The Economics of Happiness

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Welfare and well-being have traditionally been gauged by using income and employment statistics, life expectancy, and other objective measures. The Economics of Happiness, which is based on people’s reports of how their lives are going, provides a complementary yet radically different approach to studying human well-being. Typically, subjective well-being measures include positive and negative feelings (e.g., momentary experiences of happiness or stress), life evaluations (e.g., life satisfaction), and feelings of having a life purpose. Both businesses and policymakers now increasingly make decisions and craft policies based on such measures. This chapter provides an overview of the happiness economics approach and outlines the promises and pitfalls of subjective well-being measures.


In recent years, a growing consensus has emerged in academia and policy circles on the urgent need to broaden the conceptual and empirical base over which well-being and welfare are defined and measured. Objective welfare measures, such as income or employment, often tell an incomplete story of how people’s lives are going and whether certain policies are making people better off. As the OECD (2011, p. 265) put it, “Subjective well-being reflects the notion that how people experience a set of circumstances is as important as the circumstances themselves, and that people are the best judges of how their own lives are going.”

For instance, before the COVID-19 pandemic, traditional macroeconomic indicators in the United States pointed to robust stock markets, economic growth, and low unemployment levels. However, these statistics obscured the experiences of worry, anger, high stress, and low optimism – and associated labor force dropout – of many poor and middle-class Americans (Graham 2017; Graham and Pinto 2019). As another example, even though rising aggregate incomes are generally positively associated with higher short-run country-level life satisfaction levels, the pattern is not uniform across countries (Sarracino 2019). Notable exceptions have included China, India, the United States, Germany, and Turkey, where life satisfaction has declined despite economic growth and improvements in living standards (Easterlin et al. 2017; Graham et al. 2018; Guriev and Melnikov 2018; Sarracino 2019). Subjective and objective measures, therefore, often tell different sides of the same story, making subjective accounts of well-being a useful complement to the standard objective indicators.

Subjective and objective well-being measures may also move in the same direction and document similar trends. For example, research shows that migration improves both the incomes and subjective well-being of migrants who have moved from postsocialist countries to the West (Nikolova and Graham 2015). This example illustrates that looking beyond income and employment and incorporating subjective measures in the analysis may reveal added benefits or costs of specific decisions, actions, or policies, which can help policymakers and individuals act in a proactive and informed way.

In addition to complementing objective measures, subjective well-being is intrinsically valuable. Being happy and satisfied with life is something that many people strive for, either consciously or unconsciously. In addition, subjective well-being is important as it credibly predicts productivity, creativity, income, and job-related behaviors, such as effort and quits (Clark 2001; De Neve et al. 2013; Green 2010; Oswald et al. 2015; Nikolova and Cnossen 2020). Therefore, people’s accounts of how they are doing can provide important nuance and information that standard progress indicators or indicators of work quality measures may miss. For example, an economist looking at salary and compensation alone may not understand why workers decide to quit their job. When job satisfaction or perceptions of doing meaningful work are added to the picture, job changes and quits may indeed appear inevitable.

The happiness economics approach has several advantages, making it of interest to policymakers, academics, civic organizations, and laypeople. Nevertheless, using these measures in policy and economic analysis requires a solid understanding of their promises and challenges. This chapter serves as an introduction to the happiness economics approach. Interested readers are invited to read the other chapters in this handbook and the references contained in this chapter.

The Subjective Well-Being Approach

The subjective well-being (SWB) approach assumes that individuals experience positive and negative affect, life satisfaction, and feelings of purpose and meaning in life that can be directly measured via self-reported information (OECD 2013; see the “Measuring Subjective Well-Being” chapter in this handbook). This information comes from thousands of individuals, typically collected via probability-based nationally representative surveys that also collect data on respondents’ socio-demographic characteristics and economic circumstances.

