\;
".'
c.
Atlantic
Economic
Journal
VOLUME VI
NUMBER 2
RICHARD T. FROYEN AND LAWRENCE S. DAVIDSON
Estimates of the Fisher Effect: A Neo-Keynesian Approach
GILLIAN GARCIA
Consumer Expenditure by Bank Credit Card
MICHAEL SATTINGER
Trade Flows and Differences Between Countries
BARRY T. HIRSCH
Earnings, Occupation, and Human Capital Investment
WAYNE C. CURTIS AND LOWELL E. WI LSON
A Model to Project Loss of Earnings from Impaired or Destroyed Capacity
JANG H. YOO
Real Money Balances as Factor Saving Media: A Note
ANTHONY E. BOPP AND MITCHELL DURST
Estimated Importance of SeasonalAdjustment on Energy Forecasts
WILLIAM A. KELLY, JR.
Relative Rates of Return in the Competitive Fringe Model:
Declining Dominance and Merger Activity
.
EMILY P. HOFFMAN
Measurement of Faculty Productivity
DONALD C. AUCAMP AND ROBERT E. KOHN
A Diagrammatic Solution of the Pareto Problem
ROGER D. BLAIR
Random Prices and Estimation of the Elasticity of Substitution
W. MARK CRAIN, THOMAS H. DEATON, AND ROBERT D. TOLLISON
Macroeconomic Determinants of Tenure in the U.S. House of Representatives
EMILE GRUNBERG
Toward Compromise on Tenure: Comment
MARTIN BRONFENBRENNER
Reply to Professor Grunberg's Comments
OTHER CONTRIBUTORS
Alan J. Donziger; Edward C. Gray; Richard P. Rozek; William T. Jackson; Wayne K.
Talley and Stanley E. Warner; Usman A. Qureshi and Gary L. French; Myles Wallace;
Jae W. Chung; Francis Shieh; Kern O. Kymn; J. Wilson Mixon,Jr. and Alan L. Larson;
Joseph M. Sulock; Thomas Hyclak; John M. Virgo; Constantine E.A. Passaris;Ani! K.
Puri
JULY 1978
Earnings, Occupation, and Human
Capital Investment
BARRY T. HIRSCH*
In recent years the human capital earnings
model has been widely used as a framework for
examining the determination of earnings. A specification of the human capital earnings function
developed by Chiswick and Mincer has been employed to examine earnings determination across
individuals [Mincer, 1974b], states [Chiswick,
1974] , meLJpolitan areas [Hirsch, 1978], occupations [Rahm, 1971], and over time [Chiswick-Mincer, 1972]. However, empirical studies utilizing the human capital framework have
assumed thatthe effects of work experience (postschool investment) on earnings are identical for
individuals, regardless of occupation.
This assumption runs counter to the current
emphasis of many labor economists on the importance to lifetime earnings of gaining early access to certain occupational job ladders and of
receiving firm specific on-the-job training [Doeringer and Piore, 1971; Thurow, 1975] . Likewise,
human capital economists stress the importance
of postschool training investments and at least
recognize the fact that these investments vary
across occupations. Indeed, Mincer has stated
that [Mincer, 1974a, p. 33] :
ification of the human capital earnings function
is modified in order to focus on differences
across occupations in their earnings-experience
profiles. This makes possible several inferences
regarding the intensity, length, and rate of return to postschool human investments across
occupational job ladders. In addition, we examine the manner in which occupation interacts
with schooling in the earnings generation process
and discuss analytical problems which arise in
modeling this process. Finally, the relationship
between wage rates, weeks worked, and occupation is examined.
The Theoretical Framework
The Chiswick-Mincerspecification of the human capital earnings function relates present net
earnings in year t after schooling to all previous
investments in human capital, minus any current
investment. Let ko be the fraction of potential
earnings initially invested in postschool training
(at t = 0) and assume that kt declines linearly
over time such that kt =ko(1 - tiT), Tbeing the
number of years of positive net investment. A
number of simplifying assumptions and ingenious manipulations enables the followingwidely used form of the model to be derived [Chiswick-Mincer, 1972; Mincer, 1974]:'
"an analysis in which these effects [of schooling
and of experience] are allowed to differ [across
occupations] would be desirable for a number
of purposes, not the least of which is an insight into differential post-schooljob skill investments."
