1. Ongoing and future work to secure representativeness of longitudinal data in EU-SILC
Workshop on best practices for EU-SILC revision, −
Marie Reijo, Kaisa-Mari Okkonen

2. Content The Finnish EU-SILC
The 4-year longitudinal component: sample size
Unit non-response and sampling correction by calibration
Representativeness of longitudinal component:
Estimates accuracy:
Variance of the estimates
Estimates bias due to attrition
Household splits impact
Results and conclusions
Actions for improving representativeness
15 May 2019

3. The Finnish EU-SILC 4-year rotating panel survey since 2010
Integrated cross-sectional and longitudinal components since 2010
2-phase stratified sampling design
Sample units: individuals representing target persons; S-R in household
Tracing: initial sample persons in-scope followed over waves, not followed after non-response
Sample size (gross sample) for longitudinal component in the 1. wave:
n=2500 in 2004−2009, n=5000 since 2010−
Accepted sample size for 4-year panels larger since 2010
Extra sample for 2.−4. waves: not used for 4-year balanced panel
15 May 2019

4. The 4-year longitudinal component: sample size
Figure. Accepted sample size for the balanced 4-year panel
15 May 2019

5. The 4-year longitudinal component: unit non-response and attrition
Figure. Unit non-response and attrition for the 4-year panel, calculated from the 1. wave net sample
15 May 2019

6. Unit non-response correction by calibration
Calibration data from total register statistics by Statistics Finland
Cross-sectional component, 1.− 4. waves:
Standard demographic, region and income variables
Number of AROP persons was added for calibration from 2016 and persons by educational level from 2017 onwards
Longitudinal component:
1. wave: cross-sectional unit non-response correction
2.−4. waves: data on longitudinal development of persons by sex*age groups
15 May 2019

7. Representativeness of longitudinal component (balanced 4-year panel)
Estimates accuracy:
Since 2013 smaller standard errors than earlier due to larger samples
Estimates bias due to attrition in 2.−4. waves in 2018
The attrition is selective: e.g. 1. wave at-risk-of-poverty rate (sample) of persons is higher among those attrited and belonged to target population
Sample persons
All persons
Weight
RB050w1
RB064
Whole panel
16,8
11,9
Non attrition wave
14,8
10,5
11,6
Attrition wave
21,5
15 May 2019

8. Representativeness of longitudinal component (balanced 4-year panel)
Figure. Coverage of certain income components compared with the total registers statistics in 2018 by age groups, %
15 May 2019

9. Representativeness of longitudinal component (balanced 4-year panel)
Figure. Personal total disposable income estimates in 1.−4. waves of the 2018 balanced panel by sample and the total register data, 25th and 50th percentiles
15 May 2019

10. Sample persons attrition
Longitudinal component (balanced 4-year panel): household splits impact
Splitted household’s co-residents (n=150) are not followed:
Source FI-SILC 2018, calculated from those persons who belonged to target population; Household splits are more common among sample-persons households that attrited.
Samplew4
Attrition w2-w4
Splits
Sample persons attrition
Sample persons
2081
123
864
Co-residents 16+
1688
150
1241
All persons 16+, n
3769
273
2105
15 May 2019

11. Longitudinal component (balanced 4-year panel): household splits impact
Splitted household’s households sample members and co-residents by age group:
Source FI-SILC 2018, calculated from those persons who were belonged to target population
Agew1
Sample persons
Co-residents
Sample
persons
16-17
6,2
17,6
7,8
18-24
15,6
26,4
24,2
25-64
62,2
48,4
55,4
65+
14,9
7,6
12,6
Total
100,0
Persons 16+, N
17196
20076
115627
Persons 16+, n
123
155
Weight
RB050w1
RB064
15 May 2019

12. Results and conclusions
Representativeness of 2.− 4. waves for rotational cross-sectional component is good after calibration
Instead, longitudinal component (4-year panel) includes weaknesses because of non-random attrition
Calibration by age and sex does not correct change patterns between waves
In calibration we follow the Doc065 guidelines
Tracing co-residents in household-splits would increase costs and impact operational difficulties: no worth in relation to expected benefits for representativeness.
Nationally we use longitudinal data from Total Statistics on Income Distribution (TSID)
15 May 2019

13. Actions for improving longitudinal component representativeness
The first wave checked and improvements involved in calibration
Further actions planned to be implemented primarily for interviewed data collection:
Prioritizing fieldwork for those units that are difficult to contact in 1st wave sample of 2019:
priority: younger sample persons in low income strata
priority: not in group 1 or 3
priority: older sample persons in high income strata
Fieldwork ongoing, results seen later
Introducing CAWI interview as a pilot in 2020
Finding optimal field work strategy for population subgroups to maximize response rates (especially young sample persons)
Testing the use of substitutes in the 2020 as part of web pilot
Other actions to be considered next ? Calibration ?
15 May 2019

14. Expectations in the future
Response rates are decreasing rapidly
1st wave: from 64.1 % in 2015 to 57.1 % in 2018
Increasing difficulties in contacting by CATI
Goals of data collection set by data collection unit of Statistics Finland are not set on population group basis
Communicating data quality needs and criteria to data collection unit is difficult, criteria for many purposes, e.g. longitudinal component
FI-SILC production is facing multiple challenges in the next years
Pressure to implement CAWI in FI-SILC soon after piloting
Overall response rate and representativeness should be improved ?
Large changes in register data sources: changes in tax register, real-time income register 2020 onwards
Income register will have impact on survey and whole production process
15 May 2019