The social welfare function (SWF) framework is a powerful tool for evaluating governmental policies in light of human well-being. It represents a major step beyond cost-benefit analysis, currently the dominant policy-assessment tool. While cost-benefit analysis quantifies well-being impacts in monetary units, the SWF framework does so using an interpersonally comparable well-being measure.

The SWF proper is a rule for ranking outcomes and policies understood as patterns of individual well-being. “Prioritarian” SWFs give extra weight to well-being changes affecting individuals at lower levels of well-being—by contrast with utilitarianism, which simply adds up well-being numbers.

My book Measuring Social Welfare (Oxford U. Press, 2019) provides a rigorous but accessible overview and defense of the SWF framework. In this lecture, I will discuss Measuring Social Welfare, with a particular focus on prioritarianism.

I will first give an overview of the SWF methodology. I will then discuss one type of application: using SWFs to evaluate the many different kinds of governmental policies that seek to reduce fatality risks, such as policies that address pollution, consumer products, workplace safety, automobiles, transportation systems, infrastructure, and (most recently) the spread of the COVID-19 virus.

Social interventions are inherently complex which makes evaluating their effectiveness a challenging endeavour. Social workers and other professionals working with people with mental health problems use relationship-based skills and approaches to effect change in people’s lives. These approaches are not always possible to articulate in a manual, or replicate in a different context, though it is just as important to measure their effectiveness than medical or psychological interventions, for example. Firstly, a programme theory needs to be developed which articulates the intervention methods and processes; the contexts in which they are to be used; and the anticipated outcomes.

This is then tested for feasibility and indicative outcomes before a larger trial is possible. This opportunity to implement a theory or model in practice helps the researchers to understand the real-world challenges of using the intervention, and some adaptations may be required. Finally, large scale intervention testing requires significant efforts across multiple sites to recruit and retain participants in the trial; implementation of the intervention with fidelity to the model; and rigorous scientific data collection to ensure the outcomes can be measured without bias.

This plenary will draw upon social intervention research conducted in mental health services in England to illustrate some of the methodological challenges of evaluating the effectiveness of social interventions, and the importance of co-production with service users and practitioners throughout.