This paper examines whether schooling,
perience, and weeks worked have different
ko
in Jt = [inEo - ko(l + -)]
2
exef-
ko
[rpko+-(I+ko)]t-[
T
r(in WW) + Ut
=X+rS+,'t+r"t2
fects on individual earnings in nine separate occupational categories. The Chiswick-Mincer spec*University of North Carolina at Greensboro. The
research is drawn in part from my doctoral dissertation
on which William R. Johnson and Roger Sherman provided helpful comments. Support by a doctoral dissertation grant from the U.S. Department of Labor, Employment and Training Administration, is gratefully
acknowledged.
(1)
+rS +
rpkoT+ k~
2T 2]t
2
+
+r(inWW)+Ut,
where,
1Several recent articles survey and critique empiricalliterature in the human capital area [Rosen, 1977;
Griliches, 1977; Blaug, 1976].
31
- --
32
ATLANTIC ECONOMICJOURNAL
In}f
=
InEo =
the natural logarithm of earnings
in the year t of a working life;
natural logarithm of earningsthat
would be obtained in the absence
of human capital investl1lent;
r = the average rate of return to
schooling;
S = yearsof schoolingcompleted;
rp
=
the average rate of return to post-
school investment;
t = years of experience estimated by
the proxy (age - S - 6);
'Y= the partial elasticity of annual
earnings with respect to weeks
worked;
In WW= natural logarithm of weeks
worked during year t; and
Ut = a random error term with zero
mean and constant variance.
Human capital theory treats work experience
as an investment phenomenon and earnings-experience proftles are presumed to reflect its costs
and returns.2 Given that the cost and return
structure will differ across occupations, maximizing individualswill exhibit different earningsexperience profiles even when rates of return are
equalized among various occupational investment paths. Equation (1) does not allow the intercept or the regression coefficients on experience and experience squared to vary across occupations. This, in effect, constrains the rate of
return to postschool training, the fraction of poential earnings initially invested in postschool
training and the subsequent investment proftle,
and the number of years of positive net postschool investment to be equal among individuals
in occupations having different investment and
earnings patterns (Le., different rp, ko, and T
combinations). '
2Earnings-experience
profiles will also reflect the
effects of discrimination, non-pecuniary returns, economic growth, and aging per se. Some recent empirical
work has attempted to separate the independent effects
of experience and age on earnings [Klevmarken and
Quigley, 1976; Lazear, 1976).
'Technically, the constraint may be non-binding
We allow rp, ko, and T to vary across occupations, but continue to assume them identical
for individuals within any given occupation. Occupational variation in the shapes of earnings
profiles can thus be observed by estimating a
piecewise regression:
n
n
In}f=X+.~ J=2 t/>pCCj+rS+.~J=1 8jOCCj(t) (2)
n
+.~
J=1OjOCCj(t2)+ 'Y(lnWW)+ Vt
where the set of dummy variables OCCj with j =
(1, . . . , n) assigns each individual a I for occupation j and a 0 for all others. Each individual
will be classified in one of nine occupational categories. Whereas in specification (1) all differences in rp, ko, and T were captured in the residual Ut, in specification (2) Vt captures only intraoccupational
differences in these earnings
function parameters.
Equation (2) is employed to test the hypothesis that occupational differences in earnings
function parameters are a significant determinant of individual earnings. The relevant null
hypoth~sis, that t/>j= 0 (j = 2, . . . , n), 81 = 82 =
... = on, and 01 = O2=.. . = Onhold jointly, can
be tested by use of an F statistic comparing the
sums of squared residuals from (1) and (2). Failure to reject the null hypothesis would indicate
that the constraints imposed by the ChiswickMincer human capital earnings function are inconsequential.
While the earnings function parameters rp,
ko, and T cannot be directly retrieved from the
coefficients on t and t2, these parameters can
be identified if we make an assumption about
the net investment span T.' Since T corresponds
to the unobserved peak of earnings capacity and
precedes the observed peak of earnings by about
since there are four parameters (lnEo. rp, ko' and 1)
constrained by three coefficients (X, r', and r"). However, it seems unlikely that variation in these earnings
function parameters offset each other such that all occupations possess identical earnings-experience profiles.
'Knapp and Hansen [1976) develop a procedure
to estimate ko from longitudinal data.
