The Effects ofIndustrial, Occupational,
and Sex Stratification on Wages
in Blue-Collar Markets *
ROB E R T BIB B,
Vanderbilt University
WI L L I A M H. FOR M,
University of Illinois at Urbana-Champaign
ABSTRACT
Sociological and economic models of income determination are reviewed: the former lack
comprehensiveness and precision and the latter fail to consider social structural variables. A
structural theory is proposed which considers the economic stratification of industrial sectors,
occupational stratification, and the sexual stratification of society. The data show through the
estimation of multiple regression models, that the human capital economic theory is less
powerful than a structural-stratification theory in explaining variance in earnings for a
national sample of manual workers. Lowest incomes are found in economically weak sectors
with unorganized occupational groups disproportionately composed of women workers.
Since economic position is basic to stratification placement in industrial societies
and since most people are not self-employed, wage and salary data should be
commonplace in the stratification literature. Yet theory and research focusing
explicitly on wage differentials have been conspicuously scarce. Sociologists have
been preoccupied with other issues, such as the generational transmission of occupational prestige, educational achievement, and similar variations on the attainment
theme." As a result, economists, most of whom display an affinity for reclothed
nineteenth-century theories, have a virtual monopoly over the study of income.
This paper attempts to identify social determinants of the earnings of bluecollar workers. The principal hypothesis is that for both men and women, the
economic models based on human capital investments have limited utility in
explaining income and that a structural model which considers the stratification of
industries, occupations, and sexes is superior. We shall first review economic and
sociological paradigms of earnings, select useful indicators for each, and then apply
multiple regression analysis to assess the adequacy of the models for explaining
earnings for blue-collar men and women.
ECONOMIC AND SOCIOLOGICAL MODELS OF EARNINGS
The dominant economic explanation for wage differentials is provided by neoclassical marginal productivity theory." Put simply, this theory asserts that under
perfect competition, the wages paid to workers equal the value of output attributable
*We are grateful to Joan Huber and two anonymous readers for criticizing an earlier draft of this
manuscript.
974
Stratification & Wages / 975
to that of the last worker hired (Fleisher). Hicks, for instance, argues that in the long
run, wages are determined by competitive market forces with institutional factors
merely representing "complications." Competitive neoclassical theory does not
hypothesize that inequalities in earnings derive from difference in the capital
intensiveness, market dominance, and other features of enterprises. As an optimally
efficient allocator of resources, the market prices labor solely on the basis of
skill difference (Scitovsky). This follows from assumptions that in a competitive
economy, workers will freely seek employment at the highest wage, employers will
pay as little as possible for labor of a given quality, and capital will migrate until its
marginal revenue product is equalized.
The principal weakness of marginal productivity theory resides in its dubious
assumptions: that people are rational; that producers have choice among alternative
factors and consumers among products; that a high degree of mobility characterizes
goods, services, and workers; that information about markets is widely and quickly
available; and that societal institutions are exogenous to income determination. In
applying this theory to wages, it is assumed that workers are economically rational,
that they have abundant job choices and market information (Rees; Stigler) and that
they are always ready to move (Bunting; Burton and Parker; Lansing and Morgan),
thus equalizing net advantages to employment throughout the occupational structure.
All these assumptions have been challenged by empirical studies of labor markets.
Workers are not always rational (Homans), have limited job opportunities (Bakke),
are typically ignorant of alternative employment possibilities (McCall; Reynolds),
and often defer moving (Adams and Aronson). Moreover, the predicted decline in
earnings differentials among industries and occupations is often unrealized (Ober;
Ozanne; Perlman).
Economists have long known that some wages seem to be unresponsive to
market forces. As early as 1850, for example, Mill observed the presence of
noncompeting groups in the labor market. Succeeding economists tended to ignore
weaknesses in marginal utility theory and were not eager to face up to socially
structured discrimination. But the recent admission or discovery of discrimination
has prompted neoclassical economists to revise their theory of wages. The most
widely accepted revision is the human capital approach. Proponents of the theory
(Becker, b; Mincer; Thurow) allege that, for whatever reasons, the marginal
productivities of blacks, women, and other low-income groups is lower than that of
better paid white male workers. In the extreme, ceteris paribus, wages are thought
to be isomorphic with a ranking of productivity components. As Thurow put it,
"the distribution of marginal products is identical with the distribution of earned
income" (20). It is important to emphasize that the human capital approach is not
concerned about the causes of discrimination. It simply emphasizes that those
who suffer from discrimination exhibit characteristics which make them less productive: low educational attainment, low skill, and poor work attendance; hence,
their lower wages.
Typical indicators of human capital include years of formal schooling,
vocational training, on-the-job training, amount of work experience, and even
motivation (Mincer). Human capital theory resembles structural-functional stratification theory in characterizing as investments for future higher earnings the financial
976 / Social Forces / vol. 55:4, june 1977
sacrifices which people make to obtain education: the cost of education and the
income foregone during the training period (Davis and Moore). Both approaches
regard positional rank as a product of individual inputs (talents, skills, motivation)
and societal outputs, i.e., wages, income, and other rewards (compare Svalastoga
and Thurow). Both theories presuppose a single labor market in which workers are
queued according to skills which accord with employer preferences (Thurow). This
labor queue is subject to competitive market forces such that employment and
wages are determined in the intersection of employer demand and the labor supply
of particular human capital or talent. Thus, for both the human capital and functionalist stratification models, income inequality, poverty, and unemployment flow
primarily from the characteristics of individual workers and the functional requisites
of a dynamic economy." And, especially with regard to poverty, human capital
theory closely resembles the sociological notion of the "vicious circle of poverty,"
in that low incomes are said to result from cumulative, mutually reinforcing
disadvantages over the life cycle (in health, housing, education, motivation, etc.).
Both theories reason that policy intervention at any point in the circle will produce
"virtuous" repercussions utlimately raising individual incomes.
Sociologists (Parsons and Smelser; Stinchecombe; Weber) have long argued,
proponents of marginal theory to the contrary, that workers do not compete in a
single labor market. Rather, markets differ with respect to custom (Duwors), the
economic power of the industry (Lynd and Lynd), the organizational power of the
union (Cottrell), types of occupational sponsorship (Hall), degree of cohesivenss
among workers (Warner and Low), and job ceilings imposed by employers for races
(Cayton and Drake) and sexes (Hiller).
Instead of considering markets as products of competition, some sociologists
have argued that they can be better understood as socially structured mechanisms
which link economic and social organization through a wider range of social processes. In industrial societies, the principal structure that links economic and social
organizations is the stratification system of society. The sociological task is to
demonstrate this linkage more precisely. So far, the demonstration has not gone
much beyond the typology stage: Parsons and Smelser have described different
markets for labor, executives, and professionals, and Caplow for common labor,
craftworkers, and bureaucrats. Form and Huber have described the characteristics
of administered, self-controlled, traditional, contested, and free markets. These
typologies consider the relative power of occupational groups to control recruitment,
working conditions, and income but they typically omit two organizational realities:
(1) the relative economic and organizational power of enterprises to control their
markets, and (2) the articulation of societal stratification mechanisms with the
enterprises and occupational groups within them. In short, a sociological theory of
wages must consider in stratification terms the organizational interlocking of enterprises, occupational groups, and the society. Unfortunately, sociologists have not
proposed a theory of sufficient simplicity, coherence, and power which can predict
specific incomes and income changes. They are still at the stage where important
variables are examined post hoc for their influence on income.
