Measuring Subjective Wellbeing: Evidence from the UK Household Longitudinal Study


Measuring Subjective Wellbeing: Evidence from the UK Household Longitudinal Study


Thursday 23 February 2023, 12:30 — 13:30


Presented by Silvia Lui (Data Science Campus, Office for National Statistics)

We summarise different aspects of Subjective Wellbeing (SWB) in the UK using self-reported responses to the UK Household Longitudinal Study (UKHLS) both for the UK and UK regions. We first examine the relationship between different SWB outcomes unconditionally. We then investigate the main drivers of different SWB metrics by conditioning them on a range of socio-demographic factors such as age, gender, ethnic background, migrant status, income, and physical health. We also propose ways to summarise different SWB metrics that account for individuals’ heterogeneity in responses to individual and regional observables and unobservables. Our results show no evidence that SWB metrics are likely to be driven by some common factors. This suggests surveys that use a consensus of happiness or overall life satisfaction as a measure of SWB could risk missing important aspects. The conditional analysis reveals that health has the largest impact on wellbeing of the UK population, and that generally SWB is likely to be a function of more than income. We show that subjective wellbeing in the UK has followed a downward trend at least since 2014 at the national and regional level and along most of the dimensions of wellbeing.

Silvia Lui is a Senior Data Scientist at the Data Science Campus, ONS. She is also an ESCoE Research Associate. She has a PhD in Economics from Queen Mary, University of London. Prior to joining the ONS, she worked in both academia and research institution. She has worked on a variety of research projects throughout her professional career and has developed interests and expertise in micro and macro data analysis, administrative data analysis, time series models, longitudinal analysis, applied econometrics and the application of data science methods in economic analysis.

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