-
HIRSCH: HUMANCAPITALINVESTMENT
10 years [Mincer, 1974, pp. 20-3], estimates of
T can be obtained by:
T= t* -10=-
-b2
-10
2b3
where t* is the peak of observed earnings (where
31n Yj3t = 0), and b2 and b3 are the regression
coefficients on t and t2.
Given any value of T, the parameter rp and
ko can be identified. The Chiswick-Mincer specification implies:
ko
b2
= [rpko + -T (1 + ko)]
and
rp ko
120
b 3 =-[-+-2
2T
2T
] .
Solving for ko and rp we find:
ko =b2T+
2b3T2
and
b2
1 + ko
rp =(-;:T ).
0
In similar fashion, the regressioncoefficients
OJand OJfrom equation (2) can be utilized to
obtain estimates of T, ko, and rp by occupation.
The estimation of these important earningsfunction parameters allows inferences to be made
about differences in investment behavior across
occupations.
Data
The data used are from the 1/100 file of the
1970 Census Public Use Sample. Our sample includes 7,667 white, non-farm, non-student males
between the ages of 15 and 64 who had somelabor earnings in 1969, and who reside in 48 Standard Metropolitan Statistical Areas (which were
randomly selected from among SMSA's with
populations greater than 250,000 and less than
3 million).
33
Empirical Results
Table 1 presents regression results for earnings functions (1) and (2). Equation (1), the standard human capital earnings function, constrains
earnings-experience profiles to a common intercept and shape across occupations, while equation (2) allows profiles to vary across occupations. We calculate the relevantF statistic comparing sums of squared residuals from equations
(1) and (2) in order to test whether or not the
constraints on earnings function parameters imposed by specification (1) are of consequence.'
F;~8 is calculated to equal 16.43 and the null
hypothesis of equivalent earnings profiles is rejected at conventional significancelevels.
Thus, allowing the intercept and the shape
of earnings-experience profiles to vary for individuals in different occupations does add significantly to the explanatory power of the human
capital earnings model. This evidence is consistent with those views of the labor market which
stress the importance of on-the-job and other
postschool training investments.
Note that differences across occupations in
the intercept of earnings-experienceprofiles will
result not only from differences in ko but also
from differencesin non-pecuniary compensation
and any other unmeasurable determinants of
earnings. Whileko cannot be separated from each
estimatedrpj>wecancompareequations(1) and
(2) to another earnings function with the coefficients of t and t2 constrained to be equal across
occupation, but which allows the intercept to
vary (i.e., equation (1) plus 8 occupational intercept dummies).
When this specification is estimated and the
relevant F tests are performed, the null hypothS
The appropriate F ratio is
SSR (1) - SSR (2)/d
SSR (. )/(n - k - 1)
where, SSR is the sum of squared residuals, d is the
difference in the number of parameters between the restricted and unrestricted regressions, n is the number
of observations, and (k + 1) is the number of parameters in the unrestricted model (specification (2».
34
ATLANTICECONOMICJOURNAL
TABLE 1
REGRESSION RESULTS FROM EARNINGSFUNCTIONS(1) AND (2)
(2)
3.915
.055
(.0025)
(1)
3.839
.073
( .002)
Constant
S
(
.0539
(2
- .00089
(.00004)
.960
(.024)
(.0018)
lnWW
Occupation
Professional and
Technical
Managersand
Administrators
Sales
cf>j
--
.426
(.082)
.351
(.092)
.233
( .093)
.175
( .091)
.384
(.081)
.248
(.086)
.306
(.106)
.083
( .093)
---
Clerical
--
Craftsmen
--
Operatives
(Non-Transport)
Transport Equipment
Operatives
Laborers (Non-Farm)
.927
( .024)
--
---
--
Service
--
R2
SSR
.390
2108.2
OJ
bj
.0582
(.0044)
.0627
(.0054)
.0605
(.0060)
.0511
(.0057)
.0450
(.0036)
.0478
( .0047)
.0418
(.0071)
.0506
( .0064)
.0555
(.0067)
.420
2004.7
-.00108
(.00010)
-.00105
(.00011)
-.00107
(.00013)
- .00082
(.00012)
- .00075
( .00007)
-.00075
( .00009)
- .00063
(.00014)
-.00081
(.00013)
- .00096
(.00013)
Dependent variable is natural log of earnings in 1969. n = 7,667. Standard errors in parentheses.All coefficients
significant at .01 level, except cf>jfor Laborers and for Clerical.
cf>j= intercept
dummy
for occupationj.
bj
= coefficient
of experience
for occupationj.