Stratification & Wages / 977
THREE STRATIFICATION ENTITIES:
FIRM, OCCUPATION, AND SOCIETAL STATUS
In proposing a sociological explanation of wages, we must identify structural
variables which can be compared with industrial variables for their power in explaining blue-collar earnings. The first element in the structural model deals with the
stratification of industrial establishments in terms of their material and organizational
resources and their ability to pay high wages. In the top stratum, often referred to as
constituting "center" establishments by economists (Averitt; Bluestone), are large
capital intensive oligopolistic firms which practice economies of scale and earn
relatively high and stable profits (Galbraith). Their managers and technical staff can
therefore command the highest salaries and their employees can command the
highest wages because they belong to large and powerful unions.
In the bottom stratum are found "periphery" establishments which have few
material and organizational resources. These firms are typically small, geographically scattered, and labor intensive. They have many competitors, uncertain profits,
and pay the lowest wages because they operate in free markets in which competitors
are unable to influence the economic forces affecting them. Establishments which
fall in between the top and bottom strata vary in the characteristics we have
described.
Since it is difficult to gather national wage data on the basis of the organizational characteristics of firms, indicators which tap a wide range of organizational
characteristics must be selected. Most of these indicators broadly reflect the technology of the enterprise. For example, the industrial sector to which the firm
belongs (manufacturing, construction, transportation and utilities, wholesale and
retail trade, or services) reflects a characteristic technology. Second, technology is
associated with enterprise size: large firms tend to have technologies which require
enormous concentrations of capital. Third, firms with complex technolgies tend to
be located in metropolitan centers because (a) they need to be close to major suppliers and markets, (b) they must have ready access to good transportation facilities,
and (c) they need complex supportive services and highly trained, disciplined labor
forces. Moreover, since enterprises with costly technologies make long-term investments, they try to insure stability of operations by pursuing oligopolistic practices,
administering prices and profits, and demanding a cooperative labor force. The
superior market and profit position of top stratum enterprises enables them to pay
high wages, a precondition for the emergence of powerful occupational groups.
The second element in the structural model deals with the stratification of
occupational groups in terms of their organizational power to extract high wages
from management. The strength of an occupational group primarily depends on two
conditions: the extent of which its members possess scarce skills which are in high
demand and the extent to which workers are cohesively organized. Skill composition
of the workforce and extent of its unionization are the two indicators we shall use to
stratify occupational groups. Skilled workers are generally more socially cohesive
and better able to control their work environment than unskilled workers. The informal power of occupational groups tends to be reflected in their skill composition
(Blauner; Sayles). Labor unions provide organizational power to occupational
978 / Social Forces / vol. 55:4, june 1977
groups lacking in skill and supplement the informal power of skilled workers.
A basic tenet of our theory is that the economic stratification of enterprises is
matched by the organizational power of occupational groups. Unions are strongest
in industries which have organizational characteristics making for easy unionization:
the need for special skills, physical concentration of many workers in a small area,
large and steady profits, location in areas where other large firms compete for
workers, and dependence on a skilled or disciplined labor force (Mahler). Bottom
stratum establishments have organizational characteristics associated with powerless occupational groups: small size, low skill requirements, geographic dispersion,
labor intensive modes of production, low profits, location in small communities or
ghetto areas, and socially heterogeneous and socially disadvantaged employees.
Workers in such enterprises have low social cohesion and, consequently, they are
difficult to unionize (Goode and Fowler). 4
The third element in the structural model deals with the stratal location of
the labor force in the community stratification system. A tenet of the model is that
workers in favored social strata are employed in enterprises with the greatest
economic strength and in occupations with the greatest organizational power. The
task of both the human capital and structural models is to explain why some groups
in the community are denied access to favored enterprises or occupational groups.
The human capital model suggests that groups disadvantaged by ethnicity, race, or
sex are denied access because they are unproductive. The structural theory, in
contrast, suggests that they are denied access by purposive social action on the part
of employers, unions, and other dominant groups, and that these individuals and
organizations justify their behavior with an elaborate economic and stratification
ideology.
The data available for this research do not permit us to consider all the
societal stratification variables (age, race, ethnicity, sex) in the structural model.
The exclusive focus on sex requires an explanation since women are not typically
considered a stratum in stratification theory. In pre-industrial societies, the sexual
segregation of economic functions was necessitated in part by the restriction of
the woman's physical mobility imposed by pregnancy, lactation, and childcare
responsibilities while male roles (hunting, trade, warfare) necessitated mobility
(Boserup; Friedl). In industrial socieites, the mechanization of production, the
elimination of economic functions in the home, the reduction of family size,
compulsory education for both men and women, technological changes in medicine,
childcare, and home maintenance made it organizationally possible for both men
and women to engage in similar occupations. The persistence of the sexual stratification of occupations must therefore be explained in terms of power arrangements
in the family, occupational groups, and firms.
Meissner's study shows not only that the husband's pattern of household and
childcare responsibilities remains unchanged by the wife's labor force participation,
but men actually increase their non-home leisure time activities while the employed
wife's work load doubles that of her husband. Even when women work full-time for
many years, their wages are regarded as supplemental income and their work as
unimportant. Occupational stereotyping on the part of the male estate restricts the
occupational placement and labor mobility of women to the labor market area of the
Stratification & Wages / 979
husband's choosing and to his work schedule. The conflict between management
and unions is subordinated to a "higher understanding" that the good (high paying)
jobs belong to men. This consensus makes it possible for managers in both
capitalistic and socialistic countries to assign women lower wages even when they
perform the same work as men. The dominant sex-role ideology prompts women
workers, as well as their employers, to view female labor force activity as providing
supplementary earnings. For this reason, employers often feel justified in paying
women lower wages. Finally, male chauvinism in the unions, the dispersed nature
of the jobs and industries in which women are found, and women's need for easier
mobility in and out of the labor force hinder union organization in industries
disproportionately employing females.
The basic principle of a structural theory of income is that enterprises,
occupational groups, and society are stratified along different dimensions: how the
strata in these three arenas intersect explains the wages of any particular group. The
low income of blue-collar women cannot be explained by individual variables in a
human capital model but by the merging of three low strata: economically weak
enterprises in the peripheral sector, occupational groups with weak organizational
power, and the subordinated estate of women in society.
RESEARCH DESIGN, DATA SOURCE, AND VARIABLES IN THE ANALYSIS
Data in the analysis are from the University of Michigan Survey Research Center's
1972-73 Quality of Employment Survey, a companion to research initiated in 1969
by the Employment Standards Administration of the U. S. Department of Labor
(Quinn and Shepard). Both studies were undertaken to investigate working conditions and quality of employment, job satisfaction, and importance ratings of work
attributes. Persons eligible for interview were household members at least 16 years
old and gainfully employed for twenty or more hours per week. From the national
sample of 2,157 individuals, we selected all full-time" blue-collar workers in the
civilian nonagricultural labor force, yielding a subsample of 1,004 cases. The
respondents reported employment in eight major Census industry groups: mining,
construction, manufacturing, transportation -communications-utilities, wholesale
and retail trade, finance-insurance-real estate, services (including business and
repair, professional, entertainment, and personal services), and public administration. Occupational categories included skilled workers and foremen, operatives,
transportation operatives, laborers, service workers, and private household workers.
