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

Jenkins, Stephen P. ORCID: 0000-0002-8305-9774 and Rios-Avila, Fernando (2023) Reconciling reports: modelling employment earnings and measurement errors using linked survey and administrative data. Journal of the Royal Statistical Society. Series A: Statistics in Society, 186 (1). 110 - 136. ISSN 0964-1998

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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.

Item Type:	Article
Official URL:	https://academic.oup.com/jrsssa
Additional Information:	© 2023 (RSS) Royal Statistical Society.
Divisions:	Social Policy
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	31 Oct 2022 11:48
Last Modified:	16 Jan 2025 12:15
URI:	http://eprints.lse.ac.uk/id/eprint/117213

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