Measuring occupational sex
segregation
Stephanie Steinmetz (UvA)
InGRID expert workshop
11 February 2014
What is occupational sex segregation
and why is it important?
Women & men work in
different types of occupations and
at different occupational levels !
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Why is this of interest?
Last decades an increasing participation of women
 in education + on the labour market
But, women still predominantly choose
 typical female fields of study + typical female
occupations
And they are still underrepresented in high status
positions
 Leads togender
Persistent
inequalities
& universal
with respect to
phenomen income,
in most industrialised
power etc.
societies
4
Policy level – Equality measure

Degree/level of occupational sex segregation
provides information on




how unequal the distribution of men and women
across occupations and positions is,
how men and women are integrated in the
workplace, and
how separated they are by the work they do.
Used as an ‘gender equality measure’…
… for designing, evaluating & monitoring
employment+social programmes as well as policies!
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Occupational gender typing (ESS 2012, ISCO08-1)
DE
Share of women %
100
50
0
DK
ES
NL
PL
UK
Occupational concentration (ESS, 2012 / ISCO-3)
10
9
Female concentration %
8
7
~25% of employed
women concentrated
in five occupations
6
5
4
3
2
1
0
Shop
Domestic, hotel, Personal care
salespersons office cleaners
workers in
& helpers
health services
Secondary
education
teachers
Child care
General office
workers &
clerks
teachers' aides
Numerical
clerks
Administrative
& specialized
secretaries
Secondary
education
teachers
Nursing &
midwifery
professionals
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Measuring occupational sex segregation
Common indices

D = Index of Dissimilarity (Duncan & Duncan 1955)
1 J Fj M j
D 

2 j 1 F
M



Sex segregation = different distribution of women
and men across occupations
D=0 (complete equality) and 1 (complete dissimilarity)
Proportion of women & men who would need to
change jobs in order to remove segregation
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Alternative measures

Dst= Standardized Index of Dissimilarity (Gibbs 1965)
 not affected by occupational size effects  should therefore
measure ‘pure’ sex typing

IP index (Karmel & MacLachlan, 1988) reflects relative size of both
sexes + accounts for male & female share of all employed persons
 should not be sensitive to variations in female labor force share

Marginal Matching Index (MM)/Index of Segregation (IS)
(Blackburn 1993)  measures changes over time resulting
exclusively from changes in sex composition of occupations

Association Index (Charles & Grusky, 2004)

 based on log-linear models
WE index (OECD, 1980)
SR= Sex-Ratio Index (Hakim, 1979)

10
Used for change over time - 1992-2007
Source: Bettio & Verashchagina, European
11
Commission, 2009
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Role of definitions & classifications

Underestimation of the crucial role of definitions
and classifications in data production.

Determine

what is to be covered or not and with how much
detail a variable will be described.

the quality of resulting figures.

how well they reflect the actual situation of the
different participants in the labor market.
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Determinants of segregation indices

‘Gender blindness’ of occupational classifications
 Aggregated occupational groups masks sex
segregation
 Classifications do not adequately capture important
labour market changes

Occupational classifications
 Inconsistency

Concept of ‘occupation’
 Country-specific occupational classifications might
follow different construction principles
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Occupational classifications
‘Gender blindness’
 Classifications cover labour market developments
with some delay

Important changes (e.g. service sector expansion) are
not captured adequately (female-dominated sector)

Many new occupations evolve which are allocated to
few & heterogeneous occupational groups.

Level of detail matters!
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Occupational detail

Advantage of using disaggregated occupational
data
 broad occupational groups hide occupational sex
segregation  impacts on the calculation of
segregation indices (value of D declines with more
aggregation  it appears that there is less segregation than
there really is)
 more detailed occupations reveal a more accurate
picture of the actual work experience of men & women
 only then can gender distinction be revealed
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Which occupations are gendered?


Example: Major group 3 – professionals  ‘integrated’
But: 4-digit level!
Source:
ESS 2012
16
Change of the Index of Dissimilarity
0.700
0.600
0.500
0.400
Isco1
Isco2
Isco3
0.300
Isco4
0.200
0.100
0.000
DE
DK
ES
NL
PL
UK
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BUT…
…unfortunately, even very detailed occupational
groups may hide occupations’ sex segregation!
WHY?
 Tasks & duties of the same occupation may vary
between men and women.
 Example: cleaning occupations (Messing, 1998) &


sales occupations (Dixon-Muller, Anker 1990)
Female occupations tend to be considered too ‘general’,
 multitude of tasks linked to general skills (literacy,
numeracy & interpersonal contacts & traditional housekeeping
activities)
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Occupational classifications

Problem of inconsistency
Problem:
Changing from 1-digit to
more disaggregated
2-/3-digit level
 some occupations in
group 7 obviously require
higher degrees of skill &
longer training than some
of the occupations
classified in group 5.
ISCO-08
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Concept / Measurement of ‘occupation’

Different national & cultural contexts might create
country-specific occupational classifications
following different construction /measurement
principles
 are transferred into ISCO08 classification
 how ‘genderblind’ are these different measures?
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Conclusion



Occupational classifications should describe men &
women’s work characteristics equally well and
detailed.
Provision of additional ‘gender relevant’ (job)
information (i.e. tasks and duties, skills etc.) providing
insights into how sex segregation works within
occupations.
Use of aggregated indices as a measure of equality
to evaluate progress should be limited!
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THANK YOU!

Questions? Comments?
Contact: s.m.steinmetz@uva.nl
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