Happiness and subjective well-being (SWB) have a long tradition in economics, though not always as they are known today. The earliest “Happiness Economists” were, in fact, the nineteenth-century moral philosophers, such as Bentham and Mill, who viewed happiness or utility as the sum of good minus bad feelings. Francis Edgeworth even envisioned a “hedonometer” – a device measuring physiological manifestations of pleasure and pain, much like a thermometer measures temperature (Colander 2007).

Not everyone agreed: For example, Irving Fisher advocated deducing utility from the choices people make, rather than their feelings. Cardinal utility, or the idea that utility or well-being can be observed and measured on a numerical scale, fell out of favor in economics and was considered the subject matter of psychology (van Praag and Ferrer-i-Carbonell 2008). Consequently, mainstream economists generally abandoned efforts to measure and compare utility across individuals. The backward induction of utility based on people’s choices and revealed preferences, derived under restrictive assumptions about human behavior, became the norm. According to this preference satisfaction view, which is what most standard microeconomics courses cover, rational individuals behave as if they maximize utility, making their choices only constrained by their budget and their time. The idea is that while individuals can choose between two different goods/services or situations, they cannot assign a cardinal evaluation of that using a number, and even if they do, this number is meaningless (Kapteyn 2020).

Nevertheless, in recent years, modern economists have once again returned to the measurement of happiness and utility (see “Happiness Versus Utility” chapter in this handbook). This development has, in part, been due to advances in behavioral economics, which has challenged assumptions of the revealed preferences approach.

The information gleaned from revealed preferences can significantly differ from self-reported experiences. For example, increases in cigarette taxes decrease the probability of continued smoking and increase the probability of quitting. According to the rational addiction model, which is still conventional in economics, fully informed individuals choose to smoke by weighing the long-term costs of smoking against the short-term pleasure of “taking a puff.” According to the rational addiction model, therefore, cigarette taxes cannot increase smokers’ welfare. Research using US and Canadian data by Gruber and Mullainathan (2005) shows, however, an increase in cigarette taxes is associated with an increase in happiness among those with a propensity to smoke. These results are consistent with an interpretation that people have self-control problems and would actually like to smoke less but do not because they are impatient. Without cigarette taxes, these individuals would be unhappy and smoking in the future. By forcing a reduction in future smoking, taxes allow smokers to quit, which is something they would otherwise not have done – therefore, the taxes help smokers stop their bad habit and, as such, increase their happiness. Looking at revealed preferences alone would have actually led to the opposite conclusion – that by decreasing their cigarette consumption, taxes reduce smokers’ welfare, which is misleading.

The renewed interest in SWB is also due to the greater availability of subjective well-being data and the insights they have generated. Furthermore, the 2009 “Sarkozy Report” (Stiglitz et al. 2009) outlined the deficits of GDP as a measure of social progress, giving a major push to the subjective well-being approach. For example, GDP does not account for nonmarket transactions and externalities in production and does not measure the value of social interactions, such as friendships or family ties. The Report highlighted the insights that subjective well-being data contributed to understanding human well-being and recommended that statistical offices should collect such measures along with GDP and other objective indicators. In fact, the recommendations by Stiglitz et al. (2009) have inspired the OECD’s Better Life Index, a dashboard with 11 dimensions of well-being. As a result, the OECD (2013) also published recommendations and best practices for collecting subjective well-being measures, which have been very influential in happiness economics.

Like most economic measures, self-reported data are subject to bias due to the survey design, extraneous factors during the interview, such as the weather, or observed or unobserved characteristics of the respondent. As explained below, happiness economists usually carefully net these out by either using panel data, which trace the same individuals over time, or by statistically accounting for personal characteristics and interview peculiarities (e.g., interview mode, presence of others during the interview, and day of the week).

Dimensions of Subjective Well-Being

Happiness is a buzz word that attracts immediate attention. This explains why many social scientists use the term “happiness,” although they often mean “life satisfaction” or “subjective well-being.” Happiness is just one of many emotions that people experience at any one point in time. It differs from evaluations of one’s life in general, or satisfaction with one’s job, for example.

