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Happiness economics: Can we have an economy of wellbeing?

The UK government is the latest to consider incorporating measures of happiness in its policymaking. This column takes stock of what we know from investigations into people’s wellbeing. It concludes that there is still much to resolve before a measure of gross national happiness is possible – or indeed desirable.

At this year’s American Economics Association annual meetings in Denver, there were the usual panels on topics ranging from the international exchange-rate regime to the roots of the global financial crisis to trends in the real-estate market in the US. More unusual was a keynote session on whether happiness measures should replace gross national product. The latter was a standing-room-only event that was written up (rather sceptically) by the Wall Street Journal. As if that were not enough, in the same month there was a panel on the same topic at the World Economic Forum in Davos, Switzerland, with Jeffrey Sachs, the once wunderkind of free markets, calling for happiness as the ninth Millennium Development Goal. That session was written up (less sceptically) by The New York Times.

What is the world coming to? I participated in both of those panels. And other than kicking myself for being at two top skiing destinations in the same month without getting to ski, I was very happy about it. Until five or so years ago, I was one of a very small number of economists studying happiness; now my small circle of happiness researchers has been joined by other economists who are using happiness surveys to understand questions as diverse as the effects of commuting time on wellbeing, why cigarette taxes make smokers happier, and why the unemployed are less unhappy when there are more unemployed people around them.

The discussion has now moved from empirical studies that aim to deepen our understanding of human wellbeing to happiness as a policy objective. About a decade ago, the government of Bhutan implemented Gross National Happiness as a measure instead of GNP. In 2008, the Sarkozy Commission, chaired by two Nobel Prize-winning economists, called for a worldwide effort to develop measures of wellbeing that went beyond those based on income (see Stiglitz et al. 2008). And while the commission was criticised by conservatives in the US as a left-wing attempt to make our economy “sclerotic” like France’s, the most recent effort to add wellbeing indicators to national statistics has come from the conservative Cameron regime in Great Britain (see for example Dolan et al. 2011).

On the one hand, these are exciting times for scholars in this field, as the interest in the topic in economics and beyond opens the door to a much wider research agenda, for collaboration with a much broader set of academics, and for influence extending into the policy arena. Yet the leap into policy also raises a number of unresolved questions. The most important, in my view, are: What definition of happiness is most relevant and appropriate for policy? And how does that definition vary across different societies? These questions are the central focus of my new book, The Pursuit of Happiness: An Economy of Wellbeing (Brookings, 2011).

The need for definitional clarity raises conceptual challenges for those of us who rely on happiness surveys as a research tool – and as a new microscope for examining and understanding the determinants of human wellbeing. For the purposes of empirical research, we rely on an open-ended question, typically: “Generally speaking, how happy are you with your life?” or “How satisfied are you with your life?”. Possible answers lie on a scale running from “not at all” to “very” happy. We then compare the variance in happiness levels based on the other information that we are able to collect, such as the respondents’ income, marital status, age, residence (urban/rural), employment status, and so on. The patterns that we find are remarkably consistent across respondents worldwide, including in countries of very different development levels.

That consistency then allows us to test for the effects of other variables, such as living under different levels of inflation and/or kinds of governance and environmental regimes. We do not ask respondents if phenomena such as inflation, pollution, commuting time, and/or the nature of their government (for example) make them unhappy. Instead, once we have accounted for the effects of the standard socioeconomic and demographic variables (e.g., age, gender, income, and employment status), we can compare the variance in happiness scores that is explained by these additional variables.1

This works clearly and simply from a research perspective. Yet from a policy perspective it is more complicated. Policy is driven by factors ranging from ethical norms to aggregate welfare objectives to cultural differences. Those factors, in turn, influence the definition of happiness across individuals and countries. Centuries ago, philosophers worried about happiness. On the one hand, Jeremy Bentham’s concept of welfare was maximising the contentment and pleasure of the greatest number of individuals as they experienced their lives – that is, people feeling happy on a day-to-day basis. Aristotle, on the other hand, thought of happiness as eudaimonia, a Greek word that combined two concepts: “eu,” meaning wellbeing or abundance, and “daimon”, meaning the power controlling an individual’s destiny. This is, in the broader life-evaluation sense, the opportunity to lead a purposeful or meaningful life.2

