Understanding Well-being Data

chapter 8 Talking different languages of value

Returning to the culture – well-being relationship

The arguments of cultural value are curious, yet mundane. Chapters 6 and 7 offered glimpses of how some people argue about the value of culture one way, while others seem to speak a different language entirely. The language of data, metrics and numeric valuations of culture can feel at odds with how the majority of artists and cultural practitioners speak and think about culture. In one hand, we might hold a 2010 report of the UK’s Department for Culture, Media and Sport (DCMS)1, which offers an overview of evaluation techniques, such as Quality Adjusted Life Years (or QALYs((Quality adjusted life years (QALYs) are explained in Box 2.5, in Chap. 2.)) ). While, in the other, we might hold a copy of the Arts Council England (ACE) Strategy from the same year,((The two valuation techniques are evaluated for their possible use in culture in O’Brien2 Measuring the Value of Culture: A Report to the Department for Culture Media and Sport. The Arts Council Strategy, Achieving Great Art for Everyone3, includes a number of artists on the value of the arts, including Jeremy Deller and Tim Etchells, who are cited here.)) in which artist Jeremy Deller explains that art makes ‘life worth living’4. The report in one hand talks cost-benefit analysis, while in the other, artist Tim Etchells speaks of artistic ‘value not bound up with price’4.

Many in the cultural sector((If you are reading this chapter a while after reading previous ones, then the cultural sector is a broad description of cultural institutions like libraries, heritage sites, museums, theatres and so on. Crucially, it is not only about the buildings themselves, but all the ways people make and consume culture and can include Netflix and outdoor festivals.)) are sceptical that cultural experience can be expressed in quantitative terms5, with some being adamant that it should not be6. Academic research on cultural metrics is equally two-sided7. The gentler end of the critical scale involves damning metrics with faint praise by stating that ‘[s]tatistical data well channelled can provide useful ancillary information’8; the harsher end involves describing ‘ideas pertaining to the measurement of culture’s value’ as ‘stupid’9.

The previous chapter outlined some differences in quantitative expressions of the culture–well-being relationship. It began with a walk through some examples of how data could be and are used to understand questions about culture and well-being. This step-by-step approach aims to open ‘the black box’ of well-being data (and some culture data for good measure). It is not always easy for everyone to have a practical sense of how data are used, or how they work, with the way that arguments and workings are generally shared. Looking closely at how analysis and valuation are presented helps understand what is going on ‘under the bonnet’ but can feel intimidating. Given that how people feel about these relationships is associated with their own values, the trick is to feel more confident in making value judgements for yourself.

That is why in this chapter, there is one more example of using well-being data to understand culture and their role in social policy. We are going to look in greater detail and break down these processes further again. This includes a description of how the data were collected in a national-level survey. We look at the questions, as they appear in the survey, because it can be hard to imagine the mundane contexts that data originate from when you are looking at the complex results. It is also not easy to imagine what has happened already, or indeed, what happens next.

What does research do? How does it affect the world or change things? What do well-being data become when their analyses are presented as findings, and then reproduced? We will begin to think through some of these questions by following key findings, to see how they are interpreted in the real world, to imagine data’s capacity to change things. We will return to the conceptual work behind what is being measured before reflecting on what the analysis is trying to do, step by step. In this chapter I want to share that it is possible to think through what quantitative analyses are doing, without necessarily doing the maths or understanding the quantitative processes and their confusing terms.

The steps involved in data analysis like the ones in this chapter are designed on the basis of how concepts go together. It is possible to understand the research on this level, even if expressing terms in an equation feels intimidating. People may be ambivalent, even outraged at understanding aspects of well-being in numeric terms, and this is also true of trying to describe the role of an idea of culture in delivering well-being aims. Yet, numbers help us understand the extent of relationships in particular conditions; they do not necessarily decide whether that relationship exists at all. You can choose to retain that judgement for yourself.

  1. O’Brien 2010 []
  2. 2010 []
  3. 2010 []
  4. ACE 2010, 26 [] []
  5. Hill 2017; Oman 2013; Oman and Taylor 2018 []
  6. Nissel 1983; Meyrick and Barnett 2021 []
  7. Belfiore 2002; Merli 2002; Selwood 2002; Gilmore et al. 2017 []
  8. Phiddian et al. 2017, 179 []
  9. Meyrick and Barnett 2017, 109 []