Subjective well-being has three separate but related dimensions – affective (hedonic), evaluative, and eudaimonic (Graham and Nikolova 2015; OECD 2013; Stone and Mackie 2013) (Fig. 1). Affective subjective well-being refers to temporary experiences of emotions – positive ones such as happiness or joy, or negative ones such as stress, anger, and sadness. Such positive and negative feelings and emotions are usually short-term and are influenced by the immediate circumstances and states of being and doing. Positive and negative affects are distinct from each other (Kapteyn et al. 2015; Stone and Mackie 2013) and can coexist. For example, Graham and Nikolova (2013) find that access to information and communication technologies (ICT), such as cell phones, can enhance the daily happiness of the poorest people through the capabilities they provide, such as making a financial transaction. However, ICT access also contributes to greater stress and anger, likely because it makes these poor cohorts aware of material goods and opportunities they lack. Affective well-being is typically measured via survey responses or experientially using the experience sampling method or the day reconstruction method (see the “Measuring Subjective Well-Being” chapter in this handbook). In large-scale surveys, hedonic well-being is elicited by asking respondents how they felt during the previous day. For example, surveys ask: “Did you experience a lot of anger yesterday?” with possible responses being yes and no.
Fig. 1

Dimensions of subjective well-being. (Source: Authors)

Evaluative well-being, in contrast, is a judgment about one’s overall life circumstances and requires reflecting on life as a whole. Evaluative well-being also relates to specific assessments of life domains, such as work, family, housing, income, and the living standard. It is typically elicited using survey questions asking, “How satisfied are you with your life as a whole?” whereby respondents can choose an answer from 0 to 10 or 1 to 7. Another single-item question available in the Gallup World Poll, a large-scale survey in over 150 countries, is the Cantril ladder-of-life question (Cantril 1965). Specifically, respondents are asked to picture a ladder with steps from 0 (the worst possible life that they can imagine for themselves) to 10 (the best possible life that they can imagine) and rate their current life using this ladder. The ladder-of-life question is self-anchoring, which means that the scale is relative to each respondent’s aspirations and understanding of his/her best possible life. The correlation between ladder-of-life and life-satisfaction questions is about 0.75 (Bjørnskov 2010).

The important point about life evaluations is that judging one’s satisfaction with life as a whole requires a cognitive evaluation of one’s circumstances, both past and present. Unlike the short-run hedonic well-being, evaluative well-being measures usually reflect people’s capabilities, means, and long-term opportunities (Graham and Nikolova 2015).

That said, answers to life evaluations are also subject to focusing illusion, which refers to people’s tendency to exaggerate the importance of different factors when asked to think about them (Kahneman et al. 2006). For example, asking questions about dating, marriage, or health makes these issues more salient in people’s minds and actually changes their subsequent subjective well-being answers. In a similar vein, surveys asking respondents to evaluate their life circumstances as a whole may invoke a focusing illusion in the answers, by unconsciously prompting people to think about their relative economic standing or other material means (Kahneman et al. 2006). Nevertheless, despite these challenges, economists and students of economics and business tend to put a higher weight on measure of life satisfaction than on worthwhileness, happiness, and anxiety (O’Donnell and Oswald 2015).

The correlation between affective and evaluative measures of SWB has been reported to be between 0.4 and 0.8 (OECD 2013). That between evaluative and eudaimonic dimensions is about 0.25–0.29 (OECD 2013). Despite this correlation, the measures are also clearly distinct. For example, respondents may report being happy with their daily lives, but at the same time, have lowlife evaluations. Having a pleasant or unpleasant time is distinct from thinking that all in all, given all circumstances in one’s life, this is the best possible life one can imagine for oneself. For example, Knabe et al. (2010) show that while the unemployed in Germany are much less satisfied with their lives than the employed, there are no differences between the two groups in terms of daily positive and negative emotions. As the authors put it in the title of their paper, the unemployed are “dissatisfied with life, but having a good day,” mostly because they can spend more time on leisure activities.

