Understanding Well-being Data

Chapter 2 Knowing well-being: a history of data

Measuring well-being to improve human welfare – a brief history

The measurement of well-being and quality of life for policy-making has recently been described as ‘an idea whose time has come’1. Articles on happiness and well-being averaged less than five a year in the journals covered by the EconLit database((The EconLit database is considered the authority on economic research citations and abstracts. It is managed by American Economic Association and contains more than 1.4 million records, indexed from 74 countries, with citations and abstracts dating back to 1886.)) in the 1990s. By 2008 this had risen to over 50 each year2. Bache and Reardon3 historicise this surge in interest as a political phenomenon that they term ‘the second wave of well-being’.

The first wave of well-being evolved as a project of redistribution after World War II. Prior to this, in the 1920s, Gross Domestic Product was developed as a broad quantitative measure of a nation’s total economic activity. It was treated as a proxy for increases in individual wealth, and fluctuations in unemployment, thereby tracking material quality of life at national level. A recent history of national accounts in different countries indicates that the well-being of citizens, not their bank accounts, was considered to be the end goal of government4. The goal of collecting information on income distribution, growth and productivity was to examine how those indicators influence the welfare of the nation, according to economist Simon Kuznets, one of the originators of GDP. Although Kuznets also acknowledged that economic indicators were only one piece of the puzzle of citizens’ well-being, and that ‘the welfare of a nation can ‘scarcely be inferred from a measurement of national income’5. He was, therefore, arguing for the value of GDP as an instrument, but aware of its limitations, crucially stating:

Goals for more growth should specify more growth of what and for what.

(Kuznets in Croly 1962)

GDP and national accounts data were not only generated to go about understanding individual nations, but also meant that countries could be compared in these terms, reflecting a broader trend towards comparable data across nations at this time. In 1924, the League of Nations Health Organisation created the Permanent Commission on Biological Standardisation to monitor drug tests. This increasing momentum to share information on populations, including unemployment, wages and migration led to the new International Statistical Commission in 1947. The modern term ‘statistics’ was, in fact, coined with the invention of new system of accounting for national governance to ascertain ‘the quantum of happiness’ with a view to using these data to govern the nation better6.

Growing concerns evolved in the 1950s that personal prosperity created social costs which manifested as public poverty((Similar to contemporary inequality arguments, such as Piketty 2013.))7. There was also growing recognition that these social costs could not be captured by GDP. It was decided that this needed to be addressed through the development of new measurement tools that could help track whether life was actually getting better. These were hoped to be able to compensate for some of the shortcomings of GDP as a measure of human progress.

This is what came to be known as ‘the social indicators movement’, which emerged in the spirit of redistribution and an aspiration for new levels of knowledge of everyday life, birthing new surveys, such as the Level of Living Survey8. These alternative but ‘objective’ benchmarks of progress grew in relevance on the international political agenda9. The economic collapse of the 1970s is believed to have compromised the impact of these new indicators. The fact that economics had failed to avert economic crisis10, alongside a growing distrust of government, prevented the social indicator movement from toppling GDP as the primary measure of prosperity, and thus the focus on progress as growth remained.

The ‘second wave’ of well-being began in the comparative prosperity of the late 1990s1 and was cemented in the high-profile commission of leading international economists.((The Commission on the Measurement of Economic Performance and Social Progress (CMEPSP) is also referred to as the Stiglitz-Sen-Fitoussi Commission after the surnames of those who led it. It was a commission of inquiry created by the French Government in 2008 and so is also referred to by the name of Sarkozy, as France’s president.)) This responded to ongoing work of the OECD and concerns that material growth was impacting negatively on the planet11. It also responded to what has become known as the Easterlin paradox12: the discovery that rising wealth was not—in fact—improving people’s life satisfaction. The commission recommended, with considerable influence, that an alternative benchmark of progress should be found that was able to measure more than GDP and that all nations find a way to measure their own well-being. This task was taken on by most OECD countries, in different ways, and its timing in the UK resulted in its branding as Conservative Prime Minister of the Coalition Government, ‘Cameron’s happiness index’, when it was a far bigger movement that started a decade earlier.

The second wave also coincided with recent developments in subjective well-being data collection. The ONS example which they called Personal Well-being was introduced in April 2011.((The ONS began measuring personal well-being in April 2011 to provide the indicator that the ONS call ‘Personal Wellbeing’ (see e.g. ONS 2015 for more detail). )) The measurement of subjective well-being for policy emerges from ‘happiness economics’13, which builds on work in the positive psychology movement14 and which we explore in Chap. 4. Richard Layard15 used the term ‘hedonic treadmill’((The term was in fact coined by Brickman and Campbell in 1971.)) in response to the Easterlin paradox. It describes how we adapt to increasing wealth, resulting in a need for more income to maintain the levels of life satisfaction we are accustomed to. This results in greater consumption, which causes material growth and negative planetary impacts. Around the same time, other research was beginning to note the positive impacts of more social aspects of life on subjective well-being: social interaction, faith, intimate relationships, government spending and different political-institutional frameworks1.

