The small area estimation (SAE) theory is widely used when local or domain-specific reliable estimates based on survey data are needed. Small area model-based estimates use a model that links the response variable to some auxiliary information borrowing strength from the related areas. When geographical information on the areas of interest is available, the specification of a spatial area level model can increase the estimates’ efficiency, depending on available auxiliary data. In this article, we first review the most popular area level spatial models, and we then compare their performance under two alternative scenarios of auxiliary information availability to estimate the average equivalized household income in Italian Local Labour Market Areas (LLMAs) using the EU-SILC (European Union Statistics on Income and Living Conditions) survey data. Our findings suggest that the spatial information can “fill the gap” when the covariates do not have a high predictive power, a crucial result when there is lack of auxiliary data.
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