of study). The
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in it). D is often explained in a shorthand way as the percentage of women
(men) who would have to “trade” fields with a man (woman) to achieve
perfect integration, the state in which women’s representation in each field
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number of such integrative trades possible starting from perfect segregation. The denominator depends on the relative number of men and women
in all fields combined; it is maximized when men and women are each 50%
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the experience of the average person is.
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based upon log linear models. (SI Appendix, Figs. S14 and S15 compare results
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conclusion that desegregation has stalled holds with this measure as well.
Analysis of Segregation of Occupations. To examine trends in occupational
gender segregation, we use data from IPUMS decennial Census samples for
1970, 1980, 1990, and 2000, and American Community Survey (ACS) samples for
2001 through 2016. For Census and ACS samples, IPUMS provides a harmonized
occupation variable, OCC1990, which reflects a modified version of the Census
Bureau’s 1990 three-digit (detailed) occupational classification scheme. Because some occupations in this scheme did not exist as classification possibilities
for some particular years, and we wanted to compute indices of segregation
over consistent categories for each year, we collapsed OCC1990 into Weeden
and Grusky’s 82-category occupational microclass scheme (ref. 35, specifically
table A2). Using all employed respondents who report an occupation (n =
25,369,733), and detailed occupations collapsed into the 82 categories described above, we deleted 5 categories, leaving 77 of Weeden and Grusky’s 82
microclasses, because only these 77 have at least one observation in each
sample year. Using these constant 77 occupational categories, we calculate the
index of dissimilarity, the same measure we use to examine segregation of
fields of study and described above, to assess change in occupational sex
segregation. In SI Appendix, Fig. S16, we also calculate A, an alternate measure
of segregation (36), and it shows a similar trend of reduced segregation, but at
a slower rate in later than earlier decades.
Data Deposition. The data are publicly available. Code for data analysis is
archived on Open Science Framework (https://osf.io/kx94e/).
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INAUGURAL ARTICLE
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SOCIAL SCIENCES
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