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Published in final edited form as: Behav Public Policy. 2020 Jan 23;4(2):198–209. doi: 10.1017/bpp.2019.43

Self-reported wellbeing indicators are a valuable complement to traditional economic indicators but are not yet ready to compete with them

DAN BENJAMIN 1, KRISTEN COOPER 2, ORI HEFFETZ 3,*, MILES KIMBALL 4
PMCID: PMC8006893  NIHMSID: NIHMS1682530  PMID: 33786382

Abstract

We join the call for governments to routinely collect survey-based measures of self-reported wellbeing and for researchers to study them. We list a number of challenges that have to be overcome in order for these measures to eventually achieve a status that is competitive with traditional economic indicators. We discuss in more detail one of the challenges, comprehensiveness: single-question wellbeing measures do not seem to fully capture what people care about. We briefly review the existing evidence, suggesting that survey respondents, when asked to make real or hypothetical trade-offs, would not always choose to maximize their predicted response to single-question wellbeing measures. The deviations appear systematic, and they persist under conditions where alternative explanations are less plausible. We also review an approach for combining single-question measures into a more comprehensive wellbeing index – an approach that itself is not free of ongoing theoretical and implementational challenges, but that we view as a promising direction.

Introduction

We commend Frijters, Clark, Krekel and Layard for bringing the goal of measuring and raising people’s actual wellbeing to the forefront of the policy debate. We salute the work they and others have done – and are continuing to do – in drawing the attention of researchers and policy-makers to survey-based well-being indicators. We join them in their call for ‘a flood of experimentation by individuals, firms, communities and departments in all areas of life to see what improves wellbeing.’ And we agree with their proposed next steps: governments should systematically collect wellbeing indicators; where possible, policies should be implemented in ways that make causal inference possible; methodologies for using wellbeing indicators for policy analysis should be developed and improved; and government analysts should be trained in the interpretation and use of wellbeing indicators. Over the past decades, much has been learned through research on wellbeing indicators, and we hope to see this research continue to expand.

Where we may disagree is the extent to which current measures of wellbeing are ready for ‘primetime’. While they do not say so explicitly, Frijters et al.’s forceful presentation of the ‘case for wellbeing’ appears to suggest that existing wellbeing indicators – particularly survey data on life satisfaction – should be used now to guide policy decisions. Frijters et al. do acknowledge some key limitations of existing knowledge; for example, ‘There are at the moment conflicting estimates for the conversion rate between money and life satisfaction … Solving this puzzle … features high on the wellbeing research agenda’ (their ‘Methodology’ section). Yet, at the same time, they give the overall impression that this and other limitations are not impediments to the immediate adoption of wellbeing indicators and their preferential use in policy-making.

In our view, there are many substantial challenges to be overcome before survey-based wellbeing indicators will be ready to stand on a par with traditional economic and social indicators. We focus our paper on one of these challenges that our own research has emphasized: the failure of single-question wellbeing indicators to comprehensively capture wellbeing. As discussed further below, by ‘wellbeing’ we mean idealized choice: what people would choose for themselves if they were fully informed, clearheaded, unbiased and the like. At the end of our paper, we briefly outline some of the other major challenges on which we believe substantial progress should also be made before wellbeing indicators are widely adopted for guiding policy.

The non-comprehensiveness of single-question wellbeing indicators

As Frijters et al. eloquently articulate, the core motivation for using survey-based wellbeing indicators is that they hold out the promise of capturing all of the dimensions of subjective experience that people care about – and therefore, according to some thinkers, everything that matters for policy. Traditional economic indicators measure only some of what people care about (e.g., gross domestic product (GDP) aims to capture the consumption of market goods, investment and government expenditures), and therefore they can at best serve as non-comprehensive proxies for overall wellbeing. To the extent that a policy has important effects on unaccounted-for dimensions of wellbeing, such as health, relationships, emotions and other quality-of-life dimensions, reliance on traditional indicators alone can yield bad policy prescriptions.

