Schooling, Ability, and Earnings: Cross-SectionalFindings 8 to 14 Years
after High School Graduation
Robert M. Hauser, Thomas N. Daymont
Sociology of Education, Volume 50, Issue 3 (Jul., 1977), 182-206.
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SCHOOLING, ABILITY, AND EARNINGS:
CROSS-SECTIONAL FINDINGS 8 TO 14 YEARS AFTER
HIGH SCHOOL GRADUATION*
ROBERTM. HAUSER
THOMAS
N. DAYMONT
The University of Wisconsin-Madison
Sociology of Education 1977, Vol. 50 (July): 182-206
This paper extends and mod8es the Sewell and Hauser (1975) analysis of annual earnings
cross-sections of male Wisconsin high school graduates through the 14th year following high
school graduation. In the first part of the analysis, we estimate a simple recursive model of
earnings attainment (including social background, mental ability, and post-secondary educational attainment) for annual earnings cross-sections for the years 1965 to 1971 (8 to 14 years
after high school). We find that the degree and manner in which the variables in our model
influence earnings vary across annual cross-sections. In the second part of the analysis, we
shift earnings profiles to allow for the assessment of the returns to schooling net of work
experience. The results of the "experience controlled" analysis suggest that ( I ) the economic
returns to post-secondary schooling are approximately 10 percent per year i f a bachelor's
degree is obtained and approximately 6 percent per year if a bachelor's degree is not obtained,
and (2) the economic returns to post-secondary schooling are quite constant across experience
levels. Bias in the estimation of economic returns to schooling and interactions between the
effects of ability and other variables on earnings are also examined.
George Bernard Shaw, Major Barbara
Act I11
Undershaft: But, Mr. Cusins, this is a
serious matter of business.
You are not bringing any
capital into the concern.
Cusins:
What! No capital? Is my mastery of Greek no capital? Is
my access to the subtlest
thought, the loftiest poetry
yet attained by humanity no
capital? My character, my intellect, my life, my career,
what Barbara calls my
soul-are these no capital?
Say another word; and I double my salary.
* This research was supported by grants from the
National Institute of Mental Health (MH-06275) and
from the Social Security Administration (CRDG314). Certain data used in this report were derived
from statistics furnished by the Social Security Administration. The authors did not at any time have
access to any information relating to specific individuals or reporting units. The authors assume full
responsibility for the analysis and interpretation of
the data. We thank William H. Sewell, Zvi Griliches,
Sandy Jencks, Rafe Stolzenberg, and Paul Taubman
for their comments on earlier drafts of this paper.
In their analysis of the earnings of male
Wisconsin high school graduates, Sewell
and Hauser (1975) focused on the period 8
to 10 years after high school graduation
(1965 to 1967). This paper extends the
Sewell-Hauser (1975) analysis of earnings
cross-sections through 1971, that is,
through the 14th year following graduation
from high school. Our population definitions and methods differ in important
ways from those of Sewell and Hauser,
and for this reason we include results for
the years 1965 to 1967 as well as the more
recent period. After defining a population
of interest, we look at patterns of growth
in earnings among men who differ on parental income, measured mental ability,
and schooling. We then estimate a simple
recursive model of ability, schooling, and
earnings, and we use this model to interpret changes from year to year in the effects on earnings of parental income,
ability, and schooling. Of special interest
are our findings about bias in the schooling coefficient and our findings concerning
possible interaction effects of ability and
parental income on education and of
ability and education on earnings. Finally,
we present some relationships among
,
SCHOOLING, ABILITY, AND EARNINGS
schooling, experience, and earnings, and
we attempt to relate these to our findings
in annual cross sections.
In this analysis, we ignore nonpecuniary returns to education. This does
not mean that we think they are unimportant nor that they would not affect monetary returns. Also, we do not claim to
show how the distribution of earnings is
determined, but how people are allocated
to different positions in the distribution.
Thus, in contrast to the assumptions of
human capital theory (e.g., Becker, 1964
and Mincer, 1974) which suggest that the
variance of the earnings distribution may
be produced by schooling or other characteristics of the individual (cf. Jencks, et
al., 1972), we do not assume that the
shape or degree of variance of the earnings distribution is necessarily produced
by models like ours. The variables
analyzed here reflect only a few of the
factors which determine the distribution
of earnings; some of the other factors are
the business cycle, technology, prices and
quantities of natural resources, and power
relationships among groups with different
relations to one another or to the means of
production.
Previous Findings
Sewell and Hauser used a recursive
structural equation model to interpret the
effects on annual cross-sectional earnings
distributions of socioeconomic background, mental ability and other psychological variables, schooling, college quality, and occupational status. While the
original Wisconsin sample included almost 5,000 male graduates of 1957, the
Sewell-Hauser analysis was restricted to a
subsample of about 2,000 surviving men of
nonfarm origin who were not in school or
in military service in 1964 and for whom
several key variables had been ascertained. About two-thirds of the attrition
was attributable to the restrictions on farm
background and on military service and
school enrollment in 1964 (Sewell and
Hauser, 1975: 46).
Sewell and Hauser found that none of
the variables in their model accounted for
much of the variation in annual earnings.
For example, a regression analysis on 13
predetermined variables accounted for
less than 8 percent of the variance in 1967
earnings. Still, some findings were significant. The effects of several variables on
earnings increased regularly-in the same
proportion-from
1965 through 1967.
Most aspects of social background affected earnings only by increasing or decreasing the amount of post-secondary
schooling. Neither paternal or maternal
educational attainment nor paternal occupational status affected earnings directly.
At the same time an index of parental income in the four years following high
school graduation (1957 to 1960) had a
substantial effect on earnings of sons
which was not mediated by ability, schooling, or occupational status. One thousand
dollars of parental income was worth $112
in son's earnings ten years after high
school graduation, even after parental influences on ability, schooling, and occupational status had been taken into account (Sewell and Hauser, 1975: 108). Nor
were the effects of parental income explained by its possible influence on college
quality, labor market experience, marital
status, or military service. Further, Yang
and Sewell (1976) found that migration
and size of place of residence in 1957 and
in 1964 did not attenuate the direct impact
of parental income on earnings in the nonfarm population.
To almost the degree that parental income dominated regressions of earnings
on socioeconomic background, measured
mental ability stood out among the several
social psychological factors assessed by
Sewell and Hauser. Net of socioeconomic
background, a ten-point increase in measured IQ led to a $239 increase in 1967
earnings, of which about one half persisted when schooling and occupational
status were controlled (Sewell and
Hauser, 1975: 80-81). High school grades,
social influences of parents, peers and
teachers, and educational and occupational aspirations all affected earnings
primarily through the length of schooling.
In a way, this was to be expected, for all
but one of these variables manifestly referred to future schooling, but not to future economic achievement. Only the occupational aspiration measure-which did
have a significant coefficient in the final
184
HAUSER AND DAYMONT
earnings equation (Sewell and Hauser,
1975: 97-100)-appeared
to tap a strictly
economic dimension of achievement.
