Methodologies for integrating different sources in Official Statistics

For several years we have been testing the use of alternative sources to improve the quality of official statistics. In this work we use Small Area Estimation (SAE) techniques as an alternative to design-based approaches for the domains where the sample size does not give support to reliable results. Model-based approaches in SAE are obtained by fitting a model (often a regression model) to the data, using covariates as auxiliary information. In SAE, mixed models can be used to combine different sources of information and explain different sources of errors.
There are several examples where this methodology has been tested, such as the Labour Force Survey (in the social domain) and the production of Land use/Land Cover (LCLU) statistics (in the business domain). LCLU based on Small Area Estimation (SAE) requires new auxiliary sources, namely Annual Crop Statistics (ACS) at NUTS 3 level (available for internal use) and Farm Structure Survey (FSS). This auxiliary information comprises several sources already available for NUTS 3 (administrative data, national surveys, etc). This procedure usually works well still in small samples because estimation is based on regressions between the variables underlying the model.

small area estimation

model-based estimation

mixed models