Understanding Well-being Data

chapter 4 Discovering ‘the new science of happiness’ and subjective well-being


Looking at the invention of subjective well-being measures in the UK offers context behind the ubiquity of well-being measurement practices. Understanding the recent history behind a specific way of measuring a particular idea of well-being, that is considered robust and universal, is vital to appreciate the limitations of such projects. This chapter’s comprehensive survey and critical lens aims to offer tools to promote better understanding of the power of these well-being data, their capacity to change culture and society, and the limits of their application in areas of social and cultural policy and practice.

In short, ‘the new science of happiness’ has much to offer understandings of well-being and the human experience more generally. The techniques, whether originating as national-level social survey questions or personal psychological tests, can be adapted and applied to other environments and have been used widely to understand the impacts of COVID-19. Yet, politics, disciplinary and international competition compromise their neutrality. These contexts are vital to understanding the subjective well-being data generated through survey questions and their uses to inform important decisions in policy development, monitoring and evaluation, and the way these, then, promote behaviour change in people.

We have seen evidence that the national well-being measurers want to be top of the class, with possibilities that complexities of the questions in certain contexts were disregarded. This leaves us with questions. Could it be that in the keenness to compete in the new science and the international game of devising the best measures, considering the subjective experience of people answering questions on subjective well-being may have been side-lined? It transpires that less attention is paid to the qualitative trials of questions that end up as ‘robust measures’ than you may imagine, as I also found with some questions long-used to measure class (discussed in Chap. 9). Yet, should it be a great surprise that quantitative researchers and national statistics offices tend to overlook the qualitative aspects of their methodologies? It is hard to say because such evidence is hard to find.((As my research has found, records of the qualitative aspects of largely quantitative evidence projects for policy-making can be an afterthought or overlooked1. That the minutes of civil service meetings from a decade ago have been re-archived a number of times, and are no longer easily findable is fairly common. In the writing of this book, I discovered my own reports on policy that I had published less than 12 months earlier had been re-archived, with changed links, and the document titles changed. This is one of the trials of a policy researcher—or of trying to understand the origins of the data presented to us as facts.))

We have used data on the contexts behind subjective well-being data to understand them better: who collected them, interpreted them, looks after them and uses them. We have seen some trends emerge across people and policy, but found these contextual data have limits to what can be understood, too. It can be hard to find all the archival information we need, and it can be easy to interpret the absence of evidence as some sort of cover-up, when actually in policy-making and public services, institutional memory is often lost through the ‘churn’ of staff and these issues of paper trails. There is, sadly, ‘no culture of a repository of knowledge’2. Thus, the data we have on how data are made can be as compromised or limiting as the quantitative or qualitative data we have been discussing in these last two chapters.

This chapter has looked at the new sciences of happiness as people, publications, projects, politicians, agencies and disciplines. Easterlin is presented as the turning point in this tale, because he offers a useful narrative device. However, the limitations of how economics was used to understand human flourishing have been known longer—as presented in Chap. 2—and indeed in the introduction to Easterlin’s paper. Discovering the stories behind data in this way, we are able to see how all these different components work together to make the well-being agenda. We can also see that it is the subjective measures, rather than the compiling of objective lists, that are the greater driver of the agenda, and that this is—in part—owing to claims to innovation.

Essentially, however, the new sciences of happiness: the new measures and uses of data from old questions3, are the driving force behind the well-being agenda. At least what we have referred to as ‘the second wave’ in this book. Without the technological advances and the advocacy for the new measures, we might ask, would we have seen calls for the change in policy? Thus, the terms data-driven decision-making and evidence-based policy-making take on new meaning—where the promise of the possibilities of well-being data changes the policy rhetoric and call for more data to be collected. Data do not only capture social change, but ensure it, and as the next chapter demonstrates, it feels as if Big Data increase this pace of change, but how do they impact on well-being?

  1. Oman 2017a []
  2. Hallsworth et al. 2011, 8 []
  3. Allin and Hand 2017 []