Subjective well-being as an input to cost-benefit analysis Introduction

The first two sections of this chapter have largely been concerned with analyses in which subjective well-being is the ultimate outcome of interest. But in addition to the intrinsic value of knowing more about subjective well-being, subjective well-being data can play an important role as an input for other analyses - offering insights into human behaviour and decision-making, as well as on how other well-being outcomes develop (Box 4.6). Thanks, in part, to these kinds of insights, subjective well-being data has also been suggested as an alternative means for estimating the monetary value of non-market factors (i.e. goods and services that do not have market prices) for the purposes of cost-benefit analysis.

Box 4.6. Subjective well-being insights into health, human behaviour

and decision-making

Beyond the intrinsic value of subjective well-being, evidence suggests it is also important to other aspects of human functioning. Fujiwara and Campbell (2011) summarise a broad range of evidence suggesting that individuals with higher levels of subjective well-being are more likely to get married, earn more money and be healthier. Positive affect, negative affect and measures of life evaluations are associated with better long-term health and greater longevity (Danner, Snowdon and Friesen, 2001; Ostir et al., 2001), as well as shorter-term cardiovascular and immune system functioning (Cohen et al., 2003; Kiecolt-Glaser et al., 2002; Steptoe, Wardle and Marmot, 2005) which may mediate longer-term relationships between emotions and health. For example, Pressman and Cohen (2005) report that people with high positive affect have been shown to be less likely to become ill when exposed to a cold virus, and more likely to recover quickly.

In addition to income and health, subjective well-being may have other implications for economic performance and overall well-being. Research has found prospective links between positive emotions and workplace performance ratings and productivity (Diener et al., 2002; Estrada, Isen and Young, 1997; Wright and Staw, 1999). Keyes (2006) also reports evidence that mentally healthy individuals missed fewer days of work, were more productive at work, and had fewer limitations in daily activities. Summarising existing evidence, Clark and Oswald (2002) report that measures of subjective well-being have been shown to predict the likelihood of job quits, absenteeism and non-productive work, as well as the duration of unemployment. Bertrand and Mullainathan (2001) also found that job satisfaction was a strong predictor of the probability of changing jobs in the future.

Finally, subjective well-being data can offer insights into people's (in)ability to estimate the well-being impacts of both market and non-market factors as well as of life events - enabling us to compare estimates of what makes us happy against the level of happiness actually attained as a result. This work suggests that our ability to predict future well-being gains or losses (or our affective forecasting) is subject to various biases, such as irrelevant cues* (Sugden, 2005), lack of sensitivity to the size of the good or service valued (Kahneman and Tversky, 2000; Sugden, 2005), and focusing illusions, whereby “nothing that you focus on will make as much difference as you think” (Schkade and Kahneman, 1998; Wilson and Gilbert, 2005). These biases can produce marked discrepancies between the degree to which we think we want something, and the degree to which evidence suggests it will actually make us happy (described by Gilbert and Wilson, 2000, as “miswanting”). These findings have practical consequences for current methods of cost-benefit analysis (see below), as well as more general relevance, providing individuals with better information about the correlates of subjective well-being, so that they can make more informed choices in their own pursuit of happiness.

* An example is the starting point bias, whereby questions that begin: “Would you pay $x for...?” can heavily

bias responses towards the valuation of x used in the first question (Sugden, 2005).

This section focuses on how subjective well-being data can complement existing approaches to the valuation of non-market factors. It begins with a brief description of cost-benefit analysis and of why it is useful to place a monetary value on non-market factors. It then describes methods currently used for estimating the monetary value of non-market factors, and the ways in which subjective data may be able to complement these methods. Finally, the section briefly discusses some interpretive challenges and the caveats that need to be applied to valuations obtained through the use of subjective well-being data.

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