A novel multivariate composite estimator for the labour force survey

Abstract This paper introduces a novel multivariate composite estimator for the Labour Force Survey (LFS). The estimator improves upon traditional methods by simultaneously modelling all labour market categories, such as employment, unemployment, and nonparticipation. This multivariate approach avoids the asymmetrical treatment of a residual category and leverages the correlation structure between the different labour market categories. The paper also presents a framework for explicitly incorporating and estimating wave-specific biases, which can vary over time. We derive analytical formulas for the estimator’s variance and demonstrate its properties using data from the Norwegian LFS. The empirical results show that the estimator provides substantial precision gains for estimates of change over time compared to the direct estimator. By not assuming a smooth trend for the underlying population values, the proposed method also offers a robust alternative to state–space models, particularly during periods of high economic volatility.