Valuation Techniques for Social Cost-Benefit Analysis: - HM Treasury

6 Future Areas of Research
6.1 Valuation with Experienced Utility
The econometric recommendations set out in this paper will significantly improve the accuracy
of the value estimates derived from the life satisfaction approach. The Worked Example has
shown that value estimates come down to much more reasonable and plausible figures when
adhering to most of these recommendations. No study to date however has met these criteria
and thus we advise that value estimates from the life satisfaction literature be used with a lot of
caution in any policy analysis.
These recommendations, however, do not provide solutions to any possible measurement error
issues concerning life satisfaction. We have seen that context effects and difficulties in
remembering past experiences and in reporting well-being in an unbiased way can impact on
the robustness of life satisfaction data. In response to this we saw that experienced utility or
moment-to-moment well-being, measured with the Experience Sampling Method (or potentially
the Day Reconstruction Method), represents the gold standard in well-being measurement.
Going forward, therefore, this report recommends that methods are also developed to derive
monetary value estimates from experienced utility and many of the recommendations set out in
this report will be relevant for the development of this approach. This will be costly exercise
given the nature of the data collection techniques and there will be concerns about how
representative an ESM or DRM sample can be of the general population. In the meantime, we
propose that the life satisfaction approach be pursued as a complementary method to the more
traditional preference-based valuation approaches, especially where well-being data already
exist.
6.2 The Role of Neuroscience
Neuroscientists are now starting to collaborate with economists and psychologists and a new
field of Neuroeconomics has developed. Much of the work in this field is complementary to the
research in behavioural economics, in that the rationality of people‘s preferences and choices is
now being assessed with neurological data. The field essentially attempts to monitor activity in
brain cells (neurons) under different conditions and experiences. Much of the relevant research
uses functional magnetic resonance imaging (fMRI) scans to assess brain activity in humans and
primates during decision tasks. fMRI picks up blood oxygen level dependency (BOLD) in the brain
regions of interest (see Figure 1), which is an indirect measure of activity in the brain. fMRI does
not pick up the actual electrical stimulation in neurons during their activation (like
Electroencephalography (EEG) does), but rather the levels of oxygenated blood that converge on
the activated areas (to supply neurons with oxygen and glucose). fMRI is popular because it
allows for full spatial imaging across the brain, whereas EEG can only pick up activity in the
cortex (ie, the upper layer of the brain). For example, fMRI allows us to monitor activity in areas
such as the insula and amygdala, which are areas, situated deep in the core of the brain
structure, that are key to processing subjective values. The trade-off is that there is a lag of
about 4-6 seconds between activation of neurons (brain activity) and a BOLD signal and
therefore it is sometimes difficult to infer causality between experience/action and brain activity
with fMRI.
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