In 1965 to 1967 the effects of postsecondary schooling on earnings were
rather low. As late as 1967 an additional
year of school was worth only $200 in
additional earnings. In part this small ef-'
fect of schooling could be attributed to the
common causes of earnings and schooling; in 1967 the regression of earnings on
schooling was reduced by 36 percent
when socioeconomic background and
ability were controlled. However, even
the zero-order regression was not very
steep; each additional year of schooling
was associated with scarcely more than
$300 in additional earnings. This was
barely more than a 4 percent shift at the
mean of the earnings distribution ($7,538).
Recent Developments
The data on men in the Wisconsin sample of high school graduates have recently
been supplemented in two important
ways. First, a highly successful telephone
survey of the sample was carried out during 1975. More than 88 percent of the original members of the sample were interviewed, including 90.5 percent of the surviving men in the civilian non-institutional
population (Clarridge, Sheehy , and
Hauser, 1978). Respondents were asked
about selected characteristics of their
families of orientation and procreation and
about their schooling, military service,
labor force experience, and social participation (Sewell and Hauser, 1976). Second,
in late 1975 the Social Security Adminis-
tration updated the earnings histories of
men in the sample through 1971, that is,
by adding data for the years 1968 through
1971. These supplemented earnings histories are the subject of the present report.
They have not yet been merged with the
1975 survey data because we do not have
access to identifiable earnings data for individuals (Sewell and Hauser 1975: 203205). We are presently working with the
Social Security Administration to effect
such a merger.
Sample Coverage
The present analysis covers male high
school graduates for whom educational attainment was ascertained in 1964, for
whom parental income and other socioeconomic background characteristics had
been ascertained, who were not farmers in
1964, and whose covered earnings in the
reporting year were greater than $3,000 in
1972 dollars. Table 1 shows the effects of
these definitions on the disposition of
sample cases. Twenty-four percent of the
members of the sample were dropped because of missing data, and another 4 percent because they were farmers. Men who
worked in 1964 at an unknown occupation
and whose fathers were farmers were
treated as farmers, that is, dropped from
the analysis. However, those from farm
backgrounds who did not become farmers
were retained in the analysis. Many of the
cases with missing observations on parental income or father's occupation (13 percent) might have been retained if we were
willing to accept regression estimates of
those variables based on parental schooling.
TABLE 1
Disposition of Sample Cases: Male Wisconsin High School Graduates o f 1957
Source
Male High School Graduates
Ineligible by Reason:
1. No response in 1964 survey
2. Missing data (parental income or father's
occupation)
3. Farmer in 1964 (or 1964 occupation unknown, farm father)
Eligible Respondents
Frequency
4863
537
627
203
3496
Note: Eligibility criteria were applied sequentially in the order given.
% of Total
SCHOOLING, ABILITY, AND EARNINGS
Our purpose in modifying the population definition adopted by Sewell and
Hauser was to increase the coverage of
the initial sample while attempting to exclude from the analysis those men who
were not fully committed to the labor
market. Having no measure of labor force
participation except school enrollment
and job-holding in 1964 (which were used
by Sewell and Hauser), we were forced to
use a lower bound on the earnings distribution to define the population of interest.
While we chose $3,000 of earnings in 1972
dollars as the lower bound for the analyses
reported here, we experimented with
other limits, and our findings were insensitive to the choice of limits. In the distribution of the log of earnings, earnings less
than $3,000 are extreme negative outliers.
Even if they represent extremely low
earnings of persons fully committed to the
labor market, the inclusion in the analysis
of even a small number of such outliers
would distort our findings for the vast
majority of men. The occurrence of extremely low earnings deserves separate
study. We also experimented with truncation of some positive outliers in the earnings distribution. This had little effect on
our results, so we retained the complete
detail of earnings greater than $3,000. Of
the 3,496 men in the eligible subsample,
between 3,031 and 3,137 exceeded the
lower earnings bound in the years 1965 to
1971.
Measurement of Earnings and Other
Variables
Our measure of earnings includes all
earnings in covered employment for wage
and salary workers who earned less than
the ceiling on covered earnings in each
year. For workers who exceeded the
maximum, annual earnings were estimated by extrapolation from quarterly
earnings reports. An effort was also made
to correct for the effects of multiple jobholding and changes of employer.
Covered self-employment earnings were
ascertained exactly by the Social Security
Administration.
Most of our analysis concerns earnings
differentials within years and comparisons
of these differentials between years. Since
185
we write earnings functions in semi-log
form (unlike Sewell and Hauser, who estimated equations in current dollar earnings), these analyses are not affected by
price changes or economy-wide changes
in productivity. However, at a few points
we look at growth in earnings from year to
year, and for tKis purpose we attempted to
remove the effects of price and productivity changes from the earnings streams.
That is, we did not want to confound
growth in personal earnings with inflation
or with economy-wide productivity
changes in which all workers shared. We
adjusted earnings in each year by multiplying them by the ratio of mean personal
earnings per member of the labor force in
1972 to the mean in the reporting year
(U.S. President, 1975: 270-271, 276). In
the last section of this paper we briefly
examine the sensitivity of some of our results to our choice of this adjustment procedure. Despite our substantial adjustment, Table 2 shows that mean earnings
increased regularly from $9,010 in 1965 to
$1 1,940 in 1971, a real growth of about a
third. Inequality in the earnings distribution also increased in this period. The
standard deviation of earnings rose by 65
percent, and the coefficients of variation
of earnings and of log earnings rose appreciably after 1968.
Our analysis focuses on the associations
among socioeconomic background variables, mental ability, schooling, and earnings. Parents' income was ascertained
from Wisconsin State income tax files,
averaged across the available years from
1957 to 1960, and converted to 1972 dollars in the same fashion as earnings. The
years in which parental income was measured were those in which high school
graduates of 1957 were most likely to have
attended college. Father's occupation was
also ascertained from tax records; it was
coded in the 3-digit 1950-basis Census
classification, and these lines were assigned scores on Duncan's (1961) socioeconomic index (SEI) for occupations. A
separate dummy variable was used to
designate men whose fathers were farmers. Paternal and maternal schooling were
ascertained from the student in the spring
of his senior year in high school.
Mental ability was ascertained from the
HAUSER AND DAYMONT
TABLE 2
Earnings and Log Earnings, 1965 to 1971: Male Wisconsin High School Graduates o f 1957
Earnings
Log Earnings
Year
N
Mean
Std. Dev.
C.V.
Mean
Std. Dev.
C.V.
1965
1966
1967
1968
1969
,1970
1971
303 1
3094
3 120
3128
3135
3 137
3 101
9010
9672
10081
10503
11147
11577
11940
3242
3522
3622
3763
4269
4769
5357
.360
.364
.359
.358
.383
,412
.449
9.046
9.121
9.161
9.204
9.259
9.287
9.309
.352
.336
.343
.335
.345
.374
.392
.0389
.0368
.0374
.0364
.0373
.0403
.042 1
NOTE: Earnings were adjusted for price and productivity differences between 1972 and the reporting year. Data pertain to respondents who were not farmers in 1964 who had earnings greater than
$3000 in the reporting year, and for whom all data were present.