The analysis compares the traditional human capital model of income
determination with the structural-stratification model described in the foregoing
paragraphs. We hypothesize that multiple regression analyses of earnings will show
that incomes of blue-collar workers cannot be accounted for primarily by referring
to the human capital characteristics of individuals. Specifically, the independent
contribution of labor quality factors (education, cumulative experience with the
current employer, attachment to the labor force, and general vocational training) to
income should be substantially smaller than the contribution of factors associated
with the structural framework (stratal location of the firm's economic sector, power
980 / Social Forces / vol. 55:4, june 1977
of occupational groups, and sex composition of the labor force). We further
hypothesize that the superiority of a social structural model over the human capital
explanation will remain when the data are disaggregated by sex.
Variables in the analysis were operationalized as follows: The dependent
variable, annual earnings, refers to the total wage or salary income received by
respondents in 1971 in their regular jobs. The independent variables fall into three
main categories: (1) human capital investments of individuals, (2) economic and
organizational features of enterprises and occupations, and (3) the stratification of
sexes. The human capital variables include education (coded as years of formal
schooling completed), length of tenure with current employer (scored 1 = less than
a month to 7 = more than twenty years), specific vocational preparation-SVP-or
amount of vocational or on-the-job training (scored 1 = less than a month to
6 = four or more years)," and an indicator of the worker's attachment to the labor
force formed by computing a ratio of cumulative labor force experience to potential
labor force experience, i.e., age minus years of schooling plus six.
The second set of variables deal with characteristics of the firm. To parallel
the economists' distinction between the primary or core industrial sector and the
secondary peripheral sector, we grouped major Census industries into two strata.
The core sector of the economy (scored 1) includes durable goods manufacturing,
selected nondurable goods industries," mining, construction, transportation and
public utilities, and government. The peripheral sector (scored 0) includes the
services, wholesale and retail trade, finance, and selected nondurable goods manufacturing. Another industry characteristic, enterprise size, (scored 1 = less than 25
employees, 2 = 25 to 99, 3 = 100 to 250, and 4 = over 250 employees) was
included because of its association with other economic factors, such as the
tendency for large firms to be capital intensive (Lester) and oligopolistic in their
product markets (Weiss). Finally, residence in a Standard Metropolitan Statistical
Area (dubbed LOCATION and coded as a dummy variable) was included because
the highest paying industries are located in major metropolitan areas. While SMSA
residence is frequently construed as reflecting South-non-south income differences,
Fuchs has discovered substantial city size effects on earnings controlling for region.
However, his explanation leaves much to be desired: city size is considered merely
a proxy for a "high quality" (i.e., better educated) labor force and hence devoid of
independent contribution. By incorporating both SMSA residence and education in
our regression specifications, Fuchs' argument will be tested.
The third set of variables deal with occupational power. Skill level, dubbed
OCCGROUP, was coded 1 = unskilled laborer or service worker, 2 = semi-skilled
operative, and 3 = skilled craftworker or foreman. Skill level was included because
craftworkers are known to be able to control their work environments more effectively than the less skilled (Sayles). Major occupational groups differ in their
capacity to control wage determining market forces such as the exclusive operation
of expensive equipment, the recruitment, training, and certification of workers, and
the determination of working conditions." Unionization, coded as a dummyized
membership variable, was included because unions are attracted to industries which
Stratification & Wages / 981
are easier to organize and which have higher profit margins (Dunlop). 9 Moreover,
unions tend to invest occupational groups with organizational power.
A final societal variable, sex (scored 1 = female and 0 = male) was included in the analysis because we believe that both the market and occupational
groups respond particularistically to women's subordinate estate.
DATA ANALYSIS
Detailed comparisons of the human capital and structural-stratification models of
earnings are presented later, but a preliminary analysis of their parameters may be
derived from data presented in Tables 1-4. As suggested, the economic power of
enterprises and the social power of occupational groups are reflected in the earnings
of workers. The stratification of industrial sectors is demonstrated in Table 1 where
blue-collar earnings for males in mining, public administration, and transportation
are over 1.5 times greater than earnings in the lowest paid sector. Earnings in the
construction industry and manufacturing fall into a middle stratum, roughly 1.4
times those in the service enterprises. For blue-collar women, interindustry differences in earnings are even more dramatic. In short, the lowest earnings are in the
labor intensive sectors of finance, trade, and services and the highest in the more
capital intensive sectors.
If the distribution of blue-collar earnings among major industry groups can
be attributed primarily to differences in the market capacity of enterprises, the
pattern of earnings within industries undoubtedly reflects the power alignments of
Table 1. MEDIAN EARNINGS, UNION MEMBERSHIP, AND SEX COMPOSITION OF EMPLOYMENT FOR BLUE C(
WORKERS IN CIVILIAN NON-AGRICULTURAL lABOR FORCE, BY MAJOR INDUSTRY GROUP
Industry Group
Public
Administration
TransportationUtilities
Mining
Manufacturing
Construction
Finance
Servicest
Median Earnings
in 1969
Ratio Earnings
to Services*
Union
Membershipt
Male
Female
Male
Female
Male
$7,944
$3,667
1.55
1.61
52%
26%
7,864
7,693
7,497
7,161
5,431
5,125
4,172
5,121
3,734
4,727
2,795
2,275
1.53
1.50
1.46
1.40
1.06
1.00
1.88
2.25
1.64
2.08
1.23
1.00
71
90
63
44
18
15
44
4,877
2,721
.95
1.21
26
Female
Women as a PE
of Blue Cot,
Work Fore
9
8
6
2
30
1
22
61
21
31
-§
37
-§
-§
Wholesale and
Retail Trade
*Source: Bureau of the Census, Table 229.
tSource: University of Michigan, SRC.
:f:lncludes business and repair services, personal services, entertainment services, and professional services.
§Insufficient N.
982 I Social Forces I vol. 55:4, june 1977
participating occupational groups. Where the dispersion of earnings is broad,
occupational groups probably vary with respect to their organizational power;
where earnings are concentrated, occupational groups are either weak or strong
depending on where they are in the distribution. For instance, earnings among
blue-collar workers in transportation-communication-utilities and in the service
industries are highly concentrated yet at opposite ends of the income ladder (see
Table 2). This pattern probably stems from the presence of strong unions in the
former industry group and their absence or weakness in the latter (see Table 2).
Table 2. PERCENT DISTRIBUTION OF BLUE COLLAR WORKERS IN MAJOR INDUSTRY
GROUPS BY INCOME CATEGORY*
Annual Earnings
Industry Group
Public administration
Transportation-Uti Iities
Mining
Manufacturing
Construction
Finance, etc.
Wholesale-retail trade
Services
Less than
$5,000-
$7,000-
$5,000
6,999
9,999
11%
2
24%
7
20
1
25
36
54
22
17
50
24
26
14%
17
50
30
32
25
20
18
$10,000- $12,00011,999
30%
49
33
4
18
16
2
Over
21%
25
17
23
33
4
*Source: University of Michighan, SRC.
