Occupational Earnings in the 1980's

tal theory is that a key function of education is
“screening,” that is, identifying individuals with
preexisting skills and abilities required in par-
ticular jobs. According to this view, education is
used to sort individuals into occupations with
different pay, rather than to develop the abilities
needed for those occupations.°° A number of
other views on how education affects earnings
through the intermediary of occupations exist as
well. What is important to keep in mind is that,
regardless of one’s views on the connections
among education, occupations, and earnings, the
differences in earnings among education groups
will depend fundamentally on the occupational
structure of the economy. That is, the payoff to
education will depend on two factors: the effec-
tiveness of additional years of schooling in quali-
fying individuals for higher paying jobs than they
would be likely to obtain without the additional
schooling; and how wide the gap is between high-
paying and low-paying jobs.*! If changes in the
skills that are demanded increase the likelihood
that college graduates will move into high-pay-
ing occupations, and if the earnings of these high-
paying jobs increase compared with those of other
occupations, then the relative earnings of the more
educated will increase.

The connections among experience, occupa-
tions, and earnings are similar to those among
education, occupations, and earnings in some re-
spects. Human capital theory asserts, analogously
to the case of education, that with additional la-
bor market experience, individuals will receive
more on-the-job training, and the skills devel-
oped in this process will enable them to climb
the occupational ladder and receive higher pay.
An alternative view is that the skills of older
workers may not differ much from those of
younger workers, but seniority provisions have
given the older workers more opportunities to
move up in rank.

It is more difficult to make a case for why oc-
cupation distributions, and thus earnings, should
differ across the genders because of skill differ-
ences, as there is no reason to assume that abili-
ties should differ by gender (although differences
in schooling and experience by gender may lead
to differences in the development of skills). The
human capital view does, however, offer an ex-
planation based on skills: under the assumption
that women will participate less in the labor force
over the course of the life cycle (for example,
because they may leave the labor force to care
for a child), they will tend to invest in skills that
do not depreciate as much during their time away
from the labor force, and these skills will be suit-
able for particular occupations. For example,
women, according to this view, would be unwill-
ing to invest in the skills needed for many of the

20 Monthly Labor Review July 1994

professional occupations, because while they
were away from the labor force, these skills would
tend to atrophy.?? An alternative approach is that
the culture of a society itself encourages men and
women to follow different career paths, causing
women to be “segregated” in occupations that, on
the whole, tend to be less well paying than those
held by men.*? Finally, gender-related differences
in occupational distributions may result from dis-
crimination on the basis of gender.

From the preceding discussion, it is clear that
there are ample reasons to expect demographic
groups to be distributed differently across the
economy’s occupations. In some cases, these are
the result of skill differences across groups, but
in others, they are related to factors such as dis-
crimination or seniority provisions. Regardless
of their underlying cause, these differences in
occupational structure across demographic
groups will have an important impact on how
the groups fare when the structure shifts: those

. more heavily concentrated in occupations in

which demand is growing faster than average—
as evidenced by employment growth or earnings
increases or both—will tend to experience rela-
tive earnings gains.

Trends in employment and earnings

Employment by occupation. What were the
important occupational employment shifts of the
1980's? Table 3 shows trends in occupational
employment by the Census Bureau’s major oc-
cupation groups. Most notable is the employment
gain for sales occupations, up 3.2 percentage
points in 1979-89. Other important gains were
posted by executive, administrative, and mana-
gerial (up 1.5 points) and professional specialty
(up 1.1 points) occupations, jobs where a high
proportion of college graduates would be ex-
pected. On the other side of the ledger, a decline
in blue-collar occupations is evident, particularly
for machine operators (down 2.2 points) and pre-
cision production, craft, and repair occupations
(down 1.6 points). These are occupations in
which those without a college education histori-
cally have had a possibility of finding high-pay-
ing jobs. Administrative support occupations also
showed a decline (1.1 points) in the period.

A clue as to whether the changes in the earn-
ings structure in the 1980’s were due to trends in
occupational composition is provided by the de-
gree to which the shifts during the 1980's dif-
fered from those in the 1970’s, The decline of
the blue-collar occupations clearly predated the
1980’s, although it might have accelerated dur-
ing that period. The growth of managerial and
professional occupations also predated the
1980's. The only occupations in which there were



Table 6. Adding occupation and industry controls to estimates
of experience-earnings and gender-earnings
differentials, all workers, 1973-89

Change in
change Frags it | Change in | differential,
ange In "| differential, | controllin
Demographic Period | gitterential | CP¥Oll99 | controtiing ‘or
for industry | occupation
‘occupation land Industry
Men:
10-19 years.. | 1973-79 0.02 0.01 0,02 0.02
10-19 years... | 1979-89 -.01 00 =.01 00
20-29 years.. | 1973-79 01 03 01 02
20-29 years.. | 1979-89 03 04 02 03
30 or more
years...... | 1973-79 03 03 03 02
30 or more
years .. 1979-89 07 07 06 07
Women:
10-19 years... | 1973-79 01 01 01 01
10-19 years... | 1979-89 07 06 06 06
20-29 years... | 1973-79 ~02 -.03 02 -.03
20-29 years.. | 1979-89 MW 09 09 08
30 or more
years...... | 1973-79 00 00 00 -00
30 or more
years...... | 1979-89 04 04 03 03
Men-women.... | 1973-79 1 -A12 —12 —13
1979-89| -11 =08 =09 =07

Note: For experience-earnings differentials, base = 0-9 years of potential labor market
experience.

Summary and conclusions

Several interesting findings have emerged from
the analysis presented in this article. During the
1980’s, for education-earnings differentials,
shifts in occupational demand accounted for
roughly one-third of the change in the gap. The
analysis indicates, however, that the rise in the
demand for skills possessed by more educated
workers is not a new phenomenon: changes in
the occupational mix during the 1980's marked no
abrupt departure from patterns in the 1970’s. In light
of this relative stability, why were the changes in
the earnings structure so different across the de-
cades? Partly, this is due to a mismatch between
the growth in the demand for more educated work-
ers and the increase in the supply of such workers:
during the 1970's, the entry into the work force of

Footnotes

highly educated baby-boomers helped meet the
growing demand for more educated workers, but
in the 1980’s, the growth of the proportion of col-
lege graduates in the work force decelerated as
smaller cohorts entered the market.4

What other factors explain the change in edu-
cation-earnings differentials that occupational
forces do not? Aside from the factors already
mentioned, it seems likely that there was an in-
crease in the return to the general skills possessed
by college graduates (for example, the analyti-
cal and cognitive skills that are not specific to a
particular job), and not just to the more special-
ized skills demanded in the types of occupations
college graduates tend to fill.4°

Shifts in occupational and industrial demand
turn out to be relatively unimportant in explain-
ing changes in returns to experience during the
1980’s. Supply-side shifts also do not seem to
be part of the story: while the growth in the sup-
ply of less experienced workers was important
in explaining the rise in returns to experience in
the 1970's, the work force actually became more
experienced in the 1980's as the baby-boomers
aged. Katz and Murphy speculate that the rise in
returns to experience—having been concentrated
among the less educated—-was actually related
to the forces leading to the rise in returns to edu-
cation.“ Because older workers tend to be insu-
lated from labor market forces by seniority pro-
visions, and because they might possess skills
specific to a firm, younger, less educated work-
ers would tend to be hit harder than their older
counterparts by the forces tilting demand toward
workers with higher levels of schooling.

