chapter 3 Looking at Well-being Data in Context

Conclusion

Understanding whether data are ‘good data’, as in good quality—or whether they are data for good (and thus good for well-being) requires us to look at context. We have to consider whether international indicators appeal to certain standards, and if so, how so, or to whose standard? Data are often used as if they are neutral and context-less, yet they have rich context that is rarely acknowledged. Understanding the expertise, reflections and decisions involved in these ‘objective data’ makes them appear richer and therefore could be argued to demonstrate, rather than decrease the appearance of rigour.

This chapter has aimed to offer an overview of different contexts that dictate both what and how good well-being data are. These environments have varied from local parks to international statistics forums; from a youth club in Derry five years ago, to a presidential candidacy speech in Kansas over half a century ago. Across qualitative and quantitative data; primary, secondary and tertiary data; proxy data, administrative data, survey data and ethnographic data. Data collected from talking to people can be harder to imagine as data, because we usually think of data as numbers. However, the contexts of these data—how they are collected and analysed—are also often easier for most people to imagine than those of international statistics. This is because it is easier for most people to picture themselves being the person speaking to people, either asking questions or answering them.

Most of us don’t spend much time thinking about how data experts work. Why should we? But then how statisticians and data experts work are not transparent, or often discussed. This is, in fact, a barrier to understanding how their statistics and data work. This is not a textbook, and so looking at all these different types of data may not make you a statistician, but in reading this chapter, you may have improved your understanding of well-being data and their diversity. Looking at these data in context should also better enable you to better appreciate these data when you next see them in the media or in another government briefing (hopefully not about COVID-19).

We start this chapter by contextualising a political quote that is used a lot to justify why well-being data are good. We also look at a collection of attempts to define well-being for data across some policy documents over time that coincides with the recent rise of well-being data. The reflections on this political speech and policy documents treat these texts as data, enabling us to contextualise policy, politics and data with well-being.

The chapter then reflects on a number of different situations in which well-being data are generated, interpreted, analysed and applied. A hypothetical scenario of a well-being at work survey, a questionnaire outside a concert and real-life examples of well-being data that are relevant to social and cultural policy are shared to show the variety and accessibility of some approaches to well-being data collection, but the need for caution, consideration of others and the foregrounding of context in these matters. How you affect the data and the participants by collecting and using data is crucial to all research on society, especially that which supposedly improves it. This is not only a moral and ethical issue, but one that can limit the claims that can be made using these sorts of well-being data, should the wrong decisions be made, or should they not be explained. Therefore, the consequences of well-being data are crucial contexts, as well.

The HDI and the OECD well-being measures evolved from working within professional codes to innovate and generate the indices. It is not always obvious that this is a long process of organising and interpreting by

experts before final decisions are made. In presenting good practice and contextualising how these things work, these sections hoped to improve your capability and confidence (which we identified as data issues in Chap. 1) if you are less familiar with these data. The objective lists that feature in these new well-being indices are often made of data that have been long collected. Once this context is understood, they seem less revolutionary than the politics sometimes implies. It is actually the newer subjective well-being measures that were being developed over the 2000s that were the more novel aspects of these well-being indicators, and we come to the limits of these claims in the next chapter.

The very name ‘objective indicator’ suggests it is that: objective, but often the data does not measure what they say it measures, instead being a proxy for what would ideally be measured, were there a measure for it. You may have found yourself reading the section on quality of life indicators, thinking how these indicators would pick up on the negative well-being impacts of the bedroom tax that Bogue’s research uncovered. The answer is they are very unlikely to at national population or international population level, and were not designed to do that.

Well-being data are not all one thing. They have different purposes, pros and cons. Qualitative data are able to get closer to the meaning of well-being and the experiences of ill-being in some cases, but are often unable to generalise and are criticised for the subjective nature of the associated processes and the limits to claims of causation. We will look at how asking people how they really feel in surveys attempts to address some of these circumstantial issues of capturing the human experience in the next chapter where we put ‘the new science’ of happiness into context.