Using subjective well-being data to inform the options appraisal, design and evaluation of policies

When making funding allocation decisions, it is important for governments to have information about the efficiency with which resources can be used to achieve policy objectives. Estimating the efficiency of expenditure, often described as value for money, delivered by a project, programme or policy intervention involves quantifying the various impacts it might have on outcomes of interest - including economic outcomes (e.g. does the intervention boost jobs or decrease regulatory burdens on business?), social outcomes (e.g. does the intervention improve educational attainment or health outcomes?) and environmental outcomes (e.g. does the intervention contribute to carbon reduction or increase peoples’ access to green space in the local area?). These questions are relevant in the process of initially appraising policy options, but may also be asked as part of ongoing refinements to policy design and implementation, as well as when examining the potential impacts of stopping a particular policy intervention or regulation.²⁹

Options appraisal takes place before a policy is implemented, whereas policy evaluation involves the assessment of policy during its implementation - and might include both specifically commissioned research, as well as less formal evaluations based on existing evidence. Formal programme evaluations may involve an experimental or quasi-experimental design for investigations and include measures both before and after a policy has been introduced, enabling causal inferences to be drawn about the impact of the policy. Policy design considerations are relevant both before and during policy implementation, as well as in interpreting the evaluation of policy impacts and what can be done to enhance them.

Alongside some of the typical economic, social and environmental outcomes, subjective well-being data can provide policy-makers with an additional perspective on the potential impact of a policy. As noted earlier, subjective well-being data may offer unique insights into the effects of a given action, taking into account a variety of objective wellbeing outcomes and how they combine to produce an overall perception of well-being. It should be noted, however, that because subjective well-being has so many drivers, the impact of any one policy, particularly one that affects only a small number of people, may prove difficult to detect. This has implications for the sample sizes required, and the study design adopted in, for example, formal policy evaluations - issues that will be discussed in the section that follows.

In assessing the likely impacts of a policy intervention on subjective well-being, analysts are likely to draw on prior literature, including academic sources, and international examples. More comparable data will enhance the quality of these sources of information and provide better baseline information about the levels of subjective well-being to expect among different population sub-groups. This baseline data can provide essential information about the “do nothing” policy option - i.e. what to expect in the absence of intervention.

Diener and Tov (2012) list a wide variety of policy considerations where it may be valuable to consult subjective well-being data. These include issues such as deciding how to support day care for elderly Alzheimer patients, examining the moods and emotions of caregivers when the patient is in day care or at home and the life satisfaction of caregivers when respite care is provided; or examining the well-being benefits of parks and recreation, testing whether parks are more crucial to well-being in areas where dwellings have no outdoor space and whether life satisfaction is higher in cities with plentiful parks than in cities where parks are rare.

In terms of applied examples, Gruber and Mallainathan (2002 have used subjective well-being data to examine optimal cigarette taxation across a range of areas in the United States and Canada. Boarini et al. (2012) show how data from OECD member countries can be used to explore the impact of health co-payments and unemployment replacement rates on national levels of subjective well-being, as well as well-being among certain population subgroups, such as people working versus those outside the labour market. Using a quasi-experimental design, Dolan and Metcalfe (2008) examined the subjective well-being impact of an urban regeneration project in Wales.