1. Linking by Translation: the key to comparable codesets
Ben Hickman
Local Government Analysis & Research
19th March 2007

2. Setting the scene Require workforce data for all local government
Approx 2.2 million employees
Varied workforce
Multiple sources of data
No definitive source of data

3. Local Government Data Flows Project
Systematic evaluation of current methods
Development of data framework and quality assurance measures
Occupational Classification for Local Government
Multi-sourced database
New collections targeted to cover specific gaps

4. Issues with multiple-sources
Data sharing protocols
Lack of consistent definitions
Different census dates
Reliability of methodologies
Lack of a comparable occupational classification

6. Linkage by Translation
Dataset 1
Dataset 2
Key-Codes
Dataset 3

7. Linkage by Translation
Key-codes links each dataset
Able to aggregate to lowest common denominator
Enables comparisons across multiple datasets without continual remapping

8. Multi variable mapping
Use matrices to map all possible combinations of classification variables i.e. Role x Post x Service
Use common classifier (e.g. SOC2000)
Map all possible variables against the common classifier
Creates key-code for every possible classification value in the dataset

9. Multi-sourced key-codes
Map each dataset to the common classifier (SOC2000)
Use the common classifier as a basis for creating a single, unified list
Within each unit group identify overlaps and recode into a single variable where necessary – maintaining all available mapping data against the code

10. Schools workforce Approx. 800,000 workforce
Workforce collections undertaken by:
Department for Education and Skills
Chartered Institute of Public Finance and Accounting
Local Government Analysis and Research
Office of Manpower Economics
Office of National Statistics
Institute of Education

11. Department for Education & Skills
Schools Workforce Census
Detailed information relating to all school staff
Broken down by:
Type of School – 4 variables
9 role variables
66 post variables
Total of 304 possible variables

12. Local Government Analysis & Research
Numbers and pay research
Covers all local government staff
Approx. 100 classifications
Less detailed classifications
Not education specific

13. Office of National Statistics
Labour Force Survey
Provides details for whole economy
Occupational classification = SOC2000
Also not education specific
enables national and international comparisons

14. Schools Workforce Census
DfES Classification
LGAR Pay Research
All LG classification
Labour Force Survey
SOC2000

15. Educational Assistants
DfES has 8 post x role identifiers
But with types of schools could be as many as 32 different variables (i.e. 8 posts by the 4 school types)
3 LGAR Pay Research categories
1 SOC2000 Code

16. So, for Schools Workforce key-codes...
Post x Role = Var1
Advanced Skills Teacher
Excellent Teacher
Support Staff
Caretaker X
Classroom Teacher
Cleaner

17. Nursery and Primary Schools
Var1 x School = Var2
Nursery and Primary Schools
Secondary Schools
Special Schools
Classroom Teacher (Advanced Skills) X
Classroom Teacher (Excellent Teacher)
Cleaner

18. Var2 x SOC2000 = Schools Key-codes
2314
2315
2316
Classroom Teacher (Advanced Skills) (Nursery and Primary Schools) X
Classroom Teacher (Excellent Teacher) (Nursery and Primary Schools)
Classroom Teacher (Excellent Teacher) (Secondary Schools)

19. LGAR Pay Research Higher level Teaching Assistant Teaching Assistant
(primary and nursery schools)
teaching assistant
(primary and nursery schools)
literacy workers
minority ethnic support
learning support assistant (for SEN pupils)
learning mentor
language support
bilingual support assistant
higher level teaching assistant
(secondary schools)
teaching assistant
(secondary schools)
higher level teaching assistant
(special schools)
teaching assistant
(special schools)
higher level teaching assistant n.e.c.
teaching assistant n.e.c.
Higher level Teaching Assistant
Teaching Assistant
Educational assistant n.e.c.
6124 Educational assistant

20. Child services mapping
Without translation
DfES Classification
Pay classification
SOC2000
NMDS-SC
Child services mapping
Soulbury Committee

21. Simplify through translation...
DfES Classification
Pay classification
SOC2000
NMDS-SC
Child services mapping
Key-codes
Soulbury Committee

22. Benefits Easy to add in additional datasets
Addition or amendment of one dataset does not affect any others
Enables datasets to be analysed to varying degrees of detail
Ensures all possible classification scenarios are accounted for
Vehicle for maintaining coding information

23. Disadvantages
Mapping key-codes takes time and knowledge of each dataset
If one detailed dataset changes it can require major amendments to key-codes

24. Thanks for listening
Any questions?