The importance of sex stratification on blue-collar earnings, without regard
to industry affiliation, is shown in Table 3 which compares the distribution of
workers within income categories according to the percentage of women in various
occupations as reported in the Census. Expectedly, over three-quarters of the
workers in predominantly (over 85%) "male" blue-collar jobs fall into the two
highest income strata, while half of all workers located in predominantly (over
75%) "female" occupations fall into the lowest earnings stratum. How industry,
occupation, and sex stratification affect earnings can be readily discerned in Table 4.
Women earn substantially less than men in all major occupational groups whether in
the top industrial stratum or in the periphery. The least skilled male laborers and
service workers in the capital intensive stratum are economically better off than the
most skilled in the peripheral stratum. Clearly, both sexes are disadvantaged by
employment in the peripheral economy, but sexual earnings inequality in identical
occupational groups is generally less pronounced in the bottom than the top
industrial stratum. This situation suggests that the greater propensity of blue-collar
men to organize does not payoff as well in the economically disadvantaged and
more competitive trade and service industries.
The descriptive statistics in Tables 1-4, however dramatic, do not speak
directly to the issue of whether earnings can best be explained by the human capital
or the social structural model. The heart of our analysis involves the intercorrelations
of variables in both models and basic multiple regression estimates of the effects of
Stratification & Wages / 983
Table 3. DISTRIBUTIONOF BLUECOLLAR WORKERS WITHIN INCOME
CATEGORIES BY THESEXUAL COMPOSTION OF THEIR OCCUPATIONS'
Percent Female in Occupation
Annual Earnings Less than 5 5-14
Less than $5,000
$5,000-6,999
$7,000-9,999
$10,000-14,999
$15,000 or more
7
15
26
25
10
7
23
34
53
73
15-24
25-49
50-74
75 or more
15
1
4
1
5
22
28
18
8
18
15
4
1
2
49
24
5
2
6
'Source: University of Michigan, SRC.
Table4. MEDIAN EARNINGS OF BLUECOLLAR WORKERS IN CORE AND
PERIPHERY INDUSTRIES, BY MAJOR OCCUPATION GROUPAND SEX'
Men
Women
Earnings Ratio of
Men to Women
$8,445
7,356
7,134
$4,989
3,675
3,607
1.72
2.00
1.98
7,053
5,345
3,521
3,766
2,877
2,310
1.87
1.86
1.52
Core Sectort
Craftworkers
Operatives
Laborers and service
Periphery Sectort
Craftworkers
Operatives
Laborers and service
'Source: Bureau of the Census, Table 229.
[tncludes durable goods manufacturing, selected nondurable goods industries, mining, construction,transportation, and public utilities, and government.
:j:lncludes combined services, wholesale-retail trade, finance, and selected
nondurable goods manufacturing.
these variables on blue-collar earnings, computed first for the general sample and
then for males and females separately. Table 5 presents means, standard deviations,
and a matrix of intercorrelations among all variables used in the aggregate analysis.
Here, the correlations of the variables with earnings show stronger support for the
structural-stratification than for the human capital model. As anticipated, the societal
variable, sex, shows the highest correlation with earnings, followed in order by the
occupational, industrial, and human capital factors. The correlations of the human
capital variables (Xl - .\4) with the other sets of determinants are generally small,
with the obvious exception that vocational training is moderately correlated with
occupation and tenure with union membership. On the other hand, half of the industry variables (Xs - X7 ) show moderately high correlations with the occupational
terms (Xs - Xg ) while both of the occupational variables are negatively correlated
with sex (female). The suggestion in the economics literature that while top stratum
industries pay better wages, they obtain a higher quality labor force in the bargain
(cf. Weiss), is not supported by our data. Instead, it appears that the inter-sector
Mean
Std. Dev.
10.75
2.25
X1
3.03
.86
.184
X2
.64
.48
.59
.20
5.04
1.86
Xs
.154
.101
.033
.017
X4
-.242
.066
.527
Xa
-.140
.078
.70
.46
-.043
-.008
.157
.077
-.059
X6
2.33
1.24
.060
-.103
.197
-.038
.086
.300
X7
.41
.49
.017
-.034
.239
.131
.123
.323
.273
Xa
CORRELATION MATRIX, MEANS, AND STANDARD DEVIATIONS FOR VARIABLES IN THE ANALYSIS
Education X1
SVP
X2
Xa
Tenure
Attachmt X4
Location Xs
Sector
X6
Firmsize X7
Union
Xe
Occgroup X g
Sex
X,o
Earnings X"
Table 5.
Xg
1.99
.78
.056
.495
.154
.110
.001
.441
.044
.116
X10
.36
.48
-.067
-.212
-.056
-.250
-.077
-.288
.074
-.233
-.349
7933.31
3862.56
.147
.306
.253
.235
.229
.353
.112
.361
.371
-.627
X1 1
\0
'-l
'-l
--
~
~
::::
'-.
~~
\.Jl
\.Jl
~
..::
--..
~
~
~
"""l
c::>
~
S·
~
c::>
C')
--..
~
\0
Stratification & Wages / 985
distribution of well-educated, vocationally trained, loyal workers is remarkably
random.
Using these correlations, we now estimate three linear multiple regression models corresponding to (I) a human capital explanation, (2) our structural
model linking the stratification of industries, occupational groups, and sexes, and
(3) a composite specification incorporating the effects of both models. Table 6
presents the results of regressing blue-collar annual earnings on all indicators of the
three models. Figures in the upper panel denote standardized regression (beta)
coefficients; corresponding raw regression coefficients and their standard errors of
estimate appear immediately beneath. Referring to the coefficients of determination
(R 2 ) for models 1 and 2 respectively, our stratification model is shown to provide
superior explanatory power for blue-collar earnings: it explains nearly three times as
much variance in the dependent variable as the human capital approach, i.e., 50 percent as compared to 18 percent.
The relative importance of the determinants can be apprehended by referring
to the magnitude of the standardized coefficients in the composite regression. The
fully specified model explains exactly 55.1 percent of the variance in annual
earnings among blue-collar workers. The preponderance of industry, occupational,
and stratification effects over human capital effects in determining earnings is
suggested by the comparatively large coefficients for sex, location in an SMSA,
union membership, and core sector employment. Together these four variables
contribute two-thirds of the total explanatory power of the model, net of education,
tenure, and general vocational training.
Recalling Fuchs' contention that the higher income of city workers is
probably a manifestation of superior education, we note that the direct effect of
SMSA location exceeds that of education. Living in a metropolitan area is worth
about $1,164 for the blue-collar worker, controlling for education. That city effects
cannot be attributed to schooling immediately raises the question of what city
characteristics are relevant, a problem beyond the scope of the analysis at hand.
One possible explanation may be that as city size increases, local opportunity
structures tend to diversify, thus increasing the range of opportunities for workers to
capitalize on their special competences and to organize their occupations for wage
bargaining. Moreover, the geographic concentration of economically powerful
enterprises in SMSAs is accompanied by the geographic concentration of large
unions. Raphael and Gillaspy demonstrated in Pennsylvania a link between the
technology of industries, their location, and their wages. Where advances in
mechanization reduce dependence on highly paid male craftworkers, some industries
migrate to smaller communities and employ women for less skilled jobs because,
there, women have few alternative sources of employment. But, more importantly,
industries with primitive mechanical technologies stay in smaller communities,
employ low-paid women workers, and inhibit their migration to metropolitan areas.
Another powerful determinant of blue-collar earnings is employment sector.
