Social scientists (mostly economists and psychologists) talk about measures of ‘subjective well-being’ (SWB), which are “ratings of thoughts and feelings about life” (Dolan and Metcalfe (2012). SWB is typically thought to have three components (OECD 2013):
For a list of example SWB questions, see OECD (2013, Annex A).
Measures of SWB are often referred to as measures of ‘happiness’. This is technically incorrect and also misleading. If we define happiness as a positive balance of enjoyment over suffering, then the experience component of SWB is identical with happiness. Evaluations measure how people feel about their lives, rather than how happy they feel during them. Eudaimonic measures may tap into psychological states - ones related to meaning - that, presumably, feel enjoyable to experience and thus comprise happiness, but do not capture all the psychological states relevant to happiness. Hence, SWB is not only a measure of happiness.
The ‘gold standard’ for measuring happiness is the experience sampling method (ESM), where participants are prompted to record their feelings and possibly their activities one or more times a day. While this is an accurate record of how people feel, it is expensive to implement and intrusive for respondents. A more viable approach is the day reconstruction method (DRM) where respondents use a time-diary to record and rate their previous day. DRM produces comparable results to ESM, but is less burdensome to use (Kahneman et al. 2004).
Given we are interested in measuring happiness, we might think we should ignore the non-experience components altogether. Practically, however, this is unfeasible and we are forced to rely on life satisfaction measures as the main proxy measure for happiness (a ‘proxy’ measure is an indirect measure of the phenomenon of interest).
It is much easier to collect LS data as it requires just one quick question that takes subjects around 30 seconds to answer, whereas the DRM takes approximately 40 minutes to fill out. As a result of this ease of use, it is the SWB measure on which most data has been collected and most analysis done. It is now possible to say to what extent various outcomes cause an absolute increase in life satisfaction on a 0-10 scale, which is what we need to determine cost-effectiveness (see Layard et al. 2018). By contrast, to the best of our knowledge, there is insufficient research on experience measures to draw the same sort of conclusions.
Evaluative vs. experience measures
How much of a problem is it to use evaluative measures in lieu of experience ones? Experience and evaluative measures are conceptually different and answered in somewhat different ways. As Deaton and Stone (2013) explain:
Hedonic [i.e. experience] measures are uncorrelated with education, vary over the days of the week, improve with age,and respond to income only up to a threshold. Evaluative measures remain correlated with income even at high levels of income, are strongly correlated with education, are often U-shaped in age, and do not vary over the days of the week (Stone et al. 2010; Kahneman and Deaton 2010).
This doesn’t mean evaluative measures can’t be used as proxies for happiness. The evaluative and experience measures do correlate, suggesting evaluative judgements are, in part if not in whole, determined by how happy people are (OECD 2013, p32-34). While Deaton and Stone identify some cases where they come apart, it’s unclear if there are (many) cases where they would be different priorities, either for governments or for effective altruists, if the goal was trying to maximise life satisfaction rather than happiness.
Which measures should we use?
The sensible approach seems to be to use happiness data where it’s available, but life satisfaction data where it isn’t and, when using LS data to determine cost-effectiveness, to keep in mind how the two might differ. Further work to investigate if, and when, using one measure over another would generate different priorities is urgent and valuable.
While eudaimonia measures are regarded as a component of SWB, we will not refer to them again. Not only are they not the most relevant component, little data has been collected on them and it’s not conceptually clear what they capture.
 Arguably, an even better method would be to measure brain waves, assuming we could correlate well-being with brain states.