Abstract

The objective of this work is to propose a small area estimation strategy for an economic security indicator. In the last decade the interest for the measurement of economic security or insecurity has grown constantly, especially since the financial crisis of 2008 and the pandemic period. In this work, economic security is measures through a longitudinal indicator that compares levels of equivalized household income over time. To solve a small area estimation problem, due to possible sample sizes too low in some areas, a small area estimation strategy is suggested to obtain reliable estimates of the indicator of interest. We consider small area models specified at area level. Besides the basic Fay-Herriot area-level model, we propose to consider some longitudinal extensions, including time-specific random effects following an AR(1) process or an MA(1) process. A simulation study based on EU-SILC data shows that all the small area models considered provide a significant efficiency gain with respect to the Horvitz-Thompson estimator, especially the small area model with MA(1) specification for random effects.

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