Evaluative and affective subjective well-being are typically available in surveys and, therefore, widely used in research. Unfortunately, this is not yet the case with eudaimonic measures of well-being, which are much less well understood and measured. The economics of happiness has mostly pursued eudaimonic well-being by investigating life purpose. For example, Graham and Nikolova (2015) provide insights into the determinants of eudaimonia based on a question in the Gallup World Poll on whether respondents have meaning and purpose in their lives. They find that belief in hard work, health, and freedom perceptions are the most important determinant of having a life purpose. Nevertheless, the Gallup question on meaning was only asked in 2 years and in a very limited sample of countries.

In addition, in the psychology literature, eudaimonic well-being is broader than having meaning and purpose in life and refers to the process of living well and having aspects, such as competence, autonomy, personal growth, and relatedness (Fabian 2020; Ryff 2014). Economists are beginning to explore these factors and introduce them into standard economic models (e.g., Nikolova and Cnossen 2020). However, there is still no consensus on whether multi-item scales or single-item scales should be used to measure eudaimonia, even though the OECD recommends the latter (OECD 2013). A recent example of a multi-item eudaimonic scale from the psychology literature includes that by Marsh et al. (2020).

Analyzing Subjective Well-Being Data

The econometric analysis of subjective well-being data typically takes two forms. In the first scenario, subjective well-being is used to predict different outcomes. For example, studies have looked at how subjective well-being influences consumption and savings (Guven 2012), whether happier and more life satisfied people are more likely to migrate (Graham and Markowitz 2011; Graham and Nikolova 2018; Ivlevs 2014; Otrachshenko and Popova 2014), or whether happiness and life satisfaction influence job search and future labor market outcomes (Gielen and Van Ours 2014; Krause 2013; O’Connor 2020). In this analysis, the outcome Y of individual/country i is determined by:
$$ {\mathrm{Y}}_{\mathrm{i}}=\upalpha +{\upbeta \mathrm{X}}_{\mathrm{i}}+{\upgamma \mathrm{S}}_{\mathrm{i}}+{\upvarepsilon}_{\mathrm{i}} $$
where X denotes observable characterizes (e.g., age, education, income, marital status, in the individual-level case, or the unemployment rate, measures of institutional quality, life expectancy, and GDP in country-level analyses), and S is a measure of subjective well-being. The inclusion of X in the regression allows researchers to hold constant these observable factors and objective life circumstances. If the coefficient estimate γ is statistically significant, conditional on X, then S brings additional information and has explanatory power above and beyond the objective characteristics. In panel data estimations, it is possible to use the lag of subjective well-being and see how subjective well-being in the previous period affects current outcomes.
Instead of using S as an explanatory variable as in Eq. (1), many studies examine how different aspects of one’s living and working environment or life events influence subjective well-being. These analyses are in fact far more common in the happiness economics literature and usually follow empirical specifications taking the form:
$$ {\mathrm{S}}_{\mathrm{i}}=\upalpha +{\upbeta \mathrm{X}}_{\mathrm{i}}+{\upvarepsilon}_{\mathrm{i}} $$

If panel data following the same countries or individuals over time are available, Eqs. (1) and (2) also include individual or country fixed effects denoted by πi. Individual fixed effects can account for certain sources of endogeneity, such as those related to time-invariant unobservable factors (e.g., personality traits, or motivation and ability). Country fixed effects account for fixed traits, such as geography and culture. Finally, with both pooled cross-sectional and panel designs, Eq. (1) includes τt, which are time fixed effects that account for common shocks experienced in different time periods (e.g., the Great Recession). Studies relying on data collected at a single moment in time do not feature this term.

Estimating Eq. (2) using econometric techniques requires assumptions about the reporting function and the cardinality versus ordinality of self-assessed SWB (Ferrer-i-Carbonell and Frijters 2004). These assumptions are:
  • Assumption 1. Self-reported SWB is a positive monotonic transformation of the unmeasurable concept “well-being” or “welfare” W. Therefore, for an individual i observed at times t and j, if Sit > Sij, then Wit > Wij

    This assumption implies that measured self-reported SWB answers are reflective of the concept of welfare, which itself is “metaphysical” and unmeasurable (Ferrer-i-Carbonell and Frijters 2004).

