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.
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.