Precision gains for NUTS2 Statistics Austria: M atthias Till - - PowerPoint PPT Presentation

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Precision gains for NUTS2 poverty estimates –?

preliminary findings from an austrian exercise

Statistics Austria: M atthias Till Paris November 27th, 2012

www.statistik.at Folie 2 | 09.04.2013

Our problem: Eratic patterns over time

2 4 6 8 10 12 14 16 18 20

Oberösterreich Salzburg Tirol Vorarlberg Niederösterreich Steiermark Burgenland Kärnten Wien

APR (2005-10)

2005 2006 2007 2008 2009 2010

Statistics Austria Eu-SILC

www.statistik.at Folie 3 | 09.04.2013

AT34: a special case

13 9 13 18 12 10

2 4 6 8 10 12 14 16 18 20

Vorarlberg

APR (2005-10)

2005 2006 2007 2008 2009 2010 minus 37% plus 40%

Statistics Austria Eu-SILC

Seite 4

Our problem: precision proportionate to region size

NUTS 2 n= CI AT11 247 4,7 AT34 307 3,6 AT32 440 3,0 AT21 475 4,3 AT33 584 2,6 AT22 1.014 2,4 AT13 1.219 2,4 AT31 1.233 2,2 AT12 1.287 1,8 Total 6.806 0,8

not significantly different from AT13 (Vienna) EU-SILC 2007, number of households, 95% confidence intervall for Poverty Rate

www.statistik.at Folie 5 | 09.04.2013

11-13 May 2011

DDG Eurostat Laboratory

Our solutions:

• 1. Relying on direct estimates
• Inacceptable volatility for small regions
• 2. Aggregation over several years
• cheap and easy but not timely and limited scope
• 3. Augmenting income information in LFS (using registers)
• Larger (and in AT: disproportional sample design)
• quality concerns and unmet data requirements
• 4. Imputing poverty status in LFS (using models)
• borrows statistical strength and can enhance understanding
• Unit level is most flexible and can be augmented with region covariates
• Overlapping core variables & micro data availability of LFS & EU-SILC
• Shrinkage towards the mean = bias of between region differences
• 5. Composite of imputed (4) and direct (1) estimates
• M inimizes expected bias accross regions
• Reduces precision gain for small regions