1. Estimation of preliminary unemployment rates by means of multiple imputation UN/ECE-Work Session on Data Editing Vienna, April 2008 Thomas Burg, Statistics Austria www.statistik.at

2. S T A T I S T I K A U S T R I A April 2008 2 Outline  Description of the problem  Methods of Estimation  Preliminary estimation using MI  Results

3. S T A T I S T I K A U S T R I A April 2008 3 Quickness of results Today policy makers want to receive results as early as possible Challenging for official statistics Final results only after field work is completed Can I get figures earlier?

4. S T A T I S T I K A U S T R I A April 2008 4 Austrian Labor Force Survey Survey performed quarterly based on a rotating sample of households. Every quarter one fifth of the sample is exchanged Data collection is distributed to 13 weeks of a quarter and respondents are questioned about their labor status with reference to the week before. Most important figures: Unemployment rates

5. S T A T I S T I K A U S T R I A April 2008 5 Situation during field work End of quarter Estimation on data available on first day after Quarter ends.

6. S T A T I S T I K A U S T R I A April 2008 6 The Problem Is it possible to estimate preliminary unemployment Rates on the basis of the data already received? Available Data ~70% Missing Records ~30% Unemployment figures

7. S T A T I S T I K A U S T R I A April 2008 7 Missing Records For missing records not everything is missing…… Rotating sample Basic socio demographic information (Age, Sex, etc… Information from sampling frame Assumed household size, residence..

8. S T A T I S T I K A U S T R I A April 2008 8 Estimation Methods Weighting on basis of available data Raking procedure involving marginal distributions of the Austrian population Imputing labor status for records still to come Assumption on the set of records necessary

9. S T A T I S T I K A U S T R I A April 2008 9 Imputing labour status Available Data ~70% Missing Records ~30% To impute values on a record I definitely need records on which I can impute! Information from prior rotations and from the sampling frame

10. S T A T I S T I K A U S T R I A April 2008 10 Multiple imputation In official statistics not very common: There you like to have authentic databases with stored values Multiple imputation rather focuses on concrete estimation problems => Here I have a concrete estimation problem!

11. S T A T I S T I K A U S T R I A April 2008 11 Multiple imputation – single imputation step Analysis (I) Labour status: 4 possible values (1=’employed’, 2=’unemployed’, 3=’not relevant for employment’, 4=’military person’). Analysis of distributional differences of labour status between known and expected records based on poststratification including Sex, Age-groups, and Citizenship

12. S T A T I S T I K A U S T R I A April 2008 12 Multiple imputation – single imputation step Analysis (II) Results were also depending on the quarter. Even incorporating this figures were not satisfactory => There must be an additional factor => Weight of a person delivered desired result.

13. S T A T I S T I K A U S T R I A April 2008 13 Multiple imputation – single imputation step

14. S T A T I S T I K A U S T R I A April 2008 14 Multiple imputation Multiple Imputation smoothes out Variability of estimators

15. S T A T I S T I K A U S T R I A April 2008 15 Results (I) Results for the MI-Estimation of preliminary figures compared to the real data

16. S T A T I S T I K A U S T R I A April 2008 16 Results (II) Comparison of estimation of unemployment rate – MI, Grossing up and Real data

17. S T A T I S T I K A U S T R I A April 2008 17 Conclusions – Critical remarks Multiple imputation is a possible estimation strategy for preliminary figures Problematic assumptions concerning expected records Time series are very thin now