1. Martina Mysíková, Štěpán Tourek, Martin Zelený
Attrition in EU-SILC in the Czech Republic Presentation for ESRA Conference June 28th 2007
Martina Mysíková, Štěpán Tourek, Martin Zelený

2. Design of EU-SILC Four year rotational panel
households are visited continuously for four years
each year ¼ dropped and ¼ newly selected
sample persons moved to another private households are traced and visited
First wave: 4351 households responded (response rate 64,6%)
Second wave: 87,7% Response, 11,2% Non-response, 1,1% Out of survey population

3. Design of EU-SILC
2005
2006
2007
2008
2009 1 2 3 4

4. Households

5. Household distribution
NUMBER OF HOUSEHOLD MEMBERS
The distribution by number of household members is the same at „response“ and „non-response“
Among „out“ prevail households with one member

6. Household distribution
NUMBER OF CHILDREN
The distribution by number of children is similar at „response“ and „non-response“
Among „out“ prevail households without children

7. Household characteristics
SIZE OF MUNICIPALITY
The „non-response“ is highest in big cities (18%)
Small – less than 49999
inhabitants
Middle –
Big – more than
inhabitants

8. Household characteristics
INTERVIEWER
The change of the interviewer has some impact on „non-response“ (15%)
Domácnosti jsou ochotnější odpovídat stejnému tazateli.
Noví tazatelé jsou méně zkušení

9. Individuals

10. Individual distribution
NET INCOME DECILES
The greatest share of „non-response“ falls in higher deciles

11. Individual characteristics
GENDER
No differences by gender

12. Individual characteristics
AGE
Higher „non-response“ at middle-age groups
Highest „out“ at oldest group

13. Individual characteristics
MARITAL STATUS
Higher „non-response“ at separated (18%), divorced (13%) and single (13%)
Highest „out“ at widowed

14. Individual characteristics
ECONOMIC ACTIVITY
Higher „non-response“ at self-employed (17%)
Highest „response“ at retired (not working) and those receiving parental allowance
Highest „out“ at
retired (not working)

15. Individual characteristics
EDUCATION
„non-response“ rises with higher educational level
„out“ increases moderately with lower educational level

16. Summary Household level „Non-response“ „Out“
Higher in households with working members
Lower in households with retired (not working) members
Higher in bigger cities
„Out“
Mainly one member households (85%) – retired (institutions or decease)

17. Summary Individual level
Tight connection with time and economic activity
„Non-response“
No impact of gender
Higher at middle-age groups, self-employed, higher educated, high-income groups
Lower at old groups, widowed, retired, parental leave - probably spend more time at home
„Out“
Mainly old, widowed and retired (institutions or decease) and partly young (move out of country)
Mainly low-income groups

18. Impact on weights
Focus on impact of attrition on weights and on selected variables
Take the values from the 1st wave
Simply eliminate those who didn't response in the 2nd wave (the same weights)
Recalculate the weights excluding those who didn't respond in the 2nd wave

19. Impact on weights 1. Original sample Original weights 2. Exclusion
“Non-response” and
“Out” households
eliminated
Weights
recalculated
Optimally:
the 3rd step should bring the values closer to the original values 2.
Exclusion
Original weights 3.
Exclusion
New weights

20. Impact on weights
Dwelling
3rd step lowers the difference by one half

21. Impact on weights Problems with the dwelling
the 3rd step brings the values closer in all cases

22. Impact on weights Can not afford...
the 3rd step can also increase the difference

23. Impact on weights Regular monthly inter-household transfer
great decrease
at received
and only slight
increase at
paid transfer

24. Impact on weights Annual household income
3rd step brings the values closer to the original ones

25. Conclusion
The 3rd step
brings the values back in most cases (e.g. dwelling)
increases the difference in some cases (e.g. affordability)
The influence of the attrition on weights and results in next wave is relatively negligible but can be significant for certain variables (diagnostics)