Socio-economic Classifications derived from ISCO08 (and 88) Eric Harrison, City University London InGRID Expert Workshop Amsterdam, Feb 10-12, 2014

Socio-economic Classifications derived from ISCO08 (and 88)

Eric Harrison, City University London

InGRID Expert Workshop Amsterdam, Feb 10-12, 2014

www.europeansocialsurvey.org

‘If you want to get rid of fuzzy job titles, then the last thing you should do is ask people for their job titles’.

(Birch 2014)

For data analysts, titles are just portals to tasks


Overview

Preliminaries: Occupation and Class

The ‘ESeC’

Validation with EWCS and ESS

Conclusions


From Occupation to Social Class

The accurate measurement of occupation is valuable not only in its own right, but also as a building block needed to derive many widely-used class schemas.

In the context of cross-national research this also requires a high degree of harmonisation across countries.

Despite considerable efforts in this direction, users accept that a combination of institutional differences and human error lead to less than optimal validity.

Thus however conceptually rigorous the class measure, it is likely to be operationalised using survey data that is frequently imperfect and always incomplete.

4


Occupation-based class schemas: Briefly

Employers, self-employed, employees

Then within employees….different types of employment

Lockwood – Market situation v Work situationGoldthorpe – redevelops and formalises this in terms of ‘contractual hazards for employers’

Asset specificity (marketable skills) Monitoring problems (autonomous work situation)


Different forms of employer response

Service relationship

Labour contract

Pure and modified forms of both (lower managers & professionals, skilled workers)

‘Mixed’ forms of employment regulation where one dimension is high and one low

6


Why surveys need social class variables

Sociology’s ‘only independent variable’

It matters theoretically: central to both Marxian and Weberian discussions of inequality of resources and life-chances

It works empirically: in many different versions and schemes it predicts y and explains r2

Most of the attitudes and/or behaviours measured by surveys will be correlated with class – users will expect to be able to do analysis

7


Why class analysts need social surveys

Class positions are not inherently known to respondents – they are rooted in a person’s employment relations

Can’t measure the subtleties of a person’s individual work and market situation (latent variables)

We use a series of proxy questions to establish the employment relations that are typical of what we do know about that person’s employment relations

8


Deriving class from survey information

Current job (in work), last job (unemployed), career typical job (retired) (1)

Employment status (emp/self-emp) (1)

If self-emp how many workers, if emp how many co-workers (2)

Supervisory responsibility? If so, how many for? (2)

Industrial sector of job (1)

Occupation (3)

9


And that’s just respondents!

Not all are economically active: if not, need to ask all those questions about partner (8)

If no partner in place and/or if respondent living at home , may revert to status of principal parent

May be interested in father and mother’s class position anyway (social origins and destinations) so ask questions about them too

10


Occupation is Key

Though it’s nice to have full information, occupation drives 85%+ of a class allocation.

Employment relations emerge out of occupation-specific custom and practice

This makes its measurement crucial

Accuracy of posting to main group

Precision in specifying detailed occupation within this (up to 4 digits)

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www.europeansocialsurvey.org 12


‘ESeC’

ESeC Class Common Term Employment regulation

1 Large employers, higher grade professional, administrative and managerial occupations

Higher salariat Service Relationship

2 Lower grade professional, administrative and managerial occupations and higher grade technician and supervisory occupations

Lower salariat Service Relationship (modified)

3 Intermediate occupations Higher grade white collar workers

Mixed

4 Small employer and self employed occupations (exc agriculture etc)

Petit bourgeoisie or independents

Not applicable

5 Self employed occupations (agriculture etc) Petit bourgeoisie or independents

Not applicable

6 Lower supervisory and lower technician occupations

Higher grade blue collar workers

Mixed

7 Lower services, sales and clerical occupations Lower grade white collar workers

Labour Contract (modified)

8 Lower technical occupations Skilled workers Labour Contract (modified)

9 Routine occupations Semi- and non-skilled workers

Labour Contract

10 Never worked and long-term unemployed Unemployed Not applicable


‘ESeC’







Mixed



Not applicable


Not applicable



Mixed





Labour Contract



‘ESeC’







Mixed



Not applicable


Not applicable



Mixed





Labour Contract



ISCO and ESeC

High degree of equivalence: Isco groups 1 and 2 map to top classes, and group 9 to class 9

3000 – 7000 much fuzzier. Associate professionals, technical and service work

So need more than 1 or 2 digits


Rebasing ESeC on ISCO08


Three forms of validation

Operational – does it work, can we apply it to a range of datasets? What happens after loss of information?

Criterion – does it measure what it purports to measure?

Construct - Does it predict the sorts of outcomes that theory suggests it should?


Data from the EWCS

Fieldwork Jan – June 2010

Face to face interviews outside the workplace

43,816 respondents 15+ in employment

34 countries

Average RR =44% (31to 74%)

Most detailed survey of employment conditions and experiences

Long running series – EWCS #1 in 1991


If you compare your situation with Jan 2009...