Henmon-Nelson test, which had been
administered in the junior year of high
school. Test scores were obtained from
the Wisconsin State Testing Service and
expressed in the metric of intelligence
quotients. Educational attainment of the
son was encoded from a brief educational
history supplied by his parent in the 1964
mail survey. The Census concept of years
of "regular" school completed was
applied, except that as much as a year's
credit was given for extensive postsecondary vocational training. In most
cases a normative number of school years
completed was assigned to correspond to
the level of certification achieved, e.g., 16
years for college graduates with no further
schooling. Since this information was ascertained in the 1964 follow-up survey,
schooling is underreported for men who
completed school more than 7 years after
high school graduation. From the 1975
survey it appears that about 16 percent of
men completed their highest collegiate de-
gree after 1964 and about 3 percent completed it after 1971. We are presently unable to say how many of these men are
included in the analysis in each year, but
many have presumably been excluded by
our lower limit on earnings or for other
reasons.
Table 3 presents the means and standard deviations of several variables, other
than earnings, in the subsample covered in
1970. Compared with the total sample of
male Wisconsin high school graduates of
1957 (Sewell and Hauser, 1975: 29), we
find the earnings subsample to be of
slightly more favored background and
higher measured ability. The differences
between sample means are largest for
father's occupational status (.I4 standard
deviations) and parental income (. 12 standard deviations), suggesting that the exclusion, of farmers may be a major source of
systematic differences between the earnings subsample and the population from
which it was drawn.
TABLE 3
Means and Standard Deviations of Predetermined Variables in Log Earnings Equations: Male
Wisco~zsinHigh School Graduates o f 1957
Mean
Std. Dev.
XI
X2
Xs
X1
Xs
Xe
X7
10.18
3.11
10.51
2.96
32.9
22.2
119.4
62.4
,196
.397
101.2
15.0
13.59
1.99
NOTE: Variables are: Xl=Father's Education, XnzMother's Education, X,=Father's Occupation,
X4=Parental Income (adjusted for price and productivity to 100s of 1972 dollars), XS=Farm Background, Xo=Mental Ability, X,=Education. Data pertain to respondents who were not farmers in
1964, who had earnings greater than $3,000 in 1970, and for whom all data were present.
'
SCHOOLING, ABILITY, AND EARNINGS
187
YEAR
Figure 1: Mean Log Earnings by Parental Income, 1965 to 1971
NOTE: Data pertain to male Wisconsin high school graduates who were not farmers in 1964 and whose
adjusted earnings were above $3,000 in the reporting year. Earnings and parental income were
adjusted for price and productivity differences between 1972 and the reporting year.
Growth of Earnings
Figures 1, 2, and 3 show the temporal
pattern of the mean of log earnings by
parental income, mental ability, and
post-secondary schooling. Of course, the
interpretation of these temporal earnings
profiles depends on our adjustment of
eaniings.
Earnings grew in almost every year for
men from every income stratum, and the
growth patterns are roughly parallel
across the strata. However, Figure 1
suggests a modest divergence of growth
patterns after 1969. Earnings growth
ceased among men from the poorest economic origins, but it continued in almost a
linear path among men from high income
families.
Figure 2 shows a clear divergence in
earnings growth among men of differing
ability. As we previously noted, in 1965
there are no clear earnings differentials by
mental ability, but in later years ttre
growth of earnings varies directly with
ability. By 1968 mean log earnings vary
monotonically across six levels of mental
ability, and by 1971 men with an IQ over
120 earn an average of almost 40 percent
more than men with an IQ under 80 (even
though all of the latter are high school
graduates).
The temporal profiles of mean log earnings by levels of schooling in Figure 3
strongly suggest the extent to which variations in earnings by labor force experience
may confound cross-sectional earnings
differentials. The vast majority of the men
in the Wisconsin sample were born in
1939, so labor market experience in any
188
HAUSER AND DAYMONT
YEAR
Figure 2: Mean Log Earnings by Mental Ability, 1965 to 1971
NOTE: Data pertain to male Wisconsin high school graduates who were not farmers in 1964 and whose
adjusted earnings were above $3,000 in the reporting year. Earnings were adjusted for price and
productivity differences between 1972 and the reporting year.
calendar year is strongly negatively correlated with schooling. By the same token
the several earnings profiles in Figure 3
reflect different segments of experienceearnings profiles. There is substantial
overlap of mean earnings in the early
years, and not until 1969 are mean log
earnings monotonic in schooling. The
slopes of the earnings profiles appear to be
steeper for higher levels of schooling.
Conceivably, these differences of slope
could reflect differences in the periodspecific experience levels of men with
different levels of schooling, rather than a
true interaction of schooling with effects
of labor market experience. If labor market experience differentials confound
period measurements of returns to schooling, they may also affect differentials in
earnings by social background and ability.
We shall return to these issues in a later
section of the paper.
Regression Analysis in
Annual Cross Sections
Our interpretation of cross-sectional
earnings differentials is based on a simple
recursive model. We assume that measured mental ability depends on socioeconomic background, that post-secondary
schooling depends on socioecsnomic
background and mental ability, and that
log earnings depend on all of the preceding
variables. We note in passing that in comparison to a linear function of earnings,
the semi-log function reduces but does not
eliminate heteroscedasticity and provides
roughly the same fit; the anti-logs of predicted log earnings were almost as highly
SCHOOLING, ABILITY, AND EARNINGS
189
YEAR
Figure 3: Mean Log Earnings by Education, 1965 to 1971
NOTE: Data pertain to male Wisconsin high school graduates who were not farmers in 1964 and whose
adjusted earnings were above $3,000 in the reporting year. Earnings were adjusted for price and
productivity differences between 1972 and the reporting year.
correlated with observed dollar earnings
as were dollar earnings predicted from a
linear equation. We ignore the autoregression in earnings, so the annual earnings
equations may be regarded as successive
reduced forms. We have not included occupations in 1964 or any social psychological variables other than mental ability in
the present analysis. We believe 1964 occupations are too far removed in time
from the more recent earnings data for
their effects to be interpreted unambiguously; also, occupations were not ascertained for men who were in school or in
the military service in 1964, but for whom
more recent earnings have been ascertained. We did carry out some analyses
using the broader set of social psychological variables; they added little to the finding of Sewell and Hauser that such vari-
ables primarily affect earnings through
schooling. Thus this report focuses on a
simpler model which does extend findings
of Sewell and Hauser.
