The always readable Cato Institute gadfly Will Wilkinson has not one, but two, long posts about the supposed evils of trying to measure happiness. I only provide brief excerpts - so read the whole thing.
In the first, Effective Policy and the Measurement of Human Well-Being, he lambasts economists Andrew Oswald and Danny Blanchflower for daring to suggest that policy makers might want to measure well-being:
Human well-being, as opposed to the several dimensions or components of well-being, is pretty much impossible to measure. Why? Because the specific nature of human well-being is relative to the individual and the components of well-being are diverse and must often be traded against one another.
His second post has the wonderful title This Is My Dataset. There Are Many Datasets Like It, but This One Is Mine...
Having read a huge number of studies on "happiness research" over the past year or so, I have concluded that the data is not very good and tells us little about happiness as most of us intuitively understand it. In fact, some of the problems with the data seem so damning, and so daunting, that it has become a matter of some surprise to me that more researchers don't see the alleged problems as damning or daunting at all, and just proceed pretty much as usual.
Now, maybe my analysis of the difficulties in measuring happiness with surveys (which I would be happy to share at some other time) is wrong. But even if I and other critics of the data are wrong, it appears that many of the best criticisms aren't taken very seriously, even when they are duly noted. ...And there also seems to be a willingness to cite just about anything that superficially seems to support the validity of the measurement instrument -- a sign of a kind of confirmation bias.
Confirmation bias in the social sciences. Shome mishtake, shurely?
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