Understanding Well-being Data

chapter 8 Talking different languages of value

Conclusion: The value of valuation

Value is, in other words, both various and variable.

(Throsby 2001, 28)

Where are we with our thinking on the value of valuation? If some people in the sector work in the sector because they know it improves happiness from their own experience, do they need proof that this is true? Even if this validation comes from research that is not immediately legible to them, is it necessary to understand the findings cited in detail? How important are the various contexts of this research and the potential limitations of its findings for those who want to use it? The Museums Journal described the report as having ‘found museums improve people’s happiness and perception of good health, even after other factors that might be influencing them are accounted for’1, and goes further than the original report by claiming that visiting museums ‘boosts’ happiness, as opposed to museum-goers being happier. While the research project aimed to contribute to the evidence base on the value of museums, its findings are extended by those who wish to see such positive results. How does this impact positively or negatively on the status of evidence in this area—and the arguments for the value of culture?

Previous chapters have explained why there is an avalanche of numbers, and the various stories of why quantitative approaches to understanding well-being tend to dominate research used in policy. Population-level understandings of well-being are necessary to understand geographic, racial and gendered disparities. Revealed discrepancies can then indicate where policy investment should focus (in theory, although these analyses were not included in the ‘Museums and Happiness’ research). But we must scrutinise the relationships involved in these processes—theoretically and empirically. To do this requires more people feeling like they could understand well-being data. This takes practice and familiarity, but most importantly, more care is needed in research and data communications to move towards more shared understandings.

This book aims to help people feel more comfortable with data by explaining what is going on. This chapter has offered snippets of a step-by-step consideration of the data contexts: their origins, how the data were used, how researchers arrived at these results, and how these findings were then used by others. We looked at all the decisions made, and how reproducing methods with the same data does not always lead to the same results. We also considered how claims were made, and findings subsequently

shared. More care is required around transparency around research: even when reports are transparent, more effort could be put into doing transparency differently, to improve understanding and enable people to use research more fully. While these valuations may work for HM Treasury, there are multiple audiences for research like this, and those who present it, could try harder to speak different languages and be more understandable.

At the moment, this sort of research is not published in a way that makes it accessible. Instead, the culture of this kind of research more broadly tends to mean that only headline findings are accessible to cultural and social policy practitioners, who are reliant on data and expressions of data for advocacy, yet are not necessarily comfortable with their origins. Stating one thing in headline findings, but explaining how the meaning is slightly different in practice in bits and pieces further into the report is not necessarily making it as understandable as it could be, and yet it is the norm. The Happy Museum aspired to produce compelling statistics to bridge the gap of cultural values and valuations, and the research behind the report aimed to meet this challenge. However, the research met the aims of valuation, rather than the needs of those who need the research. Acknowledging this demands resource and skill in and of itself, but the culture of research for policy and social policy organisations could change to make the ways in which it uses data and discusses limitations and caveats more easily understandable.

This chapter presented one example in great detail to be a reference point for readers to come back to, to aid future understandings of how well-being data can be used. As this book has acknowledged elsewhere, there are still many issues in data and evidence that are relied on for cultural and social policy. In the age of well-being measures and measurers, it is important that we all feel able try and engage with the data and the claims on our terms—should we wish to. Given that how people feel about these relationships is imbued with their own values, the key is to feel more confident to ask questions and make value judgements for yourself.

  1. Harris 2013 []