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A comparison of sample survey measures of earnings of English graduates with administrative data

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  • Jack Britton
  • Neil Shephard
  • Anna Vignoles

Abstract

Administrative data sets are increasingly used in research because of their excellent coverage and large scale. However, in the UK the use of administrative data on individuals’ earnings, and particularly graduates’ earnings, is novel. Understanding the strengths and weaknesses of such data is important as they are set to be used extensively for research and to inform policy. Here we compare survey‐based labour earnings data from the UK's Labour Force Survey (LFS) with UK Government administrative sources of individual level earnings data, focusing separately on young (up to age 32 years) graduates and non‐graduates. This type of administrative data set has few sample selection issues and is longitudinal and its large samples mean that the earnings of subpopulations can potentially be studied with low error. Overall we find a similar share of individuals with zero earnings in the LFS and administrative data, but a considerably higher share (conditionally on working) earning below £8000 in the administrative data. The LFS has generally higher earnings right through the distribution, though above the median a large share of the differences can potentially be explained by employee pension contributions. We also find considerably larger gender difference in the survey data. The findings hold for both graduates and non‐graduates. These differences are substantively important and suggest different conclusions about the gender wage gap, the graduate earnings premium and the extent of earnings inequality.

Suggested Citation

  • Jack Britton & Neil Shephard & Anna Vignoles, 2019. "A comparison of sample survey measures of earnings of English graduates with administrative data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 719-754, June.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:3:p:719-754
    DOI: 10.1111/rssa.12382
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    Cited by:

    1. Britton, Jack & van der Erve, Laura & Belfield, Chris & Vignoles, Anna & Dickson, Matt & Zhu, Yu & Walker, Ian & Dearden, Lorraine & Sibieta, Luke & Buscha, Franz, 2022. "How much does degree choice matter?," Labour Economics, Elsevier, vol. 79(C).
    2. Jenkins, Stephen P. & Rios-Avila, Fernando, 2021. "Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data," IZA Discussion Papers 14405, Institute of Labor Economics (IZA).
    3. Kilic,Talip & Van den Broeck,Goedele & Koolwal,Gayatri B. & Moylan,Heather G., 2020. "Are You Being Asked ? Impacts of Respondent Selection on Measuring Employment," Policy Research Working Paper Series 9152, The World Bank.
    4. Britton, Jack & Gruber, Jonathan, 2020. "Do income contingent student loans reduce labor supply?," Economics of Education Review, Elsevier, vol. 79(C).
    5. Arun Advani, 2022. "Who does and doesn't pay taxes?," Fiscal Studies, John Wiley & Sons, vol. 43(1), pages 5-22, March.
    6. Advani, Arun & Summers, Andy & Tarrant, Hannah, 2020. "Measuring UK top incomes," CAGE Online Working Paper Series 490, Competitive Advantage in the Global Economy (CAGE).
    7. Emmenegger Jana & Münnich Ralf, 2023. "Localising the Upper Tail: How Top Income Corrections Affect Measures of Regional Inequality," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(3-4), pages 285-317, June.
    8. Jack Britton & Neil Shephard & Laura van der Erve, 2019. "Econometrics of valuing income contingent student loans using administrative data: groups of English students," IFS Working Papers W19/04, Institute for Fiscal Studies.

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