Occupational Crowding
Spring 2010
Rosburg (ISU)
Occupational Crowding
Spring 2010
1 / 21
Occupation Crowding: occupational segregation that results in “male”
and “female” jobs [can also be segregated by other factors such as race,
age, etc.]
First introduced by Barbara Bergman (1974)
May lead to salary gaps between members of different groups
Examples:
Women tend to be concentrated in office, administrative support, and
service occupations referred to as “pink-collar” jobs
Men more likely to be in “blue-collar” jobs
Group-specific jobs have changed over time
Industry segregation may also occur
Rosburg (ISU)
Occupational Crowding
Spring 2010
2 / 21
Evidence
Evidence - Past segregation patterns
Distribution of Tasks across 863 Nonindustrial Societies
Only or
Both men
Only or
Activity
mostly men and women mostly women
Hunting
100
0
0
Metal Working
100
0
0
Boat Building
96
3
1
Fishing
79
15
6
House building
75
10
15
Animal husbandry
64
22
14
Leather working
46
5
49
Weaving
30
12
58
Pottery Making
9
5
86
Gathering
8
14
78
Agriculture
32
32
36
Source: Murdock (1967) referenced in Baker and Jacobsen (2007)
Rosburg (ISU)
Occupational Crowding
Spring 2010
3 / 21
Evidence
Evidence of Segregation in 2005
Occupation
Architects and engineers
Security guards
Computer programmers
Physicians & surgeons
Lawyers
Marketing & sales managers
Postsecondary teachers
Sales occupations
Bakes
Waiters/Waitresses
Elementary/middle teachers
Nursing Aids
Registered Nurses
Secretaries and admin.
assistants
Female %
13
24
25
33
34
39
40
44
50
66
82
89
92
97
Gender
earnings ratio
0.83
0.80
0.90
0.61
0.77
0.69
0.79
0.63
0.74
0.86
0.89
0.95
0.92
0.85
Median
weekly earnings
1105
481
1086
1547
1609
1235
1072
622
411
352
826
388
935
562
Source: Jacobsen (2007)
Rosburg (ISU)
Occupational Crowding
Spring 2010
4 / 21
Evidence
Evidence
Simple correlations:
Earnings and female %: -0.47
Gender earnings ratio ( EEW
) and female %: 0.50
M
Wages decreasing as percentage of females in occupation rises
Gender earnings gap decreases as percentage of females in occupation rises
Gap in male and female pay due mainly to men and women being in
different jobs
Rosburg (ISU)
Occupational Crowding
Spring 2010
5 / 21
Segregation index
Measuring Segregation
Segregation index is a summary measure that characterizes overall level
of segregation
Comparison over time in degree of segregation
Differences between countries
Differences between demographic groups
Comparison between sectors within a country
Rosburg (ISU)
Occupational Crowding
Spring 2010
6 / 21
Segregation index
Duncan Index of Dissimilarity
The most commonly used segregation index is the Duncan index of
dissimilarity:
1 n
Σ |(Mi − Fi )|
2 i=1
where
n = number of occupations
100 ∗ Men in occupation i
Total men employed
100 ∗ Women in occupation i
Fi = % of women in occupation i =
Total women employed
Mi = % of men in occupation i =
This index is generalizable for comparison between any two groups [race,
age, neighborhoods, etc.]
Rosburg (ISU)
Occupational Crowding
Spring 2010
7 / 21
Segregation index
Duncan Index of Dissimilarity
Derivation:
Find percentage of men and women in each occupation
Take absolute value of difference by occupation
Sum across all occupations
Divide by 2
Characteristics:
0 if distribution across occupation categories equal between two
groups
100 if all occupations segregated
Percentage of either group that would have to switch occupations in
order to achieve complete integration
Rosburg (ISU)
Occupational Crowding
Spring 2010
8 / 21
Segregation index
Calculating Duncan Index of Dissimilarity
Percentage in each occupation
Case 1
Case 2
Occupation
Construction
Lawyer
Teacher
Nurse
Total
Male
60
25
10
5
100
Female
0
10
40
50
100
Male
30
25
35
10
100
Female
10
20
35
35
100
1
(|60 − 0| + |25 − 10| + |10 − 40| + |5 − 50|) = 75
2
1
DI2 = (|30 − 10| + |25 − 20| + |35 − 35| + |10 − 35|) = 25
2
DI1 =
Interpretation?
Rosburg (ISU)
Occupational Crowding
Spring 2010
9 / 21
Segregation index
Gender and Racial Segregation Indexes
Occupational gender segregation indexes
1960 1970 1980 1990 2000
All
64
66
59
53
52
Whites
63
66
59
55
53
Nonwhites
70
64
56
50
50
Occupational racial segregation indexes
1960 1970 1980 1990 2000
Male
45
38
28
24
23
Female
50
63
26
22
20
Segregation has decreased overtime, but faster across races than across genders.
