Combining National Surveys with Composite Calibration to Improve the Precision of Estimates from the United Kingdom's Living Costs and Food Survey

Abstract

Abstract The United Kingdom’s Living Costs and Food (LCF) Survey has a relatively small sample size but produces estimates which are widely used, notably as a key input to the calculation of weights for consumer price indices. There has been a recent call for the use of additional data sources to improve the estimates from the LCF. Since some LCF variables are shared with the much larger Labour Force Survey (LFS), we investigate combining data from these surveys using composite calibration to improve the precision of estimates from the LCF. We undertake model selection to choose a suitable set of common variables for the composite calibration using the effect on the estimated variances for national and regional totals of important LCF variables. The variances of estimates for common variables are reduced to around 5 percent of their original size. Variances of national estimates are reduced (across several quarters) by around 10 percent for expenditure and 25 percent for income; these are the variables of primary interest in the LCF. Reductions in the variances of regional estimates vary more but are mostly large when using common variables at the regional level in the composite calibration. The composite calibration also makes the LCF estimates for employment status almost consistent with the outputs of the LFS, which is an important property for users of the statistics. A novel alternative method for variance estimation, using stored information produced by the composite calibration, is also presented.