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Longitudinal LFS

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

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Longitudinal LFS

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  1. Longitudinal LFS Catherine Barham and Paul Smith ONS

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

  3. 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

  4. What can this data be used for? • Movements between E, U and N • Enables calculation of gross flows • Impact of government policies

  5. Illustration of gross and net flows Employed Unemployed Inactive

  6. People unemployed at both quarters

  7. Unemployed people moving to employment and inactivity

  8. 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

  9. Methodological issues • Non-response bias = people dropping out between interviews • Response error bias = incorrect answers to questions

  10. 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

  11. 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

  12. Implications of findings • LLFS still considered ‘experimental’ • ONS carrying out further work to investigate findings in more detail

  13. 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

  14. 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

  15. 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)

  16. 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)

  17. 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

  18. 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

  19. 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

  20. 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

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