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The perils of pre-filling: lessons from the UK's Annual Survey of Hours and Earning microdata

Whittard, D., Ritchie, F., Phan, V. , Bryson, A., Forth, J. ORCID: 0000-0001-7963-2817, Stokes, L. & Singleton, C. (2023). The perils of pre-filling: lessons from the UK's Annual Survey of Hours and Earning microdata. Statistical Journal of the International Association of Official Statistics, 39(3), pp. 661-677. doi: 10.3233/sji-230013


The role of the National Statistical Institution (NSI) is changing, with many now making microdata available to researchers through secure research environments. This provides NSIs with an opportunity to benefit from the methodological input from researchers who challenge the data in new ways. This article uses the United Kingdom’s Annual Survey of Hours and Earnings (ASHE) to illustrate the point. We study whether the use of prefilled forms in ASHE may create inaccurate values in one of the key fields, workplace location, despite there being no direct evidence of it in the data supplied to researchers. We link surveys to examine the hypothesis that employees working for multi-site employers making an ASHE survey submission are more likely to have their work location incorrectly recorded as the respondent fails to correct the work location variable that has been pre-filled. In the short-term, suggestions are made to improve the quality of ASHE microdata, while longer-term, we suggest that the burden of collecting additional data could be offset through greater use of electronic data capture. More generally, in a time when statistical budgets are under pressure, this study encourages NSIs to make greater use of the microdata research community to help inform statistical developments.

Publication Type: Article
Additional Information: The article has been published in Statistical Journal of the IAOS by IOS Press, DOI: 10.3233/SJI-230013. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Publisher Keywords: microdata; response burden; measurement error, ASHE; spatial
Subjects: H Social Sciences > HA Statistics
Departments: Bayes Business School > Management
SWORD Depositor:
[thumbnail of Whittard et al (2023) The perils of pre-filling - accepted manuscript (Statistical Jnl of the IAOS).pdf]
Text - Accepted Version
Available under License Creative Commons Attribution.

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