1. Effect of Panel Length and Following Rules on Cross-Sectional Estimates of Income Distribution: Empirical Evidence from FI-SILC
Marjo Pyy-Martikainen
Workshop on best practices for EU-SILC revision
London

2. Outline
Background
Target population and following rules in household panel surveys
EU-SILC: target population, following rules & structure
Empirical analysis with FI-SILC and register data
Results
Conclusions
16 September 2015
Marjo Pyy-Martikainen

3. Background (1)
Task force on the revision of the EU-SILC legal basis, subgroup 5: Longitudinal component, with the following goals:
To increase the consistency between SILC cross-sectional and longitudinal components
To assess the drawbacks of the SILC longitudinal component
To find ways to increase the quality of longitudinal estimates
To find ways to reduce the implementation burden of the longitudinal component
16 September 2015
Marjo Pyy-Martikainen

4. Background (2)
A study ”Assessment of the future design of the EU-SILC longitudinal component”: The six-wave rotating panel should be the preferred design
The advantages of a six-wave design over a four-wave design:
More precise estimation of persistent poverty
More possibilities for dynamic analyses
Less burdensome fieldwork as the share of w1 sample diminishes
Issues of concern:
Attrition
Response burden
Cross-sectional representativity of the panel
16 September 2015
Marjo Pyy-Martikainen

5. Target population and following rules in household panel surveys
The composition of the target population, usually private hh’s and their members, changes over time
Person-level changes:
Exits through death, emigration, entering an institution
Entrances through birth, immigration, leaving an institution
Household-level changes:
Entrances through immigration, leaving an institution, divorce, children leaving their parental home
Changes in the composition of existing hh’s
How to take these changes into account in order to maintain the cross-sectional representativity of the panel? Following rules
16 September 2015
Marjo Pyy-Martikainen

6. Classical following rule
Aims at maintaining the cross-sectional representativity of the panel
Follow-up procedure:
At w1, choose a sample that represents the target population
Define all members of w1 sample hh’s as sample persons
Follow all sample persons and attempt interviews with all eligible members of sample persons’ hh’s
Panel surveys using the classical following rule:
Survey of Labor and Income Dynamics (SLID)
Panel Study of Income Dynamics (PSID)
Survey of Income and Program Participation (SIPP)
16 September 2015
Marjo Pyy-Martikainen

7. Target population and following rules in EU-SILC
EU-SILC target population:
All private hh’s and their current members residing in the territory of the member state at the time of data collection (DocSILC065)
EU-SILC aims to remain cross-sectionally representative of hh’s and their members over time
Two different following rules applied:
Classical following rule (currently incomplete implementation)
Cohort panel following rule: only 1 person in hh followed (DK, NL, SI, FI, SE, NO, IS)
Does the cohort panel following rule maintain the cross-sectional representativity of the panel?
16 September 2015
Marjo Pyy-Martikainen

8. Current structure of EU-SILC
EU-SILC X
wave 1 2 3 4 5 6
Sample 1 W1 W2 W3 W4
Sample 2 W1 W2 W3 W4
Sample 3 W1 W2 W3 W4
Sample 4 W1 W2 W3
EU-SILC L
16 September 2015
Marjo Pyy-Martikainen

9. Suggested structure of EU-SILC
EU-SILC X
wave 1 2 3 4 5 6
Sample 1 W1 W2 W3 W4 W5 W6
Sample 2 W1 W2 W3 W4 W5
Sample 3 W1 W2 W3 W4
Sample 4 W1 W2 W3
Sample 5 W1 W2
EU-SILC L W1
Sample 6
16 September 2015
Marjo Pyy-Martikainen

10. Empirical analysis with FI-SILC and register data
Key questions:
What is the effect of following rules on the cross-sectional representativity of the panel?
How does the lengthening of the panel affect cross-sectional estimates of income distribution?
Does the choice of following rules affect cross-sectional estimates?
Can possible biases be corrected with re-weighting?
16 September 2015
Marjo Pyy-Martikainen

11. The data (1)
Two entirely register-based hh panels of six years with classical/cohort panel following rules were constructed
Basis for both panels: FI-SILC 2008 w1 sample
Sample persons’ PINs -> domicile codes -> Dwelling Units
PINs of DU members -> DU income, background variables
Benchmark data: Total population data on income distribution
Deviance of panel estimates from benchmark estimates: indication of bias
16 September 2015
Marjo Pyy-Martikainen

12. The data (2) Year Units Panel 2007 2008 2009 2010 2011 2012 Dwellings
Cohort
7,338
7,251
7,159
7,064
6,963
6,843
Classical
8,146
8,650
9,064
9,385
9,669
Persons
19,324
18,643
18,086
17,605
17,182
16,685
20,533
21,219
21,761
22,122
22,426
16 September 2015
Marjo Pyy-Martikainen

