Understanding Well-being Data

chapter 7 Evidencing Culture for Policy

Well-being as evidence for social policy

Now, of course we’ve already got some very strong instincts—even prejudices, sometimes—about what will improve people’s lives, and we act on those instincts … These are instincts we feel to the core, but it’s right that as far as possible we put them to the practical test, so we really know what matters to people. Every day, ministers, officials, people working throughout the public sector make decisions that affect people’s lives, and this is about helping to make sure those government decisions on policy and spending are made in a balanced way, taking account of what really matters.

(David Cameron, Prime Minister’s Speech on Wellbeing, 25 November Cameron 2010)

Using well-being data is thought to improve how we understand human progress and development, as we discovered in the first half of this book (particularly Chap. 2). Chapter 6 looked at two further reasons to use well-being data: to evaluate policy decisions that have been made and to predict the impacts of possible policy change. In the case of cultural policy, the common rationale for using well-being data is to argue for more investment or to ‘defend’1 the existing funding and status of the policy sector.

Shortly after the turn of this century, we saw an international commitment to well-being data that has been called ‘the second wave of well-being’2. The UK’s Office for National Statistics3 conducted a national debate so it could understand what people thought should be measured. The above quote is taken from a prime minister’s speech that launched the Measuring National Well-being (MNW) programme and this debate. He talks of having instincts about what matters, but these need to be put to the test. Chapter 6 concluded with how, when the UK began measuring well-being, there was no measure for culture. This was despite the instinct that culture is good for well-being. It was also in spite of advocacy to that effect and efforts to collect more robust data, analyse them better and present compelling evidence.

Various areas of social policy have claimed their contribution to personal or societal well-being to differing degrees over the last 25 years4. Notably, these appeals are rarely evaluated on their own terms5. The previous chapter (Chap. 6) looked at the relationship between culture and well-being because of its reliance on data and because the cultural sector((The cultural sector is a broad description of cultural institutions such as libraries, heritage sites, museums and theatres. Crucially, it is not only about the buildings themselves, but all the ways people make and consume culture and can include anything from Netflix to gaming (video games) and outdoor festivals.)) has sought a clear identity through arguing its value to well-being4. It also discussed how this policy sector in particular often adopts what has been called a ‘special case’ rhetoric6, meaning it argues that it has unique or exceptional qualities. These are enmeshed in claims to the historical traditions of ideas of culture and its relationship to societal well-being7 that have become naturalised and popularised. In other words, the relationship between culture and well-being seems almost natural, and common sense, whilst also appealing and almost taken for granted.

Alongside these processes of naturalisation and popularisation described in the previous chapter, investment in forms of research to generate well-being evidence for advocacy has also increased8. This form of research is often commissioned to support an argument in policy or political arenas, and we have looked at this as ‘instrumentalisation’ in the area of culture as social policy. This type of commissioned research is common in the UK and is meant to build an argument that a particular activity or service is good for well-being4.((In some ways, this may be an expected development of the aspects of wellbeing data usage from Chaps. 3 and 4, where part of this work is to establish a connection between, say, income and happiness (as with Easterlin 1973, see Chap. 4), or housing in the OECD index (Chap. 3).)) However, commissioning research to make evidence to support the value of a service, and therefore maintain its subsidy, affects the relationship between data, researcher and evidence.

How does commissioning research to support the arguments people want to, and need to make, change the nature and role of evidence in different social policy areas? How does this affect overall knowledge of ‘what works for well-being’((‘What Works’ is a programme across areas of government that is about evidence for what works in policy (Cabinet Office 2019). There is a What works for well-being centre, focussed on well-being evidence (What Works Wellbeing n.d.).)) in terms of social policy? Importantly, how does ‘capitalising’ on well-being data affect its capacity to do social good or to be good data? Do the economic value of data and their analysis change the relationship between well-being data and a good society? It is important to ask questions about research that seeks to prove something which is of financial and political value to particular groups.

In this chapter, we will look at some examples of data and evidence used to make specific arguments about the relationship between culture and well-being (the culture–well-being relationship), alongside evidence that might trouble some of the assumptions outlined previously. The examples in this and Chap. 8 are primarily focussed on cultural policy as a form of social policy. These case studies present issues for well-being data, evidence, knowledge and understanding that can be generalised more broadly to other domains of social policy, but focussing on cultural policy as one area makes the contradictions starker.

When you encounter research findings in your day-to-day life, you are most likely to see them in the media. Journalists don’t often have time to sit and read a whole piece of research, and so you are likely to see the reproduction of a headline finding only. Sometimes this is directly from the researcher’s own writing up, and sometimes it is reproduced second hand in others’ summaries. There is an example of this in Sect. 7.4. It is less common to see the inclusion of caveats, methods, limitations and discussions when you see headline findings reproduced in the media, which limits how we understand well-being and data, as we shall go on to discover.

Can you think of a newspaper article you’ve read that says something like ‘Loneliness is killing us’9 in its headline, which then moves on to clarify that this is actually not quite the case, the headline exaggerated the research that this article is based on and actually the research itself has many caveats? No, me neither. Media reporting of research is not renowned for this detail. Dramatic headlines are one thing in a newspaper article, where we have a shared understanding—to a degree—of how newspapers report information. Arguably, we have a different expectation when it comes to reading official reports. These can also lack detail on contextual information, caveats and limitations, as we discovered at the end of Chap. 4, with the testing of the ONS4 questions. This not only has a bearing on our understanding, but how we trust how data are reported. Often, it is just convenient to read headlines of research as they are presented to us, even believing they represent a body of evidence. The examples presented in the remainder of this chapter highlight that conclusive answers are difficult to find to questions about the well-being of any particular group of people, and the role of culture—or work—or leisure—in this. Crucially, looking at these examples, or ‘problems’ in detail, and putting them in context, generates additional well-being data to further improve understanding.

  1. Belfiore 2012 []
  2. Bache and Reardon 2013 []
  3. ONS 2011a, 2011b []
  4. Oman and Taylor 2018 [] [] []
  5. Oakley et al. 2013 []
  6. O’Brien 2013 []
  7. Belfiore and Bennett 2008 []
  8. Oman and Taylor 2018; Oman 2020 []
  9. e.g. Perry 2014 []