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Revisiting Sample Bias in the UK's Annual Survey of Hours and Earnings, with Implications for Estimates of Low Pay and the Bite of the National Living Wage

Author

Listed:
  • Forth, John

    (City University London)

  • Bryson, Alex

    (University College London)

  • Phan, Van

    (University of the West of England, Bristol)

  • Ritchie, Felix

    (University of the West of England, Bristol)

  • Singleton, Carl

    (University of Stirling)

  • Stokes, Lucy

    (Competition and Markets Authority)

  • Whittard, Damian

    (University of the West of England, Bristol)

Abstract

The Annual Survey of Hours and Earnings (ASHE) is based on an annual one per cent sample of employee jobs and provides many of the UK's official earnings statistics. These statistics are generated using official weights designed to make the achieved sample in each year representative of the population of employee jobs in Britain by gender, age, occupation, and region. However, we find that jobs in small, young, private-sector organisations remain under-represented after weighting. Additionally, there is evidence of systematic year-to-year longitudinal attrition among employees who remain in scope, for which no official weighting adjustment exists. We develop new weights to address these issues, demonstrating their importance through policy-relevant examples. Our new estimates suggest that the bite of the National Living Wage is greater, and that progress toward the target for eradicating low pay has been faster, than previously understood.

Suggested Citation

  • Forth, John & Bryson, Alex & Phan, Van & Ritchie, Felix & Singleton, Carl & Stokes, Lucy & Whittard, Damian, 2024. "Revisiting Sample Bias in the UK's Annual Survey of Hours and Earnings, with Implications for Estimates of Low Pay and the Bite of the National Living Wage," IZA Discussion Papers 17291, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17291
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    References listed on IDEAS

    as
    1. Schaefer, Daniel & Singleton, Carl, 2019. "Cyclical labor costs within jobs," European Economic Review, Elsevier, vol. 120(C).
    2. Gary Solon & Steven J. Haider & Jeffrey M. Wooldridge, 2015. "What Are We Weighting For?," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 301-316.
    3. Giulia Giupponi & Robert Joyce & Attila Lindner & Tom Waters & Thomas Wernham & Xiaowei Xu, 2024. "The Employment and Distributional Impacts of Nationwide Minimum Wage Changes," Journal of Labor Economics, University of Chicago Press, vol. 42(S1), pages 293-333.
    4. Daniel Schaefer & Carl Singleton, 2023. "The Extent of Downward Nominal Wage Rigidity: New Evidence from Payroll Data," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 60-76, December.
    5. Mark Pont, 2007. "Coverage and non‐response errors in the UK New Earnings Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 713-733, July.
    6. Michael W. L. Elsby & Donggyun Shin & Gary Solon, 2016. "Wage Adjustment in the Great Recession and Other Downturns: Evidence from the United States and Great Britain," Journal of Labor Economics, University of Chicago Press, vol. 34(S1), pages 249-291.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    earnings; non-response bias; attrition; survey weighting; low pay; national living wage;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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