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June 8,1 984
Women's Wages
It is a widely held view that women are
discriminated against in wages and employment in the marketplace. To support this
view the statistic often cited is that women
earn on average less than two-thirds of what
their male counterparts earn. In addition,
other statistics show that women are not
evenly represented in all occupations.
Women hold over 95 percent of nursing and
secretarial positions, for example, but less
than a third of the jobs in engineering, construction and the technical professions. This
seeming imbalance has led some to contend
that employers actively keep women out of
certain occupations.
These aggregate statistics are commonly
used as evidence of widespread discrimination against women in the marketplace.
Upon closer examination, however, that
interpretation becomes less obvious.
Indeed, available evidence largely supports
. the idea that competitive forces in the labor
market effectively combat the effects of
discrimination. This Letter examines the
paradox and criticizes some policy implications that arise from one diagnosis of what
causes differences in male and female
employment situations.
Economicsof discrimination
In discussing this complex issue, it is helpful
to distinguish between two types of discrimination: discrimination based on prejudice
and statistical discrimination. In one form of
discrimination based on prejudice, employers are willing to sacrifice p'roductivity and
profits (by overpaying men or failing to hire
qualified women) to satisfy a personal prejudice. In another, employers discriminate
against women not because of their own
prejudice, but because they fear that
productivity would be adversely affected if
male employees find it uncomfortable to
work with WOmen. The second form might
be called the "male club" view of
discrimination in the workplace.
In statistical discrimination, the employer
has no desire to satisfy a taste for prejudice,
but uses the sex of an employee (or some
other characteristic) as a proxy for productivity-related attributes that cannot be
observed easily. The employer may use the
sex of an employee as a proxy for, for
example, physical strength,mathematical
aptitude or job commitment. It may be
economically rational for the employer to
"statistically" discriminate (despite the fact
that the stereotype does not fit anyone
employee accurately), if the cost of obtaining more accurate measuresof the desired
attribute are too high.
Competition anddiscrimination
Although such prejudice and cost-ofinformation arguments are often accepted as
the explanation for male-female differences
in the labor market, it is easy to overstate
their likely importance. In a competitive
economy, there are extremely strong forces
at work to minimize their effect on wages
and employment. If a firm could hire
females who were as productive as males at
60 percent of the male wage rate, for
example, itwould have a considerable costadvantage over its competitors and, hence, a
strong incentive to hire women at a lower
wage. Prejudiced employers would presumably be driven out of the marketplace by
more profitable, unprejudiced employers.
Similarly, competitive firms have an incentive to avoid the arbitrary use of statistical
proxies; firms that devise inexpensive
means of obtaining more accurate information on prospective employees wi II have a
more productive work force than their
prejudiced counterparts. In a competitive
economy, the effects of the formation of
"male clubs" also would be limited by the
ability of females to form their own competitive enterprises. Although this would lead to
segregatedenterprises, one would not
expect it to lead to male-female wage
differentials or segregation by occupation.
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Some paradoxes
More important than these theoretical challenges to the conventional wisdom concerning sex discrimination are some seeming
paradoxes in the statistical record of female
labor market performance. If one believed,
for example, that there was widespread
economic discrimination against women in
the economy, significant wage differentials
wou Id be expected in all worker age groups.
Yet data from a National Science Foundation
study in 1977 show that the earnings of new
entrants to the labor market, such as recent
male and female college graduates, are
virtually identical for males and females
regardless of the individual's field of study.
The average across all business and
engineering fields was $12,548 for males
and $12,580 for females, for example.
This is an extremely difficult question to
answer. Sufficient data to account for all
relevant worker and job attributes exist - if
at all-only with an individual firm's
employment records. Such information,
however, is rarely made available to academic analysts. A studyby Malkiel and
Malldel of a 'major white-collar enterprise
found that male-female wage differences
were essentially explained by differences in
worker qualifications. A study by Siebert
and Sloane in Great Britain used similar
techniques (but much less detailed data) to
study the compensation practices of firms
engaged in various professional, public
service and manufacturing activities. In only
one case (an engineering firm) was there a
wage differential unexplained by the available..data on the workers'qualifications.
Employer discrimination against women
could also be expected to cause women to
escape unfair labor practices by seeking selfemployment. We thus might expect to see a
higher proportion of women than men in
self-employment, and self-employed
women's wages to be relatively higher than
what women earn in comparable wage and
salary employment. Yet studies by Fuchs in
1960 and more recently by Moore have
failed to find such patterns. Not only do
relatively fewer females seek selfemployment, their relative wages are either
equal to or less than what they earn in the
wage and salary sector.
The two studies suggest that economic or
statistical discrimination by employers does
not playa major role in generating observed
wage differences in men and women.
Rather, as the model of competitive labor
markets would suggest, overall, employers
make their decisions based on work-related
attributes of the worker.
labor force attachment
One of the most important characteristics of
the worker (male or female) is continu ity of
service in the labor force. Human capital
(the knowledge and abilities we have or
acquire that are valued by the marketplace),
Iike physical capital, deteriorates over time.
Prolonged absences from the labor market
can be expected to decrease the value of a
worker to an employer. A recent study by
Mincer and Ofek, for example, found that
each year in which a worker did not participate in the laborforce resulted in a 3-8
percent deterioration in h is or her wage from
what it would otherwise have been. Since
women spend approximately three times as
much of their working life outside the labor
force as men, the deterioration of their
"human capital" may result in apparent
disparities between average male and
female wages.