Do single-question wellbeing indicators, such as questions about happiness or life satisfaction as advocated by Frijters et al., fulfill the promise of being comprehensive measures of wellbeing? Before we turn to research that addresses this question directly, we note that some of the work that has studied wellbeing indicators may raise doubts (that go beyond the conflicting income coefficients mentioned by Frijters et al.). For example, Stevenson and Wolfers (2009) report a ‘paradox’: despite improvement in their lives on many objective measures, women’s happiness in the USA declined between 1972 and 2006. There are plausible explanations for this decline – for example, women may be held to higher expectations that are more difficult to meet – and understanding the reasons for the decline may yield important insights for policy. Nonetheless, for those of us with strong priors that women are better off today than half a century ago, the paradox suggests skepticism about treating happiness self-reports as comprehensive measures of wellbeing.

Evidence from several papers more directly addresses the question of whether wellbeing indicators are comprehensive measures of wellbeing, including two of ours that we briefly describe below (see also Adler & Dolan, 2008; Fleurbaey & Schwandt, 2016; Adler et al., 2017; Szabó & Ujhelyi, 2017). Like Adler (2011) and others, our benchmark for assessing whether a measure comprehensively captures wellbeing is idealized choice. This benchmark is widely – albeit not universally – accepted in modern moral philosophy. One classic definition is: ‘[A]n individual’s good consists in what he would want himself to want, or to pursue, were he to contemplate his present situation from a standpoint fully and vividly informed about himself and his circumstances, and entirely free of cognitive error or lapses of instrumental rationality’ (Railton, 1986, p. 16). Since idealized choice is unobservable, in our empirical work we attempt to find situations – hypothetical and real – where observed choices and stated choices would come as close as possible to such idealized choices.1

Our first paper (Benjamin et al., 2012) is nicely summarized by Frijters et al. We present survey respondents with hypothetical choices between two options – for example, a job with higher income versus a job allowing for more hours of sleep – and ask them two questions: Which of the two options do you think would make you happier (or more satisfied with your life, etc.)? Which of the two do you think you would choose? If people were seeking to maximize their happiness or life satisfaction, then, to the extent that stated choice is a reasonable approximation of idealized choice in the context of the scenarios, our respondents would answer the two questions similarly. We find that they often – but far from always – do, and we identify additional factors – such as predicted sense of purpose, control over one’s life, family happiness and social status – that help explain stated choice.

In our second paper (Benjamin et al., 2014a), we replace the hypothetical choices from our first paper with real choice data, collected by surveying 561 students from 23 US medical schools shortly after they submitted their choice rankings over residencies to the National Resident Matching Program. We elicit a list of predictions regarding happiness, life satisfaction, and residency features (such as prestige and future career prospects) for respondents’ top-ranking programs and compare reported program choice rankings with happiness- or life-satisfaction-prediction rankings. Our evidence from this context, which is real world and high stakes and in which the medical students deliberate about their choices, is consistent with our hypothetical choice evidence from the earlier paper.

In this second paper, we also provide evidence on the wellbeing indicator-based approach to cost–effectiveness analysis, advocated by Frijters et al. in their ‘Cost–effectiveness’ section. With our data, we can directly estimate the medical students’ willingness to trade off between perceived features of the residency programs by regressing residents’ preference rankings – as revealed by the actual residency choices they report to us – on their beliefs about these features and calculating the ratio of estimated coefficients. We treat these ratios as choice-based estimates of the students’ ‘marginal rates of substitution’. We then compare them with estimates obtained by a well-being-indicator-based approach: we use the exact same method as above, but instead of regressing actual choice, we regress predicted happiness (or life satisfaction) for a residency program on beliefs about program features and calculate the ratio of estimated coefficients. We find that these well-being-indicator-based ratios are typically different from the choice-based marginal rates of substitution by 60% or more. This evidence – that the coefficients from wellbeing regressions may be rather different from those from (the informed and deliberated) choice regressions – provides an additional reason to be concerned about the use of coefficients from wellbeing regressions for policy analysis (in addition to Frijters et al.’s concern about conflicting income coefficient estimates). We therefore caution against a blanket recommendation to use them in cost–effectiveness analysis.

The findings from these two papers suggest that people care about more than just what is measured by standard, single-question survey measures of ‘happiness’ or even ‘life satisfaction’. Thus, these measures do not solve the basic problem that motivates their use as an alternative to traditional economic indicators. Using such measures to replace more traditional methods could amount to merely replacing one non-comprehensive proxy for wellbeing with another.