Changes in occupational demand played a
nonnegligible, but small, role in narrowing the
earings gap between men and women in the
1980’s. What else has been important? As men-
tioned earlier, the disproportionate effect of the
decline in unionization on men is one factor. In
addition, a decline in differences across the gen-
ders in actual labor market experience and in the
return to that experience has been found to have
been important. Finally, a decline in discrimina-
tion may have had an impact as well. im

ACKNOWLEDGMENT: The author thanks Michael Horrigan
and Mary Joyce of the Bureau of Labor Statistics; Professor
David Howell of the New School for Social Research; and
Professor Edward Wolff of New York University for helpful
comments and discussions. This article would not have been
possible without the generous assistance of Lynn Weidman
of the Census Bureau, who provided the software necessary
for recoding occupations.

"See Claudia Goldin and Robert A. Margo, “The Great
Compression: The Wage Structure in the United States at
Mid-Century.” Quarterly Journal of Economics, February
1992, pp. 1-34, for a long-term view of U.S. earnings in-
equality trends.

2 For a survey of this literature, see Frank Levy and Richard
J, Murnane, “U.S. Earnings Levels and Earnings Inequality: A
Review of Recent Trends and Proposed Explanations,” Jour-
nal of Economic Literature, September 1992, pp. 1333-81.

3 One reason for the lack of focus on actual jobs in the
economy is that changes in the occupational classification
scheme by the Census Bureau have made it difficult to com-
pare the occupational structure of the economy before and
after 1983. To circumvent this problem, occupation data
taken from the crs before 1983 were recoded in post-1983
terms. (See the appendix at the end of the article.)

4 It should be stressed at the outset that skills are not the
only factor involved in the distribution of demographic

Monthly Labor Review July 1994 25



Occupational Earnings in the 1980's

collar machine operators, whose average earn-
ings declined in the 1980's, had been among the
few gainers in the 1970's.

Changes in the demand for skills

What have been the implications of the shifts in
occupational demand for changes in the demand
for skills? As previously described, changes in
the demand for skills can come from shifts in
industrial composition, changes in occupational
patterns within industries, and changes in the
skills required by occupations.

Impact of occupational and industrial employ-
ment shifts. ‘The changes in occupational com-
position discussed in the previous section, which
incorporate the impact of the first two catego-
ries of change mentioned, indicate that in the
1980’s, demand was growing for the skills re-
quired in professional and managerial occupa-
tions. In these occupations, cognitive and inter-
personal skills tend to be in high demand, but
motor skills do not. Again, itis important to keep
in mind that, in terms of compositional shifts,
the 1980’s may represent an acceleration in the
Tate at which the demand for cognitive and in-
terpersonal skills is increasing, but not a major
break from previous trends.

A study by Kevin M. Murphy and Finis Welch
for the period 1940-90 has arrived at a finding
consistent with the view that the demand for
skills does not show a major break in trend in
the 1980’s.3” Using education as the measure of
skill,>® they found that over this period employ-
ment shifted toward occupations with higher lev-
els of schooling, but that there was no evidence
that the demand for education grew particularly
rapidly in the 1980's. In another study assessing
the impact of changes in industrial and occupa-
tional composition on cognitive, interactive, and
motor skill requirements, Howell and Wolff?
determined that, with the exception of motor
skills, changing employment patterns have had
the effect of raising skill requirements. But they
also found a sharp deceleration in the rate of growth
in these requirements between 1960 and 1985.

The changing skill requirements of jobs. The
studies just mentioned provide a perspective on
the effects of occupational and industrial employ-
ment shifts on the skill requirements of jobs. But
the lack of good data makes it difficult to assess
trends in the third category of shifts affecting
changes in the demand for skills: the skill re-
quirements of jobs. For many years, there has
been an ongoing debate in the social sciences
about the effects of new technology on the de-
mand for skills. One view is that technical change
tends to increase the demand for cognitive skills

22 Monthly Labor Review July 1994

as the need for physical labor is reduced by au-
tomation. Another philososophy holds that tech-
nology is inherently “de-skilling,” as manage-
ment tries to reduce the amount of control
workers have over their jobs. Yet a third school
of thought argues that it is not possible to deter-
mine from first principles the effect of new tech-
nology on the demand for skills.” In a recent
study of manufacturing establishments in the
1980’s, Peter Cappelli has found that there was
a significant increase in the level of skills re-
quired for most production jobs, but that clerical
jobs are evenly split between those that have been
“upskilled” and those that have been “de-
skilled.”*! Except for some cases studies, the lit-
erature is virtually silent on trends in the skill
requirements of other occupations within manu-
facturing and of nonmanufacturing as a whole.

Quantitative aspects of the changes

Demand indexes. As a first pass at assessing
whether the changes in occupational demand
noted in the previous section play an important
role in the changes in relative earnings by de-
mographic group, an index of demand based on
occupations is devised. For a given demographic
group i, the shift in demand relative to other
groups can be calculated as the weighted per-
cent change in employment share by occupation;
that is,

AD,

AEMP,
a) D

“EMP,”

where D, is the demand for demographic group
i, a, is the proportion of that group employed in
the jth occupation in the base year, and EMP, is
the share of employment in the jth occupation.
Table 4 shows this measure for different demo-
graphic groups; the occupations are the Census
Bureau’s detailed occupations listed in the tech-
nical documentation to the CPS, the sample is all
workers, and the measure is computed as an an-
nual average.*?

