I wonder whether it would be better to measure subjective wellbeing by 1st finding the relationship between biological indicators (like heart rate) and self-reported happiness over a short period (like 1 day), and then tracking such indicators.
It would be interesting to know to which extent the reported LS predicts the corrected LS better than the logarithm of consumption.
Self-reported life satisfaction (LS), usually measured on a 0–10 scale, has remained remarkably flat over the last few decades, even in countries like Germany, the UK, China, and India that have experienced huge GDP growth.
I wonder which metrics assessing subjective wellbeing are more comparable across time. Self-reported happiness (which is different from LS) has increased with real gross domestic product (real GDP) per capita within the vast majority of countries.
Both are strong, difficult to verify assumptions. Briefly – on 1), I can’t think of a good psychological theory why the true effects should be falling, after accounting for the variables mentioned above.
Have you accounted in some way for consumption tendentially increasing across time? People consuming more may be more or less resilient against life events.
By assumption 1): if the effect of life events has fallen by 40%, we know that the happiness scale might have stretched by a factor of 1⁄0.6 ≈ 60%
Nitpick. I think you mean the happiness scale stretched by 66.7 % (= 1/(1 − 0.4) − 1), which is roughly 70 %, not 60 %.
Calibrating with biological measures. Hm, could be a interesting, albeit labour intensive … !
I’ve seen this graph a couple times on the Forum, now. I am confused why these lines are going up, but LS is generally flat. The one thing that stands out to me is that the timeframes are generally smaller than multidecade ones used for most studies on the Easterlin Paradox.
I’d also guess it’d be harder to calibrate the categorical response happiness question (This’d certainly be the case if you used my method, here.)
On income increasing over time. I discuss this more in the paper. We think that increasing income is the main pathway that rescaling occurs through. So, including it as a control could introduce over-control bias.
Oh, and I rounded from .62 something to .6 for the indexed effect size :)
What do you think of OWID’s dissolution of the Easterlin paradox? In short:
OWID say Easterlin and other researchers relied on data from the US and Japan, but…
In Japan, life satisfaction questions in the ‘Life in Nation surveys’ changed over time; within comparable survey periods, the correlation is positive (graphic below visualises this for ~50 years of data from 1958-2007, cf. your multidecade remark)
In the US, growth has not benefitted the majority of people; income inequality has been rising in the last four decades, and the income and standard of living of the typical US citizen have not grown much in the last couple of decades
so there’s no paradox to explain.
(I vaguely recall having asked you this before and you answering but may be confabulating; if that’s happened and you feel annoyed I’m asking again, feel free to ignore)
I don’t think you did mention this before...! I think this graph is just for 1 country. Perhaps Japan.
To be honest, I don’t know what to think of the Wolfers/Stevenson objections! My only thought is: differences of, e.g,. 0.2 points, would look pretty small in comparison to the potential rescaling effects I suggest here.
self-reported happiness over a short period (like 1 day)
Not exactly what you meant, but you may be interested in Jeff Kaufman’s notes on his year-long happiness logging self-experiment. My main takeaway was to be mildly more bearish of happiness logging than when I first came across the idea, based on his
Overall my experience with logging has made me put less trust in “how happy are you right now” surveys of happiness. Aside from the practical issues like logging unexpected night wake-time, I mostly don’t feel like the numbers I’m recording are very meaningful. I would rather spend more time in situations I label higher than lower on average, so there is some signal there, but I don’t actually have the introspection to accurately report to myself how I’m feeling.
Scattered quotes that made me go “huh”:
When I first started rating my happiness on a 1-10 scale I didn’t feel like I was very good at it. At the time I thought I might get better with practice, but I think I’m actually getting worse at it. Instead of really thinking “how do I feel right now?” it’s really hard not to just think “in past situations like this I’ve put down ‘6’ so I should put down ‘6’ now”.
