Reliability of estimates in socio-demographic groups with small samples

D.Buono

Statistical Office of European Union19 August 2016, SAE, Maastricht

All expressed opinions are of the author

Facts and figures about Eurostat

• About 800 people with 28 different nationalities• Small central methodology team

• TS, Econometrics, SDC, research & EA• Plus domain methodologists networking

• Statistical Office but not independent authority, General Directorate of the European Commission• Subsidiary principle!

Eurostat core business

• Euro-zone (19) & EU (28) aggregates

• harmonization, best practices, guidelines, trainings & international cooperation

Why interested in SAE?

• European regional policies • Different sizes of Member States, primary data providers

• According to the EU 2011 Population Census there are 79,652,380 residents in DE and 512,353 in LU!!!

• Some dilemmas: • How big is a small area? • Can SAE help with data breakdown demand by users?

Outline

• Reliability of indicators• At-risk-of-poverty indicators• SAE techniques for Official Statistics• Application for 2 EU countries• Learnings and open questions

• ADS and EU research funds for SAE expertise

Some Notation• U – finite population of size N• D – number of socio-demographic groups in the target

population• s – sample of size n• sd – sub-sample from domain d of size nd

• r – not sampled elements of size N-n• rd – not sampled elements from domain d of size Nd-nd

• y – target variable• X – vector of auxiliary information

Indicator of interest: ARPT

Estimation methods

Empirical Bayes (EB) method

Hierarchical Bayes (HB) method

packages and functions used• sae.R

• Functions:directebBHFpbmseBHF

• hbsae.R• Functions:

fSAEfSAE.Area

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

Application 1: Results

Application 1: Results

Application 2: Results

Application 2: Results

Learnings and future work • By applying model-based SAE techniques reliability of

estimates could be increased

• Enlargement of number auxiliary variables

• Further investigation is needed to assess the most appropriate estimator (call for harmonization?)

• Extension to additional countries and socio-demographic groups

Open questions on SAE

• EB vs. HB dichotomy calls for harmonised practices in Official Statistics?

• Design based to model based to algorithm based: maybe there is a possible link between SAE and statistical learning?

• Reversing the approach: starting from the data rather than from the goal?

• How about the use of SAE for data protection?

Advertisement CESS2016, Conference of European Statistics StakeholdersBudapest, 20–21 Oct 16 (by ESTAT, ECB & HCSO), free!

• Session B3: Official statistics on cross-border phenomena• Session C9: Small area estimation and weighting

NTTS2017, New Techniques and Technologies for StatisticsBrussels, 14–16 March 17 (by ESTAT), free!

• abstract by 28 Oct 16, track C includes SAE

Research funds under Horizon 2020 TOPIC : Towards a new growth strategy in Europe - Improved economic and social measurement, data and official statisticsOpening: 4 of October 2016 Closing: 2 of February 2017For more info here to submit a proposal here

"Disaggregation of statistics - geographically, or by other domains (e.g. identifying vulnerable population groups) - to provide greater insights and providing evidence allowing more focused policy decisions should be covered. At the same time data protection concerns should be addressed. Small Area Estimation expertise could cover the geographical/domain disaggregation aspect"

Thank you!

dario.buono@ec.europa.euhttp://ec.europa.eu/eurostat