Even in the completely specified model, the metric coefficient associated with this
variable is large. The economic strength of the industrial sector is a powerful factor
986 / Social Forces / vol. 55:4, june 1977
Table6. COEFFICIENTS FOR THREE MODELS OF EARNINGS FOR
BLUECOLLAR WORKERS (standard errors in parentheses)
Independent
Variable
Education
SVP
Tenure
Attachment
Location
Sector
Firmsize
Union
Occgroup
Sex
Model 1
Model 2
Model 3
.170
.089
.058
.162
.133
-.508
.087
.164
.148
.013
.144
.141
.038
.139
.010
-.489
.165
.251
.166
.171
METRIC COEFF ICIENTS
Education
SVP
Tenure
Attachment
283.68
(60.00)
1122.71
(151.88)
343.98
(81.00)
3366.66
(786.27)
Location
Sector
Firmsize
Union
Occgroup
Sex
Constant
R2
-2177.92
.183
1373.07
(200.17)
746.62
(251.89)
179.41
(83.01)
1275.01
(211.38)
661.66
(139.76)
-4081.35
(219.18)
148.70
(45.56)
731.71
(136.02)
307.35
(64.06)
245.41
(615.14)
1164.30
(205.82)
1185.36
(264.46)
117.13
(86.15)
1095.56
(218.42)
49.39
(167.25)
-3933.82
(231.99)
6555.10
2684.78
.503
.551
influencing workers' incomes: to be employed in the core rather than in the
peripheral sector is, for the typical worker, worth nearly $1,200. To be sure, a
substantial portion of the relationship is attributable to the fact that the stratification
of industries coincides with the stratification of sex estates: jobs in the bottom
peripheral sector are disproportionately reserved for women.
Proponents of the human capital model are apt to respond that this line of
reasoning is spurious because it obscures the fact that women and other minorities
Stratification & Wages / 987
are deficient in human capital, and this deficiency alone accounts for the low
productivities and low earnings. However, the impact of sex stratification on
earnings is only negligibly mediated by schooling because, as we shall see, the
educational biographies of blue-collar men and women scarcely differ. In fact, the
understandardized regression coefficient for education suggests that an increase in
formal schooling from eight years to high school graduation would increase yearly
earnings by only $595 (or about $149 for each additional year) when all other
factors are taken into account. By way of contrast, union membership is worth
$1,096 net of other factors. Adding length of employment with current employer
and labor force attachment does not seem to dramatically attenuate the direct effect
of being female. Although the measure of vocational training (SVP) fares somewhat
better in this respect, it leaves the observed effect of sex on earnings substantially
unexplained. Indeed, the actual dollar cost to the typical female worker is over
$3,900 annually, ceteris paribus. Insight into how this differential arises can best be
achieved by reestimating the three models disaggregated by sex, thereby illuminating differences in the wage determination process for men and women.
THE INCOME DETERMINATION PROCESS FOR MALE AND FEMALE
BLUE-COLLAR WORKERS
Means, standard deviations, and correlation matrices for the models disaggregated
by sex appear in Table 7. Perhaps the most striking feature is the similarity of male
and female mean values on the human capital variables and dissimilarity with
respect to industry sector, union membership, and occupational affiliation. While
blue-collar women, on the average, trail men in educational attainment by about
two months, they are more than twice as likely to be found in the periphery and
about half as likely to be union members.
Ironically, while women closely approximate men in their human capital
investments, these factors are far less important for women in determining their
income. Coefficients of determination for models 1 and 2 in Table 8 reveal that
wages among women are more strongly determined by the structural characteristics
of firms and occupations: our stratification model explains 24 percent of the
variance in women's earnings but only 18 percent of males'. Men appear to be at a
considerable advantage in capitalizing on education, vocational preparation, and
lengthy tenure.
Referring now to the composite specification (model 3), we note that
significant sexual disparities persist for most of the determinants. Education and
vocational training continue to provide less payoff for women. The single most
important determinant in the female equation, as gauged by the beta coefficients,
appears to be the industry variable, enterprise size. For men, tenure with the present
employer is most important probably because of the concentration of male bluecollar workers in core firms where internal labor markets reward orderly career
progression. Thus, since training and education have been netted out as "produc-
F
M
F
M
SO's
Xl
X2
X3
X4
Xs
Xs
X7
Xs
Xg
XlO
Means:
Education
SVP
Tenure
Attachmt
Location
Sector
Firmsize
Union
Occgroup
Earnings
1.77
2.49
10.54
10.78
.180
-.188
-.385
.158
-.030
.073
.005
.127
.136
Xl
4.90
5.08
1.73
1.96
.85
.86
.565
.031
.127
.203
.267
.139
.288
-.137
-.044
X3
2.78
3.14
.106
.048
.030
.035
-.152
-.126
.708
.221
.132
X2
4715.04
9766.05
1946.45
3454.25
.63
.76
.44
.50
1.24
1.25
.50
.42
.49
.47
.20
.19
.045
.084
.263
.112
.167
.324
.424
.177
.333
Xl0
1.63
2.24
.223
-.135
-.003
.197
-.022
-.028
.768
.391
.249
Xg
.26
.50
-.014
.255
-.071
-.028
.193
.023
.107
.255
.289
Xs
2.45
2.30
.331
-.112
.151
.014
-.001
.215
-.063
.030
.452
X7
.52
.77
.297
.327
.226
.230
-.194
-.250
.187
.001
-.159
Xs
.59
.66
-.049
.138
.119
-.024
.229
.100
.172
-.023
.016
Xs
.52
.62
-.013
.004
.013
.089
.054
.091
-.074
-.049
.535
X4
Table 7. CORRELATION MATRIX, MEANS, AND STANDARD DEVIATIONS FOR MODELS DISAGGREGATED BY SEX, FEMALES ABOVE THE
DIAGONAL, AND MALES BELOW
~
-.....:I
-.....:I
\0
.......
~
;:::
$:::
'-.
~~
U)
U)
~
~
c;..,
~
('"')
""'t
~
<::l
5'
-
('"')
<::l
-
gg
\0
Stratification & Wages / 989
TableS. COEFFICIENTS FORTHREE MODELS OF EARNINGS FOR BLUECOLLAR WORKERS,
DISAGGREGATED BY SEX (standard errors in parentheses)
Model 1
Independent
Variable
Education
SVP
Tenure
Attachment
Location
Sector
Firmsize
Union
Occgroup
Model 3
Model 2
F
M
F
,179
.108
.317
.007
.129
,114
.157
234
-.018
,190
.152
006
,140
.038
.080
,100
.138
.064
.167
181
.282
-.007
.075
1545.84
(311.86)
984.26
(388.51 )
140,36
(130.31 )
1217.67
(319.54)
951.73
(200.86)
701.26
(210.63)
420.46
(335.70)
498.99
(92.72)
32.06
(243.42)
399.89
(250.57)
158.03
(68,05)
626.66
(253.28)
411.51
(98.34)
-320,67
(1018.49)
138306
(324.94)
1236.82
(403.76)
16.90
(135,76)
965.38
(339.74)
174.62
(294.87)
88.10
(61.66)
228.34
(136.30)
156,04
(76.91)
641.73
(652.89)
660.51
(223.33)
705.79
(376.97)
444,88
(101.14)
-30.06
(257.66)
231.92
(275.05)
5915,96
2615.84
2661,28
530.18
M
M
F
.152
.161
,323
-.041
,072
.086
.296
-037
.212
.121
.051
.176
.209
METRICCOEFFICIENTS
Education
SVP
Tenure
Attachment
211.61
(70.14)
643.59
(186.62)
567.82
(97.47)
-734.82
(1065.99)
79.01
(67.63)
19708
(139.81)
332,91
(80.76)
--371,91
(709,59)
Location
Sector
Firmsize
Union
Occgroup
Constant
R2
3029.77
,146
1897,27
,084
,180
.244
244
.287
tivity" components, tenure may be construed as a proxy for seniority. Given males'
favorable industrial and occupational distributions, together with their superior
organizational clout, the rewards accruing to lengthy seniority are obvious.
