Understanding Well-being Data

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

Establishing a new science of happiness

Layard’s1 book, Happiness: Lessons from a New Science emerged from a series of public lectures called ‘Happiness: Has Social Science a Clue?’2. The LSE’s well-being programme was founded as a result of Layard’s public lectures. The website states:

Research from the programme has been devoted to understanding the causes of wellbeing and how wellbeing affects other outcomes that policy-makers care about (such as education and physical health).

(LSE Centre for Economic Performance n.d.)

The LSE’s well-being programme foregrounds making well-being knowledge popular by way of ‘lessons’, making knowledge ‘that policy-makers care about’. These words might imply that the aspects of happiness that policy-makers don’t care about fall outside of the remit of the centre. This is indicative of a general feeling amongst some social policy areas that the work that they do is ‘invisible’ to policy-makers3. Such a feeling is corroborated by academic research4 and evidence that some domains of social policy hold more sway with policy-makers than others.

Knowledge that policy-makers care about is, therefore, very much a concern. Let’s remember from Chap. 1 that the very idea of using well-being data to inform policy decisions (evidence-based policy) hangs on the idea that policy-makers can make neutral and objective decisions—if fed the right evidence. We have discovered already many indications to the contrary, as with the different interpretations of poverty data to suit political arguments in Chap. 1. We also know that ‘facts’ which reinforce established moral beliefs (or what we feel is right) are attractive to policy-makers and the public5 as confirmation biases. What we see here is the possibilities for the new ‘science[s] of happiness’ to become influential, with some believing the field is dominated by economics’ adaptations of psychology’s tools.((I encountered this in my observations, discussions and informal interviews with well-being experts in my PhD fieldwork (2012–2015).)) It is easy to see how this might be the case, as a result of their capacity for persuasive arguments that we come to later in this chapter.

Economics (and its sub-disciplines) tend to have much influence with governments and multi-lateral institutions (like the UN, where many countries are represented in the decision-making processes). However, economists have not necessarily presented ideas in accessible ways as a rule. Their relevance to decision-making institutions is also a matter of tradition: they have long-held sway and so are highly represented in the decision-making process. Similarly, decision-makers tend to be literate in the principles of economics and in the UK, there is a trope that all MPs attend the very same course at Oxford or Cambridge universities: PPE (Philosophy, Politics and Economics)—to the extent that it ‘runs Britain’6. Decision-making processes are reputedly controlled by Treasury’s economic approaches, including the valuation techniques discussed in Chap. 2. Economics for well-being is an easier message to communicate than economics’ more abstract ideas, and borrowing the language of positive psychology is useful in promoting ideas that governments are, and individuals should be, taking positive action themselves.((Much of the commercial side of happiness economics, as with Paul Dolan’s book Happiness By Design (2014) is about finding our own ‘route to happiness’ through exercises to locate pleasure and purpose in relation to what we do, and to be more strategic. In a broader sense, a crucial critique of positive thinking (which is different from positive economics, but linked) is Barbara Ehrenreich’s Smile or Die: How Positive Thinking Fooled America and the world (2009). She states in a presentation to the Royal Society of Arts, ‘Encouraging patients to “be positive” only may add to the burden of having cancer while providing little benefit’ (Ehrenreich 2010).))

What we can also see, therefore, is the appeal of happiness in making economics an applied and more relatable discipline. This attraction can be seen in the increase in journal articles on well-being in the EconLit database7. Yet, despite the increase in happiness economics papers and emphasis on the increasingly robust ‘science’ of well-being8, the lack of conceptual consensus outlined in Chap. 2, and expanded on in Chap. 3, has remained a concern for policy-making9. Layard himself told a journalist10 a decade ago that we were a decade away from well-being measures that are good enough for policy to be made using them. Yet numerous policy recommendations have been made on account of these measures over the last decade, as this book can attest to.

In their advisory paper to the ONS’ MNW Programme, Dolan, Metcalfe and Layard explain that any measure of well-being must be ‘empirically rigorous’, by which they mean ‘that the account of wellbeing can be measured in a quantitative way that suggests that it is reliable and valid as an account of wellbeing’11. Although the insistence that any empirically robust account must always be quantitative is preferred practice for certain disciplines, that does not mean it should not be questioned. Measurement of well-being basically wants to understand either change over time or difference between people or groups of people. These data can be captured by qualitative approaches, such as diaries or photographs, as described in Chap. 3, and do not need to actually be quantitative, therefore.

