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 Unemployed Inactive

People unemployed at both quarters

Unemployed people moving to employment and inactivity

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, its 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 quarters population data by sex by age (single year to 24, then 5-year bands) –second quarters population data by region –second quarters EUI estimates –first quarters EUI estimates (adjusted to second quarters total through I estimate)

Longitudinal weighting- 5 quarters Final weights for two-quarter data constrained to reproduce: –fifth quarters population data by sex by age (single year to 24, then 5-year bands) –fifth quarters population data by region –fifth quarters EUI estimates –first, second, third and fourth quarters EUI estimates (adjusted to fifth quarters 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