european socio-economic classification: a validation exercise

European Socio-Economic Classification: A Validation Exercise

Figen DevirenOffice for National Statistics

Introduction• The UK context

• Creating E-SeC

• Validation

• Using the Labour Force Survey

• Results

• Conclusions

The UK context8 classes 5 classes 3 classes

1 Higher managerial and professional occupations

1.1 Large employers and higher managerial occupations

1 Managerial and professional occupations

1 Managerial and professional occupations

1.2 Higher professional occupations

2 Lower managerial and professional occupations

3 Intermediate occupations 2 Intermediate occupations 2 Intermediate occupations

4 Small employers and own account workers

3 Small employers and own account workers

5 Lower supervisory and technical occupations

4 Lower supervisory and technical occupations 3 Routine and

manual occupations

6 Semi-routine occupations 5 Semi-routine and routine occupations

7 Routine occupations

8 Never worked and long-term unemployed

Never worked and long-term unemployed

Never worked and long-term unemployed

Deriving NS-SeC

Questions asked about occupation

SOC 2000Questions about

Employment status

Questions on

Size of organisation

NS-SeC

Deriving NS-SeC

Questions asked about occupation

SOC 2000Questions about

Employment status

Questions on


NS-SeC

SupervisorSelf-employed

Deriving E-SeC

SOC 2000

ISCO-88Employment

statusSupervisory

responsibilities

Working alone

E-SeC

Deriving E-SeC

SOC 2000

ISCO-88Employment

statusSupervisory

responsibilities

Working alone

E-SeC


Validation

For our purposes validation meant

Will E-SeC provide a representative picture of the UK that is comparable to the one provided using the NS-SeC?

Does E-SeC have a similar predictive power to that of NS-SeC?

Choice of survey

• The Labour Force Survey – Sample size, 72,500 of working age (men aged 16 - 64, women aged 16 - 59)

– Recent quarterly data – Autumn 2005

– Available at both individual and household levels

– Relevant questions

Comparison of E-SeC and UK NS-SeC (reduced categories)

0 5 10 15 20 25 30

routine

Lower sales, service andtechnical

lower supervisors andtechnicians

small employers and self-employed

intermediate occupations

lower mgrs/professionals,higher

supervisory/technicians

large employers, highermgrs/professionals

E-SEC

NS-SEC

Source: Labour Force Survey, Autumn 2005

Case comparabilityCase comparability

Agree at 7 categories

Agree at 3 categories

No agreement

Source: Labour Force Survey,

Autumn 2005

A Comparison of E-SEC and NS-SEC for males

0% 5% 10% 15% 20% 25%

Routine occupations

Semi-routine occupations

Lower supervisory and technical

Small employers and own accountworkers

Intermediate occupations

Lower managerial and professional

Higher managerial and professional

E-SEC

NS-SEC


Lower sales, service and technical

A Comparison of E-SEC and NS-SEC for females


0 5 10 15 20 25 30 35

routine


lower supervisors and technicians

small employers and self-employed


lower mgrs/professionals, highersupervisory/technicians


E-SEC

NS-SEC

European Socio-Economic Classification by sex

0 5 10 15 20 25 30

routine

Lower technical

Lower sales and service

lower supervisors andtechnicians

small employers and self-employed (agricultural)

small employers and self-employed (non-agricultural)


lower mgrs/professionals,higher supervisory/technicians


Male

Female

Source: Labour Force Survey Autumn 2005

Lower managers, professionals, higher supervisory and technicians: E-SeC and NS-SeC by age and sex.

0 5 10 15 20 25 30 35 40

Age 16-24

Age 25-34

Age 35-44

Age 45-54

Age 55-64

E-SeC female

NS-SeC female

E-Sec male

NS-SeC male


Routine occupations:E-SeC and NS-SeC by age and sex

0 5 10 15 20 25 30 35

Age 16-24

Age 25-34

Age 35-44

Age 45-54

Age 55-64

E-SeC female

NS-SeC female

E-SeC male

NS-SeC male


Comparison of E-SeC and NS-SeC at household level

0 5 10 15 20 25 30

Routine

Lower sales, service andtechnical

Lower supervisors andtechnicians

Small employers and self-employed


Lower mgrs/professionals,higher supervisory/technicians

Large employers, highermgrs/professionals

E-SEC

NS-SEC


European Socio-Economic Classification by sex of household reference person

0% 5% 10% 15% 20% 25% 30%

Routine

Lower technical

Lower sales and service

Lower supervisors andtechnicians

Small employers and self-employed (agriculture)