Statistical problems
Why then do the aggregate statistics appear
to show women at such a severe economic
disadvantage? Much of the explanation lies
in the job-related characteristics of the
worker, such as level and type of education
and the amount of work experience accumulated. In studies using publicly available
data, about two-thirds of the male-female
"wage gap" can be explained by such
simple worker characteristics. Is the remaining wage gap due to discrimination or
insufficient data for identifying other
important worker attributes?
2
Chart 2
MalelFemale Earnings & Child-Bearing
(1955.1982)
Chart 1
Relative Participation Rates of WomenI Men '
(1984)
Blrlhs/1000people
Rilio
26
1.90
1.0
24
0.9
22
0.8
20
0.7
16
0.8
16
10
20
30
40
50
60
1.60
1955
70
1960
1965
1970
1975
1980
14
1985
measuring skill requirements across diverse
occupations. Nevertheless, using coarse
occupational categories, Polachek found
that differences in labor force participation
alone accounted for much of the observed
occupational "segregation." Specifically,
women's share of professional positions
would increase by 35 percent and their
share of menial jobs would decrease by 25
percent if they had the same labor force
participation behavior as men. Although
Polachek's simple tests are hardly definitive,
they do point out the tenuousness of the
assumption that conscious employer
discrimination is the primary cause of the
large observed occupational differences
between the sexes.
The major causes of reduced female labor
force participation and lower relative wages
appear to be marriage and child rearing. As
Chart 1 illustrates, the major anomaly in the
pattern of relative male and female labor
force participation rates occurs in the
twenties and thirties, when many individuals are forming families and bearing and
raising children. Female participation in the
labor market is lower relative to its trend
relationship to male participation rates. This
is consistent with the observed relationship
between earlier decisions to bear children
and subsequent earnings disparities between
men and women (Chart 2). An increase in
the birth rate appears to cause a rise in the
male-female earnings ratio on average
about ten years later, asthe women return to
the labor force with their "depreciated"
human capital.
Comparable worth
It has become increasingly important to
resolve these and other issues regarding
disparities in male-female compensation
and employment patterns. In a recent
decision in the State of Washington, for
example, the court concluded that women
were discriminated against because the
wages paid in predominantly female occupations were less than in predominantly
male occupations of "comparable worth"that is, in occupations requiring "similar"
skills. If the decision were upheld, the State
of Washington's liabilities to its female employees would be in the billions of dollars.
Women who choose not to form families
should be less likely to be absent from the
I abor force for prolonged periods and, therefore, shouId experience fewer of the resuItant
effects on their market wage. Data from the
National Longitudinal Survey confirms this
hypothesis-single women who have never
been married have wages virtually identical
to their male counterparts.
"Women's work"
The uneven distribution of male and female
employment by occupation also may be
attributed to differences in labor force participation propensities. If the average female
worker is (or plans to be) out of the labor
force for an extended time, she would be
rational to seek employment in an occupation that does not depend upon continuous
labor force participation to maintain job
skills. One might argue, for example, that
the skill requirements of secretarial, clerical
and primary school teaching positions probably are influenced less by extended labor
force detachment than, say,those of university teaching or corporate management.
Yet the economic theory and research cited
here suggeststhat apparent wage and employment disparities between males and
females may be due less to widespread
prejudicial practices than to the simple
interaction of forces of supply and demand.
If this were the case, "corrective" policies
such as comparable worth compensation
wou Id lead to a less efficient allocation of
labor, with resultant higher levels of
unemployment and lower levels of output
for the economy as a whole.
Randall J. Pozdena
This is an extremely difficult proposition to
demonstrate because of the difficulty in
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BANKING DATA-TWElFTH FEDERAL
RESERVE
DiSTRiCT
(Dollar amounts in millions)
Selected Assetsand Liabilities
Large Commercial Banks
Loans, Leases and Investments 1 2
Loans and Leases1 6
Commercial and Industrial
Real estate
Loans to Individuals
Leases
U.S. Treasury and Agency Securities2
Other Securities 2
Total Deposits
Demand Deposits
Demand Deposits Adjusted 3
Other Transaction Balances4
Total Non-Transaction Balances6
Money Market Deposit
Accounts -Total
Time Deposits in Amounts of
$100,000 or more
Other Liabilities for Borrowed MoneyS
Weekly Averages
of Daily Figures
Amount
Outstanding
5/23/84
179,676
160,274
48,637
59,855
28,042
4,988
11,864
7,538
186,209
43,121
28,072
11,994
131,094
-
39,303
-
39,442
19,647
Weekended
OS/21/84
Change from 12/28/83
Percent
Dollar
Annualized
Change
from
5/23/84
468
322
122
21
20
- 15
136
- 10
-1 ,679
-2,262
-1 ,245
- 137
720
-
-
-
3,651
4,919
2,674
956
1,391
75
643
625
4,788
6,116
3,259
781
2,109
34
-
294
415
972
-
1,277
3,360
-
-
-
5.1
7.8
14.4
4.0
12.9
3.6
12.7
18.9
6.2
30.7
25.7
15.1
4.0
1.8
8.2
- 36.1
Weekended
05/07/84
ReservePosition, All Reporting Banks
Excess Reserves (+ l/Deficiency (-)
Borrowings
Net free reserves (+ l/Net borrowed( - l
-
16
55
71
89
147
58
Includes loss reserves, unearned income, excludes interbank loans
Excludes trading account securities
Excludes U.S. government and depository institution deposits and cash items
ATS, N OW, Super N OW and savings accounts with telephone transfers
Includes borrowing via FRB, TI&L notes, Fed Funds, RPsand other sources
6 Includes items not shown separately
Editorial commentsmaybe addressedto the editor (GregoryTong)or to the author .. .. Freecopiesof
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Bank of San Francisco,P.O. Box 7702, SanFrancisco94120. Phone(415) 974-2246.
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