1. All expressed opinions are of the authors
Reliability of estimates in socio-demographic groups with small samples
D.Buono & A.Bikauskaite
15 March 2017, NTTS, Brussels
All expressed opinions are of the authors

2. Why interested in SAE?
European regional policies defining the variables of interest
Small sample sizes by some breakdowns
Reliable data breakdown demanded by policy makers and customers

3. Indicators of interest: ARPT

4. Estimation methods

5. tools and methods used
Empirical Bayes (EB) method based on the nested error model
Package: sae.R
Functions:
direct
ebBHF
pbmseBHF
Hierarchical Bayes (HB) method based on the Fay-Herriot model
Package: hbsae.R
fSAE
fSAE.Area

6. Application: Target and data
Target: Calculate direct and indirect at-risk-of-poverty rate estimates by socio-demographic breakdowns
Data sources: Survey on Income and Living Conditions (EU-SILC) and Census data of some EU countries in 2011
Sample: divided in 18 disjoint socio-demographic groups of small and large sizes
Auxiliary variables: unit level information on economic activity status and highest level of education attained

7. Application: Results

8. Learnings and future work
SAE techniques improve reliability of estimates
Solves the issue of no respondent above/below the threshold
Increase data availability
Further investigation is needed to assess the most appropriate estimator
Enlargement of number auxiliary variables
Harmonization of practices
Extension to additional countries and socio-demographic groups