Understanding Well-being Data

Chapter 2 Knowing well-being: a history of data

Conclusion: well-being as a tool of policy

There are rising numbers of well-being metrics, which are increasingly used by those who want to know more about people and populations. These data influence national policies and international initiatives. The use of well-being data to make policy decisions is said to be premised on Jeremy Bentham’s Greatest Happiness principle: that ‘the right moral action is the one that produces the greatest happiness’, and therefore, ‘the best public policy is the one that produces the greatest happiness’.((This description of ‘the Greatest Happiness principle’ is taken from Layard’s introduction to Bentham, in his book, Happiness: Lessons from the New Science (2006, 5). Although a footnote later in the book points to the fact that Bentham corrected this phrase later, saying that he meant the greatest total sum of happiness (2006, 262). This is further discussed in Chap. 4 in the section on the Greatest Happiness principle.)) As the introduction outlines, for some years there have been hopes to understand the well-being of a population at any given moment, which can then be traced over time. New models have been developed with the aspiration to appraise the impact of particular policy interventions by assessing their impact on specific measures of well-being.

An evaluation of how a particular action has impacted on the well-being of people or populations allows for predictions as to how similar choices will impact in the future. We may not know what will happen, but people in power like to make educated guesses. Governments and other agencies use this information to judge which policies are thought to ‘maximise’ well-being. According to the rationales of NPM, it is considered possible to estimate the most efficient way of increasing well-being by making decisions using econometric models and subjective well-being data to estimate impact valuations.

The supposedly neutral frameworks and technologies used to decide which lives benefit, and which do not, are, of course, never truly impartial1. Choices are made at all junctures when evaluating a policy action, and in the ‘science’ which informs the evidence: what is measured: what is included and excluded from the models and what proxies will be used. In times of increasing inequality, improving the well-being of the majority, a little bit, is potentially all the more dangerous for those with the least well-being, especially as it is ‘easier to improve the quality of life of people who have relatively high levels of well-being to start with’2. This opens up questions for how knowledge about well-being is used, and in turn, affects well-being?

This naturalised belief that progress is about striving for well-being is engrained in society, becoming a central logic of policy-making and in our everyday lives. Yet, well-being is not a fixed concept; it shifts depending on who is using it when, and in what context. As we have seen, it has different levels of influence and impact and can be dangerous if used neglectfully. As a tool of policy, well-being is a concept that is applied in various ways which can be implicitly or explicitly guided by valuation. These definitions, histories and contexts are important and come to guide our knowledge of, understandings, measurements and policy implementations of well-being. Thus, reviewing how they all work together, as this chapter has done, is a useful exercise in introducing how we know well-being through data. Crucially, this background forms what well-being data are, where well-being data come from and how they are analysed, as we shall discover in the next chapter.

  1. Williamson 2015; Oman 2015 []
  2. Oakley et al. 2013 []