If workers of a particular group are overrep-
resented in an occupation, the growth of employ-
ment in that occupation will tend to increase the
demand for that group and thus raise its relative
pay. The demand indexes will then be highest
for groups that are concentrated in growing oc-
cupations. Which groups have benefited most
from the shifts in occupational demand during
the 1980s? Table 4 indicates clearly that occu-
pational shifts favored the more educated: for
both men and women, the changes in the demand
index are ordered by education. It is apparent,
though, that this trend did not begin in the 1980's:
in 1973-79, the demand for college graduates
grew more quickly than for high school gradu-



Table 3. Employment shares and earnings of all workers by occupation, selected years,
Employment shares (in percent) Logarithm of earnings*
Occupation
1973 1979 1989 1973 1979 1989

Executive, administrative, and managerial 92 9.8 13 10.24 10.13 10.19
Professional specialty... | 108 11.9 13.0 9.86 9.79 9.94
Technicians . 25 3.0 36 9.72 971 9.85
Sales : a5 8.4 113 9.12 9.03 9.24
Administrative suppor 17.5 18.4 17.3 9.24 9.25 9.35
Private household . . 13 8 7 7.39 7.58
Protective service . 18 15 9.70 9.70
Other service .. 106 113 8.59 8.65
Farming, forest 9 22 19 ‘ 8.53 8.60
Precision production, craft, and r 13.7 13.0 114 9.89 9.84
Machine operators... 11.2 a7 78 9.48 9.40
Transportation and mat

‘equipment ... 49 48 43 9.73 9.61
Handlers, cleaners,

laborers: 62 57 48 9.09 9.07 8.93

1 Earnings are deflated into 1989 dollars using the gross domestic product deflator for personal consumption expenditures.

important turnabouts between the two decades
were sales and administrative support. The lack
of a sharp contrast between the decades suggests
that changes in occupational demand may not
provide the complete explanation for the shifts
in the earnings structure.

The changes in the occupational mix just noted
can be attributed either to changes in industrial
employment or to changes in occupational staff-
ing patterns within industries. To determine
which is the case, the following technique was
used: the occupational composition for 1989 was
predicted under the assumption that, for each
industry, occupational staffing patterns did not
change from 1979, so that the only changes in
occupational employment that could have re-
sulted were from shifts in industrial employ-
ment.*4 From these calculations, it is apparent
that shifts in industrial employment generally
have been in a direction consistent with the
changing occupation mix. In other words, em-
ployment tended to shift toward those industries
containing a relatively high proportion of the
growing occupations and away from those sec-
tors with a large share of declining occupations.
The magnitudes of these shifts, however, are in-
sufficient to predict the changes in occupational
composition, indicating that other forces (such
as technical change, “outsourcing,” and changes
in management techniques) were leading to shifts
in staffing patterns within industries. For ex-
ample, the method predicted that the share of
executives, adminstrators, and managers in total
employment would rise from 9,83 percent to
10.00 percent between 1979 and 1989, but the
actual gain was to 11.30 percent.

Recent research has found some support for a
role for technical change in shifting the compo-
sition of employment in manufacturing indus-

tries toward occupations requiring higher levels
of education. For example, Ernst R. Berndt,
Catherine J. Morrison, and Larry S. Rosenblum
determined that the growth in white-collar, non-
production-worker hours is positively related to
increases in what they term the “high-tech com-
position of capital.”*> Similarly, Eli Berman,
John Bound, and Zvi Griliches have found a shift
away from production workers to be positively
related to investments in computers and com-
puter-related technology, as well as to expendi-
tures on research and development.** It is an open
question whether these results would hold for
nonmanufacturing industries.

Earnings by occupation. Table 3 also provides
earnings trends by Census Bureau major occu-
pations. In the 1980’s, those occupations with
the fastest real earnings growth were sales (up
21 percent), private household occupations (up
19 percent), professional specialty occupations
(up 15 percent), and technicians (up 14 percent).
Those with real earnings declines were ali blue-
collar occupations, with the largest drops posted
for handlers, cleaners, helpers, and laborers
(down 14 percent) and transportation and mate-
rial-moving equipment occupations (down 12
percent). In general, these trends suggest, not
surprisingly, that occupations with more educated
workers tended to experience gains in average eamn-
ings. The implications of the trends for gender and
experience differentials are less transparent.

In contrast to the case of employment, earn-
ings trends show a clear shift across decades.
Some of the white-collar occupations—for ex-
ample, executive, administrative, and manage-
rial occupations, as well as professional specialty
occupations—that did reasonably well in the
1980's had fared poorly in the 1970's; and blue-

Monthly Labor Review July 1994 21



Occupational Earnings in the 1980's

ferential “net” of occupation factors with the pre-
viously calculated change (for example, a,,
—4,,) allows a calculation of the share of the edu-
cation-earnings differential that can be attributed
to occupational factors. To summarize, changes
in occupational demand will have two effects that
lead to changes in relative earnings by demo-
graphic group. The first effect is to change the
distribution of demographic groups across oc-
cupations, benefiting those groups with relative
shifts to higher paying jobs. The second effect is
to change the earnings premium associated with
particular occupations, which favors those groups
disproportionately concentrated in occupations
with rising earnings premiums. It is also possible
to assess the impact of industry factors in the
same way, as will be done momentarily.

What would have happened to education-earn-
ings differentials in the 1980's in the absence of
shifts in occupational demand? Table 5 indicates
that these differentials would have widened, but
by a substantially smaller amount. For example,
the differential between male college graduates
and high school graduates increased by 16 per-
cent, but without the shifts in occupational de-
mand, this differential would have widened by
10 percent. And controlling for shifts in occupa-
tional demand reveals that the earnings of male
college graduates relative to high school drop-
outs would have increased by 16 percent instead
of 23 percent. The results for education-earnings
differentials for females are similar, with occu-
pational effects accounting for roughly one-third
of the widening gap. In other words, as suggested,
college graduates have benefited disproportion-
ately from the changes in occupational demand:
relative to those with less education, they were
able to shift into better paying jobs (such as those
in the professions and management), and the jobs
that they tended to hold became higher paying
in relative terms as well (as shown in table 3).

Table 5 also shows how important industry
factors have been in the widening of education-
earnings differentials. In general, shifts in indus-
try demand have not been quite as significant as
changes in occupational structure. Finally, table
5 indicates that industry and occupation factors
working together are not that much more potent
than the effect of each separately, a reflection of
the fact that many occupations are concentrated
in a small number of industries.

Table 6 shows the result of an exercise con-

ducted for experience-earnings differentials simi-
lar to the exercise carried out to generate table 5.
As noted earlier, the increase in returns to expe-
rience during the 1980's was less dramatic than
the rise in returns to education, providing less
scope for occupational factors to have played a
role. Even so, it is striking to note how little shifts
in occupational demand have affected returns to
experience: the inclusion of controls for occu-
pation has virtually no impact on the changes in
relative earnings by experience group. The same.
is true for the effect of industry controls.
* Occupational factors played a nonnegligible, but
still fairly small, role in the earnings gain of women
relative to men during the 1980’s, accounting for
roughly one-quarter of the gain, as shown in table
6. Given the popular perception that women re-
cently have made further inroads into the profes-
sions, it may seem surprising that they have not
experienced greater movement into higher paying
jobs relative to men. It should be kept in mind, how-
ever, that the secular increase in the labor force
participation of women has meant an increase in
the number of women in jobs across the entire earn-
ings distribution, not just at the top. In addition,
while the decline in high-paying blue-collar jobs
certainly hurt men more than women, men ben-
efited more from the rise in pay in the professional
occupations, given their greater concentration
there.

