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Editing Procedure for Low-Pay Data in Survey Analysis

Learn a comprehensive editing procedure for survey data with low-pay issues, targeting unbiased estimates and reducing validation costs. Strategies include preliminary edits and comparison techniques.

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Editing Procedure for Low-Pay Data in Survey Analysis

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  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

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