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Working time estimates from surveys can be unreliable, especially for atypical workers. This study improves accuracy by using time-use data to adjust survey figures, enhancing economic forecasting tools.

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Area of Science:

  • Economics
  • Sociology
  • Statistics

Background:

  • Accurate working time estimates are crucial for economic forecasting and policy.
  • Measurement errors in surveys can impact the reliability of working time data, particularly for subgroups like part-time or atypical workers.
  • Self-assessed (stylised) working time estimates may differ significantly from more truthful time diary recordings.

Purpose of the Study:

  • To investigate the reliability of paid weekly working-time measurement instruments in the UK Labour Force Survey.
  • To compare stylised estimates with time diary data.
  • To explore methods for improving stylised estimates in surveys lacking time diary data, by contrasting 'usual' and 'actual' hours.

Main Methods:

  • Utilizing calibration weights derived from the Work Schedule in the 2000 and 2015 UK Time Use Surveys.
  • Applying statistical matching to up/down scale stylised estimates in the 2000 and 2015 UK Labour Force Survey.
  • Comparing estimates from self-assessed instruments with those from time diaries.

Main Results:

  • Discrepancies exist between stylised and time diary estimates of working hours.
  • Calibration weights derived from time use data can adjust stylised estimates.
  • Statistical matching techniques show potential for improving measurement accuracy in large-scale social surveys.

Conclusions:

  • Working time estimates in surveys like the Labour Force Survey may contain significant measurement errors.
  • Adjusting stylised estimates using time diary data and statistical matching can improve reliability.
  • These cost-effective techniques offer a pathway to enhance the quality of working time data for policy and research.