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

Collecting the household data as a sub-sample. Rome May 2014 Jonas Kylov Gielfeldt. The broader frame – why are we collecting household data?. Are households the “natural” unit for collecting LFS variables? Not very often! (jobless households and…)

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

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

  2. The broader frame – whyarewecollectinghousehold 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. The economy of it all… • 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?

  4. The Danish case • In Denmark wecollect the core-LFS through CATI-mode – this model is bettersuited for individuals as the unit. • Weareobliged to collect the household data, this is done in a combination of CAWI/CATI • Sincecollecting on householddoes not fitourcollection-mode and we do not see the substantialreason for collecting LFS-variables weuse a sub-sample to minimizecosts.

  5. The core-sample and the sub-sample • 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

  6. Why to use a sub-sample • If the NSI primarilyuses CATI - collecting the wholehouseholdthroughthis mode willincreasecostssignificantly. • Collectinghousehold as the core-sample quadruples the costs! Otherwisediminish the sample size, riskingincreased bias/clustereffect (householdmembersareoftenequal)

  7. Different sample sizes – meansdifferentweighting models • The weighting model of the Core-LFS

  8. On the core-LFS weighting model • Quite a big non-response in the Danish LFS, but a lot of highquality registers. • This is used as auxiliary information in a rathercomplexweighting model. • The weighting model is optimized for the number of individuals in the population and especiallywants to control bias on fx labourmarket status, education etc.

  9. The weighting model of the household-sample

  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

  12. The auxiliary information on education • The difference between Core and household-LFS shows that the auxiliary information helpsdealing with the overrepresentation of highereducated. • But it is not possible to usethis information in the household model, since it wouldmake it toocomplex. • The household model does not handle the bias at all

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