Table 5. Adding occupation and Industry controls to estimates of education-earnings
differentials, all workers, 1973-89
Change
change in arenes" | change in | ditferential,
ographic grou Period * | differential, | controlling
Dem group od | dittorentiat | COMMONS | controlling for
for Industry | occupation
occupation and industry
Male college graduate—
High school graduate... 1973-79 | -0.04 0.01 0.02 0.01
High schoo! graduate. . 1979-89 16 10 1 09
Less than high school graduate... 1973-79 | ~.03 03 ot -04
Less than high school graduate |. 1979-89 23 14 16 “12
Female college graduate—
High schoo! graduate. 1973-79 |  -.06 ~.03 -05 -.
High school graduate... | 1978-89 14 09 08 or
Less than high school graduate . 1973-79 | -07 00 ~.05 ‘00
Less than high school graduate - 1979-89 24 13 14 "10

24 Monthly Labor Review July 1994



Maury Gittleman

Maury Gittleman is an
economist in the Office of
Publications and Special
Studies, Bureau of Labor
Statistics.

Earnings in the 1980’s:
an occupational perspective

The earnings gap between more and less
educated workers increased during the 1980's;

changes in occupational demand

accounted for roughly a third of the increase

Ts? 1980’s were a decade of dramatic
change for the earnings structure in the
United States. Differentials in earnings by
education widened considerably, the average pay
of older workers increased relative to that of
younger workers, and the earnings gap between
men and women narrowed markedly. By some
measures, these and other changes in the wage
structure caused overall levels of earnings in-
equality to rise to heights not previously seen in
the post-World War II period.!

Much research conducted in an attempt to
document and explain the recent changes had
focused almost exclusively on the demographic
characteristics of workers.” A great deal has thus
been learned about which groups have made rela-
tive gains, and which have lost, in the labor mar-
ket. Unfortunately, much less is known about
what kinds of jobs these different workers hold,
how their distribution among jobs has changed
over time, and what the trends imply about the
match between skills being demanded by em-
ployers and those available in the work force.>

Explanations for the changes in the earnings
structure can be classified into three categories:
changes in the supply of labor of different types,
changes in the demand for this labor, and changes
in wage-setting institutions. The analysis set forth
in this article fits best under the second category,
as it assesses changes in the demand for skills in
the workplace.

Directly measuring trends in the market for
skills is obviously a formidable task. Accord-
ingly, the analysis takes the approach that sub-
stantial insights into this issue can be gained by
focusing on occupations as indicators of the skills
being demanded in the workplace. Given that

16 Monthly Labor Review July 1994

each occupation differs in terms of the bundle of
skills it requires, focusing on recent develop-
ments concerning the distribution of employment
across occupations and concerning earnings by
occupation (which can be thought of as a return
to the bundle of skills utilized on the job) will
give a better understanding of the changing mar-
ket for skills. In part because demographic groups
differ in the skills they have acquired, they will
differ in how they are distributed across occupa-
tions and thus in the impact changes in occupa-
tional demand will have on their earnings.* The
article assesses how much trends in the skills
market—looked at through the proxy of occu-
pations—have influenced the aforementioned
eamings trends. Or, put in the form of a ques-
tion, Were those groups that gained ground in
the 1980s able to do so because the skills they
possessed—as evidenced by the occupations they
were concentrated in—were in growing demand?

Changes in relative earnings

The relative earnings trends that are at the focus
of the analysis presented in this article are shown
in table 1.> The calculations use the March Cur-
rent Population Surveys (cps) for 1974, 1980,
and 1990, which contain data on earnings for the
preceding year.® The shifts between 1979 and
1989 are the primary focus, with the data for
1973-79 providing a context for comparing the
1970’s with the 1980’s and with the data for
1973-89 showing how demographic groups fared
in the entire period since real wage growth be-
gan to stagnate in the mid-1970’s.

A number of important trends are apparent
from the earnings changes shown in the table for



32 For an exposition of this aspect of the human capital theory,
see Solomon William Polachek, “Occupational Self Selection:
‘A Human Capital Approach to Sex Differences in Occupational
Structure,” Review of Economics and Statistics, February 1981,
pp. 60-69.

33 For a recent analysis of segregation by gender and its
impact on the earnings gap between men and women, see
Judith Fields and Edward N. Wolff, “The Decline of Sex
Segregation and the Wage Gap,” Journal of Human Re-
sources, Fall 1991, pp. 608-22.

34 The distribution of employment in the Census Bureau's
13 major occupations was calculated for each of the Census
Bureau's 44 detailed industries at the beginning of the pe-
riod. Assuming that the proportion of employment by occu-
pation stayed constant, the distribution of employment by
industry at the end of the period can be used to predict what
the occupational composition would have been, given that
staffing patterns did not change within industries.

35 See Ernst R. Berndt, Catherine J. Morrison, and Larry
S. Rosenblum, “High-Tech Capital Formation and Labor
‘Composition in U.S, Manufacturing Industries: An Explor-
atory Analysis,” NBER Working Paper No. 4010, March 1992.

3 See Eli Berman, John Bound, and Zvi Griliches,
"Changes in the Demand for Skilled Labor within U.S.
Manufacturing Industries: Evidence from the Annual Survey

of Manufacturing,” unpublished manuscript, August 1992.

37 See Kevin M. Murphy and Finis Welch, “Occupational
Change and the Demand for Skill, 1940-90,” American Eco-
nomic Review, May 1993, pp. 122-26.

38 Tt should be kept in mind that there is some contro-
versy over the use of education as a measure of skill. The
level of education of those in the work force will be influ-
‘enced not only by the demand for skills developed in for-
mal schooling, but also by the supply of skills, which in
turn will be influenced by how well society can afford
schooling. In other words, the well-known increase in the
average schooling level of the work force is a result not only
of rising demand for schooling, but also of society’s in-
creased ability to afford it. In addition, Howell and Wolff
argue that “the usefulness of schooling measures is limited
by such well-known problems as variations in the quality of

APPENDIX: Data and methods

schooling, both over time and among regions, the use of
credentials as a screening mechanism, and inflationary trends
in credential and certification requirements” (Howell and
Wolff, “Growth and Distribution of Skills,” pp. 487-88).
They also note that, while educational indices are highly
correlated with cognitive and interactive skills, this is not
the case for motor skills.