While union membership figures prominently in the wage determination
process of blue-collar males, women, when viewed across industries and occupations, appear to derive no economic advantage by joining unions.!" In fact,
organized women workers in the aggregate earn on the average about $30 less than
their nonunion counterparts. At first blush, this defies conventional wisdom, but it
probably reflects the fact that unions with the majority of women members are
990 / Social Forces / vol. 55:4, june 1977
concentrated in low-paying industries as, for example, hospitals, restaurants, and
other various services. J. Lewis, probing the wage effects of unions on black
incomes, reached substantially the same conclusion about the type of industries and
occupations in which blacks are organized. For blue-collar women, employment in
unionized low-stratum industries is more of an economic liability than employment
in nonunionized large manufacturing firms.
That the economic power of large firms shelters women from the ravages of
the market in much the same way as the social power of unions protects blue-collar
men is suggested in Table 9. Across industry sectors and occupational groups, firm
size for women substitutes for males' more effective union organization in providing
higher earnings. The importance of firm size is most important for women who are
unskilled and service employees while for men the union is more effective than firm
size at all skill levels (see Table 10).
Table 9. ASSOCIATIONS (GAMMAS) BETWEEN
EARNINGS AND ENTERPRISE SIZEAND UNION
MEMBERSHIP, BY SEX AND INDUSTRY SECTOR
Women
Men
.138
-.045
.140
.313
.481
.215
-.053
.652
Core Sector
Earnings / firm size
Earnings / union
Periphery Sector
Earnings / firm size
Earnings .I union
Table 10. ASSOCIATIONS (GAMMAS) BETWEEN
EARNINGS AND ENTERPRISE SIZEAND UNION
MEMBERSHIPS, BY SEX AND MAJOROCCUPATION
GROUP
Women
Men
-.059
-.259
.225
.462
.168
.002
.213
.270
.505
.194
.021
.555
Craftworkers
Earnings / fi rm size
Earnings / union
Operatives
Earnings / firm size
Earnings / union
Laborers and Service
Earnings / firm size
Earnings / union
Stratification & Wages / 991
DISCUSSION
The foregoing analysis has located an important nexus of blue-collar earnings
inequality in the stratification of enterprises, occupational groups, and sexual
estates. We have discovered that, on the average, the median annual earnings of
working-class men and women differ by as much as $5,000 (see Table 7). How to
apportion this differential among human capital and stratification components of
labor markets remains to be shown, although we have already demonstrated in a
general fashion the superiority of the effects of industry and occupational variables.
A cogent strategy would be to compare actual dollar earnings increases for bluecollar women after (1) equalizing their human capital investments with those of
men, and subsequently, (2) distributing them among industry sectors, locations,
occupational groups, etc., as males are. Since the dollar rates of return on these
variables (reflected in the metric regression coefficients) are known for women, we
simply substitute mean male values in the female equation.
Doing this, we discover that increasing women's human capital investments
to accord with the investments made by men yields a total increase in annual
earnings of only $195. Merely raising women's educational attainment to equal
men's nets only $21, while providing women with vocational training on a par
with blue-collar males is worth an additional $82. On the other hand, if women
are assigned locational, sectoral, and occupational distributions identical to those
of men, they might expect an average earnings increase of about $430. Thus,
again, we conclude that human capital investments are less crucial in the income
biographies of blue-collar women than industrial and occupational stratification.
Admittedly, we have accounted for only a portion of the earnings differential
between the sexes by resorting to a substitution of means approach. Yet, when we
consider that a redistribution of women among industries and occupational groups
would necessarily imply a larger redistribution of men and, hence, a substantial
decrease in average male earnings, the residual inequality appears much smaller.
Further, we can be reasonably certain that the inequality which remains must stem
from at least one of the following general sources: (1) additional income rewarding
forms of human capital not specified in the model, (2) subtleties in the occupational
distributions of the sexes not captured in our variables, and (3) direct economic
discrimination against women. Regarding the first, formal schooling, vocational or
on-the-job training, length of tenure with one's employer, and actual attachment to
the labor force have been controlled and we are hardpressed to think of additional
important human capital investments. On the other hand, despite passage of the
1964 Equal Pay Act, lackadaisical enforement has probably contributed to the
persistence of discrimination against women. But, more importantly, within both
core and peripheral sectors and within major occupational groups, blue-collar
women usually occupy the lowest paying, least desirable positions, a nuance not
reflected in our aggregate measures. As laborers and service workers, women are
disproportionately found as laundresses, domestics, and waitresses while unskilled
males are found in better-paid occupations such as longshoremen, barbers, construe-
992 / Social Forces / vol. 55:4, june 1977
tion laborers, and cooks. Similarly, sex stratification probably extends into the core
industrial sectors with males hegemonizing employment in the best jobs regardless
of skill.
Other literature lends support to this notion. Hodge and Hodge have shown
that the entry of women into many occupations has been attended by progressively
deteriorating wages. They argue that employers need not bid high to attract workers
into jobs disproportionately filled by women. Similarly, Taeuber et aI., and Treiman
and Terrell (b) report that women fill low paying jobs abandoned by men who enter
better paying ones. Women filled such jobs during World War II, but after the war
they were expelled, thus increasing competition for low-wage employment. 11
To determine the magnitude of the crowding of women into a small number
of industries, all 55 three-digit industrial categories of employment reported in the
Quality of Employment Survey were scrutinized. We selected a random sample of
328 men from the 581 total to match the total number of women respondents in the
sample and noted their distributions in the industries which hired the largest number
of men and women workers respectively. Half of the men were found in 15
industries which had the largest concentrations of male employees, while half of the
women appeared in only 5 industries which were heavily female. The two largest
industries (building and motor vehicles) employed only 15 percent of the men,
while the two largest for women (textile manufacturing and hospitals) employed
25 percent of the female workers.
The allocation of blue-collar women to low paying, predominantly serviceoriented jobs reflects what Smelser terms the "stratificational boundaries" of
economic life. Historically, labor markets have been racially and ethnically segmented with dominant groups restricting subordinate group access to the better
paying jobs (Bonacich; Cayton and Drake). While some writers (cf. Becker, a)
contend that everyone loses by such arrangements, in the sense that they pay a
luxury tax for indulging their taste for discrimination, recent evidence (Bergmann)
suggests that workers from the dominant ethnic group derive modest, yet tangible
benefits from the overrepresentation of minorities in the low paying jobs of the
bottom stratum enterprises. Whether the wages of white males who remain in
sectors increasingly staffed by minorities suffer from competition is still unclear.
