Estimation in the Swedish LFS –

an example of combining survey data from independent samples

Martin Axelson & Frida VidellStatistics Sweden

e-mail: firstname.lastname@scb.se

Outline

• The current sampling design for the Swedish LFS

• Combining data from independent samples – some theory

• Estimation in the Swedish LFS

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The current sampling design for the Swedish LFS – project 1• Carried out by Statistics Sweden in 2008

• In response to increased interest in groups outside or with a weak attachment to the labour market

• Goal: To suggest a cost-efficient way to secure better statistics for domains of particular interest

• Suggestion: Combine data from two samples

• One drawn according to the design in use

• One drawn according to a design constructed with high precision for specific parameters and domains of study in mind.

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The current sampling design for the Swedish LFS – project 2

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• Stratification after• Region

• Sex

• Age

• Born in Sweden (Y/N)

• Not working (Y/N)

• Not working• Derived using available register information

• Important register: Longitudinal integration database for health insurance and labour market studies

• Prediction of “Not working” during the reference week at the individual level

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Combining data from independent samples – some notation

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Combining data from independent samples – explicit weighting

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Combining data from independent samples – implicit weighting

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Combining data from independent samples – some relevant facts

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Estimation in the Swedish LFS – project 1• The possible gains in precision presented by

project 1 were unrealistic

• Numerical results on expected gain based on (almost) optimal explicit weighting of GREG-estimators

• Such weighting amounts to using parameter specific weight systems, i.e., different weights are used for different parameters, which NSIs typically try hard to avoid

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Estimation in the Swedish LFS – project 2

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Estimation in the Swedish LFS – project 3

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Estimation in the Swedish LFS – today

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Estimation in the Swedish LFS – examples of precision gains

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5.0, )]ˆ(ˆ/)ˆ(ˆ[ LFSOLFS tVtV

Estimation in the Swedish LFS – examples of precision gains

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Table 1: Population 20-64 by age and labor status, January 2013

Age 5.0

, )]ˆ(ˆ/)ˆ(ˆ[ LFSOLFS tVtV

Unemployed Not in the labor force

20-24 0.86 0.85 25-34 0.83 0.82 35-44 0.79 0.81 45-54 0.82 0.77 55-64 0.76 0.88

.

Estimation in the Swedish LFS – examples of precision gains

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Table 2: Population 20-64 not in the labor force by gender, January 2013

Sex 5.0, )]ˆ(ˆ/)ˆ(ˆ[ LFSOLFS tVtV

Full-time students

Working at home

Jobseekers, not available

Retired Long-term ill

Others

Male 0.83 0.72 0.79 0.93 0.80 0.86 Female 0.84 0.85 0.78 0.91 0.82 0.90

Estimation in the Swedish LFS – concluding remarks

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Finding solutions to methodological challenges – reflections from a methodologist

•Typically requires using (challenging) theory and methodology

•Theory and practice must go hand in hand – let implementation be part of the challenge

•Should be seen as an investment, as the short-term cost is expected to be followed by long-term savings

•Sampling design and estimation are best treated together, given the strong dependency between them

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