1. Stratification, calibration and reducing attrition rate in the Dutch EU-SILC
Judit Arends

2. Overview The Dutch EU-SILC (2016 redesign)
Stratification: oversampling low income groups
Calibration
Reducing attrition, nonresponse rate
Plans and discussion points

3. Dutch EU-SILC: sources of data
Statistics Netherlands (SN)
Register country: most information from registers
Selected respondent: only one person is interviewed
Income component Mostly registers (t-1) child support, students, hh transfer
Material deprivation All items: Survey
Work intensity Register: employee status, source
Survey: working hours, current status
Some other target variables from register: country of birth, citizenship, NUTS, ethnic origin, child-care costs, rent, housing costs

4. Data collection strategyw1
w2-w4
Response = yes: Recruitment next poll
64-
LFS
cati
Intro letter
“Old”
65+
BRP
cati
“New”

5. Data collection strategyw1
w2-w4
Response = yes: Recruitment next poll
64-
LFS
cati
cati
Intro letter
Intro letter
“Old”
65+
BRP
cati
cati
“New”

6. Data collection strategyw1
w2-w4
64-
LFS
cati
Response = yes: Recruitment next poll
cati
Intro letter
Intro letter
“Old”
65+
BRP
cati
cati
Intro letter
BRP
cawi
Response = yes: Recruitment next poll
nonresponse with phone-
number
“New”
cati 64-
cati 65+

7. Data collection strategyw1
w2-w4
Response = yes: Recruitment next poll
64-
LFS
cati
cati
Intro letter
Intro letter
“Old”
65+
BRP
cati
cati
Intro letter
Intro letter
BRP
cawi
Response = yes: Recruitment next poll
cawi
nonresponse with phone-
number
nonresponse with phone-
number
“New”
cati 64-
cati 65+
cati

8. Panel – Old & New design “Old” “New” Interview year t-3 t-2 t-1 t t+1
Sampling year
t-1 w1 w2 w3 w4 t w1 w2 w3 w4
t+1 w1 w2 w3 w4
Not sure of everyone is familiar with ‘structure’ of SILC so I will briefly explain …
“New”
t+2 w1 w2 w3 w4
t+3 w1 w2 w3 w4

9. Sampling frames at CBS
Municipal basic registration of population data.
gender
date of birth
marital status
native country
native country parents
nationality
type of household
position in household
RIN number
address
municipality
district code
Not listed in CBS register:
name
telephone number

10. Sampling frames at CBS Additional register information: income
self-employed
benefit
employed / unemployed
student grant
disability
addresses of institutional population
Tax authorities
Employment office
Ministry of Education
Ministry of Social Affairs and Employment
Municipalities

11. Sampling frames: addresses (households)
10 %

12. Completing the samples
Deleting sample elements with missing or incorrect address information
Deleting institutional population
Deleting addresses that are selected in a different survey in the last 12 months
Adding telephone numbers
(Under- or oversampling for subpopulations)
Reducing sample to desired size in each stratum
< 12
65 +

13. Sampling design 2016
Sample persons were drawn form the sampling frame of persons from the Population Register (BRP)
Stratified sampling design
Strata: income, household size, and 16 years
30 strata (22 – 21 strata):
10 decile income groups (t-2),
16 years
household size 17+ (1 – 2 or more)

14. Sampling size Screening: - 7% 1.07*2.84*16.268=49.435
strata
age
Hh size
Income decil
total
population
over-sampling 1
17+
1301
441262
2.45 2
1402
409657
2.84 3
1229
389943
2.62 4
911
352467
2.15 5
613
311516
1.63 6
496
275288
1.50 7
402
245862
1.36 8
346
219692
1.31 9
287
190783
1.25 10
289
173555
1.38 11
2 or more
643
539456
0.99 12
788
575937
1.14 13
930
733345
1.05 14
896
849475
0.88 15
815
990995
0.68 16
851
0.63 17
859
0.58 18
898
0.56 19
931
0.54 20
1022
0.59 21
1 or 2 39
15017
2.18 22 52
18777
2.27 23 45
17759
2.10 24 35
16760
1.75 25 34
20404
1.37 26
22900
1.26 27 32
23481
1.16 28 31
22678
1.12 29
20753
1.08 30
20641
1.18
Tot
16268
1.00
Screening: - 7%
1.07*2.84*16.268=49.435
Thinning out: each strata