...have you experienced a change in the number of hours you work per week?

...have you experienced a change in your salary or income?

Responses: Decrease, No change, Increase


Change in Working time since 2009

ESeC 1 ESeC 2 ESeC 3 ESeC 4 ESeC 5 ESeC 6 ESeC 7 ESeC 8 ESeC 90

5

10

15

20

25

Increase in hours

Decrease in hours%


Change in earnings since 2009


5

10

15

20

25

30

35

40

45

50

Increase in salary

Decrease in salary%


Generally, does your main paid job involve...

...meeting precise quality standards?

...assessing yourself the quality of your work?

...solving unforeseen problems on your own?

...monotonous tasks?

...complex tasks?

...learning new things?

Dichotomous response


Job Content & Quality


10

20

30

40

50

60

70

80

90

100

Complex tasksLearning new thingsSelf-assessed qualityProblem solvingMonotonous tasksQuality standards


Is the pace of your work dependent on...

...the work of colleagues?

...demands of customers, passengers, clients etc?

...production or performance targets?

...movement of a machine or product?

...the direct control of your boss?



Pace of work determined by...


10

20

30

40

50

60

70

80

90

MachineryBossColleaguesTargetsCustomers


Are you able to choose or change...

...your order of tasks?

...your methods of work?

Your speed or rate of work?



Autonomy over aspects of job


10

20

30

40

50

60

70

80

90

100

Order of tasksMethods of workSpeed of work


Discussion

Broad ‘tip to toe’ fit with expected class ‘gradient’

Distinctive self-employment spikes consistent with theory and experience

Anomalies in classes 3, 6 and 7 largely consistent with theory: Classes are relational not hierarchical

Some interesting results on individual measures

Customer /market ethos pervades modern societies

Total quality improvement and target setting do not respect employment relations boundaries


Three forms of validation

Operational – does it work, can we apply it to a range of datasets? What happens after loss of information?

Criterion – does it measure what it purports to measure?

Construct - Does it predict the sorts of outcomes that theory suggests it should?


ESS R5 data on work and wellbeing in recession

ESS rotating module geared specifically to concepts of interest

Asset specificity (how easy for you to get another job + how difficult for employer to replace you?)

Monitoring problems (how difficult for your immediate boss to know how much effort you’re putting in?)

Job quality (variety in work + learn new things + support from co-workers + security)


Analysis Strategy

Pooled R5 dataset with controls for country

Dummies for each class (reference class 9)

Controls for age, age sq, education (high, medium, ref= low), gender.

Separate models for each of four derivation methods:• 3 digit occupation + supervision• 2 digit occupation + supervision • 3 digit occupation minus supervision• 2 digit occupation minus supervision


Asset Specificity (0-20 scale) OLS coefficients (ref = class 9)

Class 1 Class 2 Class 3 Class 6 Class 7 Class 8 Class 90

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

3 digit +2 digit +3 digit -2 digit -


Monitoring Problems

• Few if any significant results...


Job Quality (0-12 scale) OLS coefficients (ref = class 9)

Class 1 Class 2 Class 3 Class 6 Class 7 Class 8 Class 90

0.5

1

1.5

2

2.5

3

3 digit +2 digit +3 digit -2 digit -


Subjective Health (1-5 scale)

Class 1

Class 2

Class 3

Class 4

Class 5

Class 6

Class 7

Class 8

Class 9

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Most infoLeast info


Happiness (0-10 scale)

Class 1

Class 2

Class 3

Class 4

Class 5

Class 6

Class 7

Class 8

Class 9

0

0.1

0.2

0.3

0.4

0.5

0.6

Most infoLeast info


Discussion

Classes 3 and 6 sometimes in ‘wrong order’ but significantly distinct from each other – ‘mixed classes‘

Class 8 outperforms 7 on criterion measures but this is reversed for construct validity

Incomplete information has minimal impact on utility of scheme – but class 6 remains a problem

Country by country analysis and experiments with different and fewer classes needed


Challenges to measurement of class through occupation

Working with international standard instrument - the new ISCO revision will take time to work through

Improving measurement in existing modes and adapting to changes in mode (self-administered web surveys)

Enforcing better harmonisation across countries in both their collection of national data and their mapping to international measures

ESRA, Lausanne, 2011 39


Remaining Issues

Sticking to 2 (or even 1!) digit ISCO would save time, reduce resource burden, increase national comparability

But over time would undermine theoretical foundations, i.e. individual occupations are where ER are embedded

Could use collapsed class schemas for general surveys but devote more resources to specialist research into social stratification and mobility across Europe (and beyond…)


Thank you for listening.

Correspondence: [email protected]

Documents

Socio-economic Classifications derived from ISCO08 (and 88) Eric Harrison, City University London InGRID Expert Workshop Amsterdam, Feb 10-12, 2014