1. An Editing Procedure for Low Pay Data Salah Merad, Mike Hidiroglou and Fiona Crawford Office for National Statistics, UK Survey Methods Division

2. Outline Background Problem Solution

3. Background Annual Survey of Hours and Earnings –Statistics produced include estimates of average pay and distribution of hourly pay around the National Minimum Wage (NMW) in domains of interest Basic hourly pay obtained in two ways –Directly: Stated hourly rate (available in 45% of records) –Indirectly: Derived basic hourly rate Derived basic weekly pay/Average weekly hours

4. Problem Selective editing is applied to the whole data set –Targets estimates of averages and totals overall and in important domains Picks up large errors Need to target estimates of the number of employees below the NMW –Small errors can be important –Additional editing Validation costs high: reduce editing costs whilst resulting estimates of the number of employees below the NMW are nearly unbiased

5. Solution: Outline of editing strategy Stated hourly rate available: preliminary edit followed by main low pay edits –Preliminary edit based on difference between Stated and Derived Threshold determined so that resulting bias is small Threshold value depends on position of Stated and Derived in relation to NMW –Main low pay edits: compare current and previous Derived, and use other relevant information Stated hourly rate not available: main low pay edits Large number of failed records: manually edit a random sample, and impute remainder using data from edited records