Table 4 gives estimated coefficients of
the first two equations of the model, those
for mental ability and post-secondary
schooling. We shall not comment on these
equations at length, since they have been
interpreted in detail elsewhere (Hauser,
1972; Sewell and Hauser, 1975). However. we think it worth noting that in contrast to our findings in the earnings equations, parental income does not dominate
the ability or schooling equations. This is
important: given the way in which other
background variables were measured, one
could plausibly argue that the greater validity and reliability of parental income,
relative to other background variables,
190
HAUSER AND DAYMONT
TABLE 4
Regression Analysis o f Mental Ability and PostSecondary Schooling: Male Wisconsin
High School Graduates of 1957
Dependent Variable
Predetermined
Variables
XO
X7
A . Regression Coeflicients
XI
.583
,0999
( .lo51
Xa
XU
X,
XS
( .0132)
,596
,0853
( . 101)
( ,0128)
.0392
.00971
( .0148)
( ,00186)
.0264
(.ooso)
-.22
(.71)
.00563
( .ooo63)
.087
(.090)
X7
.0695
( .0121 )
.0542
(.0117)
.00766
( ,0017o)
.00425
(.ooos8)
099
(.082)
G
,0522
Xe
( .0021)
Const.
84.6
10.67
6.26
B . Regression Coefficientsin Standardized Form
XI
.121
,156
,109
Xn
.I18
.I27
.081
Xa
.058
.lo8
,110
,177
XI
.020
Xs
-. 006
.017
,394
XO
R"
.095
.I77
,318
aog5
NOTE: Variables are: X,=Father's Education,
X2=Mother1s Education, XsFather's Occupation, X,=Adjusted Parental Income (in $loo's),
Xo=Farm Background, X,;=Mental Ability, X7=
Education. Data pertain to male Wisconsin high
School graduates who were not ftw'i'lers in 1964,
who had reported adjusted earnings above $3,000
in 1970 and for whom all data were present. The
parenthetical entries are standard errors.
smaller coefficients for mental ability and
schooling but not for parental income. We
excluded farmers from the analysis, while
Sewell and Hauser dropped all men with
farm background, but we doubt that this
contributed significantly to the observed
differences. This would require the process of economic achievement to diverge
between men from farm background who
do not become farmers and men from nonfarm backgrounds. We believe the differences in the coefficients are attributable to our procedures for eliminating those
not fully committed to the labor force. If
this is the case we may have underestimated the effects of mental ability and
schooling in our equation for log earnings.
Figures 4 and 5 summarize our findings
about the changing influence on earnings
of parental income, mental ability, and
post-secondary schoo1ing.l Throughout
the years covered in our analysis the eff e c t ~on earnings of socioeconomic background variables other than parents' income are negligible, and we have not
presented them in the figures. As an aid to
exposition the figures show the effec.t on
the log of earnings (times 100) of a onestandard deviation shift in each of the predetermined variables. Figure 4 gives total
effects, that is, the coefficient of each
variable in an equation which excludes
subsequent intervening variables (Alwin
and Hauser, 1975). Figure 5 shows the
direct effects, which are coefficients in the
earnings equation in each year' Of
course the total and direct effects of
schooling are the same.
During the 8 to 14 years after high
school graduation the total effect of parental income on son's earnings persisted and
even increased slightly. An upward standard deviation shift in parental income increased .son's earnings by 4.8 percent in
1965 and by 6.4 percent in 1971.2 However, this increase in the total effect was
accounts for its dominance in the earnings
functions. This interpretation is difficult t~
reconcile with the large effects of other
variables in the ability and schooling equations.
AS indicated earlier, we were forced to
use a lower bound on the earnings distribution to define annual samples because
we measured labor force participation
The regression equations from which these estionly in 1964. TO obtain a rough idea of mates were obtained are available from the authors
how this may have affected our results, on request.
Throughout we have interpreted small effects on
we
some regressions of dollar
the log of earnings as proportionate or percentage
earnings in Our
with those of changes,
since ex= 1 + x where x is .loor less. In
Sewell and Hauser for the years 1965 to other cases we have interpreted the anti-logs of re1967. AS shown in Table 5 we obtained . gression coefficients as proportionate shifts.
SCHOOLING, ABILITY, AND EARNINGS
191
TABLE 5
Comparison of Results of Present Analysis with Those of Sewell and Hauser (1975) for the Regression
of Earnings on Education, Mental Ability, and Social Background, 1965 to 1967
Dependent Variable
Yes
YGO
Sewell and
Predetermined Present
Variable
Analysis Hauser (1975)
X,
Xe
Xl
R2
7.97
3.7
2
.018
Yo1
Present
Analysis
Sewell and
Hauser (1975)
Present
Analysis
Sewell and
Hauser (1975)
7.92
7.7
161
.035
7.63
11.6
222
,049
7.28
15.0
223
,056
7.61
20.6
26 1
.062
6.63
12.8
118
,026
NOTE: Variables are X,=Parental Income (in $100s), X,=Mental Ability, X1=Education, and Y,=
Earnings in year i. Results from Sewell and Hauser (1975) come frcm their Table 3-15, lines 9, 13,
and 17. The other predetermined variables included by Sewell and Hauser (1975) were: Father's
Education, Moth'er's Education, and Father's Occupation. We included these plus a dummy variable
for Farm Background. Coefficients were adjusted to reflect adjustments in earnings and parental
income for price and productivity differences between 1972 and the reporting year.
PARENTAL INCOME
+--,
NTAL ABILITY
YEAR
Figure 4: Total Effects of Parental Income, Mental Ability, and Schooling
NOTE: Coefficients show the effect of a one standard deviation change in the regressor. Earnings and parental
income ,were adjusted for price and productivity daerences between 1972 and the reporting year. Data
pertain to male Wisconsin high school graduates of 1957 who were not farmers in 1964 and had earnings
above $3,000 in the reporting year.
192
HAUSER AND DAYMONT
YEAR
Figure 5: Direct Effects of Parental Income, Mental Ability, and Schooling
NOTE: Coefficients show the effect of a one standard deviation change in the regressor. Earnings and
parental income were adjusted for price and productivity differences between 1972 and the reporting
year. Data pertain to male Wisconsin high school graduates of 1957 who were not farmers in 1964 and
had earnings above $3,000 in the reporting year.
attributable to the influence of parental dar years in the make-up of the cohort by
income through ability and schooling and work experience and the curvilinear efthe changing effects of those variables. In fects of work experience on earnings.
1965 the total and direct effects of parental Growth in the total effect of mental ability
income were the same (4.82 percent), but" is slower; a standard deviation shift in IQ
the direct effect peaked at 5.3 percent in leads to a 7.7 percent shift in earnings in
1968 and declined regularly to 4.2 percent' 1971. As in the case of parental income,
by 1971. Thus, the data may portend a growth in the total effect of mental ability
decline in the influence of economic ori- is partly attributable to its effect on
gins on earnings as the sample approaches schooling and to changes in the effect of
schooling. The direct effect on earnings of
midlife.
The total effects on earnings of mental a standard deviation shift in mental ability
ability and post-secondary schooling rise increases linearly from zero in 1965 to 3.8
in tandem from zero in 1965 to about 6.2 percent in 1969. In 1970 and 1971 the dipercent (per standard deviation shift) in rect effect of ability was stable, and it may
1969. After that the schooling coefficient even have declined. In summary, as long
continues a rapid increase to 10.5 percent as 8 years after high school graduation,
in 1971. To anticipate, our findings in the ability and schooling had no, discernible
last section of this paper suggest that the effects on the distribution of annual earnincrease in the effect of schooling on earn- ings, but earnings were affected by parenings is due to heterogeneity across calen- tal income in the years just after high
SCHOOLING, ABILITY, AND EARNINGS
school. The influence of parental income
persisted over the next six years, but the
effects both of ability and schooling rose
rapidly in the same period. The direct influence of mental ability appears to have
peaked by the 12th year after high school
graduation, but the influence of schooling
was still on the rise. Continued increases
in the overall influence of parental income
and mental ability were sustained by virtue of their effects on earnings through
schooling.