A wide range of research findings suggests that which dimension matters to a particular person is, at least in part, determined by that person’s capacity to pursue a meaningful life. In the absence of that capacity – due, for instance, to government restrictions or a lack of wealth or education – people may place more value on simple, day-to-day experiences, such as friendship and religion.3 Those with more capacity have less time and interest in day-to-day experiences, particularly if they are very focused on some overarching objective or achievement. (Think of the scientist trying to cure cancer who sacrifices leisure time and personal relationships in favour of bench time).

Some societies might be comfortable emphasising happiness-as-day-to-day-contentment as a policy objective. Others, such as the US, which has the “pursuit” of happiness in the Declaration of Independence and has traditionally emphasised the importance of equal opportunities over that of equal outcomes, would likely opt for a definition of happiness based on the opportunity to pursue life fulfilment. Yet promising that in turn requires providing citizens with the tools and agency to do so.

In short, there is much to resolve before we can agree that developing a measure of gross national happiness is a desirable and shared public objective. Yet many countries – with Britain standing out as the clearest example – are at the cusp of developing and utilising wellbeing metrics in the policy discussion. As a low-cost and risk-free first step we could consider the merits and demerits of that approach by simply adding a few tried-and-true questions to our statistical gathering. It would require us to reconsider our benchmarks of progress and think deeply about questions such as whether we value opportunity or outcomes more; achievements or process (e.g., life evaluation versus day-to-day experiences); and how much we should emphasise things such as health, leisure, and friendships over productivity and longer working hours. The results from the metrics themselves will provide important fodder for discussion.

At least for now, that is as far as we can or should go. We can compare income across people with a broad consensus on the metric and what it seeks to measure, i.e. changes in the amount that each economy produces. While we have made great strides in developing robust and comparable measures of the various and distinct dimensions of happiness, we do not yet have the same kind of consensus on the aggregate concept that we are seeking to measure and aim for as a policy objective.

Happiness is, in the end, a much more complicated concept than is income. It is also a more ambitious and laudable policy objective. The fact that it is seriously on the table reflects what a parameter-shifting moment it is in economics and in policy debates more generally. Indeed, at a time when so many of our public and political debates are divided and contentious, exploring new parameters and metrics that provide tools for evaluating the wellbeing of our citizens rather than emphasising the roots of their divide is a welcome change. For those of us studying the topic, this change provides great impetus to get the nascent science right.

A slightly different version of this piece appeared in Johns Hopkins University’s One magazine.


Diener, Ed, W Ng, J Harter, R Arora (2010), “Wealth and Happiness across the World: Material Prosperity Predicts Life Evaluation, whereas Psychosocial Prosperity Predicts Positive Feeling”, Journal of Personality and Social Psychology, 99:52-61.

Dolan, Paul, Richard Layard, and Robert Metcalfe (2011), “Measuring Subjective Wellbeing for Public Policy”, Office of National Statistics, February.

Graham, Carol (2009), Happiness Around the World: The Paradox of Happy Peasants and Miserable Millionaires, Oxford University Press.

Kahneman, Daniel and Angus Deaton (2010), “High Income Improves Evaluation of Life but Not Emotional Wellbeing”, Proceedings of the National Academy of Sciences, 4 August.

Sisella Bok (2010), Exploring Happiness: From Aristotle to Brain Science, Yale University Press.

Stiglitz, Joseph Amartya Sen, and Jean-Paul Fitoussi (2008), “Report by the Commission on the Measurement of Economic Performance and Social Progress”.


1 For a summary of the approach and basic findings across countries, see Graham (2009).

2 For a fuller discussion, see Bok (2010).

3 Two recent studies suggest that happiness questions that capture different dimensions of wellbeing – experiencing daily life (hedonic wellbeing) versus life purpose and life evaluation (eudaimonic wellbeing) – correlate differently with income, with the latter being more closely correlated with income than the former. See Kahneman and Deaton (2010) and Diener et al. (2010).

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