Converting measures of mental health and wellbeing into WELLBYs
Summary
At the Happier Lives Institute, we evaluate the effect of interventions using subjective wellbeing (SWB) as our primary outcome. This is typically measured with questions asking people to self-report how happy or satisfied they are on a scale of 0 to 10. However, there is often too little ‘typical’ SWB data available about the interventions we evaluate. Therefore, as a proxy for SWB, we often use measures of internalising distress symptoms, such as measures of symptoms of mental distress, stress, depression, and anxiety. We refer to these as affective mental health (MHa) measures. Is it appropriate to use these as a proxy for SWB?
We have discussed this briefly before in different reports (e.g., McGuire et al., 2023). In this report we give a fuller standalone explanation of our methods. We explore whether standardised effects on MHa are an acceptable proxy for standardised effects on SWB. This is an ongoing topic of interest which we expect to update over time.
MHa measures’ commonality is that they ask about low moods, which overlaps with the wellbeing concept of happiness (a balance of positive over negative experience; a hedonic theory of wellbeing). While distinct, this shows some theoretical overlap between MHa and happiness. However, our question here is an empirical one: whether results on the proxy (MHa) predict results on the primary measure (SWB); namely, is the effect of an intervention substantially different when measured on a SWB or a MHa measure?
We use four different data sources: psychotherapy in low- and middle-income countries, psychotherapy in high-income countries, psychological interventions in high-income countries, and cash transfers in low- and middle-income countries.
Although the results for the four sources vary, the differences between MHa and SWB tend to be small and non-significant. Overall, when averaged, the evidence suggests that effects on SWB are slightly larger than the effects on MHa. Hence, including MHa most likely plays a conservative role rather than an overestimating role when estimating the impact of an intervention on SWB, at least in these cases. Further work could explore how far this generalises, but this indicates that substituting one for another is not clearly a problem considering the dearth of data we face.
Just because the results are very similar between the two broad types of measures does not mean that the results are identical nor guarantee that they are measuring the same concept. Nevertheless, we think these findings suggest it is reasonable to treat MHa and SWB as a suitable substitute during the current lack of SWB data for evaluating interventions. We are tentatively excited about this result, as it has the potential to unlock data for ourselves and other researchers to do more and more extensive analyses where only non-ideal data are available. Of course, the long-term solution would be for those who conduct interventions to collect more typical SWB data.
Notes and Acknowledgments
Publication note: This report was initially ready for publication in October 2024 and had a publication page on our website (so we could refer to it across our work) but no full report. While the results were ready, and have not substantially changed, we took time to refine the presentation before publishing it. Hence, this document is now fully published in November 2025.
Author note: Samuel Dupret, Joel McGuire, and Ryan Dwyer contributed to the conceptualization, investigation, analysis, data curation, and writing of the project. Michael Plant contributed to the conceptualization, supervision, and writing.
The views expressed in this document do not necessarily reflect the perspectives of reviewers.
Reviewer note: We thank, in alphabetical order the following reviewers for their help: Sam Bernecker (University of Washington), Mark Fabian (University of Warwick), Huw Evans (Kaya Guides), Sara MacLennan (LSE), Lord Richard Layard (LSE), Isaac Parkes (LSE), Alberto Prati (UCL), and Ekaterina Oparina (LSE).