Hodge and Hodge argue that blacks and women pose a competitive threat to the
earnings of white male workers and that some jobs are low paying because
minorities are willing to work for less. Other researchers (Taeuber et al.) have found
that the low pay of some occupations occurs independently of their occupancy by
minority workers. Our data suggest a more complex explanation: while bottom
stratum enterprises may be inefficient and marginally profitable, sex stratification
mediates industrial stratification to prolong their economic survival.
The disproportionate allocation of blue-collar women to some industrial
sectors and their conspicuous absence in others should surprise no one acquainted
with census data. What is less understood is how women's manual jobs are
distributed in manufacturing enterprises which vary in economic strength and,
especially, in the economically powerful enterprises. We suspect that analyses of the
Stratification & Wages / 993
internal markets of such firms will reveal stratification patterns similar to those
found in this intersector analysis. Whether the depressed earnings of female manual
workers derive from the way they are socialized not to compete for higher paying
jobs or whether they result from conscious market discrimination, the occupational
and industrial segregation of women culminates in two related market processes:
(1) in the absence of competition from women, the labor supply curve for male
dominated industries shifts left, increasing the equilibrium wage for men, and
(2) the separation of women in female dominated industries shifts the labor supply
curve there to the right, lowering the equilibrium wage for women. 12
The relatively small sample used in this study has limited our analysis to
women as a whole as well as to rather global industry and occupational categories.
But women vary in their stratal and social location in society. The analysis would be
improved by determining how the ethnic or racial status of blue-collar women
affects their market allocation in different types of communities (large or small, one
dominant industry or industrially heterogeneous, North or South, etc.) and among
detailed 3-digit jobs and industries. The recently available Public Use Samples of
the 1970 Census make the exploration of these problems possible. 13 This paper has
focused on only one aspect of earnings inequality for blue-collar women. The
weakness of the human capital variables in explaining their earnings suggests that
women operate under unique market and institutional arrangements. These arrangements are better understood when the economic stratification of enterprises is
considered as interacting with occupational groups of varying organizational power
and the stratification of society into sexual estates. The open acknowledgment of
sex discrimination in the economy today has stimulated government and women's
groups to reduce it. Their current ineffectiveness attests to the strength and invisibility of the institutional arrangements which perpetuate the sexual stratification of
labor markets.
NOTES
l.Duncan et aI., Sewell and Hauser, Treiman and Terrell (a), and others have extended the attainment
model for occupational mobility to income, but this approach does not consider the market and economic
organizations as variables.
2. Radical economic theory (e.g., Gordon; Sweezy) is a competing paradigm which considers capitalist
institutions as structuring labor markets and as deserving analysis.
3. Grandjean's trenchant explication of the functionalist theory of stratification found that while talent
and training had some influence on social rewards, the effect of "functional importance" was not
discernible through economic analysis.
4. Goode's and Fowler's early study verged on the formalization of a sociological theory of income
distribution by explicitly identifying the differential power of occupational groups in the market.
5. Employed at least 35 hours per week.
6. For detailed Census occupations, SVP measures the amount of training time required to learn the
techniques associated with an average performance in a specific job-worker situation. Specific vocational
preparation includes training obtained from vocational education, apprenticeships, in-plant instruction,
on-the-job training, or essential experience in other jobs.
7. Averitt's five-factor classification of manufacturing industries was followed in allocating nondurable
goods enterprises between core and peripheral sectors. Thus, due to their key location with respect to
other industries, market concentration, capital intensive modes of production, vertical integration, etc.,
the following manufacturing industries are considered to be core: chemicals and allied products,
994 / Social Forces / vol. 55:4, june 1977
petroleum and coal, and rubber and miscellaneous plastics. Not exhibiting core features in Averitt's
typology are the food industries, tobacco manufactures, textiles, apparels, paper and allied products, and
printing.
8. To test for nonlinearities in the relationship between occupational group and earnings, two preliminary
regression analyses made use of, respectively, a dummyized Skilled vs. "Other" variable and the
Unskilled vs. "Other" variable. Regression estimates proved to be superior in the present case using the
intervalized three category occupation factor.
9. The issue whether unionized workers earn higher wages than nonunion workers is distinct from the
question whether unions have increased the income share of labor as a whole. Many economists feel that
unions have not garnered a larger portion of the pie for labor and that their gains have been won at the
expense of unorganized workers rather than privileged strata (cf. Johnson and Miezkowski).
10. Whether unionization produces significant net increases in earnings within occupations and industries
which employ predominantly women remains an unresolved issue. Without regard to industry sex
composition, most available data suggest that unions do produce modest wage advantages within
detailed industries, usually on the order of 4-7 percent. But the relative wage effects of unionization
have been shown to vary dramatically from 42 percent net advantage in the male dominated bituminous
coal industry to virtually no effect in the female dominated footwear, cotton textile, and men's clothing
industries (H. Lewis). In general, the ability of unions to wring wage concessions from employers is
constrained by the technological makeup of industries and the nature of the market firms confront.
11. McNally showed that while women accounted for 29 percent of all blue-collar manufacturing
workers in 1947, in 1967 they accounted for only 19 percent.
12. See Fusfeld for a discussion of the effects of imbalanced labor supply on the marginal productivities
and wages of "primary" and "secondary" workers in ghetto labor markets.
13. We should caution that many important indicators of the determinants of earnings, including union
membership, firm size, and length of work experience are not available in the Census User Tapes or in
the published volumes. To construct adequately specified income determination models similar to those
in our analysis would require that Census records be supplemented with data drawn from other sources.
Our decision to opt for the Quality ofEmployment Survey to estimate exploratory models was dictated in
part by these considerations.
REFERENCES
Adams, Leonard P., and Robert L. Aronson. 1957. Workers and Industrial Change: A Case
Study of Mobility. Ithaca: Cornell University Press.
Averitt, Robert T. 1968. The Dual Economy. New York: Norton.
Bakke, E. Wright. 1940. The Unemployed Worker: A Study of Making a Living Without a
Job. New Haven: Yale University Press.
Becker, Gary S. a:1957. The Economics ofDiscrimination. Chicago: University of Chicago
Press.
___ . b:1964. Human Capital. Chicago: University of Chicago Press.
Bergmann, B. R. 1971. "The Effects on White Incomes of Discrimination in Employment."
Journal of Political Economy 79(March-April):294-313.
Blauner, Robert. 1964. Alienation and Freedom: The Factory Worker and His Industry.
Chicago: University of Chicago Press.
Bluestone, B. 1970. "The Tripartite Economy: Labor Markets and the Working Poor." Poverty and Human Resources Abstracts 5(July-August):221-38.
Bonacich, E. 1972. "A Theory of Ethnic Antagonism: The Split Labor Market." American
Sociological Review 37(October):547 -59.
Boserup, Ester. 1970. Woman's Role in Economic Development. London: Allen & Unwin.
Bunting, R. L. 1961. "A Test of the Theory of Geographic Mobility." Industrial and Labor
Relations Review 15(October):75-82.
Burton, J., and J. Parker. 1969. "Interindustry Variations in Labor Mobility." Industrial and
Labor Relations Review 22(January):199-216.
Caplow, Theodore. 1954. The Sociology of Work. Minneapolis: University of Minnesota
Press.
