Identifying potential policy problems

An important feature of measures of subjective well-being is their ability to provide an insight into human behaviour and decision-making. In particular, measures of subjective well-being can help researchers to understand the difference between the ex ante beliefs that people hold about their future well-being (which form the basis for decisions) and the ex post outcomes that people achieve in terms of their subjective well-being. A better understanding of these issues is important both for policy-makers and for the broader public. Policy-makers have an interest in understanding why people make the decisions that they do, because much public policy involves dealing with the consequences of systematic poor decisionmaking by individuals. Similarly, businesses and the general public have an interest in understanding how people’s subjective well-being shapes their behaviour.

One way in which measures of subjective well-being are useful to businesses and the broader public is by providing information on the characteristics of good places to live and work. There is clear evidence that subjective well-being predicts future behaviour. Clark (2001), for example, has shown that measured job satisfaction predicts the probability of an employee going on to leave their job. Thus businesses might well have an interest in the measured job satisfaction of their employees and in understanding the determinants of job satisfaction.

Measures of subjective well-being can also help shed light on various biases in the way people make decisions. Although people are generally able to predict whether events are likely to be pleasant or unpleasant, Wilson, Gilbert and colleagues have described various ways in which affective forecasting can be biased or faulty, particularly with regard to the intensity and duration of emotional reactions to future events (e.g. Wilson, Wheatley, Meyers, Gilbert and Axsom, 2000; Wilson and Gilbert, 2006). Kahneman et al. (2006), for example, show that people are prone to over-estimate the impact of income gains on their life satisfaction. When evaluating those factors that people expect to contribute to a positive mood, people tend to focus on conventional achievements, thus over-estimating the role of income relative to other factors. By way of contrast, other activities that are less commonly used as a reference for conventional measures of status get under-estimated with respect to their impact on subjective well-being. Commuting, for example, has been found to have a strong negative impact on both measures of affect (Kahneman et al., 2006) and life evaluations (Frey and Stutzer, 2008). This suggests that people may be prone to over-estimating the positive impact of, for example, a new job with a higher salary but a longer commute.

Faulty affective forecasting is significant in this context because it suggests that decisions reflected in market choices will not always serve to maximise subjective well-being in practice. Individuals may have a substantial interest in better understanding the factors affecting the level of well-being that they actually achieve. Hence, a sound evidence base derived from measures of subjective well-being is of potential interest to the general public. There are also direct policy applications for better understanding the human decision-making process and the various biases and heuristics involved in it. Consider the case of policy options that incorporate a “default” option, for example, workplace retirement schemes that are set up on a basis of either “opt in” clauses, where a new employee does not join the scheme unless he/she ticks a box to join, or “opt out” clauses, where the reverse is the case. The fact that people respond differently depending on which default is selected - despite the fact that in neither case is there any compulsion - has raised policy interest in the idea of “libertarian paternalism”, which focuses on achieving better outcomes be setting policy defaults to influence people’s behaviour in positive directions. Dolan and White (2007) note that information on subjective well-being can be used to help set policy default options appropriately, by indicating which default options contribute most to subjective well-being.

 
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