  • Assumption 2. Self-reported SWB data are ordinally comparable across individuals i and k: if Si > Sk, then Wi > Wk

    The second assumption requires that individuals answering SWB questions have a common understanding of what well-being is and report their innate well-being in the same fashion based on the given scale. Therefore, according to this assumption, the interpretation of “completely satisfied” or “completely dissatisfied” and its translation in terms of a numerical scale is the same across individuals.

  • Assumption 3. Self-reported SWB answers are cardinally comparable across individuals. As such, Si – Sk = y(Wi, Wk), where y(Wi, Wk) is assumed to be: Wi – Wk and Si – Sk = Wi – Wk

Estimating Eq. (2) using Ordinary Least Squares (OLS), a common practice in the literature, requires Assumption 3 about cardinality (MacKerron 2012). A major advantage of the cardinality assumption is the ability to apply fixed effects estimators, which net out the influence of time-invariant unobservables. When using individual-level data, these include motivation, ability, and personal idiosyncrasies in answering SWB questions and the interpretation of the SWB scale. Nevertheless, cardinality assumptions are quite strong. For example, for a life satisfaction question measured on a scale from 0 (not at all satisfied) to 10 (completely satisfied), the cardinality assumption implies that the relative difference between responses at 0 and 1 is the same as the relative difference between responses at 8 and 9.

In contrast, ordinality assumptions underpinning ordered logit or probit estimators are weaker because they accept that the relative difference between the answers at different points of the scale is unknown. Still, people are assumed to share the same interpretation of each answer on the scale (Ferrer-i-Carbonell and Frijters 2004). The probit and logit estimators take the SWB scales as arbitrary and regard the ordinal response to SWB questions as the discrete manifestations of a continuous latent SWB. Using maximum likelihood methods, estimators such as ordered logits or ordered probits, estimate a coefficient vector that predicts the latent SWB variable based on the set of control variables X and a set of cutoff points where the values of the latent SWB variable switch between the different values of the observed scale. The general disadvantage of using latent models is that they cannot be used with fixed effects, though workarounds and alternatives exist, such as the blow-up and cluster estimator (see Riedl and Geishecker 2014). Other estimates that have been used in the literature include generalized ordered models (which allow for the impact of X to vary along the distribution of outcomes), fixed effects ordered logits, and probit-adapted OLS (POLS) (see MacKerron 2012 for an overview).

In practice, using OLS or ordered probit/logit results has yielded similar results in terms of the statistical significance of the included covariates and the relative magnitudes (i.e., the size of one coefficient estimate relative to another one) (Ferrer-i-Carbonell and Frijters 2004; Riedl and Geishecker 2014). Nevertheless, recent research (Bond and Lang 2019; Schröder and Yitzhaki 2017) has challenged the plausibility of the three assumptions and existing approaches to estimating Eq. (2) using SWB data and has claimed that most of the findings in the SWB literature can be reversed with certain monotonic increasing transformations of SWB data. Kaiser and Vendrik (2019) show, however, in most instances, reversals of the sort Bond and Lang (2019) and Schröder and Yitzhaki (2017) describe depending on people using the SWB response scale in a strongly nonlinear fashion that is utterly implausible. In an OLS context, reversals are rarely even possible. Moreover, most of the recent criticisms already have been addressed at length, and proposed solutions include using median coefficient values rather than means, replicating findings based on very short-response scales with longer, more reliable ones, and adjusting for response scale bias based on findings from vignette analysis (Chen et al. 2019; Kaiser and Vendrik 2019).

As explained in the next sections, researchers have estimated equations such as (2) based on different datasets using different countries, time periods, and individuals. These analyses reveal remarkably consistent patterns. While not all contemporary economists are convinced of the validity of the happiness economics approach, the proliferation of peer-reviewed articles on subjective well-being in mainstream economics journals demonstrates the growing relevance of the field.

