Estimating Monthly Labour Force Figures During the COVID-19 Pandemic in the Netherlands

Abstract

Official monthly statistics about the Dutch labour force are based on the Dutch Labour Force Survey (LFS). The LFS is a continuously conducted survey that is designed as a rotating panel design. Data collection among selected households is based on a mixed‐mode design that uses web interviewing, telephone interviewing and face‐to‐face interviewing. Monthly estimates about the labour force are obtained with a structural time series model. Due to the COVID‐19 pandemic, face‐to‐face interviewing stopped. It was anticipated that this would have a systematic effect on the outcomes of the LFS and that the lockdown at the same time affected the real monthly labour force figures. The lockdown indeed marked a sharp turning point in the evolution of the series of the monthly labour force figures and strongly increased the volatility of these series. In this paper, it is explained how Statistics Netherlands produced monthly labour force figures during the COVID‐19 pandemic. It is shown how the sudden change in the mode effects, because face‐to‐face interviewing stopped, were separated from real period‐to‐period changes in the labour force figures. It is also explained how the time series model is adapted to the increased volatility in the labour force figures.

Structural Break in the Norwegian Labor Force Survey Due to a Redesign During a Pandemic

“…In Norway, the COVID-19 shutdown took place in March 2020, so we must allow for the change in the hyperparameter in January 2020. Results in Van den Brakel et al (2022) support that the hyperparameter for the slope should be increased two months before the COVID-19 shutdown. Furthermore, Gonçalves et al (2022) find that an increased hyperparameter for the slope in the six first months of 2020 improves the fit of a model for LFS in Brazil.…”

“…In the Netherlands, the Labor Force Survey estimates are improved by applying a state-space model; see Van den Brakel and Krieg (2009). During the COVID-19 pandemic, they had to modify the state-space model to account for the more rapid changes in the labor market; see Van den Brakel et al (2022). They did so by allowing for a timevarying hyperparameter for the slope of the trend.…”

“…Gonçalves et al (2022) applied the same approach for the LFS in Brazil. However, neither Van den Brakel et al (2022) nor Gonçalves et al (2022) considered to include an auxiliary variable. Bollineni-Balabay et al (2016) applied a similar approach when considering both level and variance breaks due to survey redesign.…”

“…When estimating the structural break due to the redesign, we pursue the same approach to accommodate the large fluctuations in the labor market. In contrast to both Bollineni-Balabay et al (2016) and Van den Brakel et al (2022), we include an auxiliary variable. We use time-varying hyperparameters for both the LFS variables and the auxiliary variable in a model for quantifying the structural break due to survey redesign.…”

Time Series Modelling of Repeated Survey Data for Estimation of Finite Population Parameters

“…Another major topic occupying many statisticians throughout the world for more than two years is how to model, or account for the effects of COVID‐19. In the context of time series analysis of repeated surveys, van den Brakel et al (2021) modified the model of van den Brakel and Krieg (2016) to account for the effects of the pandemic by increasing the variance of the slope disturbance during that period. This way, the trend estimates assign more weight to the current survey estimates and less to past data.…”