39 Howell and Wolff, “Growth and Distribution of Skills.”

4© For a more detailed discussion of the issue, see Peter
Cappelli, “Are Skill Requirements Rising? Evidence from
Production and Clerical Jobs,” Industrial and Labor Rela-
tions Review, April 1993, pp. 515-30.

41 Cappelli, “Are Skill Requirements Rising?”

“2 There are many other ways to calculate this measure.
Ideally, the measure would control for hours, but because
of limitations in the crs noted earlier, that is not possible.
An alternative control for hours is to use only full-time, year-
round workers, which gives results qualitatively similar to
those shown in the table. Katz and Murphy, “Changes and
Relative Wages,” argue that wages of workers should be
taken into account as well, to measure employment in terms
of efficiency units. Again, it is unlikely that such a proce-
dure will lead to results very different from the ones shown
in the table.

43 The changes in relative earnings by demographic group
calculated in this way give a picture similar to one that could
be calculated from the earnings trends shown in table 1.
There are two important differences, however: first, in the
current calculations, regression techniques are used to con-
trol for other factors that may affect earnings; second, the
sample here includes all workers, rather than just full-time,
year-round workers, with differences in hours worked across
individuals controlled for by the inclusion of variables for
part-time status and weeks worked.

“For a further discussion of this issue, see Katz and
Murphy, “Changes in Relative Wages.”

45 Some support for this notion is found in Erica Groshen,
“Rising Inequality in a Salary Survey: Another Piece of the
Puzzle,” Federal Reserve Bank of Cleveland Working Pa-
per No. 9121, December 1991.

46 See Katz and Murphy, “Changes in Relative Wages.”

Occupational recoding. The Census Bureau
made dramatic changes in the scheme it used to
classify occupations between the 1970 and 1980
Censuses of Population. Because the same
scheme is used in the Current Population Sur-
vey, it is now exceedingly difficult to compare
the occupation structure before 1983 (when the
1980 occupation system was adopted) with that
after 1983. The Census Bureau has, however,
developed imputation techniques to assign 1980
occupation codes to individuals, based on their
1970 occupation code and a number of demo-
graphic characteristics. (See Lynn Weidman,
Final Report: Industry and Occupation, SRD Re-
search Report Number Census/SRD/89/03, Aug.
20, 1989.) The Census Bureau generously pro-
vided the Bureau of Labor Statistics with the
software used to recode occupations in the 1970
census, and this software was then adapted for

use with pre-1983 Current Population Surveys.
Because the techniques involved rely on impu-
tation, rather than on an exact match between
1970 and 1980 occupations, the Census Bureau
recommends that they be applied five times and
the results of statistical analyses be averaged over
these five imputations. This is the approach fol-
lowed in the body of this article.

Top coding. One difficulty in using the Cur-
rent Population Survey to study relative earnings
trends is that the true value of an individual’s
earnings is not revealed if the individual earned
more than the amount specified in a top code. To
circumvent this problem, earnings reported be-
low the top code were fit to a Pareto distribu-
tion, and the parameters of the distribution were
then used to predict the mean value of earnings
for those above the top code.

Monthly Labor Review July 1994 27



Occupational Earnings in the 1980's

groups across jobs. For example, differences between men
and women in occupational attachment may be due to dis-
crimination—both before entry into the labor market and at
the workplace—and older workers may be higher up on the:
job ladder than their younger counterparts due to seniority.
5 In this table, average earnings are calculated only for
full-time, year-round workers, in an attempt to control for
differences in hours worked across groups. To assess trends in
as broad a portion of the labor force as possible, the remaining
tables include all workers and use regression techniques where
appropriate to control for differences in hours worked.

® The crs is a monthly survey of approximately 60,000
households conducted by the Bureau of the Census for the
Bureau of Labor Statistics, For inclusion in the samples used
in the analysis presented in this article, the individual (1)
had to be between the ages of 18 and 64 years, (2) could not
be self-employed, and (3) had to have worked at least 1 week
during the year in the civilian economy. Note in table | that
because the March 1974 cps does not contain a continuous
variable for weeks worked and usual hours worked, it is not
possible to calculate an hourly wage rate for all 3 survey years
combined.

7 Throughout this article, changes in the natural logarithm of
earnings are used to approximate percent changes in earnings.

® Because the crs does not measure actual labor market
experience, potential labor market experience is used instead.
This is defined as age, minus number of years of schooling,
minus 6 years, to account for the preschool period, In some
cases, the result of the calculation is a negative number, so a
zero is substituted.

9 For women, the patterns are less clear and the signifi-
cance even less so, given that the number of years of poten-
tial experience may not be a good proxy for actual labor
market experience for many women and that the relation-
ship between these two has changed over time as women’s
labor force participation has risen.

"See, for example, Finis Welch, “Effects of Cohort Size
on Earnings: The Baby Boom Babies’ Financial Bust,” Jour-
nal of Political Economy, Vol. 81, pp. S65-S97.

'\ There are a number of alternative methods for calculat-
ing supply indexes, The indexes shown here measure the
logarithm of the change in the share of employment of all
workers by demographic group. It would have been prefer-
able to weight the employment by the number of hours
worked during the year, but this was not done because the
March 1974 ces does not contain data on usual hours worked
per week, and the data for weeks worked during the year
are grouped into categories. Another alternative (see
Lawrence F, Katz and Kevin M. Murphy, “Changes in Rela-
tive Wages, 1963-87: Supply and Demand Factors,” Quar-
terly Journal of Economics, February 1992, pp. 35-78)
would be to use an efficiency unit concept, where employ-
ment is weighted by earnings. The evidence indicates that
all of the alternatives provide similar results.

"2See George J. Borjas, Richard B. Freeman, and Lawrence
F. Katz, “On the Labor Market Effects of Immigration and
Trade,” nner Working Paper No. 3761, June 1991.

3 See June O'Neill and Solomon Polachek, “Why the
Gender Gap in Wages Narrowed in the 1980's,” Journal of
Labor Economics, January 1993, pp. 205-28. This is an
sue that cannot be explored using the ces, since, as men-
tioned earlier, that survey can measure only potential rather
than actual labor market experience.

“4 See, for example, McKinley L. Blackburn, David E.
Bloom, and Richard B. Freeman, “The declining economic
position of less-skilied American men,” in Gary Burtless,
ed., A Future of Lousy Jobs? (Washington, pc: The
Brookings Institution, 1990). See also Paul Ryscavage,

26 Monthly Labor Review July 1994

“Gender-related shifts in the distribution of wages,” this is-
sue, pp. 3-15.

