Unemployment estimation: Spatial point referenced methods and models

Abstract

From the fourth quarter of 2014, the Portuguese Labour Force Survey (PLFS) started geo-referencing the sampling units, namely the dwellings, in which the surveys are carried out. This opens new possibilities in analysing and estimating unemployment and its spatial distribution across any region by employing point referenced methods and models. According to a preestablished sampling criteria, the labour force survey selects a certain number of dwellings from across the nation to study, and establishes the number of unemployed people in each dwelling. Based on this survey, the National Statistical Institute of Portugal presently uses direct estimation methods to estimate the national unemployment figures. Recently however, there has been increased interest in estimating these figures in smaller areas. Due to reduced sampling sizes in small areas, direct estimation methods tend to produce fairly large sampling variations. Therefore, model based methods should be favoured as these tend to borrow strength from area to area by making use of the areal dependence. These model based methods tend to use areal counting processes as models and typically introduce spatial dependence through the model parameters using a latent random effect.In this paper, we suggest using point referenced models as an alternative to the traditional small area estimation methods for unemployment estimation. Specifically, we model the spatial distribution of residential buildings across Portugal using a log Gaussian Cox process, and the number of unemployed people per residential unit as a mark attached to these random points. Thus, the main focus of the study is to model the spatial intensity function of this marked point process. The number of unemployed people in any region can then be estimated using a proper functional of this marked point process. The principal objective of this point referenced method for unemployment estimation is to produce reliable estimates at higher spatial resolutions, and at the same time to incorporate into the model any available auxiliary information of the residential units, such as mean age or education level as compared to areal unit averages used in small area estimation.