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Abstract

There is increasing evidence that official population statistics are inaccurate at the local authority level, the fundamental administrative unit of the UK. The main source of official population statistics in the UK comes from the decennial census, last undertaken in 2011. The methodology and results of official population counts have been criticised and described as unfit for purpose. The three main purposes of population statistics are resource allocation, population ratios, and local planning and intelligence.

Administrative data are data that is routinely collected for administrative purposes by organisations, government departments or companies and not for statistical or research purposes. This is in contrast with surveys which are designed and carried out as a specific information gathering exercise. This thesis describes a methodology for linking routinely collected administrative data for counting and profiling populations and other purposes at the local level.

The benefits of this methodology are that it produces results more quickly than the decennial census, in a format that is more suitable for accurate and detailed analyses. Utilising existing datasets in this way reduces costs and adds value.

The need and the evolution of this innovative methodology are set out, and the success and impact it has had are discussed, including how it has helped shape thinking on statistics in the UK. This research preceded the current paradigm shift in the UK for research and national statistics to move towards the use of linked administrative data. Future censuses after 2021 may no longer be in the traditional survey format, and the Office for National Statistics are exploring using a similar administrative data method at the national level as an alternative. The research in this thesis has been part of this inevitable evolution and has helped pave the way for this.

Publication Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HA Statistics
Departments:	Bayes Business School Doctoral Theses Bayes Business School > Bayes Business School Doctoral Theses

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