Small employers and self-employed (non-agriculture)


Lower mgrs/professionals, highersupervisory/technicians

Large employers, highermgrs/professionals

Male

Female


Predictive power

• NS-SeC is accepted as a predictor of ill-health

• Linear regression – binary outcome yes/no

• Choice of variables

• Significance of classifications

Chronic morbidity for males (individual level)


0% 5% 10% 15% 20% 25% 30% 35%

Routine

Lower sales, service and

technical

Lower supervisors and

technicians

Smal l employers and sel f -

employed


Lower mgrs/ prof essionals,

higher

supervisory/ technicians

Large employers, higher

mgrs/ prof essionals

E-SeC

NS-SeC

Chronic morbidity for females(individual level)


0% 5% 10% 15% 20% 25% 30% 35%

Routine


Lower supervisors and technicians

Small employers and self -employed


Lower mgrs/ professionals, higher

supervisory/ technicians

Large employers, higher

mgrs/professionals

E-SeC

NS-SeC

Predictive power – Individual level

-using NS-SeC as an independent variable

B S.E. Exp(B)

sex -0.01 0.001 0.992

Age25_34 0.18 0.002 1.196

Age35_44 0.49 0.002 1.63

Age45_54 0.93 0.002 2.541

Age55_64 1.51 0.002 4.513

ethn2 0.13 0.002 1.142

quals -0.21 0.001 0.813

degree -0.39 0.002 0.68

nsec_h2 0.13 0.002 1.139

nsec_h3 0.22 0.002 1.242

nsec_h4 0.13 0.002 1.14

nsec_h5 0.34 0.002 1.403

nsec_h6 0.39 0.002 1.481

nsec_h7 0.48 0.002 1.612

Constant -1.92 0.003 0.147

Chronic morbidity - using E-SeC as an independent variable

B S.E. Exp(B)

sex -0.01 0.001 0.995

Age25_34 0.26 0.002 1.291

Age35_44 0.56 0.002 1.75

Age45_54 1.00 0.002 2.726

Age55_64 1.58 0.002 4.846

ethn2 0.16 0.002 1.175

quals -0.22 0.001 0.802

degree -0.40 0.002 0.67

esec_h2 0.16 0.002 1.177

esec_h3 0.22 0.002 1.251

esec_h4 0.15 0.002 1.162

esec_h5 0.36 0.002 1.438

esec_h6 0.35 0.002 1.421

esec_h7 0.47 0.002 1.596

Constant -2.02 0.003 0.133

Results of the regression analysis containing age, ethnicity and educational attainment

Predictive power – Household level

Chronic morbidity - using E-SeC as an independent variable

B S.E. Exp(B)

sex -0.17 0.001 0.848

Age25_34 0.22 0.002 1.245

Age35_44 0.54 0.002 1.717

Age45_54 0.97 0.002 2.638

Age55_64 1.58 0.002 4.835

ethn2 0.12 0.002 1.124

quals -0.42 0.001 0.655

degree -0.22 0.002 0.804

esec_h2 0.11 0.002 1.113

esec_h3 0.26 0.002 1.296

esec_h4 0.16 0.002 1.175

esec_h5 0.33 0.002 1.391

esec_h6 0.45 0.002 1.566

esec_h7 0.48 0.002 1.615

Constant -1.79 0.004 0.168

-using NS-SeC as an independent variable

B S.E. Exp(B)

sex -0.17 0.001 0.842

Age25_34 0.27 0.002 1.306

Age35_44 0.59 0.002 1.801

Age45_54 1.02 0.002 2.764

Age55_64 1.62 0.002 5.072

ethn2 0.13 0.002 1.143

quals -0.45 0.001 0.637

degree -0.24 0.002 0.786

nsec_h2 0.15 0.002 1.160

nsec_h3 0.26 0.002 1.297

nsec_h4 0.17 0.002 1.187

nsec_h5 0.34 0.002 1.409

nsec_h6 0.38 0.002 1.468

nsec_h7 0.48 0.002 1.614

Constant -1.83 0.004 0.160

Conclusions

• The picture of the UK using E-SeC is broadly similar to that obtained when using NS-SeC

• Differences observed between the two classifications for lower managers/professionals and routine occupations by age and sex

• E-SeC is comparable to NS-SeC when used as a predictor of chronic morbidity.

• More validation needed?

european socio-economic classification: a validation exercise

Documents

lower supervisors

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lower mgrsprofessionals

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higher mgrsprofessionals18

sexesec routine