One might conjecture that since responses to happiness or life satisfaction questions plausibly capture more of what people care about (despite not being fully comprehensive), they must at least improve on traditional economic indicators. Whether this conjecture is true depends on whether people’s responses to these survey questions accurately weight the dimensions of wellbeing relative to each other. This remains an open question, but some of the evidence is negative. For example, in our hypothetical choice paper mentioned above (Benjamin et al., 2012), we find that people are more likely to choose options involving higher income than to think that these options will increase their life satisfaction or happiness. This finding suggests that the survey measures underweight the opportunities afforded by a higher income relative to other dimensions of wellbeing – again, providing evidence that adds to Frijters et al.’s conflicting coefficients concern. In other words, while traditional indicators likely overvalue income and consumption, single-question wellbeing indicators may swing too far in the opposite direction, undervaluing them.

An approach to more comprehensive survey-based wellbeing indicators

While effort is ongoing to find a single survey question that would more comprehensively capture wellbeing, another idea – which to us seems more promising at present – is to ask a set of survey questions that (to a reasonable approximation) are: (1) more comprehensive when taken all together; and (2) non-overlapping (or, if overlap is unavoidable, with the weights used to aggregate the questions adjusted to correct for ‘double-counting’).2 The set of questions may include single-question measures of emotions (e.g., happiness, anxiety) and broad evaluations (e.g., life satisfaction, Cantril ladder), but also questions about other dimensions of wellbeing that people care about, such as health, sense of purpose or supporting their families financially. Responses to the set of questions are then aggregated into an overall wellbeing index that can be used as a comprehensive survey-based measure of wellbeing. To inform that aggregation, other survey questions are used to elicit relative local preferences over changes in the different dimensions of wellbeing.

We are aware of two approaches to wellbeing indices grounded in economic theory that have been explored in the literature to date. For concreteness, here we briefly describe one of them (the other is described in, e.g., Fleurbaey & Blanchet, 2013; Decancq et al., 2015). The approach we sketch here is detailed in Benjamin et al. (2014b) and is summarized and updated in Benjamin et al. (2017), from which we borrow much of the text below. It aims to derive an individual-level wellbeing index that can be tracked over time or used as an outcome in policy evaluation. To identify the dimensions of wellbeing to measure, we began by constructing a list that includes over 100 potential survey questions, drawing on proposals from psychologists, philosophers and economists, from Maslow (1946) to Stiglitz et al. (2009) and beyond. We then expanded this list to over 2000 potential dimensions and proposed ways to eventually shorten them to a manageable list that retains what the data tell us are, empirically, the most important dimensions.

The theory for measuring wellbeing is analogous to that underlying the measurement of aggregate consumption. In the case of aggregate consumption, a utility function u(c) is defined over a consumption vector for M goods. A traditional aggregate consumption index, ∑m=1Mpm¯cm, weights each good’s consumption, cm, by its price held fixed at a baseline level, pm¯. In the face of small changes in consumption, changes in the index approximate changes in utility (up to a multiplicative constant): ∑m=1Mpm¯Δcm∝∑m=1M∂u(c)∂cmΔcm≈Δu.

In the case of a survey-based wellbeing index, the vector of M consumption goods, c, is replaced with a vector of J dimensions of wellbeing, w. These dimensions are the final goods that people ultimately care about (market goods are now treated as intermediate goods in the production of dimensions of wellbeing, à la Becker, 1965). The levels of w are assumed to be what are measured by responses to the survey questions.

Because the dimensions are not traded in markets, price data are unavailable, and different individuals may have different marginal rates of substitution across the dimensions. Nevertheless, a wellbeing index can be constructed using each individual’s marginal rates of substitution for the dimensions as weights. Specifically, the index is given by ∑j=1J∂u(w)¯∂wjwj, where the marginal utilities are defined relative to an arbitrary numeraire dimension. Small changes in this index provide a first-order approximation of changes in the individual’s (ordinal) utility (even if the individual’s preferences are non-linear).

In practical terms, then, this approach requires: (1) identifying a set of dimensions of wellbeing to measure; (2) asking survey questions that measure each dimension; and (3) asking additional, stated preference survey questions that measure how respondents would be willing to make trade-offs between the dimensions. The marginal rates of substitution estimated from responses to these stated preference questions are then used as weights to construct an index that aggregates across the dimensions of wellbeing.