Stratification & Wages / 995
Cayton, Horace B., and St. Clair Drake. 1945. Black Metropolis. Vol. 1. New York: Harper
& Row.
Cottrell, Fred. 1940. The Railroader. Stanford: Stanford University Press.
Davis, K., and W. E. Moore. 1945. "Some Principles of Stratification." American Sociological Review 1O(April):242-49.
Doeringer, Peter B., and Michael J. Piore. 1971. Low-Income Labor Markets and Urban
Manpower Programs: A Critical Assessment. Research and Development Findings No.
12. Washington: Department of Labor.
Duncan, Otis D., David L. Featherman, and Beverly Duncan. 1972. Socioeconomic
Background and Achievement. New York: Academic Press.
Dunlop, John T. 1957. The Theory of Wage Determination. New York: Macmillan.
Duwors, R. E. 1948. "Custom and Contract: A Functional Analysis of the Wage System in
Atlantic Fisheries." American Sociological Review 13(February):55-61.
Fleisher, Belton. 1970. Labor Economics: Theory and Evidence. Englewood Cliffs:
Prentice-Hall.
Form, W. H., and J. Huber. 1976. "Occupational Power." In Robert Dubin (ed.), Handbook
of Work, Organization and Society. Chicago: Rand McNally.
Friedl, Ernestine. 1975. Women and Men. New York: Holt, Rinehart & Winston.
Fuchs, V. R. 1967. "Differentials in Hourly Earnings by Region and City Size." Occasional
Paper 101. Washington: National Bureau of Economic Research.
Fusfeld, Daniel R. 1973. The Basic Economics of the Urban Racial Crisis. New York: Holt,
Rinehart & Winston.
Galbraith, John K. 1967. The New Industrial State. Boston: Houghton Mifflin.
Goode, W. H., and I. Fowler. 1949. "Incentive Factors in a Low Morale Plant." American
Sociological Review 14(October):618-23.
Gordon, David M. 1972. Theories of Poverty and Underemployment. Lexington, Mass:
Heath.
Grandjean, B. D. 1975. "An Economic Analysis of the Davis-Moore Theory of Stratification." Social Forces 53(June):543-52.
Hall, O. 1946. "The Stages of a Medical Career." American Journal of Sociology
53(March):327 -36.
Hicks, John R. 1964. The Theory of Wages. 2d ed. London: St. Martin's.
Hiller, E. T. 1947. Social Relations and Structures. New York: Harper.
Hodge, R. W., and P. Hodge. 1965. "Occupational Assimilation as a Competitive Process."
American Journal of Sociology 61(November):249-64.
Homans, George C. 1950. The Human Group. New York: Harcourt Brace.
Johnson, H. G., and P. L. Miezkowski. 1970. "Effects of Unionization on the Distribution
of Income." Quarterly Journal of Economics 84(November):539-61.
Lansing, J. B., and J. N. Morgan. 1967. "Effects of Geographic Mobility on Income."
Journal of Human Resources (Fall):449-60.
Lester, R. 1967. "Pay Differentials by Size of Establishment." Industrial Relations
6(October):512-18.
Lewis, H. Gregg. 1963. Unionism and Relative Wages in the United States. Chicago: University of Chicago Press.
Lewis, J. D. 1974. "Union Activity and Black Income in Central Cities." Working Paper
7420, Program in Applied Social Statistics. Urbana: University of Illinois.
Lynd, Robert, and Helen Lynd. 1937. Middletown in Transition. New York: Harcourt Brace,
Jovanovich.
McCall, J. J. 1970. "Economics of Information and Job Search." Quarterly Journal ofEconomics (February): 177-89.
McNally, G. B. 1968. "Patterns of Female Labor Force Activity." Industrial Relations
7(May):206-12.
996 / Social Forces / vol. 55:4, june 1977
Mahler, J. E. 1961. "The Wage Pattern in the United States." Industrial and Labor Relations
Review 15(October):3-20.
Meissner, M. 1975. "Sexual Division of Labour and Inequality: Labour and Leisure." Unpublished manuscript, Sociology Department, University of British Columbia.
Mill, John Stuart. 1969. On Liberty, Representative Government, and the Subjection of
Women. London: Oxford University Press.
Mincer, J. 1970. "The Distribution of Labor Incomes: A Survey with Special References to
the Human Capital Approach." Journal of Economic Literature 8(March): 1-26.
Ober, H. 1948. "Occupational Wage Differentials, 1907-1947." Monthly Labor Review
67(August): 127-34.
Ozanne, R. 1962. "A Century of Occupational Wage Differentials." Review of Economics
and Statistics 44(August):292-99.
Parsons, Talcott, and Neil J. Smelser. 1956. Economy and Society. Glencoe: Free Press.
Perlman, R. 1958. "Forces Widening Occupational Wage Differentials." Review of Economics and Statistics 40(May): 107-15.
Quinn, Robert P., and Linda J. Shepard. 1974. The 1972-73 Quality of Employment Survey.
Ann Arbor: Institute for Social Resarch, University of Michigan.
Raphael, E. E., and R. T. Gillaspy. 1975. "Population Redistribution and Industrial Changes
in the Nonmetropolitan Labor Force." Population Issues Research Office. University
Park: Pennsylvania State University.
Rees, A. 1971. "Information Networks in Labor Markets." American Economic Review
61(June ):290-310.
Reynolds, Lloyd G. 1951 The Structure ofLabor Markets. New York: Harper & Row.
Sayles, Leonard R. 1963. Behavior ofIndustrial Work Groups: Prediction and Control. New
York: Wiley.
Scitovsky, Tibor. 1964. Welfare and Competition. London: Allen & Unwin.
Sewell, William H., and Robert M. Hauser (eds.). 1975. Education, Occupation, and Earnings: Achievement in the Early Career. New York: Academic Press.
Smelser, Neil J. 1963. The Sociology of Economic Life. Englewood Cliffs: Prentice-Hall.
Stigler, G. J. 1962. "Information in the Labor Market." Journal of Political Economy
70(October):94-1 05.
Stinchcombe, A. 1965. "Social Structure and Organizations." In James G. March (ed.),
Handbook of Organizations. Chicago: Rand McNally.
Svalastoga, Kaare. 1965. Social Differentiation. New York: McKay.
Sweezy, P. 1970. "Toward a Critique of Economics." Review ofRadical Political Economics
2(Spring):88-109.
Taeuber, A. F., K. E. Taeuber, and G. G. Cain. 1969. "Occupational Assimilation and the
Competitive Process: A Reanalysis." American Journal of Sociology 72(November):273-85.
Thurow, Lester. 1969. Poverty and Discrimination. Washington: The Brookings Institute.
Treiman, D. J., and K. Terrell. a:1975. "Sex and the Process of Status Attainment: A Comparison of Working Men and Women." American Sociological Review 40(April):174200.
___ . b:1975. "Women, Work, and Wages." In Kenneth C. Land and Seymour Spilerman
(eds.), Social Indicator Models. New York: Russell Sage Foundation.
Warner, W. L., and J. O. Low. 1947. The Social System of a Modern Factory. New Haven:
Yale University Press.
Weber, Max. 1947. The Theory of Economic and Social Organization. New York: Oxford
University Press.
Weiss, L. L. 1966. "Concentration and Labor Earnings." American Economic Review
56(March):96-117.