Methodological and Conceptual Issues

This section provides a brief overview of some of the key methodological and conceptual issues, which are discussed in much detail in OECD (2013), Stone and Krueger (2018), and Stone and Mackie (2013). Over four decades of research on SWB demonstrates that these metrics are useful, valid, and reliable, and most, though not all, issues related to SWB measurement have now been tackled (Stone and Krueger 2018; Stone and Mackie 2013). Since 2013, the OECD and the US National Academy of Sciences have provided much guidance on what subjective well-being is and how such measures should be collected validly and reliably, not just by academics, but also by official statistics offices around the world (Durand and Smith 2013; OECD 2013; Stone and Mackie 2013). Increasing use of these best practices has certainly helped resolve certain methodological issues, such as framing and context effects (Stone and Krueger 2018).

Validity and Reliability

Validating SWB measures is challenging and can only be done indirectly. This is because subjective well-being is not like height or temperature – it is abstract and metaphysical. That said, SWB measures plausibly predict future behavior and choices (De Neve et al. 2013), attesting to their convergent validity. For example, subjective well-being measures explain voting patterns and do so even better than macroeconomic factors, such as GDP per capita, inflation, and unemployment (Liberini et al. 2017; Ward 2020). The fact that subjective reports of well-being can explain future actions (e.g., migration or job switching) provides some reassurance that these measures have informational value and are not simply noise (Clark 2016a). Also, SWB measures have meaningful and logical correlations with other variables (Di Tella and MacCulloch 2006). For example, if they were simply noise, these measures would be (statistically) unrelated to life events and circumstances, such as unemployment, marriage, or death of a family member; nonetheless, many cross-sectional and panel data find that they are, suggesting their construct validity. Other indirect validations show that SWB measures correlate with the frequency of “genuine” Duchene smiles, biological markers, such as brain activity and cortisol, and ratings made by friends and partners (OECD 2011).

Reliability refers to internal consistency and test-retest reliability. Since this chapter’s focus is on single-item measures of subjective well-being where computing a standard measure such as Chronbach’s alpha is not possible, we only discuss test-retest reliability. Krueger and Schkade (2008) report test-retest correlations of life evaluations and affect measures of about 0.5–0.7 for 1–14 days. Moreover, reliability tests suggest that SWB is relatively stable over the life course (Ehrhardt et al. 2000; Headey and Wearing 1991). In general, the test-retest reliability of affective measures is lower than that of evaluative ones, and there is not much evidence on the test-retest reliability of eudaimonic well-being. Moreover, the test-retest reliability of SWB measures is lower than that of objective variables such as income or education (Krueger and Schkade 2008; OECD 2013). Finally, unsurprisingly, the test-retest reliability of SWB measures is higher at the country than the individual level (OECD 2013).

Much like their objective counterparts, SWB indicators are imperfect, though in different ways. Certain response modes (e.g., phone vs. in-person interviews), temporary moods, or the presence of others can distort the answers to SWB questions (Conti and Pudney 2011; Deaton and Stone 2016; Dolan and Kavetsos 2016; Krueger and Schkade 2008). For example, asking about politics, health, or social capital before a subjective well-being question influences the responses (Deaton and Stone 2016; Lee et al. 2016; Nikolova and Sanfey 2016). This is why the OECD (2013) recommends that surveys first elicit subjective well-being questions and then proceed to other topics.

Limits to Validity: Differences in Response Styles and Comparability of SWB Scores

By now, most of the validity and reliability issues have been addressed – either through the recommendations by the OECD (2013) on how to collect SWB measures or via statistical techniques, including interviewer fixed effects in regression analyses.

Systematic differences in the interpretation of the subjective well-being questions or scales based on culture, expectations, or language may be problematic if researchers compare unadjusted SWB levels between different groups (e.g., comparisons of SWB scores across countries or between men and women). This is less of an issue if researchers analyze changes in SWB between different countries or groups rather than levels. Therefore, rankings of raw SWB scores, which often make for interesting headlines in the media, should be treated with extreme caution.