Of course, such an approach to constructing a wellbeing index is data hungry, both in absolute terms and, especially, compared with a single wellbeing question. We certainly acknowledge the practical challenges of implementing the index approaches – the higher costs of data collection requirements that they place on governments, the effort they impose on respondents given the survey length and format, the difficulty of explaining the wellbeing measurement methodology to constituents, etc. – all of which may make it tempting to take the single-question data we already have at present and run with them. However, these practical challenges should not be confused with arguments about what is the appropriate methodology for measuring wellbeing in a way that is normatively appealing and theoretically sound.

Other issues for ongoing research

We have highlighted non-comprehensiveness because we think it is a major reason to be cautious about basing policy on single-question wellbeing measures – and because the search for comprehensiveness is an important reason for economists to look ‘beyond GDP’ in the first place. However, there are also a number of other not-yet-resolved challenges to the use of survey-based wellbeing for policy, regardless of whether these are single-question measures or indices (see also Adler, 2013). Some examples follow.

Which question(s) to ask

In their ‘Estimates of key effects’ section, Frijters et al. raise the question of which wellbeing indicator should be used for policy purposes. Their proposed answer: ‘The frontrunner at the moment remains life satisfaction.’ But it is widely agreed that different wellbeing measures capture different aspects of experiences (e.g., a happiness question aims to measure emotions, whereas life satisfaction gets at an overall evaluative assessment). These measures have been found to correlate differently with policy-relevant variables (e.g., Kahneman & Deaton, 2010; Stone et al., 2010). For a wellbeing index, it is not yet clear what set of questions is sufficiently comprehensive and non-overlapping to be used.

Question-interpretation differences

A related issue is that different people may interpret the question being asked differently (Ross et al., 1986; Ralph et al., 2011; Benjamin et al., 2019). For example, some people might interpret a happiness question as asking about their own happiness, while others might interpret it as asking about both their own and their family’s happiness. If so, different responses to a happiness question might reflect differences in interpretation rather than differences in wellbeing. Thus, even with standard questions about happiness or life satisfaction, it is worth investigating whether a better-worded question might lead to a more homogeneous interpretation of the question.

Scale-use differences

When asked to answer a wellbeing question on some response scale, say 0–10, different people may use the response scale differently (see Oswald, 2008, on the curvature of the ‘reporting function from objective reality to subjective feelings’). For example, some sociodemographic groups are less likely to stick to the middle response option than others (see Van Vaerenbergh & Thomas, 2013, for a recent review of this ‘response style’ literature). Thus, when some people report an increase in life satisfaction from 5 to 6, they might actually be experiencing a bigger improvement than other people who report the same increase. One approach to account for differences in scale use uses ‘vignette’ survey questions to try to measure and correct for such differences (e.g., Kapteyn et al., 2007), but that approach has been criticized (e.g., Deaton, 2011). Differences in scale use that are correlated with group differences or caused by events clearly pose a challenge for the use of wellbeing indicators subject to these scale-use differences in policy evaluation.

Adaptation

A related phenomenon is adaptation: in response to a positive life event (such as winning the lottery) or negative life event (such as becoming disabled), people’s happiness or life satisfaction changes a lot initially, but subsequently moves partway back toward its initial level (for a review, see, e.g., Lucas, 2007). Such adaptation may to some extent reflect how feelings of happiness or life satisfaction actually evolve over time, but it also seems to reflect a change in how people answer questions about their happiness or life satisfaction (Loewenstein & Ubel, 2008).

Bounded scale and top-coding

The response scale for a wellbeing survey question should allow for wellbeing improvement over time. The bounded scale and the tendency for respondents to use numbers near the top of the scale in standard questions may preclude individuals from reporting genuine improvements.

Other measurement and survey methodology issues

Due mainly to concerns about a variety of measurement issues, the US National Academy of Sciences ‘Panel on Measuring Subjective Well-Being in a Policy-Relevant Framework’, charged with studying hedonic measures (e.g., happiness questions), concluded that these measures were not yet ready to be treated as national statistics (Stone & Mackie, 2013). There are also survey methodology concerns. For example, some widely used self-reported wellbeing data are collected through surveys with low response rates (around or below 50%). In these cases, conclusions regarding cross-group differences may depend on who self-selects into responding and on how hard the survey administrators attempt to get non-respondents to participate (Heffetz & Rabin, 2013). Until the main measurement and survey methodology issues are addressed, we worry about relying too heavily on estimates from the self-reported wellbeing literature for making policy decisions.

