<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the Netherlands, very precise and detailed statistical information on labour force participation is derived from registers. A drawback of this data source is that it is not timely since definitive versions typically become available with a delay of two years. More timely information on labour force participation can be derived from the Labour Force Survey (LFS). Quarterly figures, for example, become available six weeks after the calendar quarter. A well-known drawback of this data source is the uncertainty due to sampling error. In this article, a nowcast method is proposed to produce preliminary but timely nowcasts for the register labour force participation on a quarterly frequency at the level of municipalities and neighbourhoods, using the data from the LFS. As a first step, small area estimates for quarterly municipal figures on labour force participation are obtained using the LFS data and the unit-level modelling approach of Battese, Harter and Fuller (1988). Subsequently, time series of these small area estimates at the municipal level are combined with time series on register labour force participation in a bivariate structural time series model in order to nowcast the register labour force participation at the level of municipalities and neighbourhoods.</p>
</abstract>

In the Netherlands, very precise and detailed statistical information on labour force participation is derived from registers. A drawback of this data source is that it is not timely since definitive versions typically become available with a delay of two years. More timely information on labour force participation can be derived from the Labour Force Survey (LFS). Quarterly figures, for example, become available six weeks after the calendar quarter. A well-known drawback of this data source is the uncertainty due to sampling error. In this article, a nowcast method is proposed to produce preliminary but timely nowcasts for the register labour force participation on a quarterly frequency at the level of municipalities and neighbourhoods, using the data from the LFS. As a first step, small area estimates for quarterly municipal figures on labour force participation are obtained using the LFS data and the unit-level modelling approach of Battese, Harter and Fuller (1988). Subsequently, time series of these small area estimates at the municipal level are combined with time series on register labour force participation in a bivariate structural time series model in order to nowcast the register labour force participation at the level of municipalities and neighbourhoods.

Nowcasting Register Labour Force Participation Rates in Municipal Districts Using Survey Data

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In the Netherlands, very precise and detailed statistical information on labour force participation is derived from registers. A drawback of this data source...

Nowcasting Register Labour Force Participation Rates in Municipal Districts Using Survey Data

Publicado en línea: 26 dic 2021

Páginas: 1009 - 1045

Recibido: 01 mar 2020

Aceptado: 01 abr 2021

DOI: https://doi.org/10.2478/jos-2021-0043

Palabras clave
Small area estimation, unit-level model, survey sampling, register-based statistics, data integration

© 2020 Jan van den Brakel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Nowcasting Register Labour Force Participation Rates in Municipal Districts Using Survey Data

Publicado en línea: 26 dic 2021

Páginas: 1009 - 1045

Recibido: 01 mar 2020

Aceptado: 01 abr 2021

DOI: https://doi.org/10.2478/jos-2021-0043

Palabras claveSmall area estimation, unit-level model, survey sampling, register-based statistics, data integration

© 2020 Jan van den Brakel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Small area estimation, unit-level model, survey sampling, register-based statistics, data integration