To detect nonlinearities in the earnings
equation, we carried out a multiple
classification analysis (MCA) with log
earnings in each year as the dependent
variable and parental income, mental
ability, and education as the independent
variables. Table 6 shows coefficients of
determination in the MCA and the corresponding linear regression in each year.
Overall departures from linearity decline
over time, and by 1971 they are quite
small; however, the pattern of these departures is of interest. The means of log
earnings for levels of each of the three
variables controlling the other two are
shown in Figures 6 to 8.3 In the figures,
the annual mean of log earnings was plotted at the mean of the regressor in each
interval of it. Log earnings is a fairly
smooth slightly convex function of parental income for each year (Figure 6). In the
early years, log earnings is an irregular
function of mental ability, but by 1971 the
curve has become convex and quite
smooth (Figure 7). These findings are not
unexpected since we could think of no
reason why log earnings should be a linear
function of variables that represent initial
inputs or personal characteristics, rather
than investments of time. These patterns
of curvilinearity might suggest that to obtain a better fit, one could take the log of
mental ability, parental income, and
possible other background variables before entering them into an equation of log
earnings. However, this did not improve
the fit; the R2 in such an equation was the
same as in the semi-log form in 1971 to the
fourth decimal, and it was very slightly
smaller in all earlier years.
' The source tables are available from the authors
on request.
TABLE 6
Proportion o f Variance of Log Earnings Explained (Re)b y Linear and Multiple Classification Analysis Models, 1965 t o 1971:
Male Wisconsin High School
Graduates of 1957
Model
Year
Linear
MCA
NOTE: Variables included in each model were:
Parental Income, Mental Ability, and Education.
Data pertain to respondents who were not farmers in 1964, who had earnings above $3,000 in
the reporting year, and for whom all data were
present.
Figure 8 shows that log earnings is an
irregular function of schooling, especially
in the early years. In the interval from 12
to 16 years of schooling we note a generally concave shape to the earnings curve
for the years 1970 and 1971. This suggests
a credential effect, that the average payoff
to a year of schooling is greater for those
who obtain a bachelor's degree than for
those who do not. If this is perceived by
those contemplating college then it may
have implications for decisions to attend
college. That is, if one views college as a
series of one-year decisions and begins by
considering the rate of return to only the
first year of school, he may decide not to
enroll; but if he thinks of college-going as
requiring a one-time decision of how to
spend four years, he may opt to attend.
Bias in the Schooling Coejjicient
Sociologists and economists have a
long-standing interest in the extent to
which the effects of schooling are overstated when ability and background variables are omitted from earnings functions.
The research literature of the past several
years has recently been reviewed by Griffin (1976), but no consensus on this issue
has yet been achieved. The radical political economists claim to find large biases
194
HAUSER AND DAYMONT
Figure 6: Effect of Parental Income on Log Earnings, 1965 to 1971
NOTE: The points represent the mean of log earnings for levels of parental income controlling for mental
ability and education. Earnings and parental income were adjusted for price and productivity
differences between 1972 and the reporting year. Data pertain to male Wisconsin high school
graduates of 1957 who were not farmers in 1964 and had earnings above $3,000 in the reporting year.
(Bowles, 1972; Bowles and Nelson, 1974;
Bowles and Gintis, 1976); Bowles says that
failure to include background variables
may result in a bias of about 40 percent in
an earnings function for U.S. men in the
early 1960s. Sewell and Hauser (1975:
84-85) find a 35 percent reduction in the
effects of schooling when background and
ability measures are included in crosssectional analyses of 1965 to 1967 earnings. Griliches and his associates persistently find smaller biases (Griliches and
Mason, 1972; Chamberlain and Griliches,
1975, 1977), on the order of 10 to 15 percent. Welch (1974) says that the bias due
to omitting mental ability is somewhat less
than 10 percent. However, recent research on twins and other brother pairs
suggests downward revisions in estimated
returns to schooling (Jencks et al, 1972;
Behrman, Taubman, and Wales, 1977; 01-
neck, 1976a). The picture is further complicated by measurement error in the social surveys from which returns to schooling are typically estimated. For example,
contrary to widespread belief, Bielby,
Hauser, and Featherman (1977) find that
the occupational effects of schooling are
biased downward when errors in retrospective survey reports of social background and schooling are ignored.
Table 7 shows our findings about bias in
the schooling coefficient in the years from
1965 to 1971. In that period the zero-order
regression of earnings on schooling rises
from 0.35 percent per year of postsecondary schooling to 6.70 percent. During the same years the schooling coefficient net of background and ability goes
from zero to 5.24 percent. Whether we control, ability done or both ability and background the spurious component of the re-
SCHOOLING, ABILITY, AND EARNINGS
195
MENTAL ABILITY
Figure 7: Effect of Mental Ability on Log Earnings, 1965 to 1971
NOTE: The points represent the mean of log earnings for levels of mental ability controlling for parental
income and education. Earnings were adjusted for price and productivity differences between 1972
and the reporting year. Data pertain to male Wisconsin high school graduates of 1957 who were not
farmers in 1964 and had earnings above $3,000 in the reporting year.
gression of earnings on schooling appears
to decline as a component of the total regression. The bias due to omitting ability
is about 15 to 20 percent in our sample
depending upon the year. This is somewhat greater than in studies of bias discussed by Welch (1974). He states that
failure to include pre-school ability measures results in a bias of somewhat less
than 10 percent. Part of the difference in
our results and his conclusions may be
due to the age of our sample. To the extent
that the effect of schooling continues
to become stronger and,the effect of mental ability continues to increase at a slower
rate, we can expect the bias to decrease as
people become older. The bias due to
omitting ability and the five background
variables declines to about 22 percent by
1971. In the years after 1965 this bias accounts for roughly a 1.0 to 1.5 percent
shift in earnings per year of schooling, and
it appears to increase in absolute magnitude even as it declines as a share of the
uncorrected schooling coefficient.
We conclude that bias in the schooling
coefficient cannot be ignored in any of the
earnings cross-sections we have examined. While the share of bias in the zeroorder regression of earnings on schooling
appears to decline over time, even in 1971
returns to schooling would be overestimated by more than 25 percent in an uncorrected equation. Moreover, the absolute bias in the schooling coefficient appears to increase over time.
