Improving timeliness and accuracy of estimates from the UK labour force survey

ABSTRACT

Estimates of unemployment in the UK are based on data collected in the Labour Force Survey (LFS). The data is collected continuously and the survey design is structured in such a way as to provide quarterly estimates. These quarterly estimates are published each month as ‘rolling quarterly’ estimates. Currently the Office for National Statistics (ONS) publish rolling quarterly estimates, and these have been assessed to be of sufficient quality to be badged as ‘National Statistics’. ONS also publish monthly estimates of a selection of labour force variables, but these are designated ‘Experimental Statistics’ due to concerns over the quality of these data. Due to the sample design of the LFS, monthly estimates of change are volatile as there is no sample overlap. A state space model can be used to develop improved estimates of monthly change, accounting for aspects of the survey design. An additional source of information related to unemployment is administrative data on the number of people claiming unemployment related benefits. This data is more timely than survey data collected in the LFS and can be used to provide early estimates of monthly unemployment.

KEYWORDS:

• Unemployment
• rotation group bias
• state space models

Acknowledgments

Thanks to two anonymous referees who provided detailed and constructive comments on an earlier submission. Thanks to Danny Pfeffermann for constructive conversations on developing the models. Thanks also to Bethan Russ, Alastair Cameron, Greg Dixon, and Matthew Greenaway for their contributions to providing the wave-specific monthly estimates and variances used as input data for all the modelling.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

D. J. Elliott

D. Elliott and P. Zong are methodologists at the Office for National Statistics in the UK.

P. Zong

D. Elliott and P. Zong are methodologists at the Office for National Statistics in the UK.