The cross-country comparability of SWB levels remains an open area of research. Exton et al. (2015) review the existing literature on SWB and culture and distinguish between cultural bias (i.e., measurement error) and cultural impact (i.e., culture shaping how people experience their lives). Exton et al. (2015) provide the most extensive analysis regarding the cross-country comparability of SWB scores and conclude that culture could account for between 6% and 18% of the country-specific unexplained variance in SWB scores.

Among several methods attempting to study and control for culture in SWB responses, vignettes have received the most attention among economists. This method asks respondents to rate the SWB of hypothetical individuals whose life circumstances are described in a short story (vignette) (see, for example, Angelini et al. 2014). The idea behind vignettes is that all interviewees read the same hypothetical scenario, which should have the same meaning to all of them. Therefore, cross-country differences in how people evaluate the vignettes reflect differences in the use of SWB scales across cultures. This information can be used to adjust cross-country SWB scores and net out the role of culture. Some studies using vignette adjustments find that life or job satisfaction country rankings can change (Angelini et al. 2014; Kapteyn et al. 2013; Kristensen and Johansson 2008). Therefore, cross-country differences in the vignette answers can be used to adjust respondents’ self-reports of subjective well-being.

An example of a vignette, taken from Angelini et al. (2014, p. 646) is as follows:

1. John is 63 years old. His wife died 2 years ago and he still spends a lot of time thinking about her. He has 4 children and 10 grandchildren who visit him regularly. John can make ends meet but has no money for extras such as expensive gifts to his grandchildren. He has had to stop working recently due to heart problems. He gets tired easily. Otherwise, he has no serious health conditions. How satisfied with his life do you think John is?

2. Carry is 72 years old and a widow. Her total after tax income is about 1100 per month. She owns the house she lives in and has a large circle of friends. She plays bridge twice a week and goes on vacation regularly with some friends. Lately she has been suffering from arthritis, which makes working in the house and garden painful. How satisfied with her life do you think Carry is?

Nevertheless, the vignette method has several limitations. Most importantly, real differences in life circumstances and quality of life in a country influence how people perceive the scenarios presented in the vignettes. For example, life expectancy, retirement age, norms, and attitudes regarding retirement and health expenditures differ across countries, influencing how people in different countries evaluate the circumstances described in the vignettes. Another example from Kapteyn et al.’s (2013) study assumes that having the median income in the United States and the Netherlands presents comparable economic circumstances. As such, vignettes are liable to country-level differences in public goods, norms, aspirations, and expectations, suggesting that they cannot fully provide adjustments for cross-country subjective well-being answers. However, they are an important first step toward understanding cultural differences in SWB answers.


Adaptation is one of the most critical challenges to SWB research. From a methodological viewpoint, there are two problematic aspects related to adaptation – (i) subjective well-being restoring to its predetermined baseline (i.e., “set point”) after positive and negative life events, and (ii) changes in the way people evaluate their lives over time (recalibration) (Stone and Krueger 2018). First, complete adaptation implies that life events, such as a divorce or the birth of a child, initially change subjective well-being, but after a few years, subjective well-being levels go back to the levels before the life event. If subjective well-being levels always return to a genetically predetermined set point, policy interventions can only have temporary rather than long-lasting effects. Moreover, complete adaptation would imply that the impact of different policies or life events on subjective well-being based on short-run analyses may be over-estimated (OECD 2013). Then, the key questions for policy may relate to understanding what policies improve people’s SWB without being subject to adaptation and, conversely, how policies and interventions can speed up the adaptation to negative events and encourage resilience. Indeed, the ability to adapt is a defense mechanism shielding people from adversity and, as such, is a good thing (Graham 2011). In this sense, adaptation is related to resilience.

