Understanding Well-being Data

Chapter 2 Knowing well-being: a history of data

Common definitions used with well-being data

There is no single definition of wellbeing. The terms wellbeing, quality of life, happiness, life satisfaction and welfare are often used interchangeably (although some disciplines draw distinctions between them)

(Allin 2007, 46)

Paul Allin became Director of the UK’s Office for National Statistics’ Measuring National Well-being programme. As he acknowledges above, there are a number of terms used as if they are substitutable in disciplines associated with measuring well-being. In addition to happiness, life satisfaction and quality of life are also synonymous with well-being. As we shall find out throughout the book, when it comes to data, although these ideas are linked in a common-sense way, life satisfaction metrics are largely from different sorts of data than quality of life metrics. Life satisfaction measures aim to capture how people feel and so they are from subjective evaluations. Quality of life measures are used to understand various qualities of life, such as health and relationships; the endgame is understanding how these work together, to then assess overall well-being. They are made from objective lists and measures.

Objective Well-being

This approach examines what are thought to be the components of the good life, using objective data which include resources (income, food, housing) and social attributes (education and health). Objective well-being data are then added up (aggregated) to become society-wide descriptions that imply concrete conditions, such as employment rate or life expectancy. They are objective because they measure material conditions, and are considered impartial. They are well-being data as they are used to understand how something like housing or income might impact on our lives. In other words, the can be used as a proxy measure for well-being. By proxy we mean an indirect measure. For example, someone’s income does not necessarily directly tell you about their quality of life, but because the relationship has been long-studied, assumptions can be made about well-being using what we know about how income relates to well-being – so the theory goes.

Objective well-being data predominantly come from what we call administrative data. These data are collected in the processes of our everyday lives, like taxation or the registration of births, marriages and deaths. Objective data are also collected from people using surveys. Questions that ask for details on salary and how many people live in someone’s home (like in the census), for example, are objective. Chapter 3 looks at objective lists and measures in much greater detail.

Subjective Well-being

As with health diagnoses, subjective well-being data are generated by asking people questions about how they are doing and / or how they are feeling. This can be about their material conditions: how they feel about their local area; is it clean? is it safe? It can also be how they are feeling in and of themselves. One example is the UK’s Office for National Statistics (ONS)’ four questions to understand personal well-being. We will return to ‘the ONS4’ often in this book. They ask:

  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?

People score themselves out of ten, with most scoring around a 7 out of ten for life satisfaction. These scores are aggregated to become the well-being data of a population who answered these questions. These aggregated data are used in a number of ways which can be tracked over time. Subjective measures are also used against objective measures, so if a measure of poverty spikes, we can see if this appears to be linked to anxiety using data produced by question 3. More recently, subjective well-being questions have been used to track impacts of the Covid-19 pandemic on different samples of different populations all across the world.

As we have touched on, understanding the human experience in a more scientific way is one of the key drivers of the well-being agenda. Chapter 4 looks in greater detail at the study of subjective well-being as ‘a new science’ [1]. Interestingly, this ‘new science of happiness’ is one of the academic and intellectual developments that saw a resurgence in interest in well-being measurement more generally, especially in policy. Somewhat confusingly, the well-being agenda – as the measurement of well-being – tends to be discussed in terms of objective indicators to replace GDP, rather than subjective well-being. As we discover in the next section, this is a more complex history than is ordinarily accounted for.


  1. Layard 2006[]