Reconciling reports: modelling employment earnings and measurement errors using linked survey and administrative data

Stephen Jenkins and Fernando Rios-Avila ()

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We develop and apply new statistical models for linked survey and administrative data on employment earnings, incorporating 4 types of measurement error. In addition, we allow error distributions to differ with individual characteristics, which improves model fit and allows us to investigate substantive hypotheses about factors associated with error bias and variance. Contributing the first UK evidence to a field dominated by findings about the USA, we show that measurement errors are pervasive, but the 4 types are quite different in nature. We also document substantial heterogeneity in each of the error distributions.

Keywords: measurement error; linkage error; earnings; linked survey data and administrative data; finite mixture models (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2023-01-31
New Economics Papers: this item is included in nep-ltv
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Citations: View citations in EconPapers (4)

Published in Journal of the Royal Statistical Society. Series A: Statistics in Society, 31, January, 2023, 186(1), pp. 110 - 136. ISSN: 0964-1998

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http://eprints.lse.ac.uk/117213/ Open access version. (application/pdf)

Related works:
Working Paper: Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:117213

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