OJ = coefficient
of experi-
ence squared for occupationj.
esis that intercepts are identical across occupations is clearly rejected (that is, we reject Ho:cf>2
= cf>3 = . . . = <Pn = 0). However, the null hypothesis of equivalen t slope coefficients on experience
and experience squared across occupations (Ho:
= b2 = . . . = bn and cf>l = O2 = . . . = On), while
rejected at a .05 significancelevel, cannot be rejected at a .01 level. These results imply that
variation acrossoccupations in the slopesof earnings-experience profiles may be quite moderate,
b1
6
35
HIRSCH:HUMANCAPITALINVESTMENT
after accounting for differences in intercepts,
schooling, and weeks worked.
Even though variation in the shapes of earnings-experience profiles is small, differences in
the profiles are consistent with expectations.
Those white-collar occupations where we presume the amount of postschool investment is
greatest-professional and technical, managers
and administrators, and sales-show the most
concave earnings proftles. On the other hand,
blue-collar occupations tend to have significantly flatter profiles. In addition, the empirical results indicate an inverse relationship between OJ
and OJ.Those occupations showing the steepest
rising profiles (highest OJ)also tend to show the
most rapid decline (lowest OJ)' This finding is
consistent with an investment pattern where
rates of return to occupational paths are equalized, while ko and T are inversely related.
Results from the estimation of earnings function (2) indicate that earnings peak at different
agesand years ofexperience across occupations.7
The earnings-experienceprofile is at a maximum
at t*
= -oj/20j
for occupationj.
The age at which
TABLE 2
YEARS OF EXPERIENCE AND AGE
AT WHICHCROSS-SECTION EARNINGS
PEAK, BY OCCUPATION
Professional and
Technical
Managersand
Administrators
Sales
Clerical
Craftsmen
Operatives
Transport Equipment
Operatives
Laborers (Non-Farm)
Service
t
Age
26.9
48.2
29.9
28.3
31.2
30.0
31.9
49.0
47.2
49.3
46.8
48.1
33.2
31.2
28.9
49.3
47.2
45.4
Estimates are from specification (2).
OJ
t*'= -J
20.J
Age*'= - J
OJ
-
+ S. + 6
J
cross-sectional earnings are at a maximum is estimated by adding to t* the mean years of schooling in occupation j plus 6. Table 2 shows the
Table 3 presents estimates of the investment
years of experience and age at which cross-sec- parameters T, ko, and rp by occupation. Rates
tion earnings peak by occupation. Of course, of return to postschool investment are found to
concave cross-sectional earnings profiles result, vary little among occupational paths, a finding
in part, from secularchangesin productivity (vin- consistent with equilibrium efficiency conditage effects) and in schooling quality and need tions. The estimates of ko and T suggest that
not imply that earnings for any single cohort ac- the initial investment intensity is greater in whitetually decline during their working life [Ruggles collar than in blue-collar occupations, while the
and Ruggles, 1978] .
length of the net investment span is shorter.
6 These regression results are not shown. The estiThese results are consistent with the finding of
mated equation has SSR = 2012.4. We first test the
more concave earnings profiles in white-collar
null hypothesis of equivalent intercepts and calculate
occupations. While estimates are somewhat senF~654 = 45.55, thus rejectingHo at conventional signifsitive
to alternative assumptions and specificaicance levels. We then separately test the null hypothesis of equivalent slope coefficients on t and t2 and caltions, they are highly suggestive,and in accord
culate F.,':38 = 1.63. The relevant tabular values for the
with a priori expectations.
F statistic in the latter case areF.O5 = 1.61 andF.o1 =
1.94.
Note that the existence of occupational moBecause In WW is on the right-hand side of the rebility over the life-cycle may introduce a probgression, the peak of weekly wages is, in effect, being
lem in interpreting the estimated occupational
observed. Generally, earnings peak earlier than do wage
rates.
earnings profiles. Mobility across occupational
7
20.J
------..