Direct and Indirect Effects
For each year all of the effects of parental, education on earnings are indirect
through mental ability and schooling. This
196
HAUSER AND DAYMONT
Figure 8: Effect of Education on Log Earnings, 1965 to 1971
NOTE: The points represent the mean of log earnings for levels of education controlling for mental ability
and parental income. Earnings were adjusted for price and productivity differences between 1972
and the reporting year. Data pertain to male Wisconsin high school graduates of 1957 who were not
farmers in 1964 and had earnings above $3,000 in the reporting year.
can be contrasted with the partitioned efl
fects of parental income and mental ability
which are shown in Table 8. Here we see
that virtually all of the influence of parental income on earnings was direct in 1965;
however, by 1971 only 65 percent of the
effect of parental income was direct.
Twenty-two percent of the effect of parental income in 1971 occurred by way of the
influence of parental income on schooling,
and 13 percent was mediated by ability or
by both ability and schooling. Over the
years most of the increase in the indirect
effect of parental income occurred because of the increasing returns to schooling. Bowman (1976) offers three possible
explanations for the substantial direct effect of parental income on earnings: (1)
higher parental income provides one with
greater options by allowing one to remain
out of the labor force for a longer time
while waiting for a high paying job (i.e.,
imperfect capital markets); (2) higher par:
ental income allows one to obtain better
information about monetary rewards and
other aspects of occupations; and (3)
higher parental income leads to higher income expectations. We would add another partial explanation: higher parental
income is an indicator of the wealth of the
family of orientation and, at least for
some, physical capital as well as human
capital determines earnings. This would
be especially true for those who operate
their own businesses. A partial test of this
hypothesis would involve estimating the
effect of parental income on earnings
separately for the self-employed and those
who work for others.
Even during the years when the direct
influence of ability was increasing, the direct influence of mental ability on earnings
decreased as a proportion of its total effect
becauge of the increasing returns to
SCHOOLING, ABILITY, AND EARNINGS
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schooling. From 1965 to 1969 the direct
effect of ability increased from essentially
zero to a quarter of a percent increase in
earnings per point of IQ at the same time
the direct effect declined to 61 percent of
the total. By 1971 a one point shift of IQ
increased earnings by half a percent, and
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197
Interactions Znvolving Ability
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Our analysis of the earnings distribution
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s
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g ". ". *I . .%
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SCHOOLING, ABILITY, AND EARNINGS
mentioned above, we used the normative
number of school years needed to complete each level of certification. If mental
ability is an indication of learning capacity
and influences the pace at which people
earn educational certification, this effect
on the pace of educational attainment
would not be captured in our interaction
term. Therefore, we reran our tests for
ability by education interactions from
1965 to 1971 using the total number of
years attended at college or vocational
school as our measure of education. The
mental ability by schooling interaction in
the multiple classification analysis was
statistically significant in 4 of the 7 years.
For each year, the coefficient of the multiplicative interaction term in the linear
regression was positive but not statistically significant. If these coefficients are
interpreted as changes in the coefficients
of education for different levels of ability,
then the estimates of the increased economic returns to schooling associated
with a standard deviation difference in
mental ability range from 0.1 percent to
0.6 percent with no discernible trend
across annual cross sections. Thus, to the
extent that these coefficients reflect actual
differences in returns to schooling, these
differences appear to be quite small.
In addition, we tested for abilityparental income interactions in both the
education and earnings equations. When a
multiplicative ability by parental income
interaction term was included in the education equation it had a significance level
of .004; however, the results of multiple
classification analysis suggested that this
was due to the association of ability and
parental income and a curvilinear relationship between ability and education.
The effect of ability in an IQ metric on
educational attainment is greater for
higher levels of ability than for lower
levels. Specifically, controlling the 5
background variables, the increments in
educational attainment for men in the following ability categories.over men in the
next lower category are as follows: 81 to
90, .18 additional years of education; 91 to
100, .38 additional years of education,
101-110, .59 additional years of education, 111-120, .67 additional years of education, and 120 and above, .71 additional
199
years of education. When we allow for
this curvilinearity by using a multiple
classification analysis, the significance
level of the ability by parental income interaction becomes .999. In the earnings
equation the ability by parental income
multiplicative interaction term is insignificant in all years, and experimentation with
alternative functional forms suggests that
this is not a result of curvilinearity.
Schooling, Experience and Earnings
We have already commented on the
possibility that the strong negative correlation between schooling and experience
in the Wisconsin sample may have affected our cross-sectional estimates of the
returns to schooling (and perhaps those to
parental income and ability, as well).
Earnings typically rise rapidly to a peak
level during the first several years of labor
market experience (Mincer, 1970, 1974;
Hauser, 1973; Heckman and Polachek,
1974; Stolzenberg, 1975). Consequently,
early in the career a cross-sectional comparison of the earnings of more- and lesseducated workers picks up the moreeducated workers at a lower point on their
experience-earnings profiles than the
less-educated workers, and the returns to
schooling will be underestimated. Even as
late as 1971 our estimated regressions of
schooling on earnings (6.7 percent without
controls and 5.2 percent controlling background and ability) are less than those estimated by other research workers among
older men or in samples covering a broad
age-range. In the previous argument,
however, the negative correlation between schooling and experience (within
calendar-year cross-sections) could account for the small size of these effects,
even if the effects of schooling on earnings
do not vary by labor market experience.
The vast majority of men in the Wisconsin
sample were born in 1939 so in any calendar year, that is, at any given age, the
length of work experience is determined
by age at school entry, the rate of progression through the grades, and the number
of school-years completed. Among high
school graduates in this cohort born on the
eve of World War 11, we would expect
variations in the length of schooling (in-
200
HAUSER AND DAYMONT
cluding interruptions for military service)
to be the largest component of variations
in work experience (at a fixed age). The
correlation between last year of schooling
and educational attainment is .72 in the
sample used by Sewell and Hauser (1975);
the correlation would be substantially
larger, except we coded education in
levels of certification rather than years
attended, and we did not know the exact
timing of military service completed prior
to school-leaving. These problems precluded our estimating stable effects both
of schooling and experience in calendar
year cross-sections, so we have instead
estimated the net effects of those two
variables by letting interannual variations
in earnings represent the effects of work
experience.
Figure 9 suggests the application of this
argument to our interpretation of the Wis-
consin data. We have plotted the annual
variations in mean log earnings by levels
of schooling, as in Figure 3, but with two
modifications. We have extended the
series back in time for men with little or no
post-secondary schooling, and we have
shifted the profiles to the left at higher
levels of schooling in order to make the
abscissa reflect variations in experience,
rather than calendar years. Men with 13
years of school were shifted one year to
the left, men with 14 to 15 years of school
were shifted 3 years, men with 16 years
were shifted 4 years, and men with 17 to
20 years were shifted 6 years. Of course,
variations in the rate of passage through
school and the length of military service
contribute heterogeneity to our implicit
measure of work experience. In defense of
our analysis, we would add that ours is the
same measure of experience which has
Figure 9: Experience Profiles of Mean Log Earnings by Education
NOTE: Data pertain to male Wisconsin high sch.001 graduates who were not farmers in 1964 and whose
adjusted earnings were above $3,000 in the reporting year. Earnings were adjusted for price and
productivity differences between 1972 and the reporting year.
