New estimation methodology for the Norwegian Labour Force Survey

Abstract

Labour Force Survey (LFS) is an important source of the labour market statistics that provides information about the participation of people aged 15 and over in to the labour market and people outside of the labour market. It is a rotating panel sample survey that is carried out in accordance with the European Union (EU) Council Regulation. Statistics produced are subject to both sampling and non–response errors. Sampling errors are monitored through standard errors, which are provided alongside with the point estimates for the key variables. In that respect, finding an efficient estimator is one of the main goals for the LFS. This requires data sources that includes good auxiliary variables. Thus we aim to find an estimation methodology which better utilises the auxiliary information in the light of a new available data source, namely A–ordningen. In this regard, we compare the regular generalised regression estimator (GREG) and the (multiple) model–calibration estimator, which has been shown to be optimal among a class of calibration estimators, in terms of efficiency by using the Norwegian LFS data. Standard errors are estimated by using the Jackknife linearisation (JL) variance estimator. Overall, for the data used, the (multiple) model–calibration estimators have been more efficient than than the GREG estimators. Thus the former has been chosen to be used in the production of the Norwegian labour force statistics.