Being honest to myself like this can also make me less happy. Normally if I’m negative about something I try not to dwell on it. I don’t think about it, and soon I’m thinking about other things and not so negative. Logging that I’m unhappy makes me own up to being unhappy, which I think doesn’t help. Though it’s hard to know because any other sort of measurement would seem to have the same problem.
Thanks for sharing, Mo! Very interesting. That makes me more pessimistic about finding the relationship between biological indicators and self-reported human welfare. I still think tracking more objective metrics would be helpful such that is is harder to game the system. If welfare surveys became widespread, and consistently used to make decisions, people could try to give answers which benefit them the most instead of reporting their welfare as accurately as possible. I like the assumption that welfare per human-year is proportional to the logarithm of annual consumption because this is hard to game.
Thanks for the great post, Charlie!
I wonder whether it would be better to measure subjective wellbeing by 1st finding the relationship between biological indicators (like heart rate) and self-reported happiness over a short period (like 1 day), and then tracking such indicators.
It would be interesting to know to which extent the reported LS predicts the corrected LS better than the logarithm of consumption.
I wonder which metrics assessing subjective wellbeing are more comparable across time. Self-reported happiness (which is different from LS) has increased with real gross domestic product (real GDP) per capita within the vast majority of countries.
Have you accounted in some way for consumption tendentially increasing across time? People consuming more may be more or less resilient against life events.
Nitpick. I think you mean the happiness scale stretched by 66.7 % (= 1/(1 − 0.4) − 1), which is roughly 70 %, not 60 %.
Hello Vasco, thanks!
Calibrating with biological measures. Hm, could be a interesting, albeit labour intensive … !
I’ve seen this graph a couple times on the Forum, now. I am confused why these lines are going up, but LS is generally flat. The one thing that stands out to me is that the timeframes are generally smaller than multidecade ones used for most studies on the Easterlin Paradox.
I’d also guess it’d be harder to calibrate the categorical response happiness question (This’d certainly be the case if you used my method, here.)
On income increasing over time. I discuss this more in the paper. We think that increasing income is the main pathway that rescaling occurs through. So, including it as a control could introduce over-control bias.
Oh, and I rounded from .62 something to .6 for the indexed effect size :)
What do you think of OWID’s dissolution of the Easterlin paradox? In short:
OWID say Easterlin and other researchers relied on data from the US and Japan, but…
In Japan, life satisfaction questions in the ‘Life in Nation surveys’ changed over time; within comparable survey periods, the correlation is positive (graphic below visualises this for ~50 years of data from 1958-2007, cf. your multidecade remark)
In the US, growth has not benefitted the majority of people; income inequality has been rising in the last four decades, and the income and standard of living of the typical US citizen have not grown much in the last couple of decades
so there’s no paradox to explain.
(I vaguely recall having asked you this before and you answering but may be confabulating; if that’s happened and you feel annoyed I’m asking again, feel free to ignore)
I don’t think you did mention this before...! I think this graph is just for 1 country. Perhaps Japan.
To be honest, I don’t know what to think of the Wolfers/Stevenson objections! My only thought is: differences of, e.g,. 0.2 points, would look pretty small in comparison to the potential rescaling effects I suggest here.
Not exactly what you meant, but you may be interested in Jeff Kaufman’s notes on his year-long happiness logging self-experiment. My main takeaway was to be mildly more bearish of happiness logging than when I first came across the idea, based on his
Scattered quotes that made me go “huh”:
Thanks for sharing, Mo! Very interesting. That makes me more pessimistic about finding the relationship between biological indicators and self-reported human welfare. I still think tracking more objective metrics would be helpful such that is is harder to game the system. If welfare surveys became widespread, and consistently used to make decisions, people could try to give answers which benefit them the most instead of reporting their welfare as accurately as possible. I like the assumption that welfare per human-year is proportional to the logarithm of annual consumption because this is hard to game.
Thanks, this is interesting. I wonder if this sort of individual-level noise might be smoothed out by large-n experience sampling.