Linking by Translation:the key to comparable codesets

Ben HickmanLocal Government Analysis & Research19th March 2007

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

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

Issues with multiple-sources

» Data sharing protocols» Lack of consistent definitions» Different census dates» Reliability of methodologies» Lack of a comparable occupational

classification

Linkage by Translation

Dataset 1 Dataset 2

Dataset 3

Key-Codes

Linkage by Translation

» Key-codes links each dataset» Able to aggregate to lowest

common denominator» Enables comparisons across

multiple datasets without continual remapping

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

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

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

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

Local Government Analysis & Research

» Numbers and pay research» Covers all local government staff» Approx. 100 classifications» Less detailed classifications» Not education specific

Office of National Statistics

» Labour Force Survey» Provides details for whole economy» Occupational classification = SOC2000» Also not education specific» enables national and international

comparisons

Schools Workforce Census

DfES Classification LGAR Pay ResearchAll LG classification

Labour Force SurveySOC2000

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

So, for Schools Workforce key-codes...

Post x Role = Var1

Advanced Skills

Teacher

Excellent

Teacher

Support Staff

Caretaker XClassroom Teacher

X X

Cleaner X

Var1 x School = Var2 Nursery and

Primary Schools

Secondary Schools

Special

Schools

Classroom Teacher (Advanced Skills)

X X X

Classroom Teacher (Excellent Teacher)

X X X

Cleaner X X X

Var2 x SOC2000 = Schools Key-codes 2314 2315 231

6

Classroom Teacher (Advanced Skills) (Nursery and Primary Schools)

X

Classroom Teacher (Excellent Teacher) (Nursery and Primary Schools)

X

Classroom Teacher (Excellent Teacher) (Secondary Schools)

X

LGAR Pay Research

higher level teaching assistant n.e.c.

higher level teaching assistant (special schools)

higher level teaching assistant (secondary schools)

higher level teaching assistant (primary and nursery schools)

teaching assistant n.e.c.

teaching assistant (special schools)

teaching assistant (secondary 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

Teaching Assistant Educational assistant n.e.c.

6124 Educational assistant

Data Linkages Demonstration

Without translation

DfES Classification Pay classification

SOC2000

NMDS-SCChild services

mapping

Soulbury Committee

Simplify through translation...

Key-codes

DfES Classification Pay classification

SOC2000

NMDS-SCChild services

mapping

Soulbury Committee

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

Disadvantages

» Mapping key-codes takes time and knowledge of each dataset

» If one detailed dataset changes it can require major amendments to key-codes

Thanks for listening

Any questions?