':t,
36
ATLANTIC ECONOMICJOURNAL
TABLE 3
EARNINGS FUNCTION PARAMETERS
BY OCCUPATION
T
Professional and
Technical
Managers and
Administrators
Sales
Clerical
Craftsmen
Operatives
(Non-Transport)
Transport Equipment
Operatives
Laborers
(Non-Farm)
Service
Pooled
ko
rp
(percent)
16.9
.367
7.8
19.9
18.3
21.2
20.0
.416
.390
.346
.300
8.0
7.9
8.4
8.5
21.9
.327
8.5
23.2
.292
8.7
21.2
18.9
20.3
.345
.363
.360
8.3
8.1
8.3
categories which is experience (age) related
means that the life-time pattern of earnings in a
given occupation differs from the life-time patterns of earnings of workers who start out in
that occupation. However, the occupation categories used in the above analysis are general
enough so that age related mobility across these
categories should not affect seriously the results.
Disaggregation By Occupation
In the previous section, evidence was presented indicating that postschool investment behavior differs across occupations, and that allowing
earnings-experience profiles to vary increases the
explanatory power of the earnings function to a
statistically significant degree. In this section,
we consider why it is not proper to disaggregate
by occupation if the purpose is to obtain reliable
estimates of rates of return to schooling. It can
be shown that estimating either a separate earnings function for each occupation, or interacting
years of schooling with an occupation dummy,
will result in schooling coefficients which under-
estimate "true" rates of return to schooling for
individuals in any given occupation. In addition,
the relationship between wage rates, weeks
worked, and occupation will be examined.
A massive volume of empirical research has
established that a significant positive relationship exists between education and earnings.
However, education per se has little direct effect on earnings; rather it increases earnings by
enhancing skill and productivity and/or by serving as a screening device for employers to identify productivity related characteristics (e.g.,
native ability, trainability, motivation). Occupational mobility and advancement is simply a
medium by which schooling leads to greater
earnings power.
Thus, introduction of both occupation and
schooling variables into an earnings function is
in part redundant and will bias rate of return estimates. Indeed, if occupational categories were
defined so narrowly that there were no differences among workers in skill and knowledge, the
effect of schooling on earnings could be eliminated. Rates of return estimated within occupations also will be biased downward due to a selectivity bias. Relative to an occupation's average educational level, we observe the least able
of more highly educated workers and the most
able of less educated workers.
The downward bias in estimated rates of return to schooling brought about by disaggregation is illustrated in Figure 1. Assume that all
jobs are in either a skilled or unskilled occupational category, and that additional schooling
increases an individual's probability of gaining
access to a skilled job. The estimated schooling
coefficient from either disaggregated sample will
generally be lower than the "true" rate of return
estimated from a pooled sample. In short, the
coefficient on schooling estimated jointly measures the returns from schooling from two sources.
First, it captures the return from occupational
mobility; that is, from facilitating access to higher-skill occupations. Second, it captures the returns from additional education for workers
37
HIRSCH: HUMAN CAPITAL INVESTMENT
within a particular occupational category once
access is gained. A disaggregated approach allows
only this latter effect to be captured.
FIGURE I
DISAGGREGATION BIAS IN RATE OF RETURN ESTIMATES
In Eamings
All Wo,kers
Skilled
..
Occupat;on
. /.
;.
Unsk;lIed
Occupation
with identical years of schooling, those who are
more able, more motivated, or have greater investment intensity (due to greater ability or
schooling quality) tend to be employed in whitecollar occupations in which these attributes are
more highly rewarded (productive). Note again
that these coefficients do not reflect the returns
of occupational access and mobility made possible by schooling, and should not be interpreted
as rates of return.
The coefficient of In WW, interpreted as the
partial elasticity of annual earnings with respect
to weeks worked, is found to vary across occupations. A value of 1 for'Yindicates no net correlation between weeksworked and weekly wage
rates. A value of'Y greater (less) than 1 indicates
that for individuals with similar amounts of
schooling and experience, weekly wage rates are
higher (lower) for those who work more weeks.
Two sets of factors
Years of Schooling
Table 4 presents regression results for the
standard human capital earnings function disaggregated by occupation. We calculate the F
statistic comparing the sums of squared residuals from the nine disaggregatedregressionsand
from the single pooled regression.We find F~2
= 13.17 and reject the null hypothesis that the
coefficients of the model are jointly equivalent
across occupations.
