Social Science Statistics Blog

Alas there are potential problems with using twin data. For example, measurement error in a difference estimation can lead to severe attenuation bias precisely because twins are so similar. If there is little variation in educational attainment, even small measurement errors can strongly affect the estimate. Researchers have been ingenious about this (e.g. by instrumenting one persons’ education with the level that her twin reported, as in Ashenfelter and Krueger). While this may reduce the attenuation bias it can magnify the omitted variables bias which motivated the use of twins in the first place. Because there are only small differences in schooling, small unobserved differences in ability can lead to a large bias. The culprits can be details such as differences in birth weight (Rosenzweig and Wolpin have a great discussion of such factors). In addition, twins who participate in such studies are a selected group: they are getting along well enough to participate, and many of them get recruited at “twin events.” But not all twins party in Twinsburg, Ohio.

Of course none of this is to belittle the contribution of Dr Lykken, who besides helping to start this flurry of work also was also a major contributor to happiness research.