Aggregating wellbeing indicators across individuals

As Frijters et al. acknowledge and discuss in their ‘The aggregation of happiness across people’ section, the equal utilitarian weights that they assume throughout much of their paper are not normatively compelling. The weights used to aggregate individuals’ welfare to a social welfare function should reflect society’s normative views about inequality. Put more precisely, aggregation should reflect a normative consensus about the curvature of the social welfare function. Is a half-point increase to someone beginning at 9 on a 0–10 scale really worth the same as a half-point increase to someone beginning at 3? (Top-coding naturally imposes some curvature, but this might not be the right curvature.) For a review of recent developments in social welfare theory, see Fleurbaey and Blanchet (2013).

Conclusion

We share Frijters et al.’s view that survey-based wellbeing indicators could improve policy-making, and improve people’s lives. We therefore fully agree with their next steps, including regularly estimating and reporting the effects of policy on wellbeing indicators. We view such analysis of wellbeing indicators as complementary to analysis of standard economic indicators, owing to the likely non-comprehensiveness of each.

Our disagreement is a matter of emphasis. Frijters et al. seem to argue that we should be using wellbeing indicators now for policy, and we will figure out how to overcome the challenges as we go. In our view, research on the use of survey-based wellbeing indicators for policy is in its infancy. While we agree that ‘measurement does not need to be perfect to be useful,’ we worry that fetishizing an imperfect measure could be damaging – as demonstrated by the obsession with GDP. We would like to see more progress on overcoming some of the measurement and methodological challenges before we are comfortable advocating the use of these less well-understood indicators as a primary means of policy evaluation. We hope that researchers reading Frijters et al.’s contribution will, like us, appreciate it as a call for making that progress in the required additional research. And we hope that policy-makers reading their contribution will appreciate it as a call for working closely with researchers on such research.

Understood in this way, we fully join Frijters et al.’s call. Directing national statistics offices to develop and field new wellbeing measures; making sure policy interventions and reforms are designed to help estimate their wellbeing effects; collaborating with researchers on field experimentation – in short, creating the data needed for such research and making it available to the research community – is our vision of the important role policy-makers could currently play in bringing about the change that Frijters et al. and we envision.

Acknowledgments

We are grateful to Matthew Adler, Angus Deaton and Andrew Oswald for insightful comments on an earlier draft and to participants in the Third Annual Stockholm Region Workshop on Economics & Philosophy for valuable feedback.

Footnotes

1

The use of choice as a benchmark, under circumstances that are close to (but fall short of) idealized choice, is a venerable tradition in economics and has justifications rooted in moral philosophy (e.g., Bernheim, 2016). In contrast, we avoid using revealed choice as a benchmark under circumstances where we suspect it may be far from idealized choice (in the extreme: addiction, suicide). To be clear, idealized choice is conceptually distinct from both of Kahneman et al.’s (1997) notions of ‘decision utility’ (which describes what people choose, possibly erroneously due to biases or lack of deliberation or information) and ‘experienced utility’ (which describes how people answer questions about current pleasure and pain).

2

Note that this motivation for asking multiple questions – in order to obtain a more comprehensive measure – is different from (and complementary with) the usual motivation for constructing psychological batteries, which is to minimize measurement error.

Contributor Information

DAN BENJAMIN, Center for Economic and Social Research and Department of Economics, University of Southern California, Los Angeles, CA, USA; National Bureau of Economic Research (NBER), Cambridge, MA, USA.

KRISTEN COOPER, Department of Economics and Business, Gordon College, Wenham, MA, USA.

ORI HEFFETZ, Department of Economics and Center for Rationality, The Hebrew University of Jerusalem, Jerusalem, Israel; Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, NY, USA; National Bureau of Economic Research (NBER), Cambridge, MA, USA.

MILES KIMBALL, Department of Economics, University of Colorado Boulder, Boulder, CO, USA; National Bureau of Economic Research (NBER), Cambridge, MA, USA.

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