Comparison of the slopes of the log earningsschooling profiles indicate that returns to schooling vary widely across occupations, once access
to an occupation has been gained. White-collar
occupations-particularly professional and technical, and managers and administrators-impart
significantly larger percentage rewards to more
highly educated workers (holding constant experience and employment) than do blue-collar
and service occupations. Generating this result
may be a more flexible wage structure within
white-collar occupations which is affected relatively less by seniority provisions, unions, or
institutionalized wage patterns.
Alternatively, it may be that among workers
tend
to make 'Y
<
1 (but
positive). First, the income effect in labor supply
with respect to those wage differences not explained by Sand t results in fewer weeksworked
in response to a higher weekly wage. Of course,
labor supply response with respect to explained
wage differences do not affect estimates of 'Y.
Second, to the extent that wage premiums are
attached to cyclical and seasonal jobs, 'Ytends
to be less than unity, since workers in these jobs
tend to work fewer weeks.
Several factors tend to offset the above effects and lead to estimates of'Y greater than unity. Thesubstitution effect in labor supply partially offsets the income effect discussed above.
Also, human capital theory leads to the prediction that those receivinglarger amounts of firmspecific training exhibit both higher observed
earnings (and wage rates) and greater employment stability (a higher WW), thus leading to an
'Y
> 1 [Parsons,
1972] .
An additional factor leading to an 'Y
> 1 is
that current weeks worked may be positively
correlated with weeks worked in previous years.
Fewer previous weeks worked (e.g., previous
unemployment) implies less accumulated on-
ATLANTICECONOMICJOURNAL
38
TABLE 4
REGRESSION RESULTS FROM EARNINGSFUNCTION (1)
DISAGGREGATED BY OCCUPATION
Professiona] and
Technical
Managersand
Administrators
Sales
Clerical
Craftsmen
Operatives
(Non-Transport)
Transport Equip.
Operatives
Laborers
(Non-Farm)
Service
TOTAL
n
1316
Constant
4.219
100]
3.592
623
3.791
581
3.357
1910
5.031
994
4.320
447
5.163
366
4.708
429
1.954
S
.075
(.006)
.082
( .007)
.041
(.01 ])
.055
(.009)
.040
(.005)
.037
(.007)
.037
(.010)
.042
( .012)
.036
(.012)
7667
t
.058
(.005)
.061
(.006)
.059
(.008)
.047
(.005)
.047
(.003)
.047
(.004)
.046
(.007)
.054
(.007)
.036
(.008)
t2
- .00105
(.00011)
-.00098
(.00013)
-.00105
(.00017)
- .00076
(.00010)
- .00082
(.00007)
- .00077
(.00008)
-.00073
(.0014)
- .00088
(.00014)
- .00068
(.00015)
In WW
.87
(.06)
1.00
(.12)
1.07
(.11)
1.13
(.06)
.77
(.05)
.94
(.06)
.72
(.09)
.76
(.07)
1.55
(.11)
R2
.349
SSR
348.7
.27]
316.3
.289
263.4
.525
109.0
.256
417.9
.401
193.3
.270
]09.8
.456
100.9
.425
112.6
1971.9
Dependent variable is natural log of earnings in 1969. All coefficients significant at .01 level. Standard errors in
parentheses.
the-job training and thus acts as a wage depressant [Lazear, 1976]. Final]y, a positive corre]ation between weeks worked and hours worked
per week tends to make 'Y 1. Hours worked
per week is not accounted for due to serious
measurement error in the Censusvariable.
Estimates of 'Yfrom individual data not disaggregated by occupation are close to unity.
Mincer [1974b] estimated 'Yto equal 1.17 using 1959 data while here 'Yis estimated to equal
.96 for the 1969 SMSA sample. These results
lead to the conclusion that there is little net correlation between weeks worked and weekly
wage rates. However, the variation in estimates
of 'Ywhich is found after disaggregation by occupation suggeststhat this conclusion is mis]eading. Whileit is not possible to isolate the relative
>
strength of factors within occupations generating estimates of'Y far different than one, the results presented in Tab]e 4 are suggestive. The
low estimates of'Y for those in blue-collar occupations may result from relativelystrong income
effects and from significant wage premiums for
cyclical and seasonal employment.
The reason for the high estimate (1.55) for
service workers is at best speculative. A large
number of workers in this category (e.g., food
service, health service, personal service) have
relatively weak labor force attachment. This
will generate a positive relationship between
weeks worked and weekly wage rates if weeks
worked and hours worked per week, and, current weeks worked and accumulated job training, are positively correlated. Whatever the cor-
HIRSCH: HUMANCAPITALINVESTMENT
rect explanation for the above results, it appears
that estimates of 'Yobtained from pooled regressions mask some important labor market phenomena.
