1. Candidate
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68250
1
Discuss the pros and cons of using the subjective wellbeing
(happiness) approach to environmental valuation. Use examples to
illustrate your answer.
The subjective wellbeing (SWB) approach (hereafter, life satisfaction approach
(LSA)) to valuation has been gaining popularity in recent years, in contrast to the
traditional preference-based techniques (Fujiwara & Campbell, 2011). The LSA
provides an estimate of the value of non-market goods by looking at how they impact
on people’s reported wellbeing, using regression analysis. This valuation technique
has its pros over the more traditional techniques, but also faces its own set of
limitations. Valuation using LSA relies on two assumptions; a central assumption that
reported life satisfaction (LS) is a good proxy for an individual’s underlying utility
(Frey et al., 2009), and the less fundamental but more controversial assumption of
interpersonal comparability of LS scores (Menz, 2011). The latter assumption
however is contentiously regarded as a weakness of this valuation technique.
Conversely, the strength of the LSA is that it obviates some of the major limitations
of both stated and revealed preference techniques. Furthermore, Valuation using LSA
has the advantage of being potentially more informative than the standard techniques.
Nevertheless, the most arguably significant weakness of the LSA is that many
valuation studies give implausibly high estimates, which can be counter informative.
The rest of this essay will entail a critical discussion of the pros and cons of the LSA
outlined above, using illustrative examples where relevant.
The LSA has an advantage over both the revealed and stated preference methods that,
at least, in principle, makes it a more accurate valuation method. First, an advantage
of LSA over revealed preference methods is that it does not make the assumption of
market equilibrium. The hedonic method (HM) relies on the assumption of
equilibrium in the housing market, but this assumption is often violated (e.g. Ambrey
& Fleming, 2011) which results in biased willingness to pay (WTP) estimates (Frey et
al., 2009). The LSA, however, explicitly captures individual welfare in the absence of
market equilibrium (Frey et al., 2009), thus, obviating the resulting bias caused by the
assumption of market equilibrium in HM, and, therefore, giving more accurate
estimates. Second, the LSA attempts to avoid the major concerns with the use of
stated preference methods. The hypothetical nature of questions in stated preference

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studies are likely to entail superficial answers and strategic behavior from the
respondents (Di Tella & MacCulloch, 2008). Notably, these problems are difficult to
overcome (Frey et al., 2009). Although its disputable, the LSA is not affected by
either of these limitations of the standard techniques because the only information
people provide is their life satisfaction, which is cognitively less demanding and there
is no reason to expect strategic behavior bias (Frey et al., 2009).
A limitation of the LSA identified in the literature is its assumption of interpersonal
comparability of self-reported LS. Several studies using LSA to value environmental
conditions assume cardinality (thus interpersonal comparability), and whether this is a
weakness of the LSA still remains controversial. Interpersonal comparability is an
issue for studies using cross-sectional or panel datasets on wellbeing (Fujiwara &
Campbell, 2011). Notably, Welsch (2002, 2003, 2006, 2007) compare mean LS and
mean air pollutant values at the whole-country level, thereby making a strong
assumption of cardinality. At the individual level, the problem is that people could use
the LS scales differently (Stiglitz et al., 2009). At the whole-country level, Diener et
al. (1999) found evidence that LS scales might have to an extent different meaning
between cultures (Larsen, 2008). However, Veenhoven (1993) and subsequently
Bolle & Kemp (2009) find no indication that cultural or linguistic bias may prevent a
comparison of happiness across nations. Sandvik et al. (1993) have shown that
individuals are able to recognise and predict the satisfaction of others, suggesting that
SWB is observable and comparable between individuals (Fujiwara & Campbell,
2011; Welsch, 2006). Nevertheless, the assumption of cardinality, if incorrectly made
would yield estimates from LS studies that are both biased and inconsistent (Hill et
al., 2008 in Ambrey & Fleming, 2014).
Contrary to the Welsch studies, some LSA studies do not assume cardinality. In
specifying a happiness function, there are basically two varieties: a function with
individual happiness (micro approach) or a function with aggregate (average)
happiness (macro approach) (Welsch, 2006). The micro approach does not require a
cardinal self-reported happiness function but requires control for heterogeneity in
individuals’ characteristics (Welsch, 2006). Many authors (e.g. Ambrey et al., 2014)
have shown that estimates of coefficients are virtually unchanged, regardless of what
assumption is taken, contingent on individual heterogeneity being addressed

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appropriately (Ambrey & Fleming, 2011). Also, LSA studies that are not interested in
marginal utility of income or the environmental good separately, but only the ratio of
the two (MWTP) are not sensitive to the issue of interpersonal comparability
(Levinson, 2012). Thus two inferences can be made from this. First, evidence shows
that the assumption of interpersonal comparability is not particularly a significant
limitation of the LSA, because the bias caused by the assumption are not large.
Second, assuming it is a significant weakness of the LSA, not all studies make this
assumption, and therefore, it is only at best, a weak con of the LSA.
Conversely, another strength of the LSA to environmental valuation is the possibility
of going beyond average effects, which makes LS studies more informative relative to
the standard methods. Although the focus of valuation exercises usually entail
estimates for a typical member of the population under consideration (i.e. average), it
is possible to assess how estimates differ by age, income group or other
characteristics (Fujiwara & Campbell, 2011). Several studies report differentiated
effects for different subgroups of the population such as predicted risk groups, the
elderly or environmentalists (Frey et al., 2009). A notable example is the study based
on 35,000 happiness responses from OECD countries between 1975 and 1997 by Di
Tella & MacCulloch (2008) who investigated the effect of a number of variables on
life satisfaction including environmental degradation (measured by sulphur oxides
(SOx) emissions). Not only did they find a statistically significant negative impact of
SOx pollution on happiness, they also show that its effect on young adults is more
than twice the size of the effect on the elderly and that its also more negative on LS of
the highest income group relative to the country average. Frey et al. (2009) identify
that such differentiated effects can serve as robustness and plausibility check and also
provide valuable information for policy makers (Frey et al., 2009).
Furthermore, Fujiwara & Campbell (2011) note that sufficient sample size is a
prerequisite for subgroup valuations and most LSA studies use large national or
multinational datasets (e.g. Di Tella & MacCulloch, 2008). From these large datasets
comes another advantage over preference-based techniques. The larger datasets that
LSA studies have over both revealed preference and stated preference studies mean
that sample results are more representative of the population in general (Fujiwara &
Campbell, 2011). This however, does not apply to all LSA studies because some have