15 See Francine D. Blau and Lawrence M. Kahn, “Race
and Gender Pay Differentials,” in David Lewin, Olivia S.
Mitchell, and Peter D. Sherer, eds., Research Frontiers in
Industrial Relations and Human Resources (Madison, wi,
Industrial Relations Research Association Series, 1992).

'SRichard R. Nelson and Edmund S. Phelps, “Investment
in Humans, Technological Diffusion and Economic Growth,”
American Economic Review, May 1966, p. 75.

U See Finis Welch, “Education in Production,” Journal
of Political Economy, January/February 1970, pp. S65-S97.

18See Ann P. Bartel and Frank R. Lichtenberg, “The Com-
parative Advantage of Educated Workers in Implementing
New Technology,” Review of Economics and Statistics, Feb-
ruary 1987, pp. I-11.

19 See Ann P. Bartel and Frank R. Lichtenberg, “Techni-
cal Change, Learning and Wages,” nar Working Paper No.
2732, October 1988.

20 See Lee A. Lillard and Hong W. Tan, “Private Sector
Training: Who Gets It and What Are Its Effects?” Rand
Corporation, March 1986; Indermit S. Gill, “Technological
Change, Education and Obsolescence of Human Capital:
Some Evidence for the U.S.,” unpublished manuscript, No-
vember 1989; Jacob Mincer and Yoshio Higuchi, “Wage
Structures and Labor Turnover in the U.S, and in Japan,”
Journal of the Japanese and International Economies, Sune
1988; and Jacob Mincer, “Human Capital Responses to Tech-
nological Change in the Labor Market,” Nae Working Pa-
per No. 3207, December 1989.

21 See Alan Krueger, “How Computers Have Changed the
‘Wage Structure: Evidence from Microdata,” Quarterly Jour-
nal of Economics, February 1993, pp. 33-60.

2 See Jacob Mincer, “Human Capital, Technology and
the Wage Structure: What Do Time Series Show?” unpub-
lished manuscript, August 1991.

> Blackburn, Bloom, and Freeman, “Less-skilled Ameri-
can men.”

24 See William E, Even and David A. Macpherson, “The
Decline of Private-Sector Unionism and the Gender Wage Gap,”
Journal of Human Resources, Spring 1993, pp. 279-98.

25 Blackburn, Bloom, and Freeman, “Less-Skilled Ameri-
can Men,”

Focusing on changes in occupational demand as a meas-
ure of changes in demand for labor within industries is an
approach that is complementary to assessing the impact of
technical change and other within-industry forces mentioned
earlier.

27 See, for example, Chinhui Juhn, Kevin M. Murphy, and
Brooks Pierce, “Wage Inequality and the Rise in Returns to
Skill,” Journal of Political Economy, June 1993, pp. 410-42.

28 See David R. Howell and Edward N. Wolff, “Trends in
the Growth and Distribution of Skills in the U.S. Workplace,
1960-85,” Industrial and Labor Relations Review, April
1991, p. 487.

29 See Gary S. Becker, Human Capital (New York, Na-
tional Bureau of Economic Research, 1964), for an early
elaboration of this view.

30 See, for example, Paul J. Taubman and Terence J. Wales,
“Higher Education, Mental Ability and Screening,” Jour-
nal of Political Economy, Vol. 81, 1973.

3! For a discussion of the extent to which college gradu-
ates take jobs that do not require a college degree, see Daniel
Hecker, “Reconciling conflicting data on jobs for college
graduates,” Monthly Labor Review, July 1992, pp. 3-12.



ates and dropouts, for both men and women. The
magnitudes of the changes do indicate, however,
a modest acceleration in the shift toward the more
highly educated in the 1980's, one that may even
be greater than indicated here because demand
shifts were highest for those groups with increas-
ing earnings.

Categorical statements about which experience
groups were favored by shifts in occupational de-
mand are much harder to make. For both men and
women, the differences in changes in the occupa-
tional demand index across experience groups were
relatively small in the 1980’s. For men, there is no
apparent order to the shifts, while for women, the
less experienced appear to have benefited more.

In light of the strong earnings gains by women.
relative to men during the 1980's, it is striking
to note that shifts in occupational demand, at least
by this measure, did not favor females. Shifts in
occupational demand were actually slightly more
favorable toward men than women in the 1980's,
the opposite of the situation in the 1970's. This
suggests that we must look outside the realm of
changes in occupational demand to determine
why women’s earnings gained ground on men’s
during the 1980s.

Regression analysis. Although the preceding
results are suggestive, it is useful to quantify the
impacts of occupational shifts on relative earn-
ings. To do this, regression-decomposition analy-
sis is used. This framework is a convenient tool
for attributing changes in a given earnings dif-
ferential (for example, that between college
graduates and high school graduates, or between
those with 20 to 29 years of experience and those
with 0 to 9 years of experience, or between men
and women) to shifts in the distribution of occu-
pations. The technique is applied in terms of edu-
cation-earnings differentials as follows: a regres-
sion for the base year (year s) is run, regressing
(In) earnings on variables representing years of
schooling, years of potential labor market expe-
rience, race, region, whether the individual lived
in a metropolitan area, part-time status, and
weeks worked. The formula is

In Earnings = a,, + a,, LTHS+ a,,HS
@) +a,,SOME +a,,X+e,

where LTHS is a variable indicating that the in-
dividual has less than a high school education;
HS is a variable indicating that the individual
has a high school education; SOME is a variable
indicating that the individual has some college;
X is a vector representing the other variables
mentioned; the a,,’s are the corresponding coef-
ficients for the year s, and ¢ is an error term. The
coefficients on the variables representing the

Table 4. Annual average relative changes in demand based on
detalled occupations, all workers, 1973-89

Demographic group 1973-89 | 1973-79 | 1979-89
0.25 0.24 ~0.23
-36 03 =.38
-88 1.04
~44 -62
14 19
99 1.05
-99 -98
16 —56
77 —.08
Bt 43
-29 -32
a1 -07
-.09 -13
-35 -33
30 -28
05 -.36
01 =45
37 —57

* Potential labor market experience is defined as age, minus number of years of schoo!-
ing, minus 6 years, to account for the preschool period. It is used because the Current

Population Survey does not measure actual labor market experience.

schooling groups can be used to measure the
average difference in earnings between one group
and another, after controlling for other relevant
variables that affect earning power. For example,
measures the difference between earnings of
high school graduates and college graduates. This
same regression is then rerun for the end year,
year t, and the change in the coefficients for
schooling groups can be used to measure the
change in relative earnings by schooling group.
(For example, a,,— @,, measures the change in
the college graduate-high school graduate dif-
ferential between year s and year #).*3
The same exercise is then repeated for the
beginning and ending year of the period, this time
including in the regressions variables represent-
ing the individual’s occupations:

(3) In Eamings = by, +b,,LTHS + b,,HS
+b,,SOME + b,,X+b,,0CC+e.

