Deaths, hospitalisations and cases have been the default metrics for policymakers during the pandemic. Paul Dolan (LSE) and a team of behavioural science experts propose a broader way of measuring policy outcomes that considers life experience as well as life expectancy.
The policy response to the COVID-19 pandemic has relied almost entirely on evidence about virus transmission risks, hospitalisations and mortality. It has been dictated by concerns for lives lost from COVID-19. As most people recognise, this is far too narrow a focus. Other outcomes matter too, such as the effects on livelihoods, and the life chances of children and young adults. COVID is not the last crisis the UK will face. And even in calmer times, we need a better way of analysing the effects of a policy. Ultimately, any policy will affect one or both of people’s main welfare concerns: life expectancy, and life experience.
Life expectancy can be measured through life years lost or gained. Information on life experiences can be provided by assessments of subjective wellbeing (SWB). SWB represents how people evaluate their lives overall, and/or how they feel about their moment-to-moment or daily experiences. It allows us to consider how the health, economic, and social effects of policies affect people’s life experiences. For measures of SWB to be used to evaluate policies that affect life expectancy and life experiences, we need to calculate a single measure, such as wellbeing-adjusted life years (WELLBYs). A single metric allows for the value of all possible uses of scarce resources to be estimated in terms of their relative cost-per-wellbeing adjusted life year.
Citizens and policymakers care not only about how many WELLBYs are being generated per pound spent, but also about how they are distributed across people. One of the most important distributional considerations is wellbeing over the lifetime. We care not only about how well, or badly, different groups are doing at any one point in time, but also about their flow of wellbeing from birth to expected time of death. Concerns for future generations should also be considered.
Major policy decisions affect all of us in different ways. The policymaking process should therefore be informed by people with different voices, disciplines, perspectives, and experiences. Diversity has been shown to increase performance in organisational settings. The decisions public officials take can never be completely cleansed of self-interest and bias. In academia, attempts have been made to encourage adversarial collaboration, which explicitly brings together academics with different prior beliefs to work on a research question. In a similar way, so that we are better prepared for future crises, we must start to embed practices in policy-making that actively encourage criticism and critique.
The government should be required to be more transparent about the data it is using to inform its decisions, and from whom it is seeking advice. Part of this transparency aim should be to place any numbers in context. In the case of COVID-19, most national leaders have based all their statements on COVID-19 cases and deaths, ignoring basic comparisons with common illnesses and other causes of death. The mainstream media can play a crucial role here in holding the government to account, and in ensuring that data are presented in context.
As a first step, and especially when seeking to respond to crises in a timely way, the most important impacts of major policy decisions should be set out in a checklist. Checklists serve to draw us back away from situational blindness, whereby we can miss information crucial to a good decision because we are paying undue attention to a limited number of considerations, such as death. A checklist can only get us so far, and so the long-term aim of a single wellbeing metric should help to frame the ways in which we analyse existing data relating to the checklist and collect new evidence. At the very least, it will encourage policymakers to think about the wellbeing impacts of interventions that might not typically be thought of as being expressed in wellbeing units (e.g. educational outcomes).
We propose setting up a scientific wellbeing impacts agency. This body will seek to bring together experts from a range of disciplines who have in-depth knowledge of various data sources across policy areas. Their tasks will be to a) synthesise diverse knowledge by mapping available data onto WELLBYs; and b) highlight where the most important data gaps are, thus informing priority areas for future research and data collection. A separate wellbeing commission should be established comprising different voices, including those from advocacy groups (e.g. those involved in palliative care). The commission will ensure that the ways in which WELLBYs are generated have widespread support.
These two bodies will be ready to respond to future “wicked problems”, which are characterised by radical uncertainty. They can also address ongoing challenges such as how to prepare for a future pandemic, and how best to mitigate and adapt to climate change. They will serve to enhance decision-making in calmer times, too. Whatever shape the post-COVID world takes, the time has arrived for wellbeing over the lifetime to be the unit analysis in policy.
This post represents the views of the authors and not those of the COVID-19 blog, nor LSE.