To what extent can econometric techniques mitigate the difficulties identified in the previous Section?114
This Section briefly discusses the question. With apologies to some readers, it presupposes a basic knowledge of econometrics.
Assume—to begin—that the pool of respondents has homogeneous preferences. However, they are characterized, potentially, by scale heterogeneity, evaluation error, and miscommunication.
The standard approach researchers employ is to estimate the determinants of SWB using ordinary least squares (OLS). The estimating equation is SWBit = βxit + εit, with xit being a vector of
individual i’s attributes at time t, including income. The ratio of the coefficient in for some nonincome good, to the coefficient on income, immediately yields an estimate of individuals’ WTP for the good.
The error term in this equation, εit, serves to handle certain kinds
of scale heterogeneity, evaluation error, and miscommunication— namely, when these are caused by unobserved factors that are uncorrelated with the observed attributes catalogued in xit. For
example, random variations in day-to-day weather may deflate or inflate individuals’ moods, randomly changing the mix of information about attributes that is salient to individuals. Transient psychological factors may cause an individual to shift upward or downward the preference scale used to express the (common) attribute ranking.115
However, the flaw in this strategy—well-recognized by many economists in the SWB literature116
—is that there may be unobserved
114. For helpful discussions of econometric issues in SWB surveys, see Andrew Clark, Fabrice Etilé, Fabien Postel-Vinay, Claudia Senik & Karine Van der Straeten, Heterogeneity in Reported Well-Being, 115 ECON.J. C118 (2005); Ada Ferrer-i-Carbonell & Paul Frijters, How Important Is Methodology for the Estimates of the Determinants of Happiness?, 114 ECON.J. 641 (2004); Simon Luechinger, Valuing Air Quality Using the Life Satisfaction Approach, 119 ECON. J. 482 (2009); Erzo F.P. Luttmer, Neighbors as Negatives: Relative Earnings and Well-Being, 120 Q.J.ECON. 963 (2005); and Nattavudh Powdthavee, How Much Does Money Really Matter? Estimating the Causal Effects of Income on Happiness, 39 EMPIRICAL ECON. 77 (2010); Dolan et al., supra note 11.
115. Blanchflower & Oswald, supra note 24, at 1361–62.
116. Seesupra note 114; see also Marianne Bertrand & Sendhil Mullainathan, Do People Mean What They Say? Implications for Subjective Survey Data, 91 AM.ECON.REV.(PAPERS & PROC.)67, 67, 71–72 (2001) (arguing against the use of SWB and similar surveys to predict the effect of observable individual attributes on individuals’ attitudes because of the correlation of measurement error with those attributes).
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individual-specific factors that both cause variation in stated SWB and cause (or otherwise are correlated with) the attributes in xit. For
example, the fact that an individual is unusually happy may both
make her prone to evaluation errors or miscommunications that shift upward the preference-utility scale and cause her to earn higher income.
In response to difficulties of this sort, panel data is often used to estimate an OLS equation with individual fixed effects: SWBit = βxit + fi + εit. But there are a number of reasons to think that
this strategy is not a full response to the difficulties under discussion. One point, a technical one, is that OLS with individual fixed effects assumes a dependent cardinal variable. As discussed earlier, it is far from clear whether an individual in responding to a life- satisfaction question is articulating her perceived cardinal (rather than merely ordinal) utility. Ordinal utility should be estimated via ordered probit or logit—and incorporating fixed effects in these models yields a biased estimate of β.
A second and more substantive worry is that the fixed-effect methodology controls for time-invariant sources of scale heterogeneity, evaluation error, or miscommunication. But one theme in the discussion in Section A was that the processes leading to individual SWB responses may change along with the change in individual income or other attributes. Intrapersonal scale
recalibration is just this: an individual with a higher income, health,
and so forth, may tend to use a different scale to express his preferences; this effect will show up in εit rather than in fi, yielding a
biased estimate of β. Similarly, higher levels of certain attributes may cause improvements in individual moods, in turn inducing a systematic shift upward in stated life satisfaction. Finally, cultural norms encouraging respondents to moderate (or inflate) their stated SWB may come into play just when individuals are at higher levels of income or other attributes.
Third, OLS with fixed effects controls for the possibility that time-invariant unobserved factors change the intercept of the line associating observed factors with stated life satisfaction, but not the possibility that these skew SWB in more profound ways. For example, individuals with a particular personality trait might be disposed to change the slope of the preference-utility function, not just the
intercept.117
Fourth, OLS with fixed effects has difficulty producing statistically significant estimates for the coefficients on individual observed attributes that do not vary much over time.
Fifth, and this is again a deeper worry, it is hard to see how econometric technique, however sophisticated, can cope with a certain kind of miscommunication effect. OLS with fixed effects (if used to estimate preference utility) starts with the assumption that there is a common preference ranking over attributes, captured by a common utility function with the form uit = βxit. It then allows for
random or individual-specific changes in expressed preference utility via fi and εit. But if statements of SWB are caused by some feature of
individuals’ mental states other than their preferences—for example, by their moods—it is puzzling how any such statement can be used to estimate the preference-utility function. Consider an analogy. Individuals may have quantitative beliefs of various sorts (to give one example, beliefs regarding the size of the world’s population). These beliefs may be caused by various observed and unobserved factors. Would it be sensible to estimate the coefficient on these observed factors (the extent to which they change an individual’s belief) by asking an individual an SWB question? That would be absurd, because SWB answers are not caused by beliefs about the world’s population. But—if SWB answers are indeed expressions of the respondent’s hedonic state, not his evaluation of how fully his preferences are realized—why is it less absurd to use SWB answers in estimating preference utility?
Some of these difficulties (although not the last) can be handled via instrumental-variable techniques. But these are used fairly infrequently in the SWB literature, given that valid instruments for income (or other attributes) seem difficult to find in this context.118
Introducing preference (not merely scale) heterogeneity just further complicates the picture.