Understanding Well-being Data

chapter 4 Discovering ‘the new science of happiness’ and subjective well-being

How is this well-being measure subjective?

This portrayal of the ‘new science[s]’ of happiness is (as Seligman hints) not as new as implied, but also results from fundamental theories and indicators of well-being that date back centuries. One important—yet confusing—distinction is that there is the idea of experienced well-being (how we experience well-being or happiness) that gets called subjective well-being and then there are measures of well-being that form objective lists, like the OECD’s, that are based on subjective data.

As we have seen, objective approaches to measuring well-being investigate the objective dimensions of a good life (using largely proxy indicators). However, the subjective approach examines people’s subjective evaluations of aspects of their own lives by collecting numeric data. For example: ‘on a scale of 1–10, how safe do you feel walking home at night?’ This is not the same as how people feel about their well-being.

As we have also seen already, a number of well-being indices that were established around the same time have recognised the importance of

aking people’s perceived well-being into consideration alongside objective lists in order to measure overall well-being. Subjective well-being data are generally captured using questions about how people feel they are doing. We are going into more detail about this now, in order to understand how these data can differ, and how they are different from the objective well-being indicators and the qualitative data described at length in the previous chapter. Crucially, it is the subjective well-being data about how we think our own well-being is that are the driving force of happiness economics and the second wave of well-being1. As we shall discover, this is largely down to the influence of key advocates, such as Layard, in the well-being agenda.

Let’s consider the UK’s ONS’ subjective well-being data. As we have previously discovered, it uses four questions to understand what it calls ‘personal well-being’. The questions are:

  1. Overall, how happy did you feel yesterday?
  2. Overall, how satisfied are you with your life nowadays?
  3. Overall, to what extent do you feel the things you do in your life are worthwhile?
  4. Overall, how anxious did you feel yesterday?

How are these data used? The answers to these questions are on a scale of 0–10 and could be traced over time to see how an individual is doing. This is not going to happen in an anonymous national-level survey; instead aggregated data are used to understand population-level well-being over a specific period or to compare population sub-groups by geography or ethnicity, for example.((See the ONS n.d. Well-being. Office for National Statistics: https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing.)) Some of these questions with almost identical wording have been in surveys, and therefore generated data, for decades before ‘the ONS4’ were invented. Therefore, there are baselines to measure change against. The fact that these data have been collected over time can help establish how a major event such as COVID-19 has affected the well-being of the population, as well as more minor events. Chapter 7 runs through an example of how a policy change over ten years affects life satisfaction scores over a decade, for example.

These subjective well-being data can therefore be used to see how a particular event affected anxiety, alongside other social and structural issues, such as, say, poverty. Again, this does not mean that, for example, an individual’s household income is looked at against their anxiety levels, but that average anxiety of everyone who was asked the question (or, as we might say, the population sampled) is measured against the average household income levels. There are two things to remember about samples, the first is that few surveys are completed by a whole population, so the data collected almost always come from a sample; the second is that sampling is cleverly worked out so that if you sample enough of the population, you can make generalisable claims. Therefore, while national-level surveys do not measure nations in their entirety, they can make good estimations using mathematical rules. The other thing to say is that poverty can be measured using whatever indicator has been decided to represent poverty. There are numerous poverty indicators, which could be household income, for example, or the IMD (index of multiple deprivation). As we discovered in Chap. 1, ‘Introducing Well-being Data’, poverty is not one absolute, objective thing when it is discussed in parliament. Politicians cherry-pick from absolute and relative poverty measures and across different timeframes to arrive at the most complimentary statistics for their argument. So, what subjective well-being is measured against can also be subjective, in that the data and their uses are not automatically neutral or without bias, but are indeed chosen.

  1. Bache and Reardon 2013 []