Here, the vector of occupation variables, OCC,
controls for the differences across education
groups in the distribution of these groups across
occupations, as well as for differences in aver-
age earnings across occupations. Calculating the
change in education-earnings differentials across
years in this specification gives the change in
relative earnings “net” of occupation factors (for
example, b,, — b,,). Comparing the change in dif-

Monthly Labor Review July 1994 23



Wage-setting institutions. Finally, to shift fo-
cus to institutional factors that may have affected
the wage structure, Blackburn, Bloom, and Free-
man have found that, because of the existence of
a union premium and the fact that the less edu-
cated are more likely to be union members, the
decline in unionization in the United States has
contributed to a widening of education-earnings
differentials. Recent research has also determined
that the decline in unionization is responsible for
about one-seventh of the contraction of the gen-
der gap. This result may be attributed to the fact
that union membership rates have declined more
slowly for women than for men, primarily be-
cause women were less concentrated in jobs
where unionization was falling.” On the other
hand, Blackburn, Bloom, and Freeman have
found that the decline in the real value of the
minimum wage has not played an important role
in the changes in education-earnings differentials.?5

Occupations and the demand for skills

To sum up the findings of recent research, the
evidence suggests that while supply and institu-
tional factors may have played some role, de-
mand clearly has shifted in favor of more edu-
cated workers. Breaking this down even further,
the shifts in demand have occurred primarily
within, rather than across, industries. In light of
these findings, it is natural to ask whether one
can detect, in changes in occupational composi-
tion, a shift in demand for skills that would fa-
vor the demographic groups that performed well
in the labor markets of the 1980’s.7° Before do-
ing so, however, it is instructive to outline the
connections among occupations, skills, and rela-
tive earnings. Much recent work has speculated
that many of the changes in the earnings struc-
ture—particularly the rise in returns to educa-
tion—have come about as a result of a rise in the
return to skill.27 The definition of skill is usually
not made precise, but the term is often spoken of
in connection with education, implying that skill
is developed in school. In fact, the skills required
on the job are multidimensional, and not all of
them are the kind of skills developed through
formal education. To use one taxonomy, David
R. Howell and Edward N. Wolff write of jobs as
being “defined by a set of tasks requiring some
combination of motor skills (manual dexterity,
motor coordination), interpersonal skills, orga-
nizational and managerial skills (leadership, au-
tonomy and responsibility), verbal and language
skills, diagnostic skills (synthetic reasoning abili-
ties), and analytical skills (mathematical and logi-
cal reasoning abilities).”28

Measuring the demand for skills. Given the
complexity of measuring the sets of skills re-

quired for just one job, it is apparent that deter-
mining the skill requirements for the economy
as a whole, as well as changes in such require-
ments, is a difficult task. In light of the dearth of
data directly measuring the skill requirements of
jobs, an alternative approach is to focus on the
occupational composition of the economy, as an
occupation—if defined narrowly enough—can
serve as a proxy for a set of skills. Using this
approach, we can group changes in the skill re-
quirements of jobs into three categories. First,
shifts in the industrial composition of employ-
ment—resulting from changes in the demand for
products and differences in productivity growth
across sectors—will lead to changes in occupa-
tional composition and, thus, skill requirements
because the distribution of employment by oc-
cupations differs across industries. For example,
the secular shift of the economy away from
manufacturing and toward services has contrib-
uted to a shift from blue-collar to white-collar
work, leading to a decline in demand for the
motor skills required in production work and an
increase in the cognitive and interpersonal skills
needed in clerical and professional positions.

Second, staffing patterns can change within
industries themselves as a result of “outsourc-
ing,” changes in management techniques, and
other factors. For example, if firms move pro-
duction activities abroad in order to take advan-
tage of a less expensive pool of blue-collar la-
bor, the proportion of white-collar employment
at these companies will increase.

Finally, the skills required in an occupation
itself can change, often owing to the introduc-
tion of new technology. The spread of word proc-
essors, to cite one example, has had a marked
effect on skills, requiring secretaries to leam how
to use personal computers rather than typewrit-
ers and leading many professionals to do for
themselves many of the clerical responsibilities
previously performed by their secretaries.

Skill requirements and the earnings of demo-
graphic groups. How do skill requirements of
the economy affect the earnings of particular
demographic groups? It is perhaps easiest to ex-
plain how the demand for different sets of skills
will cause earnings to differ across education
groups. A number of theories have been put forth
to describe the relationships among level of
schooling, occupation, and earnings by educa-
tion group. According to one view prominent
among academic economists studying the labor
market—known as human capital theory—
schooling develops the skills required to perform
the tasks in a particular occupation, and the re-
sulting enhancement of productivity leads to
higher earnings.”? An alternative to human capi-

Monthly Labor Review July 1994 19



Occupational Earnings in the 1980's

June O’ Neill and Solomon Polachek explore
a supply-side hypothesis for the gains in earn-
ings of women relative to men.'? They find that
much of the contraction in the gap can be ex-
plained by forces relating to actual labor market
experience: first, differences in actual labor mar-
ket experience have narrowed, and second, dif-
ferences in returns to this experience across the
genders have narrowed, which they attribute to
higher levels of on-the-job training for women.

The demand for workers. Perhaps the most
prominent hypothesis on the demand side has
been that shifts in the industrial composition of
employment—particularly as a result of the de-
cline in the employment share of manufactur-
ing—have reduced the number of high-paying
jobs available to workers with low levels of edu-
cation, Recent research'* provides some support
for this hypothesis in connection with the wid-
ening of education-earnings differentials, but
overall, most of the increase in returns to educa-
tion has occurred within industries. Similarly,
past research has found that industry effects did
not play a large role in the reduction of the earn-
ings gap between men and women in the 1980’s.5

Given that changes in industry composition
do not explain the bulk of the changes in rela-
tive earnings, it is necessary to examine the rea-

Table 2. Relative changes in the supply of all workers, 1973-89
[Change in logarithm of share of employment x 100]

Demographic group 1973-69 | 1973-79 | 1979-89
Men.. -97 62 3.5
Women. : 117 77 4.0
Years of schooling
04.8 29.9 -34.9
+465 45 -2.0
144 83 27
16 or more. 287 10.0 16.7
Women:
59.0 23.2 35.8
43 81 -38
46.4 25.0 21.4
46 of more 64.2 30.3 33.9
Years of potential experience!
Men:
0-9. -2.0 37 ~23.8
10-19 313 7.0 24.3
20-29... : -26 18.4 15.8
30 or more............ 47.0 23.5 23.5
Women:
0-9. 48 11.2 16.0
10-19... 56.4 29.9 26.6
20-29... 276 12 26.4
30 or more 23.2 125 -107
* Potential labor market experience is defined as age, minus number of years of school-

ing, minus 6 years, to account for the praschool period.
Population Survey does not measure actual labor market experience.

is used because the Current

18 Monthly Labor Review July 1994

sons why changes within industries have oc-
curred. Candidates for within-industry shifts in
demand in favor of more educated workers in-
clude changes in technology that shift demand
toward more skilled workers, “outsourcing” to
foreign locations of activities previously per-
formed by unskilled workers, and changes in
management techniques that favor one group
over another. Older workers may experience
what is in effect a favorable within-industry de-
mand shift when seniority provisions protect
them from layoffs during the restructuring of an
industry. Women, too, can find occupational pat-
ters changing as discrimination lessens or as
changing cultural mores lead to greater similar-
ity in career pursuits across the genders.

