1. UK LFS wave approach
Dean Fletcher ONS UK

2. UK LFS wave structure

3. UK LFS Quarterly datasets
Most variables asked on all 5 waves every quarter.
Some variables only asked at wave 1 and then rolled forward.
Earnings variables asked at wave 1 and wave 5 only

4. UK LFS Quarterly datasets Quarterly and Earnings variables weights
Quarterly weights using GREG
Partition 1: Individual Local Authority Districts NUTS3
Partition 2: GB/NI by sex for the ages 0-15, 16, 17, 18, 19, 20, 21, 22, 23, 23, 24 and 25+
Partition 3: Male/Female by Government Office Region (GOR)/NUTS2 and Age-Groups –Sex
Earnings weights using GREG
Partition 1: Five-year age bands by sex
Partition 2: Full-time/Part-time by Standard Occupational Classification Major Group from quarterly
Partition 3: Standard Industrial Classification Industry Sector from quarterly
Partition 4: Government Office Region (summary) by sex
By using quarterly estimates keeps earnings estimates consistent with quarterly estimates

5. AHM and some Annual wave 1 only
Weighting the AHM. Only asked at wave 1.
Uses GREG also. Calibrates to 4 quarter average so does equal annual estimate.
1) ILO status (unemployed/employed/inactive) by sex by 10-year age bands between 15 and 55
2) NUTS3
3) GB/NI by sex by single year of age between 16 and 24
4) NUTS2 by sex by 5-year agebands
Measuring different estimates.
Lose quarter on quarter change. Annual datasets no overlap so change estimates more variable. OK with AHM as only in one year.

6. Potential to use other waves
Looking to add variables only to waves 2 to 4 across 4 quarters and create an annual estimate
Issues:
Differential attrition across waves
Consistency of common variables
Number of datasets and/or weights
Estimates of change