9th Workshop on Labour Force Survey Methodology. Roma, 15-16 maggio 2014

1. Collecting the household data as a sub-
sample.
Rome May 2014
Jonas Kylov Gielfeldt

2. The broader frame – why are we
collecting household data?
• Are households the “natural” unit for
collecting LFS variables?
• Not very often! (jobless households and…)
• Are the household unit better for data
collection?
• Sometimes yes! With CAPI-mode household
is very sensible, but not for CAWI and CATI-
mode.

3. • All NSI’s work in an environment were resources are
sparse(r).
• Is it justifiable to use a lot of resources on collecting
household data if
a) there is no gain in terms of collection mode
b) there is no strict substantial reason for collecting the
variables on households instead of on individuals?
The economy of it all…
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4. The Danish case
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• In Denmark we collect the core-LFS through CATI-
mode – this model is better suited for individuals
as the unit.
• We are obliged to collect the household data, this
is done in a combination of CAWI/CATI
• Since collecting on household does not fit our
collection-mode and we do not see the
substantial reason for collecting LFS-variables we
use a sub-sample to minimize costs.

5. • The core-LFS gross sample – 40.000 persons pr. quarter
• The number of respondents pr. quarter – 22.000
persons
• The gross sub-sample – 11.000 persons (not including
Core-LFS respondents)
• The number of respondents – 6.000 persons
The core-sample and the sub-sample
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6. Why to use a sub-sample
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• If the NSI primarily uses CATI - collecting the whole
household through this mode will increase costs
significantly.
• Collecting household as the core-sample quadruples the
costs! Otherwise diminish the sample size, risking
increased bias/cluster effect (household members are
often equal)
Costs saved by sub-sampling
6000 respondents quadrupled Euro DKR (7,45)
Number of respondents 24.000
Current price in average for ca. 6000 respondents on HH 29.600 220.520
Price quadrupled 118.400 882.080
Difference (saved costs) 88.800 661.560

7. Different sample sizes – means
different weighting models
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• The weighting model of the Core-LFS
Variables Groupings
-age11 11 grp
Information is crossed -sex 2 grp
-region 5 grp
Information is crossed
-age6 6 grp
-education 3 grp
-socio-economic status 8 grp
-number of children in the
household 4 grp
-citizenship 4 grp
-registered as unemployed 12 grp
-brutto income 4 grp
-moved 2 grp

8. • Quite a big non-response in the Danish LFS, but a lot
of high quality registers.
• This is used as auxiliary information in a rather
complex weighting model.
• The weighting model is optimized for the number of
individuals in the population and especially wants to
control bias on fx labour market status, education
etc.
On the core-LFS weighting model
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9. The weighting model of the
household-sample
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Variables Groupings
-age 3 grp
Information is crossed -sex 2 grp
-family type 6 grp
-size of household 4 grp
A person from Household has
moved 2 grp
-Only danes in household or
mixed household 2 grp
-average age of the household 3 grp
-brutto household income 4 grp

10. On the household weighting model
• This weighting model is optimized for both the
number of individuals in the population, but
also the total number of households
• This means that new variables must be added
as auxiliary information (family type, size of
household etc.)
• At the same time – smaller smaple size limits
the amount of auxiliary information

11. Differences in estimates – the example
on education
• Education is added as auxiliary information in
the core-LFS but not in the household
• This means differences in estimates
Highest level of education completed
(25-64 years) - % 2011 Core-LFS 2011 HH-LFS 2012 Core-LFS 2012 HH-LFS 2013 Core-LFS 2013 HH-LFS
-At most lower secondary level 23,1 19,4 22,1 18,3 21,7 19,1
-Upper secondary level 43,2 41,6 43,1 41 42,8 40,6
-Third level 33,7 39 34,8 40,6 35,4 40,2
2011 Core-LFS 2011 HH-LFS 2012 Core-LFS 2012 HH-LFS 2013 Core-LFS 2013 HH-LFS
- min. ISCED3c long / upper
secondary level (20-24
years) - % 70,0 74,9 72,0 74,9 71,8 76,1
-Early leavers from
education and training (18-
24 years) - % 9,7 7,9 9,1 8,0 8,1 6,9

12. The auxiliary information on education
• The difference between Core and household-LFS
shows that the auxiliary information helps
dealing with the overrepresentation of higher
educated.
• But it is not possible to use this information in the
household model, since it would make it too
complex.
• The household model does not handle the bias at
all