Rosburg (ISU)
Occupational Crowding
Spring 2010
10 / 21
Segregation index
Occupational Gender Segregation
Occupational gender segregation by age 1
25-34 35-44 45-54 55-64
Index value
52
54
55
57
Occupational gender segregation by
years of schooling
0-8 9-11 12-15 16 >16
Index value
54
55
57
43
40
Index rises slightly with age and drops off greatly for top two educational groups
[segregation greater at lower levels of education]
Gains not uniform - progress greater for minorities with higher education
1
Source: Jacobsen (2007)
Rosburg (ISU)
Occupational Crowding
Spring 2010
11 / 21
Segregation index
Segregation Index Issues
Change is limited by time it takes to train and hire new employees
Occupational categories are changing so data not always comparable
Ex: Computer-based occupations
More detailed categories typically find more occupational segregation
Broad categories overlook some segregation
Need to maintain the number of categories when making comparisons
across time or countries
Lower index of segregation may not reflect real improvements in
opportunities of minorities
Government pressure may force qualified minorities into higher
positions but give them little responsibility
Jobs may become more open to minorities as skill requirements fall
from additional technology
Rosburg (ISU)
Occupational Crowding
Spring 2010
12 / 21
Segregation Theories
Theories for Segregation Persistence
Gender differences in taste for work activities
Gender differences in ability
Efficiency in separating sexes [reduced work disruption]
Differential balance between non-market labor and other familial
concerns
Imperfect information about relative abilities [statistical
discrimination]
Exploitation of minorities by another subset of society
Rosburg (ISU)
Occupational Crowding
Spring 2010
13 / 21
Segregation Theories
Occupational Segregation
Two points of view each depending on perception in differing tastes,
talents and motivations between and within groups:
1
Segregation natural and appropriate
Integration leads to economic inefficiency and personal frustration
Proponents of this view focus on similarities within groups and
differences between groups
2
Without gender stereotyping and barriers, there would be far less
occupational segregation
Removing barriers would increase efficiency and decrease frustration
Proponents of this view focus on similarities between groups and
variations among individuals within groups
Optimal amount of segregation is not necessarily precisely proportional to
distribution of groups within society
Rosburg (ISU)
Occupational Crowding
Spring 2010
14 / 21
Segregation Theories
Occupational Segregation
Rise in female labor force may lead to intraoccupational segregation and
job de-skilling
Intraoccupational segregation occurs when one group tends to be more
concentrated in higher paying jobs (ex: men)
De-skilling occurs when jobs in an occupation become more routinized
and generally involve less responsibility (technology advancement)
Insurance adjustment now uses computerized claims-processing
→ large increase in proportion of women
Rosburg (ISU)
Occupational Crowding
Spring 2010
15 / 21
Segregation Theories
Common Terminology
Tokens - Minority or unfavored group in field predominately dominated by
majority or favored group (ex: women in predominantly male fields)
Top Dogs - Men in predominantly female fields
Ex: Librarians, nurses
‘Top’ refers to the tendency for these men to move quickly up the
internal ladder
Glass Ceiling - Relatively impenetrable wall in many occupations, built
from covert discriminatory practices on part of top-level managers that
prevent one group from rising to the top (ex: women)
Set of subtle barriers impeding attempts to move up hierarchy
Glass Elevator - Term used to describe favored group’s apparent
accelerated progress to top in unfavored-group-dominated occupations
Men in female-dominated occupations
Rosburg (ISU)
Occupational Crowding
Spring 2010
16 / 21
Modeling Occupational Crowding
Models for Occupational Crowding
Two potential models:
1
Exclusion Model - minorities systematically excluded from
higher-paying jobs and hired only to fill lower-paying jobs
Pay not linked to available productivity → NOT determined by market
forces
2
Crowding model - minorities systematically excluded from more
desirable jobs and crowed into less desirable ones
Female labor supply artificially reduced in more desirable jobs
Wages determined by market forces
Rosburg (ISU)
Occupational Crowding
Spring 2010
17 / 21
Spring 2010
18 / 21
Modeling Occupational Crowding
Rosburg (ISU)
Occupational Crowding
Modeling Occupational Crowding
Crowding Model
Steps:
1
Women are excluded from the blue-collar occupations and crowded
into pink-collar occupations
2
Wages fall and employment increases in pink-collar jobs
3
At the same time, supply of labor to blue-collar jobs decreases which
raises wage and decreases employment
Assumptions:
Men and women have equal abilities in both occupations
Occupations have similar structure
Rosburg (ISU)
Occupational Crowding
Spring 2010
19 / 21
Modeling Occupational Crowding
Crowding Model Criticisms
Difference in pay and segregation may be picking up other factors that
vary systematically between male- and female-dominated occupations
More part-time work opportunities [women]
More overtime opportunities [men]
Part-time and Overtime Work Statistics in 2000
Usual hours
Usual hours
under 30 (%)
over 40 (%)
% of occupation that is female
0-24.9
25-49.9
50-74.9
75-100
Rosburg (ISU)
Women
10.6
12.2
15.0
17.9
Occupational Crowding
Men
3.7
4.2
5.5
8.4
Women
28.9
27.7
21.4
14.3
Men
42.6
46.5
41.4
26.8
Spring 2010
20 / 21
Modeling Occupational Crowding
Readings for Next Section
Institutional Barriers:
Stoll, Raphael, and Holzer (2004)
Arthur and Cook (2004)
Preston, Chapter 8
Rosburg (ISU)
Occupational Crowding
Spring 2010
21 / 21