# Unemployment estimation: Spatial point referenced methods and models

## TL;DR

This paper introduces a novel spatial point referenced method using a Log Gaussian Cox process to estimate unemployment at finer spatial resolutions by modeling residential distributions and unemployment as a marked point process.

## Contribution

It proposes a new spatial modeling approach for unemployment estimation using marked point processes, enhancing small-area estimates with auxiliary data.

## Key findings

- Effective modeling of spatial unemployment distribution.
- Improved estimates at higher spatial resolutions.
- Integration of auxiliary information into the model.

## Abstract

Portuguese Labor force survey, from 4th quarter of 2014 onwards, started geo-referencing the sampling units, namely the dwellings in which the surveys are carried. This opens new possibilities in analysing and estimating unemployment and its spatial distribution across any region. The labor force survey choose, according to an preestablished sampling criteria, a certain number of dwellings across the nation and survey the number of unemployed in these dwellings. Based on this survey, the National Statistical Institute of Portugal presently uses direct estimation methods to estimate the national unemployment figures. Recently, there has been increased interest in estimating these figures in smaller areas. Direct estimation methods, due to reduced sampling sizes in small areas, tend to produce fairly large sampling variations therefore model based methods, which tend to "borrow strength" from area to area by making use of the areal dependence, should be favored. These model based methods tend use areal counting processes as models and typically introduce spatial dependence through the model parameters by a latent random effect. In this paper, we suggest modeling the spatial distribution of residential buildings across Portugal by a Log Gaussian Cox process and the number of unemployed 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. Number of unemployed 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 get reliable estimates at higher spatial resolutions and at the same time incorporate in the model the auxiliary information available at residential units such as average income or education level of individuals surveyed in these units.