SCHOOLING, ABILITY, AND EARNINGS
been used widely by other researchers. In
later analyses we shall be able to use year
last attended school as an inverse measure
of labor market experience, and we may
be able to separate the effects of civilian
and military work experience (see Hansen
and Weisbrod, 1973 and Griffin, 1977 for
efforts to do this).
In contrast to Figure 3 the several
experience-earnings profiles in Figure 9
are almost non-overlapping, and there appears to be less heterogeneity between
schooling levels in the shape of the profiles. The results in the first year or two of
experience should not be taken too seriously; because we used schooling as an
experience proxy, they typically refer to a
reduced sample. Thus, in contrast to the
findings in calendar-year cross-sections,
within experience cross-sections the regression of earnings on schooling is evident even in the early post-schooling
years. For example, in the third year of
experience the mean log earnings of men
with no post-secondary schooling is 8.745
(in 1960), and the mean log earnings of
college graduates is 9.040 (in 1964); this
suggests an earnings differential of about
34 percent between high school and college graduates. In the tenth year of labor
market experience, the mean log earnings
of high school graduates is 9.1 13 (in 1967)
and that of college graduates is 9.503 (in
1971), which implies an earnings differential of 48 p e r ~ e n t . ~
Several researchers have argued that
the effect of experience on earnings
should be greater for men with more
schooling (Miller, 1960; Becker, 1964;
Thurow, 1967; Stolzenberg, 1974, 1975;
Heckman and Polachek, 1974). The rationale is that education and work experience have complementary effects on productivity. To some degree this interaction
is implicit in our analysis, for the semi-log
form of the earnings equation implies
complementarity in the effects of regressors on dollar earnings. However,
Stolzenberg (1974, 1975) uses crosssectional data from the 1960 Census to
argue that schooling and experience interact even in an equation for the log of earnings.
See footnote 2.
20 1
Our efforts to specify the interactions
among schooling, experience, and earnings in the Wisconsin data are necessarily
limited. There is a lower bound of 12 vears
of schooling, and we have observed only
the first few years of earnings among men
with post-collegiate education. Moreover,
our estimates of returns to experience require comparison of earnings in different
calendar years, so they are affected by
adjustments of earnings for interannual
changes in prices and productivity. On the
other hand our use of adjusted longitudinal data mav be more robust than the
analyses cited above which generalize to
experience-earnings relationships from a
synthetic cohort interpretation of crosssectional age-earnings relationships.
In the Wisconsin sample the earnings
differentials by schooling appear to be
homogeneous across experience levels.
As shown in the first column of Table 9
we fitted parabolas with differing intercepts but the same curvature to the
experience-earnings profiles of Figure 9.
Mincer (1970) suggests the parabolic form
of the experience-earnings profile. Using a
weighted least squares estimation procedure, we accounted for 98.5 percent of the
variance in mean log earnings. The difference in intercepts between high school
and college graduates implies an
(experience-constant) earnings differential
of 41 percent (first column of Table 9),
which is in substantial agreement with that
typically found in the 1950s and 1960s. As
a further check on the homogeneity of
experience-earnings profiles across levels
of schooling, we estimated separate
parabolas for each level of schooling.
These gave a negligible improvement in
fit, and there were no interpretable variations in the slopes across levels of schooling.
To assess bias in the schooling coefficient in experience cross sections, we
shall have to rearrange our unit record
data (in terms of experience-years, rather
than calendar years). This will be the next
inajor step in our continuing analysis of
the Wisconsin earnings data, but for the
present we have made a crude effort to
approximate the outcome of such an
analysis. For the years 1965 to 1971 (when
most of the men in our sample at all levels
202
HAUSER AND DAYMONT
TABLE 9
Regression of Group Means o f Log Earnings (from M C A ) on Work Experience and Education: Male
Wisconsin High School Graduates o f I957
Adjusted for Changes in Per Capita
Earnings for Members of Labor Force
Regression
Coefficients
Not
Not
Controlling
Controlling
Controlling
X,, X6; Years X,, Xs; Years Xa, Xo; Years
1958 to 1971 1965 to 1971 1965 to 1971
Adjusted for Changes
in Per Capita Income
for Males 25-34 Years
Old Standardized on Adjusted for
the Educational Dis- Changes in
tribution of Wisconsin Consumer
Sample
Price Index
Controlling
X*, Xe; Years
1965 to 1971
Controlling
X1, Xa; Years
1965 to 1971
--
A . Slope coefficients
xs
(XB)~
B. lntercepts
X7= 12
X7= 13
X7=140r15
X 7 = 16
X7 = 17 to 20
RE
.I313
.I189
.I134
.I471
.0934
- ,00294
- .00472
- .00404
-. 00400
-. 00496
0.000
.071
.I61
,346
.477
.985
0.000
.081
,180
.384
.536
.991
0.000
.062
.I39
.334
.462
,989
0 .ooo
.058
,124
.314
.434
.976
0.000
.071
.I70
.380
'543
.976
NOTE: See text for definition of work experience. Variables are: X I = Parental Income, Xa = Mental
Ability, X7= Education. and Xs = Work Experience. Data pertain to respondents who were not farmers in 1964, who had earnings above $3,000 in the reporting year, and for whom all data were present.
To facilitate interpretation we have defined the intercepts as 0 for X7=12 in each equation; the estimated intercepts for each equation, from left to right, are: 8.482, 8.282, 8.356, 8.404, and 8.109.
of schooling were in the labor force), we
fitted homogeneous parabolas to the several segments of experience-earnings profiles covered in those years. The estimates
of those parameters are shown in the second column of Table 9. Then, for each of
the 7 years we carried out a multiple
classification analysis of earnings (as in
Table 6), from which we obtained expected mean log earnings, net of the (nonlinear) effects of parental income and
mental ability. Again, we arranged these
estimated means as observations in
experience-earnings profiles and fitted
homogeneous parabola^.^ The third column of Table 9 gives the results of this
regression and Figure 10 shows the fit of
these curves to the data. They differ from
results which might be obtained from true
experience-constant regressions because
the adjustments for effects of parental income and mental ability were carried out
he source tables are available from the authors
on request.
within calendar year cross sections, and
we have shown those effects to vary from
year to year.
Even after controlling for background
and ability, our "experience-controlled' '
regressions show larger effects of schooling on earnings than we observed without
correcting for bias in any calendar year.
For example, in 1971, the calendar year in
which the effects of schooling were
greatest, the differences between the
mean log earnings of high school
graduates and men with 13, 14 or 15, 16,
and 17 or more years of schooling were
.069, .135, .301, and .395, respectively.
These are all smaller than the corresponding (unbiased)
contrasts
in
the
"experience-controlled" analyses.
