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

An Editing Procedure for Low Pay Data
Salah Merad, Mike Hidiroglou and Fiona Crawford
Office for National Statistics, UK
Survey Methods Division
Outline
• Background
• Problem
• Solution
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
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
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