Understanding Well-being Data

chapter 7 Evidencing Culture for Policy

Conclusion: well-being data and its uses to understand policy questions

We began this chapter with David Cameron promising to put ‘instincts we feel to the core’ to ‘the practical test’ so that those whose decisions on policy and spending, that affect people’s lives, take account of what matters. We end with concerns about impact and conflated variables. We considered data and evidence in cultural policy briefly, before looking at three components of the culture–well-being relationship that are relevant to our policy concerns. First, we looked at subjective well-being (measured as life satisfaction) over time and policy spending on culture in the UK over time. Second, we looked at different kinds of subjective well-being data and ‘creatives’ (broadly defined) in the UK and the US. Finally, we looked at subjective well-being and ‘cultural access’ (broadly defined) in Italy.

We had a play with different kinds of readily available data to look at the relationship between policy spend on culture and whether that impacts on national well-being. We considered the contexts of the data, the limits of what we can expect in terms of impact on life satisfaction as a measure and in terms of policy spend on a measure. Although these data were used descriptively, we found ourselves with questions as to why more research has not been done on the relationship between policy investment and well-being, given claims for investment based on improved well-being? This left us at a point of provocation: why are some data operationalised to understand the culture–well-being relationship, when other data are not?

We compared two studies that seemed to look at comparable groups, but reached different conclusions about the well-being of people who could be called creatives. Again, we reflected on the contexts of data, the ambitions of the researchers and the aims of the research to appreciate the limits and extents of claims that can be made. We spent some time breaking down how models and categories work, and why they are important for understanding what is being measured about culture and what is being measured about well-being. We also considered a much-cited study on the impact of what the authors call ‘cultural access’ on well-being. We discovered that ideas of culture and cultural access were slippery which enabled a favourable outcome. We reflected on how an outcome that might be popular, because it reinforces people’s beliefs about the culture–well-being relationship, can result in the study being frequently referred to in later, and influential literature reviews.

This chapter has tried to break down some features of how these different aspects of cultural policy (investment, labour, access) are measured. It also wanted to demonstrate that these relationships can be explored simply, using easily available data. The lack of relationship between life satisfaction and GDP (the Easterlin paradox) is lauded as the starting point for a whole new area of research in happiness economics and positive psychology. Yet, the lack of relationship between life satisfaction and arts subsidy is not discussed as an important research question. We might be similarly interested in how little research has happened since the two projects on being an artist or the creative occupations, to further understanding of the complex relationship between professional creative practice and well-being.

The final question for this chapter, though, is are we using data to establish evidence or finding data to suit arguments? There are frequent calls that more evidence is needed to support the cause of cultural policy to argue its value as social policy. Why are there not more analyses of the data already available, even if they reveal a possibly uncomfortable relationship, as in the case of cultural funding, or other aspects of delivering social policy and well-being? Perhaps this might be where more complex relationships between well-being, inequality and culture might be explored. Despite the crudeness of tracking arts funding and life satisfaction data together, they tell a simple and effective story and definitely warrant future research. Or, at least ask questions of existing research. In the next chapter we will explore one of a number of studies that use increasingly complex quantitative techniques to express the relationship between culture and well-being differently. Thus, continuing our exploration of evidencing culture for policy.