  We use novel nonparametric techniques to test for the presence of
non-classical measurement error in reported life satisfaction (LS) and study
the potential effects from ignoring it. Our dataset comes from Wave 3 of the UK
Understanding Society that is surveyed from 35,000 British households. Our test
finds evidence of measurement error in reported LS for the entire dataset as
well as for 26 out of 32 socioeconomic subgroups in the sample. We estimate the
joint distribution of reported and latent LS nonparametrically in order to
understand the mis-reporting behavior. We show this distribution can then be
used to estimate parametric models of latent LS. We find measurement error bias
is not severe enough to distort the main drivers of LS. But there is an
important difference that is policy relevant. We find women tend to over-report
their latent LS relative to men. This may help explain the gender puzzle that
questions why women are reportedly happier than men despite being worse off on
objective outcomes such as income and employment.