13. Measures of income distribution
Register-based income information (disposable money income) adjusted by the dwelling unit’s size and composition ≈ equivalised disposable income
Estimated measures of income distribution:
Dwelling units’ median equivalised disposable money income
Income decile shares
At-risk-of-poverty rate
Gini coefficient
16 September 2015
Marjo Pyy-Martikainen

14. Calculation of weights
Design weights were calculated for w1 as inverses of selection probabilities of dwelling units
A weight share procedure was applied to design weights for w2+ in order to include cohabitants in the analyses
The design weights were calibrated using two different calibration models:
Cal1 (demographic variables): region, size of dwelling unit, degree of urbanisation of the municipality, gender and age
Cal2 (FI-SILC calibration variables): Cal income variables
2 panels * 6 years * 2 calibration models -> 24 calibrated sets of weights
16 September 2015
Marjo Pyy-Martikainen

15. Number of dwelling units in years 2007-2012
Cohort and classical panel estimates calculated with design weights
16 September 2015
Marjo Pyy-Martikainen

16. Number of persons in years 2007-2012
Cohort and classical panel estimates calculated with design weights
16 September 2015
Marjo Pyy-Martikainen

17. Number of persons aged 16-25 in years 2007-2012
Cohort and classical panel estimates calculated with design weights
16 September 2015
Marjo Pyy-Martikainen

18. Dwelling units’ median equivalised disposable money income
Cohort
Classical
Benchmark
Year Dw
Cal1
Cal2
2007
19,080
18,910
18,880
18,780
2008
20,210
19,970
19,920
20,010
19,900
19,890
19,840
2009
20,840
20,460
20,420
20,490
20,410
20,400
20,320
2010
21,250
20,800
20,870
20,790
20,760
2011
22,050
21,640
21,470
21,650
21,480
2012
23,090
22,570
22,380
22,460
22,490
22,350
22,200
16 September 2015
Marjo Pyy-Martikainen

19. Dwelling units’ median equivalised disposable money income, persons under 25
Cohort
Classical
Benchmark
Year Dw
Cal1
Cal2
2007
12,990
12,940
12,020
2008
13,050
12,540
12,920
2009
13,300
13,080
12,400
12,670
12,340
12,250
2010
13,150
13,400
13,000
12,180
12,280
12,150
12,430
2011
14,800
14,610
14,480
13,290
13,360
13,330
13,110
2012
15,970
16,000
14,070
14,450
14,400
13,810
16 September 2015
Marjo Pyy-Martikainen

20. At-risk-of-poverty rate of persons, %
Cohort
Classical
Benchmark
Year Dw
Cal1
Cal2
2007
13.94
13.96
14.18
15.03
2008
13.74
14.04
14.19
14.21
14.39
14.89
2009
14.03
14.40
14.58
15.05
14.82
2010
13.63
14.06
14.71
14.24
14.28
14.90
14.88
2011
14.41
14.61
14.96
15.00
2012
12.75
13.18
13.08
13.88
13.81
13.86
16 September 2015
Marjo Pyy-Martikainen

21. Gini coefficients, % Cohort Classical Benchmark Year Dw Cal1 Cal2 2007
28.09
28.26
29.01
29.51
2008
26.81
26.85
27.70
27.11
27.12
28.04
28.37
2009
26.49
26.68
26.84
26.99
27.04
27.22
27.56
2010
26.98
27.14
27.55
27.51
28.00
28.18
2011
27.57
27.86
27.66
28.11
28.17
28.53
2012
25.52
25.79
25.83
26.29
26.30
26.38
27.19
16 September 2015
Marjo Pyy-Martikainen

22. Summary of results
The cohort panel severely underestimates the totals of persons and dwelling units.
The cohort panel overestimates the median income of dwelling units with a reference person under 25. The amount of bias increases over time
The estimates of at-risk-of poverty rate and Gini coefficient from the cohort panel are unsatisfactory especially towards the end of the panel
The total population estimates of income benefit from the use of income information in the calibration model, age-specific income estimates problematic
16 September 2015
Marjo Pyy-Martikainen

23. Conclusions
The cohort panel does not fully depict changes in the population over time
This implies a risk of increased bias in cross-sectional estimates of income, if panel extended to 6 years
Results of this study are based on a single rotation group,
the use of fresh subsamples in EU-SILC is likely to diminish bias
A topic for further study: assessment of bias with data comprising of several rotation groups
16 September 2015
Marjo Pyy-Martikainen

24. Thank you for your attention!
Marjo Pyy-Martikainen