As in the cross-sectional analyses, our
"experience-constant" analyses do display bias in the schooling coefficient. The
size of the bias depends on which levels of
schooling are contrasted. If we contrast
high school graduates with men completing 13 or 14 and 15 years of school, the
SCHOOLING, ABILITY, AND EARNINGS
203
WORK EXPERIENCE
Figure 10: Goodness of Fit of Additive Model of Log Earnings, 1965 to 1971
NOTE: Each line represents the expected experience profile of earnings for a given level of education
controlling parental income and mental ability. The data points are means of log earnings for levels of
education and work experience controlling parental income and mental ability. Earnings were
adjusted for price and productivity differences between 1972 and the reporting year. Data pertain to
male Wisconsin high school graduates who were not farmers in 1964 and had earnings above $3,000
in the reporting year.
corrected schooling coefficients are 77
percent as large as the uncorrected coefficients. But in comparisons of high school
graduates with college graduates or those
with graduate or professional training, the
bias is less. In these contrasts the corrected
coefficients are 87 percent and 86 percent
as large, respectively, as the uncorrected
coefficients.
It is instructive to contrast these results
on bias with cross-sectional findings in
1971. Recall that 1971 is the most recent
year in our series, and one in which bias in
the schooling coefficient was less (proportionately) than in earlier years. If we contrast raw and adjusted (in a multiple
classification analysis) mean log earnings
in 1971 of high school graduates with
those among men with 13, 14 or 15, 16,
and 17 or more years of schooling, the
corrected coefficients as a percentage of
the observed coefficients are 58 percent, 60 percent, 78 percent, and 81
percent, respectively. Moreover, in the
annual cross section biases were larger
absolutely, as well as relatively, than in
our constructed experience-earnings profiles, even though returns to schooling
were consistently larger in the constructed
data. Thus, controlling for experience, we
increase the estimated effects of postsecondary schooling on earnings, and we
appear to reduce the ability-background
bias in the schooling coefficient both in
absolute and relative terms.
As in the cross-sectional analysis, we
note a possible credential effect in the
"experience-controlled"
analysis. For
example, the difference in log earnings between high school and college graduates is
.334, implying an average effect of 9.9
percent per year of college; but the dif-
204
HAUSER AND DAYMONT
ference between high school graduates
and those with 14 to 15 years of school is
.139, implying an average effect of only
about 5.7 percent per year.
From our experience-earnings profiles
we have estimated returns to labor market
experience which are comparable (in the
early years of work) to the effects of
schooling. In the corrected estimates from
1965 to 1971 data, the initial return to
experience is almost 12 percent per year.
(In the annual cross-sections the estimated effects of experience, measured inversely by last year in school, are very
small and unstable.) The negative quadratic coefficient (-.00404) implies that
maximum earnings are achieved after 14.7
years of labor market experience.
Whereas the results of our annual
cross-sectional regression analysis are not
sensitive to our procedure for adjusting
earnings, the results of our "experiencecontrolled" analysis are sensitive to such
adjustments. We adjusted earnings by the
mean earnings of members of the labor
force in order to assess changes in earnings over time net of price and productivity changes. However, in using this
procedure we did not control changes in
the composition of the labor force. In attempting to control these changes we repeated our analysis of experienceearnings profiles after adjusting annual
earnings for changes in the mean income
of males 25 to 34 years of age, standardized on the educational distribution of
the Wisconsin sample (U.S. Bureau of the
Census, 1974: 17); these results are shown
in the fourth column of Table 9. In this
analysis our adjustments of annual earnings are unaffected by changes in the age,
sex,, and educational composition of the
labor force; however, the Census series
includes non-earned income as well as
earnings, to the extent that the former is
reported in the Current Population Survey. We see from Table 9 that the difference resulting from this alternative adjustment is small. ~ e l a t i v eto the adjustment we used throughout the analysis, the
alternate adjustment yields a slightly
poorer fit using homogeneous experience
curves and slightly lower estimates of the
effects of education and work experience.
The fifth column of Table 9 presents the
results when earnings were adjusted only
for price changes according to the consumer price index (U.S. President, 1975:
300). Relative to our adjustment for price
and productivity changes, the adjustment
for price alone yelds a slightly poorer fit
for the homogeneous experience curves
and larger estimates of the effects of
schooling and work experience.
Conclusion
We have used the present analysis to
play out the interpretative scheme used by
Sewell and Hauser over a longer span of
years and in a broader segment of the Wisconsin sample. Our findings suggest the
utility of representing earnings data in
terms of work experience cross sections,
or perhaps in person-years, rather than in
calendar-year cross sections. Even before
we merge the earnings data with variables
from our 1975 survey, we expect this alternative perspective on the data will lead
us to new insights into the interconnections among social background, ability, the
quantity and quality of schooling and the
distribution of personal earnings.
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DIFFERENCES IN THE OCCUPATIONAL ACHIEVEMENT PROCESS
BETWEEN MALE AND FEMALE COLLEGE GRADUATES*
JOEL. SPAETH
University of Illinois at Urbana-Champaign
Sociology of Education 1977, Vol. 50 (July): 206-217
In the college graduating class of 1961, mean 1968 occupational achievement for men and
women was similar. The standard deviation for men was greater than that for women, indicating that occupational opportunities of women were more constricted than were those of men.
Basically the same pattern holds for occupational expectations held in the freshman and senior
years in college and three years after graduation. On the other hand, women's achievement of
education beyond the bachelor's degree lagged behind men's. Regression analysis reveals that
compared with men, women showed greater instability in occupational expectations, reaped
lesser returns in occupational status from investments in advanced education, and were less
likely to realize their occupational expectations.
INTRODUCTION
Compared with the rather extensive literature on the socioeconomic achievement process among males, research on
that process among females has tended to
lag. For males, the basic outlines of the
socioeconomic life cycle have been established by Blau and Duncan (1967) and
Duncan, Featherman, and Duncan (1972),
and the role of social-psychological variables in the young adult years has also
been well documented (Sewell, 1971;
Sewell and Hauser, 1975; Spaeth, 1968,
1970; Spaeth and Greeley, 1970).
* An earlier version of this paper was presented at
the annual meetings of the American Sociological
Association, San Francisco, August, 1975. The
author wishes to thank David L. Featherman, Robert
M. Hauser, Joan Huber, and an anonymous referee
for helpful comments. This work was supported in
part by a grant from the Research Board of the University of Illinois at Urbana-Champaign and by
Grant NIE-G-76-0077 from the National Institute of
Education, Department of Health, Education, and
Welfare. The opinions expressed herein do not
necessarily reflect the position or policy of the National Institute of Education, and no oficial
endorsement by the National Institute of Education
should be inferred.
Achievement models have recently
been extended to females. Based on data
collected in the two Occupational
Changes in a Generation (OCG) surveys,
Featherman and Hauser (1976b) report
that for persons in the experienced civilian
labor force, the processes determining
educational and occupational achievement are basically similar for men and
women, and that levels of occupational
and educational achievement differ only
slightly by sex, although standard deviations are somewhat lower for women than
for men. Similar results are reported in
Treiman and Terrell (1975). Data for the
general population seem to indicate that
occupational returns to schooling do not
differ by sex-a year of schooling produces roughly the same amount of occupational status for a woman as for a man.
This similarity by sex in parameters of
models of educational and occupational
achievement seems counterintuitive. Not
only the claims of feminists but also common observation indicate that women are
excluded from occupations with high
socioeconomic status. If women are excluded from the highest status occupa-