using esec to look across and within classes

Using EseC to look across and within classes

Workshop on Application of ESeC

Lake Bled, 29-30 June 2006

Eric Harrison & David Rose

ISER, University of Essex

Purposes of Paper

• Replicate initial analysis using the new three digit matrix (‘Euroesec’)

• Explore new variables now available in round two of the European Social Survey

• Trial analysis using the draft Socio-economic groups (SEGs)

European Social Survey

• Rapidly becoming primary European dataset:– A more all-purpose instrument than LFS, with numerous socio-

political attitude measures– A more precise set of information for constructing ESeC than

ECHP and many more countries

• Two rounds now available (Round 3 in progress)• Round 1: 22 countries, 42,359 cases• Round 2: 24 countries, 45,681 cases (Italy still to

deposit)• Most or all of the information needed to make an ESeC,

i.e. 3 or 4 digit ISCO (e.g. French R2), employment status and supervision questions)

Sample Sizes in ESS 1 & 2

0 500 1000 1500 2000 2500 3000 3500

United Kingdom

Ukraine

Switzerland

Sweden

Spain

Slovenia

Slovakia

Portugal

Poland

Norway

Netherlands

Luxembourg

Italy

Israel

Ireland

Iceland

Hungary

Greece

Germany

France

Finland

Estonia

Denmark

Czech Republic

Belgium

Austria

co

un

try

achieved n

R2

R1

Three Performance Targets for EseC

• Does it work? Can it be operationalized?

• Can it measure what it purports to measure, over and over again?

• Does it discriminate and structure with regard to predicting values of related variables?

EseC Distributions in ESS 1&2

0

5

10

15

20

25

%

Largeemployers,

higher mgrs &professionals

Low er mgrs &professionals,

highersupervisory &technicians

Intermediateoccupations

Small employersand self-

employed -non-agriculture

Small employersand self-

employed -agriculture

Low ersupervisors and

technicians

Low er salesand service

Low er technical Routine

Class

R1

R2

ALL

The Treatment of Employment Status in R2

• Self-employed, supervisors, employees• Employment relation variable – France and

Hungary inserted extra categories. These can be collapsed back into the main dataset

• No problem with self-employed in R2:– Family workers (small N) treated as employees

• Supervision – remains ambiguous in social surveys

• Management – rely on ISCO codes

Redistribution of Class 2 Supervisors

Class 3

Class 4

Class 5

Redistribution of Class 6 Supervisors

Class 7

Class 8

Class 9

ESeC Distributions for ESS countries

0%

20%

40%

60%

80%

100%

%

AT BE CH CZ DE DK EE ES FI FR GR HU IC IE IL IT LU NL NO PL PO SI SK SW UA UK

Country

Routine

Lower technical

Lower sales and service

Lower supervisors andtechnicians

Small employers and self-employed -agriculture

Small employers and self-employed -non-agriculture

Intermediate occupations

Lower mgrs &professionals, highersupervisory & technicians

Large employers, highermgrs & professionals

Measuring Employment relations in the ESS

• Looking for core variables over numerous rounds of the surveys:

• Round 1 Citizenship module had two questions now part of core in Round 2 (organisation of work and policy decisions)

• In Round 1 many questions only asked to those who worked in previous week

• In Round 2 also asked for information about last job = larger n

Influence over organisation of daily work

0

1

2

3

4

5

6

7

8

9

10

Largeemployers,


Lower mgrs &professionals,

highersupervisory &

technicians


Smallemployers andself-employed -non-agriculture

Smallemployers andself-employed -

agriculture

Lowersupervisors

andtechnicians

Lower salesand service

Lowertechnical

Routine

Class

Me

an

sc

ore

Two measures of asset specificity

• ‘Using this card, how difficult or easy would it be for you to get a similar or better job with another employer if you wanted to?’

[adapted from Citizenship R1 module]

• ‘In your opinion, how difficult or easy would it be for your employer to replace you if you left?’

[new question]

Two measures of asset specificity

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Largeemployers,



highersupervisory &

technicians


Lowersupervisors and

technicians

Lower sales andservice

Lower technical Routine

Class

me

an

sc

ore

/1

0

Get new job

Difficult to replace me

Statements about current job (R2)

• Family, work and well-being module• Battery of questions about aspects of job quality:

– variety, on the job learning, security, effort bargain, support from co-workers, time-keeping, health and safety (4 point T/F)

– work effort, work intensity, promotion opportunities (5 point A/D)

• Initial analysis suggests tapping quite different constructs

‘Opportunities for advancement in my job’

0

0.5

1

1.5

2

2.5

3

3.5

Large employers,higher mgrs &professionals


higher supervisory& technicians


Lower supervisorsand technicians

Lower sales andservice


Class

Me

an

sc

ore

/5

Subjective General Poor Health

0

0.5

1

1.5

2

2.5

3

Largeemployers,



highersupervisory &technicians


Smallemployers andself-employed -non-agriculture

Smallemployers andself-employed -

agriculture

Lowersupervisors and

technicians

Lower salesand service


class

mea

n s

core

Looking Within Classes

• ESeC was designed as a ‘nested hierarchy’: each class has a number of distinct groups below the top level.

• Revised ESeC now has 41 active SEGs

• Coding structure offers chance to make fine distinctions among the inactive groups which can be used in modelling

Examples of SEGs

Class 1:

11. Employers (non-agric) with 10+ employees12. Large business farmers13. Higher managerial and administrative14. Higher professional occupations (employees)15. Higher professional occupations (self-

employed)

Examples of SEGs

Class 2:

21. Lower managerial and administrative occupations

22. Lower professional occupations (employees)

23. Lower professional occupations (Self-employed)

24. Higher technician occupations (employees)

25. Higher technician occupations (self-employed)

26. Higher supervisory occupations

Employment Relations through Work Autonomy (difficulty of monitoring)

The ESS invited respondents to say• ‘how much the management at your work allows

you….• to be flexible in your working hours?• To decide how your own daily work is

organised?• To influence your environment?• To influence decisions about the general

direction of your work?• To change your work tasks if you wish to?

Five-item work autonomy scale:Employees in Class 1 and 2

0

1

2

3

4

5

6

7

8

Higher man & admin Higher prof emps Low er man &admin Low er prof emps Higher tech emps Higher supers

Influence on organising own work: SEGS in class 1 and 2

0

1

2

3

4

5

6

7

8

9

10

Largeemployers

Higher man& admin

Higher profemps

Higher profs/e

Low er man&admin

Low er profemps

Low er profs/e

Higher techemps

Higher techs/e

Highersupers

class

mean

sco

re

Subjective Poor Health: Classes 1 and 2

1.85

1.9

1.95

2

2.05

2.1

2.15

2.2

2.25

2.3

Largeemployers

Higher man& admin

Higher profemps

Higher profs/e

Low er man&admin

Low er profemps

Low er profs/e

Higher techemps

Higher techs/e

Highersupers

SEG

mea

n s

core

Conclusions

• ESeC classes discriminate remarkably well:– a range of ‘employment relations’ questions– significant differences between every class, not just

contract types

• Little or no discernable loss of power in adopting an ESeC based on three digit ISCO

• SEGs offer chance to discriminate and structure within classes, but more reliant on precise ISCO

using esec to look across and within classes

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