Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data

Stephen Jenkins and Fernando Rios-Avila ()

No 14405, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: We contribute new UK evidence about measurement errors and employment earnings to a field dominated by findings about the USA. We develop and apply new econometric models for linked survey and administrative data that generalize those of Kapteyn and Ypma (Journal of Labor Economics, 2007). Our models incorporate mean-reverting measurement error in administrative data in addition to linkage mismatch and mean-reverting survey measurement error and 'reference period' error, while also allowing error distributions to vary across individuals. Annualised survey earnings underestimate true annual earnings on average. Mean-reversion in survey measurement errors is absent. Both earnings sources underestimate true earnings inequality. The survey earning measure is more reliable than the administrative data earnings measure, but hybrid earnings predictors based on both sources are distinctly more reliable than either source-specific measure. The models with heterogeneous measurement error distributions indicate how data quality may be improved. For example, for survey quality, our results highlight how respondents showing payslips to interviewers have smaller survey error variances. For administrative data, our results suggest that greater error variances are associated with non-standard jobs, private sector jobs, and employers without good payroll systems.

Keywords: measurement error; earnings; survey data; administrative data; finite mixture models (search for similar items in EconPapers)
JEL-codes: C81 C83 D31 (search for similar items in EconPapers)
Pages: 92 pages
Date: 2021-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Published - published in: Journal of the Royal Statistical Society , 2023, 186 (1), 110 - 136

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Working Paper: Reconciling reports: modelling employment earnings and measurement errors using linked survey and administrative data (2023)
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