Longitudinal LFS

Catherine Barham and Paul Smith

ONS

Outline

• Introduction to LLFS• Examples of analyses• Potential quality issues• Weighting• Attrition bias and gross flows• Conclusions

Introduction to LLFS

• LFS panel structure designed for cross-sectional data

• BUT potential to link individuals• First LLFS datasets released 2001• Back to winter 1992/93• All working age people who responded at

each of the waves• Subset of variables

What can this data be used for?

• Movements between E, U and N

• Enables calculation of gross flows

• Impact of government policies

Illustration of gross and net flows

Employed

UnemployedInactive

People unemployed at both quarters

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Male UU Female UU

Unemployed people moving to employment and inactivity

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5%

10%

15%

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25%

30%

35%

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Male UE Male UN Female UE Female UN

Other examples of types of analyses

• ONS – People leaving employment, trends and characteristics

Inactivity flows by reason for inactivity (LMT articles)

• DTI – impact of EU directive on hours worked

• Bank of England – gross flows, measuring labour availability, ‘non’-employment

Methodological issues

• Non-response bias = people dropping out between interviews

• Response error bias = incorrect answers to questions

Response error bias

• Common survey problem, errors cancel out in cross-sectional data

• LLFS – impacts on gross flows between economic activity statuses.

• Likely to bias estimates of gross flows upwards• Transitions likely to be most affected are: U to N,

part-time E and either U or N, for women any transition involving U and for students moves between E and U

• Some inconsistencies may be caused by general volatility

Further work

• PhD thesis: Measurement error with application to the LFS, Southampton University

• Completed 2003• Main findings:

1 Existence of measurement error can result in alteration in direction of gross flows

2 Using Swedish re-interview data, it’s possible to account for the measurement error

3 More work is needed to quantify the detailed effects of this methodology on gross flows

Implications of findings

• LLFS still considered ‘experimental’

• ONS carrying out further work to investigate findings in more detail

Options for weighting

• LFS data currently weighted by– person– household

• Longitudinal dataset relies on matched households, which means

– Sample smaller (non-matches discarded)– Sample has different representation

Longitudinal weighting

• Only 15-59/64 year-olds included• Longitudinal weights are person-level weights• initial weights to reproduce first quarter tenure

categories:– owned– rented from LA/housing association– privately rented

• initial weights scaled so that population total recovered

Longitudinal weighting - 2 quarters

• Final weights for two-quarter data constrained to reproduce:

– second quarter’s population data by sex by age (single year to 24, then 5-year bands)

– second quarter’s population data by region– second quarter’s EUI estimates– first quarter’s EUI estimates (adjusted to second

quarter’s total through I estimate)

Longitudinal weighting - 5 quarters

• Final weights for two-quarter data constrained to reproduce:

– fifth quarter’s population data by sex by age (single year to 24, then 5-year bands)

– fifth quarter’s population data by region– fifth quarter’s EUI estimates– first, second, third and fourth quarter’s EUI estimates

(adjusted to fifth quarter’s total through I estimate)

How might things be different?

• LFS quality review recommended investigating “all aspects of LFS weighting”

• Household level weighting• Household basis for EUI estimates• Wave-specific weighting

Quality issues in the longitudinal data

• Measurement error • Movers

– LFS has address-based sample– movers into/out of an address do not match - excluded

from longitudinal dataset– too few movers

• Attrition bias– non-response not constant across waves– people responding in all waves more likely to have

certain characteristics– too many of these people

Weighting “solutions”

• Wave-specific weighting helps compensate for attrition bias in cross-sectional (EUI) data…

• …which are used to weight longitudinal data• General use of household weighted datasets would

promote consistency through all LFS databases• requires methodological issues to be resolved

• other solutions require resources and methodological development

Conclusions

• There are biases in gross flows data from non-response, attrition and measurement error

• It is likely that changes in gross flows will be more accurately estimated

• The longitudinal LFS still provides useful information on changing working patterns

• The quality deficiencies should be taken account of when using the data