Linking by Translation: the key to comparable codesets Ben Hickman Local Government Analysis & Research 19th 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 1Dataset 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 Research All LG classification Labour Force Survey SOC2000

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 X Classroom Teacher XX Cleaner X

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

Var2 x SOC2000 = Schools Key-codes 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 AssistantEducational assistant n.e.c Educational assistant

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

Simplify through translation... Key-codes DfES Classification Pay classification SOC2000 NMDS-SC Child 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?