8
Summary
Recent labor market researchhas emphasized
the importance of on-the-job training and occupational access as avenues toward higher earnings. Empirical studies employing the human
capital framework, however, assume that the relationship of earningswitheducation,experience,
and employment is similar for individuals in all
occupations. In order to gain insight into the
earnings generation process, this study allows
the effects of years of schooling, years of experience, and weeks worked to vary across individuals in nine occupational groupings.
Earnings-experience profiles, whose shapes
reflect the initial investment intensity, the investment span, and the rate of return to postschool training, are found to differ across occupations in a manner consistent with a priori
expectations. Occupations which are believed
to impart relatively greater amounts of postschool training exhibit the most concave profiles, such that earnings profiles which rise most
steeply also show the most rapid decline. Empirical estimates of the investment parameters T,
39
ko, and rp are obtained by utilizing prior information about T. Rates of return to postschool
training are found to vary little across occupations, while the initial investment ratio and the
investment span appear to be inversely related.
Earnings functions disaggregated by occupation are also estimated. It is shown that schooling coefficients obtained in this manner should
not be regarded as rate-of-return estimates since
they do not capture the returns from occupational mobility. However, the percentage return
to years of schooling, once access to an occupation is gained, is found to differ widely between
white and blue-collar occupations.
Finally, the conclusion from previous studies
that there is little net correlation between wage
rates and weeks worked is found to be misleading. The partial elasticity of annual earnings
with respect to weeks worked varies significantly from unity across occupations, indicating
correlation between weekly wage rates and
weeks worked. We speculate on the various phenomena generating this result. Hopefully, future
research can disentangle several of the offsetting
labor market effects which form the basis for
the relationship between earnings, wage rates,
and weeks worked.
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Zvi Griliches, "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, 45,
January 1977, pp. 1-22.
BarryT. Hirsch, "Earnings Inequality Across Labor
Markets: A Test of the Human Capital Model," Southern Economic Journal, 45, July 1978, forthcoming.
Anders Klevmarken and John Quigley, "Age, Experience, Earnings, and Investments in Human Capital,"
Journal of Political Economy, 84, February 1976, pp.
47- 72.
Charles Knapp and W. Lee Hansen, "Earnings and
Individual Variations in Postschool Humanb).vestmen t,"
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Mark Blaug, "The Empirical Status of Human Capital Theory: A Slightly Jaundiced Survey," Journal of
Economic Literature, 14, September 1976, pp. 827-55.
Alan Blinder, "On Dogmatism in Human Capital
Theory," Journal of Human Resources, 11, Winter
1976, pp. 8-22.
Barry Chiswick,Income Inequality: Regional Analyses within a Human Capital Framework, New York:
National Bureau of Economic Research, 1974.
Barry Chiswick and Jacob Mincer, "Time-Series
Changes in Personal Income Inequality in the United
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Peter Doeringer and Michael Piore, Internal Labor
8
As Blinder [1976] points out, it is not obvioushow
'Y f 1. Since some characteristics
to interpret values of
may affect both wage rates and weeks worked (e.g.,
motivation),
it is analytically difficult to have a partial
elasticity
of earnings with respect to weeks worked.
The exclusion of slope dummies for In WW in specification (2) does not substantially affect the earlier results with respect to occupation1l1 differences in investment parameters. .
40
ATLANTIC ECONOMICJOURNAL
Edward Lazear, "Age, Experience, and Wage
Growth," American Economic Review, 66, September
1976, pp. 548-58.
Jacob Mincer, "Progress in Human Capital Analyses
of the Distribution of Earnings," National Bureau of
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-,
Schooling, Experience, and Earnings, New
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Donald Parsons, "Specific Human Capital: An Application to Quit Rates and Layoff Rates," Journal of
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Carl Ra1m, "Investment in Training and the Occu-
pational Structure of Earnings in the United States,"
unpublished Ph.D. dissertation, Columbia, 1971.
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in Labor Economics, Greenwich, CT: JAI Press, 1977.
Nancy Ruggles and Richard Ruggles, "The Anatomy
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Lester Thurow, Generating Inequality: Mechanisms
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