Chapter 1 Introducing well-being data
Why well-being data?
Well-being data can be about individuals, such as Fitbit data, or population data, such as the census. They include health data and poverty data; information on how we feel, on how we live and how long we live. This book focusses on well-being data for a number of reasons. Firstly, it is easy to assume that well-being data are similar in some way, because they are about the ‘same thing’: we will look at how diverse well-being data are. It is also through trying to understand ‘well-being data’ as a thing that I came to know data in general.
To come to know well-being data, I had to spend years trawling through books from within and beyond economics, psychology, statistics, policy, politics and philosophy. This was a slow process, and an uncertain process, which fuelled my feelings of imposter syndrome. All these different disciplines used different language that I had to be familiar with. Or worse, the same words to mean different things, which I try to overcome as much as possible in this book. It was years before I slowly gained confidence in my own common sense when reading about either well-being or data. The very idea of data and academic or policy language means we stop trusting our own common sense. We shouldn’t. To be honest, some academics do too. They also shouldn’t.
Secondly, well-being ‘as the aim of all policy-making’ (we’ll come to this in the next chapter) has unique relevance for areas of social and cultural policy. This is because—in common-sense terms—culture and society are undisputedly about people, and those working in these policy domains often aim to either improve people’s quality of life or interrogate what improving lives might actually involve! Unlike other aims of policy, well-being, as a concept, makes sense to those working in it and those affected by it—which is everyone.
Thirdly, well-being is about experience. Some people find it hard enough to explain how they feel with words, let alone using the same words. It is even harder to capture experience with numbers. I mean, for thousands of years, people haven’t even agreed on what well-being is exactly and statisticians also admit it’s impossible to agree on a definition, even, as we shall see! How do you know what you are measuring when you don’t know what it is? We’ll find out how people have tried and why they have tried.
Fourth, we all have a sense of what well-being is. We also have a sense of doing what is good for us and knowing what has been bad for us or others. We all make decisions daily that are well-being related—that balance of going to the pub versus going to the gym. Maybe it’s not getting takeaway coffees and sandwiches for a month to save for a holiday. These decisions we make are based on pleasure and purpose at different moments in time, that’s all well-being. We are all well-being experts and we all ignore the evidence (except that app that told me I was happiest in a beer garden with my friends; I listened to that and return to it in Chap. 5).
Fifth, it is also all too easy to forget that not everyone has the same idea of well-being: what makes some people feel better can actually be bad for others.. I found that when I searched for the word ‘well-being’, the majority of images comprised stock images of white people who were able-bodied and doing yoga or jumping, or they were a middle-class family sitting down to a healthy dinner together with perfect teeth. These very ideas of what well-being looks like, who has well-being and who doesn’t are reinforced by government health messaging. This changes what we think well-being means.  For example, not all religions and cultures will feel as at home in a British pub as I do on a sunny day: not all activities are available or desirable to everyone. Even formal well-being advice from governments and the media in the pandemic has routinely forgotten you can’t go for a walk to make you feel better if: you are home alone with three kids, are in the middle of a long shift or are indeed unable to walk. It is important to remember that exposure to well-being solutions is a reminder of what is not available for some, which is inevitably bad for their well-being. We also need to be mindful of when ignoring ‘evidence’ is better for well-being and that universal solutions do not work.
Lastly, data affect people’s well-being. As I’ve already said, it may seem like data are neutral, but they are used to inform decisions that are political because they affect people—and some people more than others. I ask my students to think about good data and data for good. Good data might be thought of as an issue of quality. In the case of statistics, this means they ‘fit their intended use, are based on appropriate data and methods and are not materially misleading’ according to the government statistical service . The GSS also state that their statistics ‘serve the public good’, not only because they capture aspects of society, but because they are shared. So, how data and the information they are capable of providing are shared is implicit in an idea of ‘good data’. However, more attention should be paid to how this is shared understanding (which is where we shall conclude this book).
- For further discussion of ideas of well-being: Sara Ahmed compellingly explains how the ideals of happiness are not available to all: they are reliant on race, class, gender and sexuality (2010). I have tested this using a Google search over different years ((see Oman 2015b as an example[↩]
- See Ryan (2021) for some alternative messages.[↩]
- GSS n.d.[↩]