1. Do local social problems need centralized statistics?
Statistics Austria:
Matthias Till
Paris
June 27th, 2012
Potential for small area poverty estimates

2. Focus on the poor for improving QoL …because the rich may be saturated

3. stimulate Interest to learn about inform Decision to fight
Poverty statistics may meet needs on different levels
create Awareness of poverty
stimulate Interest to learn about
inform Decision to fight
support Action to alleviate
global
local
Indicators 3

4. National data can not guarantee regional precision
National rates for poverty&exclusion are estimated from EU-SILC (harmonized data)
> 200k hhds in 30 countries (together ~ 50 years of interviewing time)
Same regional precision would require little less effort (per region!)
Collection effort may triple for NUTS1 regions and be tenfold for NUTS2
Annual data collections of such magnitude are unrealistic!
Small municipalities may never be included in a sample

5. Example: ARP, NUTS2 Austria
source: Statistik Austria EU-SILC
source: Statistik Austria EU-SILC
not significantly different from AT13 (Vienna) 5

6. Project on NUTS2 poverty in AT
Jointly commissioned by the “Länder” to ensure comparability ( )
harmonisation of SILC/LFS variables for indirect estimation
-> model based projections impute propensities for poverty in LFS
Additional (national) module on income in LFS as a reality check
Review of a number of recent initiatives in small area estimation (EURAREA, SAMPLE, AMELI, for comprehensive documentation and software see )
Synthesis and evaluation of competing strategies

7. Principle of Small Area Estimation
find optimum between
low variance with acceptable bias,
& large, unbiased variance x x x x x x x x x x x x x x x x x x
Expected value
e.g. estimate mean directly from area sample
e.g. estimate mean indirectly from grand mean
Seite 7 7

8. MSE = Variance + Bias2
Seite 8 8

9. Preliminary conclusions
Always calculate standard errors!
If errors are large (e.g. CV > .165) derive structural information from 3-yr average ys as simplest and most flexible solution
Analysis of risk factors and their harmonized measurement in core variables is essential for enhancing precision by using auxiliary information. We can have more stable results!
For small numbers of regions and highly informative sampling designs model assisted methods (GREG or calibration estimators) appear closer to application than model based
Need for long term capacity building for practical SAE application