Understanding Well-being Data

chapter 8 Talking different languages of value

The well-being data available in the Taking Part Survey

Happiness taps in to people’s emotions, technically their affective state, and hence tries to gauge people’s moods at that moment.

(Fujiwara 2013, 12)

As we saw in Chap. 6, part of thinking through how humans experience well-being, is acknowledging these processes are cultural and centre ideals of ‘society’; they also involve imagining moments of social or cultural engagement and how they affect people on an individual level. Questions of when and how we experience particular well-being effects (or, perhaps, different kinds of well-being) are a key part of the puzzle of philosophers’ thinking for centuries. As we have also seen in Chap. 4, this problem has driven recent developments in well-being measurement, arguably shaping what we have called the second wave of well-being and happiness economics. Understanding people’s emotions in this way is used in various research contexts: whether using the diary reconstruction method (DRM) outlined in Chap. 4 to understand how people are doing in the day-to-day life, or to understand how a major event, such as the financial crisis of 2007/2008 or COVID-19 has impacted on people’s well-being at scale.

What is hopefully clear by now is that deciphering which particular moment is actually being captured when attempting to measure an ‘affective state’ (such as happiness), and whether that is the moment that is relevant to your research question, has proved complex for a long time. Chapter 4’s Fig. 4.1 and the related section outline how approaches to understand this differ, yet are related. ‘Museums and Happiness’ uses TPS data, which now include all of the UK’s Office for National Statistics’ four well-being questions (ONS4),((Table 4.3 shows a selection of the surveys that the ONS4 have been added to.)) and has since the 2013–2014 dataset. However, the research we are looking at analysed data from 2005 to 2011, so before this change. Therefore, the question is similar, but not identical, to the ONS4 experience measures which ask about happiness and anxiety yesterday (see Table 4.2). The TPS data we are looking at in ‘Museums and Happiness’ understand happiness through the following question:

“Taking all things together how happy would you say you are?” on a scale from 1–10 where 10 is described as “extremely happy” and 1 as “extremely unhappy”.

The report says (as cited at the beginning of this section) that the data from this question establish someone’s mood at that moment. The report continues:

This differs to wellbeing questions that contain an evaluative judgment such as life satisfaction or eudemonic((This book uses the alternative spelling of ‘eudaimonic’.)) wellbeing. Life satisfaction is held to contain a response about one’s current emotions together with an evaluation of their life overall (how it measures up to their goals for instance) and eudemonic wellbeing questions tap in to people’s perceptions of whether they are living a meaningful life.

(Fujiwara 2013, 12)

If you return to Fig. 4.1 while reading this, you can see how this explanation maps onto the figure and the descriptions of approaches in Sect. 4.3 that follows it on how these measures are used. Notably, the ‘taking all things together’ part of the question makes it a ‘general happiness’ question, which is sometimes approached using Cantril’s ‘ladder of life’ (Fig. 4.2). I say this, so you can probably imagine different ways you might answer this particular question.

There is a broader consideration with using national-level survey data to understand someone’s ‘happiness’ in any moment. We have also encountered this before in Chap. 4, discussed in the section on experience measures. The ideal way of understanding happiness as an affective state is to ask people repeatedly during a particular day, over a period of days about how they feel in the moment. In other words, you would collect a sample of their moods and ask them what they are doing at that moment (which is why it is called the experience sampling method). This method is hard to translate into a survey because it is too time-consuming—for the

interviewer and the interviewee to repeatedly ask and answer questions. This would make it too expensive to run, and difficult for many people to be available to participate in, which would then affect your sample—or, who you are talking to, and limit understanding. As an alternative, the rationale with the ONS4 experience measures is to ‘replicate’ or ‘proxy’ ESM approaches by asking respondents for their experiences and feelings relating to a whole day (yesterday).

So, let us briefly consider what is being captured by the question: these data are collected through a national-level survey and therefore at a time and in a place that is most likely completely unrelated to a museum visit. The implications of the headlines of this report is that the ‘affective state’ is ‘gauge[d]… at that moment’, but that moment is—of course—not the moment in the museum, but when the survey interviewer is in someone’s home. On top of that, the question asks you how happy you feel you are overall, so it is not directing you to consider a period of time (as the ONS4 experience measures do), let alone a specific moment. So, we are beginning to encounter some limits, but this is not necessarily abnormal, because, as we know, all measures will have their pros and cons.

You may remember the difficulties in establishing whether a concert changed someone’s well-being, even when you ask them immediately afterwards (Chap. 3). When the question is presented to someone by the TPS interviewer, that person may struggle to even remember the last time they were inside a museum. In truth, that is not even asked. As the box in the previous section demonstrates, the questions are about the last 12 months in general, not specifically the length of time since someone’s last visit. Also, the survey did not request that they rate their happiness whilst in the museum (or before and after), but to comment on their happiness overall. Therefore, talking about measuring happiness in this way may feel confusing, because the happiness derived from visiting a museum in-the-moment is not what is captured directly in the data that are available for analysis. The title of the report implies that there is a relationship between museums and happiness, which at glance for some will undoubtedly confirm their belief or personal experience that museums make them happier, and encourage better overall well-being. This, of course, may well be true. However, we must remember that not everyone is the same, and to question what the data that are available for analysis are telling us. We must remember that it might be that—in general—people who go to museums tend to be happier than those who do not. A causal relationship may be difficult to demonstrate.

Box 8.3 Causal Inference: A Reminder

Causal inference describes the process to identify whether there is a relationship that involves the independent variable (culture) affecting the dependent variable (well-being). It means that there is an effect in the connection under study.

When looking to identify and measure causal relationships, we analyse the relationship between the cause variable and the effect variable.

To find that cultural participation is a cause of improved well-being (as the phenomenon), we need to establish that the cause precedes the effect, which means eliminating other plausible alternative causes. This is difficult because you cannot test this question in the real world.

The classic example is if we found a relationship between whether people were wearing shorts and whether they were buying ice cream, it wouldn’t mean that wearing shorts caused people to buy ice cream, or that buying ice cream caused people to wear shorts. There is something else affecting this relationship that needs to be found and accounted for.