The authors continue by making an important point regarding any measure of well-being: that it should ‘be sensitive to important changes in well-being and insensitive to spurious ones. In practice, distinguishing between the two is quite a challenge and often relies on judgement based on a priori expectations’11. Returning to the well-being data examples we have already come across in Chap. 3, whether the OECD indicators or a small-scale questionnaire, understanding someone’s well-being using data gathered from any questions will have limits.

Recalling our hypothetical example of understanding whether a concert in a local park might improve well-being, how do we understand which aspects of the experience were the contributing factors? How can you disaggregate the contribution of the park, from the music itself, the people you were with, or the quality of the hotdogs for sale or the length of the toilet queue? Let alone understand which contributes to longstanding well-being or momentary happiness? Distinguishing between important changes to well-being and spurious ones is difficult, and therefore well-being data do not always meet Dolan et al.’s12 criteria. Evidence of the impacts of particular activities and interventions on well-being is often criticised, as we discovered in Chap. 3: generally, if you ask certain questions because you seek a causal relationship, you are most likely to find it. The same is therefore an issue for well-being research more generally. The theory of confirmation bias is an account of how people tend to respond to causal messages which reinforce what they already believed or which suits their way of living and or thinking.

Thinking of the Facebook posts that have appeared on my feed in recent years, many different accounts, traditions and philosophies (that we have touched on briefly in this book) appear in the posts: we should try harder, we are trying too hard; we should visualise what we want and go for it, we spend too much time living in the future and not enough in the present and so on. All of these memes get shared because they appeal to things the person sharing already believes. Well-being wisdom repackaged is a large part of the wellness industry without any of the concerns with contradictions or evidence against the claims made. It appears that happiness economics may be similarly equipped to package simple ideas and positive psychology with long-held traditions, empirical evidence and call itself a new science.

There are several takeaways from this overview of the new sciences of happiness. First, that happiness economics seems to dominate the social sciences of well-being. Bearing in mind that all social sciences could be argued to be about understanding and improving well-being in some way, it is happiness economics that appears to be at the forefront—and that has certainly seen the largest increase as a discipline. This is because it has gained ‘scientific authority’ based on a couple of factors. First, is the combination of historical examples of moral philosophy, narratives of innovation and claims that the measures are growing increasingly robust. Second, these aspects are presented as simply as possible for media, policy and public audiences. Yet, the multidimensional nature of well-being means that it remains extremely difficult to remove confounders which include philosophical and empirical contradictions. It is, therefore, challenging to make and substantiate simple claims to know ‘the causes of well-being’, for example. Econometric models typically used to analyse subjective well-being data may lay claims to robustness, but are still not economically sound13 and use data collected by questions that do not necessarily translate to the general public (as we shall discover later in the chapter).

These measures are, by the admission of prominent well-being experts, not neutral or objective measures of subjective well-being, but also involve subjective categorisation lists of people’s strengths or moral character (such as that in positive psychology) or a country’s development (as in the Human Development Index), as well as being the result of a process of decision-making when it comes to which data and how to model them. Having looked at the disciplines that have led to this new science of well-being, we will now turn to the data that inspired it and are generated by it. Specifically, we look at the ideas of subjective well-being and the methods that have shaped subjective well-being data and their prominence.

  1. 2006 [↩]
  2. Layard 2003 [↩]
  3. as with Holden 2012, in the case of culture [↩]
  4. e.g. Stevenson et al. 2010; Gray 2004 [↩]
  5. Davies 2018 [↩]
  6. Beckett 2017 [↩]
  7. EconLit (n.d.) and see Chap. 2 [↩]
  8. O’Donnell et al. 2014; Helliwell et al. 2015; ONS 2015a and 2015b [↩]
  9. Fleche et al. 2012, 11 [↩]
  10. Rustin 2012 [↩]
  11. Dolan and Metcalfe 2012, 411 [↩] [↩]
  12. 2011a, b [↩]
  13. see Cooper, in McKenzie 2015 [↩]