Resampling-based Variance Estimation for Labour Force Surveys

Abstract

Labour force surveys are conducted to estimate quantities such as the unemployment rate and the number of people in work. Interest is typically both in estimates at a given time and in changes between two successive time points. Calibration of the sample to force agreement with known population margins results in random weights being assigned to each response, but the usual methods of variance estimation do not account for this. This paper describes how resampling methods—the jackknife, jackknife linearization, balanced repeated replication and the bootstrap—can be used to do so. We also discuss issues of implementation, and we compare the methods by simulation based on data from the UK Labour Force Survey. The broad conclusions are these: bootstrap and jackknife linearization are less computer intensive than the other resampling methods for such applications and give better standard errors; ‘standard’ methods can be badly biased downwards; and it is essential to take variability of the weights into account.