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small sample sizes (e.g. Mackerron & Mourato, 2009) and unrepresentative sample
groups (e.g. Mackerron & Mourato, 2013). Also, it should also be borne in mind that
many of these studies with large datasets such as Welsch (2002, 2003, 2006, 2007)
and Di Tella & MacCulloch (2008) face problems such as unobserved heterogeneity
from aggregated data that are regarded as serious limitations. Therefore, while the
relatively larger datasets of LSA studies and the potential of going beyond average
measures are seen as advantages, its validity will depend on the specific studies, their
methodology and the particular limitations they face.
In contrast, it is recognised that environmental valuation using LSA produces
implausibly high valuations, which presents itself as a significant con of the LSA. The
problem here involves the estimation of marginal utility of income in the LS
regression, where the coefficient of income tends to be statistically significant but
small, which often results in implausible WTP estimates for the environmental good
(Dolan et al., 2011). The causes often cited in the literature for the downward bias on
the estimate of the income coefficients are the issue of reverse causality between
income and LS and the closely related issues of adaptation to income and influence of
relative aspects of income on an individual’s LS. Many LSA studies have generated
implausible valuations (Fujiwara & Campbell, 2011). For example, in a study on air
pollution in London, Mackerron & Mourato (2009) find that a 1% increase in NO2
levels is equivalent, in LS terms, to a drop of 5.3% in income. They identify that that
this value is unrealistically high, both intuitively and in comparison with results from
revealed and stated preference studies. Such high values tend to be fairly common in
LS research to date (Mackerron & Mourato, 2009). As acknowledged in various
studies, including Mackerron & Mourato (2009), valuation estimates from most LSA
studies are somewhat higher than those calculated using stated and revealed
preference techniques (Menz, 2011). As a result, interpretation from LSA studies is a
controversial issue and a cautionary approach towards this approach is recommended
in the literature.
However, the issue of high valuation may not necessarily be as big a problem as it
often regarded. Firstly, some LSA studies estimate values using subjective wellbeing
score instead of monetary valuation (e.g. Ambrey & Fleming, 2011). Secondly, not all
LSA studies face the same outcome of implausible high valuations. Some valuation

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studies such as Luechinger & Raschky (2007) LS have produced reasonable estimates
that fall in a similar range as the preference-based techniques (table 1). Thirdly,
investigating the relationship between income and life satisfaction is a fast growing
area of research and better estimates of marginal utility of income are evolving
(Ambrey & Fleming, 2011). For example, Ambrey & Fleming (2014) use a subset of
windfall income (restricted windfall income) in place of the more conventional
household income measure in their study and find that the causal effect of income on
life satisfaction is substantially higher (and MWTP estimates substantially lower)
when restricted windfall income is used. Likewise, many other studies have
developed ways such as controlling for new relative income variables to reduce the
downward bias on estimate of income in LSA (e.g. Luttmer, 2005;Powdthavee, 2009;
Luechinger 2009a).
This essay shows that the LSA has its own pros and cons, but more importantly, there
are nuances within these well-identified factors. In relation to revealed preference
method, LSA has an advantage of avoiding the often-violated assumption of market
equilibrium that gives biased estimates. Similarly, the LSA has advantage over
revealed preference techniques because it circumvents the major issues of superficial
answers and strategic behavior inherent in hypothetical questions, but it also faces it
own related problems that have not been discussed here. Whether or not assuming
interpersonal comparability is a weakness of the LSA remains debatable.
Nevertheless, I argue that it is, at best, a weak con of this approach. Studies that adopt
the LSA can have an advantage of going beyond average estimates and tend to have
larger and more representative samples. However, the validity of these advantages
depends on individual studies. Arguably the most significant con of the LSA is the
relatively high and sometimes implausible valuation estimates studies produce.
Nonetheless, I debate that it is not as big a con as it portrayed because some studies
do not produce monetary valuations, others also produce estimates in a similar range
as the preference-based techniques and notably, the LSA is still evolving with
increase in research towards its improvement. It is important to note that there are also
other cons of the LSA including measurement errors and bias from reported LS as a
result of mood states, self-selection and the influence by immediate context, that have
not been discussed here. The nice feature of this approach is that, while it avoids the
problems of the preference-based techniques, its shortcomings also differ markedly

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from theirs, making it useful for comparison. Fujiwara & Campbell (2011)
recommend the LSA to be currently regarded as a complement to the more standard
methods. The UK Government is committed to improving the way that wellbeing and
social impacts are incorporated into policy decisions (Fujiwara & Campbell, 2011).
With the increase in research towards improving the LSA and the stagnant status of
preference-based techniques, I believe the LSA has the potential of overcoming its
cons and significantly contributing to the government’s commitment to improvements
in the future.

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Appendix 1. TABLE
Table 1: valuation of PM10 using non-market valuation techniques. From Menz
(2011).

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Appendix
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