The demise of the social indicators movement in the 1970s was arguably not only the result of economic downturn16. Instead weaknesses in the objective indicators and data themselves made them unsustainable. Described as a ‘bewildering array’, these metrics were not linked to a robust theoretical or ideological analysis of what quality of life was exactly. The metrics and their analysis did not answer what needed to be achieved for whom and how17. Thus, the second wave appealed to these proclaimed deficiencies.

The history of well-being measurement raises important questions regarding what measures are suitable for policy. Experts argue that the science behind measuring well-being is becoming more robust18, but do the indices address the fundamental question of what ‘quality of life’ is? Do they accommodate how people will find different qualities more valuable in various circumstances? Also, if wealth remains a proxy for well-being for some, and addressing well-being inequality((See, for example, the What Works for Wellbeing website (2016) on addressing well-being inequalities.)) is a new policy focus, has it been decided how redistribution of well-being would be undertaken in practice?

The very essence of well-being, as it is generally understood (particularly subjective well-being), not only is attached to the lived experience, but should encompass it. Instead, well-being is often discussed in a detached way as an object of politics that changes over time. Some argue that this is as a consequence of it becoming measurable19 which means well-being assumed its own agency, and in ways which are not necessarily understood by the general public. Others argue that this is the very consequence of attributing value to values20. This obscures the political motivations, and the power of those creating and operationalising the measures and models, for policy evaluation. Remember when we were thinking about the idea of facts being neutrally observed, as objective and neutral, without factors which can affect judgement? Power is one reason why neutrality is harder to prove or argue than is always recognised.

These are the politics of data. It is imperative to consider these issues if we are to respond to the well-being agenda, including calls to move from ‘national well-being measurement to a national well-being strategy’ in a report by the All-Party Parliamentary Group (APPG) on Wellbeing Economics21. Furthermore, different policy domains take different positions in a national well-being strategy. A well-being strategy might imply working towards a better social infrastructure, thus improving welfare provision overall, but it may actually be about foregrounding any one of a number of issues attached to the well-being agenda: social care, mental health resources, more NHS nurses, decarbonisation or increasing the minimum wage.

To understand how well-being data might enable a well-being strategy, we need to side-track briefly into some other historical contexts. We have mainly talked about national indicators: the social indicators’ movement as an international imperative to change the way progress was measured (in the 1960s) as a project of redistribution, or the more recent second wave (of the 1990s and 2000s) encouraging individual nations and international bodies to devise more complex indices of objective and subjective well-being. The same kinds of data can be collected to evaluate policy decisions, actions and investments, and there are numerous techniques used in policy evaluation. These were generated to value the non-economic in the audit society, but ‘they are too liable to be co-opted, in support of some broader notion of efficiency’22. The following sections explore how we arrived at what has been called ‘the cult of the measurable’23 and what that means for well-being data and what we value.

  1. Bache and Reardon 2013 [↩] [↩] [↩]
  2. Fleche et al. 2012, 8 [↩]
  3. 2013 [↩]
  4. Perlman and Marietta 2005 [↩]
  5. Kuznets 1934, report to congress, cited in OECD 2007 [↩]
  6. Sinclair 1798, vol. 20, xiii [↩]
  7. Noll 2002 [↩]
  8. The Swedish Institute for Social Research 1968; ONS 1970 [↩]
  9. Scott 2012; McGillvray 2007 in Bache 2012 [↩]
  10. Bache, 2012 [↩]
  11. Bache 2012 [↩]
  12. 1973 [↩]
  13. Layard 2006 [↩]
  14. e.g. Seligman and Csikszentmihalyi 2000 [↩]
  15. 2006 [↩]
  16. Scott 2012 [↩]
  17. Scott 2012, 36 [↩]
  18. O’Donnell et al. 2014; Helliwell et al. 2015; Cameron 2010; ONS 2015 [↩]
  19. Beer 2016; Davies 2015; Doria 2013; White 2014 [↩]
  20. Doria 2013; Kaszynska 2021 [↩]
  21. Berry 2014, 4 [↩]
  22. Davies 2014, 193 [↩]
  23. Belfiore and Bennett 2007, 137 [↩]