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 

Persistent & universal

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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 DK ES NL PL UK 100 50 0

Occupational concentration

(ESS, 2012 / ISCO-3)

10 4 3 6 5 9 8 7

~25% of employed women concentrated in five occupations

2 1 0 Shop salespersons Domestic, hotel, office cleaners & helpers Personal care workers in health services Secondary education teachers Child care workers & teachers' aides General office clerks Numerical clerks Administrative & specialized secretaries Secondary education teachers Nursing & midwifery professionals 7

Measuring occupational sex segregation

Common indices



D = Index of Dissimilarity

(Duncan & Duncan 1955)

D

 1 2

j J

  1

F j F



M j 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 9

Alternative measures

      D st = 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

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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 13

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 15

Which occupations are gendered?

 Example: Major group 3 – professionals  ‘integrated’ 

But:

4-digit level!

Source: ESS 2012

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Change of the Index of Dissimilarity

0,700 0,600 0,500 0,400 0,300 0,200 0,100 0,000

DE DK ES NL PL UK

17 17 Isco1 Isco2 Isco3 Isco4

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) 18



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? 20

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: [email protected]

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