15. Response distribution income group
Strata
Age
Hhsize
Incomedecil
Response 1
17+
26% 2
29% 3
33% 4
37% 5
42% 6
41% 7 8
46% 9
47% 10
39% 11
2 or more
35% 12
31% 13 14
45% 15
49% 16 17
51% 18
54% 19
56% 20
57% 21
1 or 2
1 to 5 22
6 to 10
Total

16. Response distribution: income group

17. Response probabilities 2017
Hh size = 1
Hh size = 2+
16 y
Ptotal = 42,0% (average 2 types of incentives)

18. Weighting adjustments
Four weighting adjustments are applied at SN (all towards population of 16 years and older)
Wave 1
Wave 2 to 4 (separately)
Cross-sectional
Longitudinal

19. Administrative variables
General socio-demographic
Gender, age, province, household type and size, ethnicity, country region, urbanization
SILC-specific
Income (personal and household), house ownership, socio-economic status (SES)
SES: employee, other active, allowance, pension, other
From income data, three variables are derived:
Household income deciles
Household income below SN threshold
Household income below poverty threshold EU

20. Weighting wave 1 Model = Gender (2 classes) × Age (15 classes) +
Province/NUTS2 (12 classes) × Age (2 classes) +
Household size (5 classes) +
NUTS2 (12 classes) +
Ethnicity (3 classes) +
Low income category SN (3 classes) +
Degree of urbanization (5 classes) × EU poverty (3 classes) +
Region/NUTS1 (4 classes) × EU poverty (3 classes) +
NUTS1 (4 classes) × Income deciles (10 classes) +
Tenure status/Houseownership (3 classes) +
Activity status/SES (5 classes)

21. Weighting waves 2 to 4 Model = gender × age14 + province × age2 +
hhsize4 +
lowincome3 +
urbanization × EU poverty +
region × EU poverty +
income deciles +
houseownership3
Like wave 1 but less detailed

22. Weighting longitudinal data
Model = gender × age15 +
province × age2 +
hhsize5 +
lowincome3 +
urbanization × EU poverty +
region4 ×EU poverty +
region4 × income deciles +
houseownership3 +
SES
Like wave 1 but without ethnicity.

23. Weighting cross-sectional data
Final model Model without SILC-specific variables
Model = gender × age15 +
province × age2 +
hhsize5 +
province +
ethnicity3 +
lowincome3 +
urbanization × EU poverty +
region4 ×EU poverty +
province × income deciles +
region4 × houseownership3 +
province × SES +
gender × age15 × hhtype
Model = gender × age15 +
province × age2 +
hhsize5 +
province +
ethnicity3 +
urbanization +
region4 +
gender × age15 × hhtype

24. Weighting cross-sectional data
Income-related variables decrease estimates, i.e. provide a more positive view on poverty
Standard errors also strongly deflated by the addition of the extra terms

25. Reducing attrition, nonresponse
Age (especially younger people)
In order to obtain a better estimate of the risk of poverty by age, the weighting model will be expanded with a crossing of age class and AROP
Movers (CAWI)
Question at the end: about their plans
W2-W4: BRP before fieldwork?
Incentives
10 euro’s (10% more response)
iPad lottery: effect on response in W2
W2-W4: 5 euro’s
Feedback of the results of the previous year?
s addresses?

26. Reducing attrition, nonresponse
Invitation letters: receiving before the weekend
Reminders: 2 letters
CAWI - CATI
CATI in W1 and CATI in W2: worked well
CATI
65 min and 65 plus
Morning, afternoon, evening
Recruiting W2
About 80%: YES
65% in W2  52% response
Experiment: not asking: 67% response

27. THANK YOU FOR YOUR ATTENTION
Judit Arends-Tóth
Bart Huynen