Of the forces leading to shifts in demand for
labor within industries, the one that has received
the most attention is the influence of technical
change on the demand for educated workers. A
number of related hypotheses involving the con-
nection between technical change and returns to
education have been put forth. One explanation
draws on work by Richard R. Nelson and
Edmund S. Phelps, who suggest that “the rate of
return to education is greater the more techno-
logically progressive the economy.”"© This may
occur because, in a technologically dynamic en-
vironment, educated workers have better
“allocative ability in the sense of selecting the
appropriate input bundles and of efficiently dis-
tributing inputs between competing uses,”!” be-
cause educated workers have a comparative ad-
vantage in adjusting to and implementing new
technology, '® or because the introduction of new
technology increases the need for learning by
workers, and better educated workers are better
learners.!? Apart from these studies, the argument
that the returns to education are higher in indus-
tries with rapid technological progress has received
empirical support from a number of sources.°

Despite the empirical support for the linkage
between technical change and returns to educa-
tion at a given point in time, the findings about
the relationship between technical progress and
changes in the return to education over time are
mixed. Alan Krueger observes that, because
workers who use computers eam more than other
workers, and because such workers are likely to
be better educated, the expansion of computer
use during the 1980's can account for an impor-
tant part of the change in returns to education?!
Similarly, Jacob Mincer has found that, over time,
expenditures on research and development and
on new capital equipment have had a positive
impact on the education wage premium.” On the
other hand, McKinley Blackburn, David Bloom,
and Richard Freeman could not find evidence of
such a relationship.”



full-time, year-round workers. Most striking is
the performance of men who did not graduate
from high school. In real terms, their earnings
declined by nearly 20 percent between 1979 and
1989, and by nearly 25 percent relative to col-
lege graduates.’ For women, there is a similar
widening of the gap between the more and less
educated, although in absolute terms, only those
who did not graduate from high school suffered
a decline in purchasing power. The gains by more
educated groups are in sharp contrast to trends
in the 1970's, when, on the whole, these groups
Jost ground. Also of note is that there has gener-
ally been an increase in returns to experience®
for men. To give the most dramatic example,
between 1973 and 1989, earnings of men with
30 or more years of experience rose by 14.5 per-
cent (0.034 — (-0.111) = 0.145) relative to those
with 10-19 years of experience.9 Finally, women
have gained substantial ground on men in recent
years—12.3 percent between 1979 and 1989 alone.

Explanations of the changes

Why did these profound changes in the earnings
structure occur in the 1980’s? As noted earlier,
alternative hypotheses can be grouped into three
categories: changes in the supply of labor of dif-
ferent types, changes in the demand for this la-
bor, and changes in institutions that affect the
setting of wages.

The supply of workers. Economic theory tells
us that an increase in the relative supply of a
demographic group tends to depress that group’s
earning power. To give a prominent example,
supply-side changes resulting from the entry into
the work force of the baby-boomers was an im-
portant factor in the rise in returns to experience
during the 1970’s.'° An examination of supply
patterns, however, reveals little support for
simple supply-side explanations for the changes
in the 1980’s. The calculation of relative supply
indexes (a measure of the share of jobs held by
specific demographic groups), shown in table 2,
demonstrates a striking increase in the average
education level of the labor force.!! For both men
and women, college graduates were the fastest
growing education group in the 1980's, with
those without any college at all showing declines
as a share of the labor force. Yet, this is exactly
the opposite of what a supply-side explanation
would predict. Indeed, the data show that those
education groups with the fastest growth in earn-
ings had the biggest rise in their share of the la-
bor force. Similarly, women posted gains in their
share of the labor force at the same time their
earnings were moving closer to men’s levels.
With regard to potential experience, the picture
is somewhat less clear. It suffices to say, how-

ever, that there is no obvious pattern indicating
that the groups with the largest increases in earn-
ings have declined in their relative shares of the
labor force.

With a simple supply-side explanation ruled
out, it is worthwhile to consider more sophisti-
cated supply-side hypotheses for the rise in re-
turns to education. Some observers have specu-
lated that a decline in the quality of high school
education, perhaps coinciding with a decline in
test scores that occurred during the 1970’s, may
have contributed to a widening gap between the
productivity and, thus, the earnings of those who
have gone on to college and those who have not.
This hypothesis is inconsistent, however, with
the fact that education-earnings differentials wid-
ened for cohorts that entered the labor force well
before the postulated decline in the quality of
high school education. Past research has found
some support, however, for the idea that the po-
sition of the less educated—particularly those
without a high school education—has worsened
owing to the increase in the relative supply of
less skilled labor resulting from a rise in the flow
of immigrants into the labor force.!?

Monthly Labor Review July 1994

Table 1. Changes in real earnings of full-time, year-round
workers, 1973-89
[Difference in averages of logarithms]
Demographic group 1973-89 1973-79 1979-89
0.023 0.019 0.004
-.050 -018 =.082
137 047 091
Years of schooling
Men:
on —.247 051 196
12... 155 -.041 =.113
13-15 -071 ~.044 ~.028
16 or more 024 -.073 049
Women:
O14... -.018 041 -.059
12... 042 028 015
13-15 076 .005 071
16 or more 122 ~.019 141
Years of potential experience’
Men:
0-9-2... 082 -.002 -.080
10-19... nat -.026 .085
20-29 -.049 -.033 -.016
30 or more 034 028 005
Women:
0-9 .. 107 055 .051
10-19... 135 042 093
20-29....... 157 033 125
30 or more .. 095 043 051
* Potential labor market experience is defined as age, minus number of years of school-
ing, minus 6 years, to account for the preschool period. It is used because the Current
Population Survey does not measure actua/ labor market experience.
Nore: Earnings are deflated into 1989 dollars using the gross domestic product defla-
tor for personal consumption expenditures,

17