Economists and psychologists have studied how people’s happiness and life satisfaction change before and after different life events, i.e., whether they anticipate and adapt to them (Clark et al. 2008b). Economists tend to reject claims of complete adaptation, and psychologists tend to believe in set-point theory and the idea that people have predetermined levels of happiness to which they return after life shocks. Research generally shows that individuals adapt to most positive and negative life shocks and events, such as divorce, the death of a spouse, marriage, or the birth of a child, though there are some country differences (Clark 2016b). For example, in all contexts in which it has been studied, the birth of a child is preceded by an increase in subjective well-being (in anticipation) and then complete adaptation to it over time (Clark 2016b). Nevertheless, the results are more mixed when it comes to marriage. For instance, the evidence from Germany, Switzerland, the UK, and Australia points to complete adaptation to marriage. However, marriage leads to lasting increases in life satisfaction in Russia and South Korea (among men).

However, many recent studies show that adaptation to income and other aspects of economic and social life is incomplete. In other words, people’s life satisfaction and happiness levels may recover after a shock, but not go back to their original levels. For instance, people do not fully adapt to disability. Oswald and Powdthavee (2008) use British longitudinal data to document that only half to a third of the subjective well-being dip following disability dissipates over a 5-year period.

Moreover, people do not adapt at all to unemployment, pollution, or poverty, meaning that their life satisfaction and happiness levels continue to decline with time spent in the condition (Clark 2016b; Clark et al. 2016). Using cohort data and applying a life-course approach to subjective well-being, Clark and Lepinteur (2019) also document, rather depressingly, that past unemployment continues to reduce current life satisfaction. As such, past unemployment experiences are really scarring. In addition, Nikolova et al. (2020) show that there is no adaptation to involuntarily losing a salaried job or a business – the dramatic dip in life satisfaction following both events lasts 2 or more years, albeit being much stronger for failed entrepreneurs (Fig. 2). In general, it seems that people adapt to situations that bring pleasant and unpleasant certainty, while they fail to adapt to situations of uncertainty and volatility (Graham 2011).
Fig. 2

Life satisfaction patterns before and after losing self-employment and salaried employment. (Source: Nikolova et al. (2020) based on data from the German Socio-Economic Panel v.34. Notes: The figure illustrates the within-person changes in life satisfaction following the switch from self-employment to unemployment (left panel), and salaried employment to unemployment due to a company closure (right panel) based on fixed-effects regressions. The reference category is 3 or more years before becoming unemployed. The analysis sample in this graph is based on respondents who are observed 3 or more years before becoming unemployed and then remain unemployed for 2 or more years. The dashed lines refer to the 95% confidence interval. The x-axis denotes the number of years before and after becoming unemployed. The y-axis designates the change in life satisfaction. Life satisfaction is measured on a scale ranging from 0 (completely dissatisfied) to 10 (completely satisfied). The findings should be interpreted as the within-person change in life satisfaction with respect to the score 3 (or more) years before that person becomes unemployed)

There is a second issue related to adaptation that poses challenges for happiness researchers. Specifically, recalibration refers to the fact that people may change how they report their subjective well-being over time (Ubel et al. 2010). For example, suppose that Person A, who experiences no change in life circumstances themselves, initially rates their life satisfaction at 5 on a scale of 0–10. Suppose that Person A’s childhood friend becomes seriously injured in an accident and must spend the rest of their life in a wheelchair. As a result of this accident and seeing how the friend’s life circumstances and opportunities have changed, Person A revises their life evaluation and now rates their life as a 7 (on a scale of 0–10) as opposed to 5 but has experienced no changes in life circumstances.

Both issues related to adaptation are still subject to ongoing research. The goal is to distinguish between resilience and habituation and develop statistical techniques to deal with recalibration (Stone and Krueger 2018).

Determinants of Subjective Well-Being

Subjective Well-Being and the Individual

There is now a large body of literature on the correlates of happiness and life satisfaction at the individual level, which reveals universal patterns across different contexts (Bhuiyan and Szulga 2017; Blanchflower and Oswald 2004). With the availability of the Gallup World Poll now surveying about 150 countries worldwide since 2005/2006, studies have shown that the key individual-level subjective well-being determinants are generally similar across different societies and levels of development (Graham 2009; Helliwell and Barrington-Leigh 2010). Happiness and life satisfaction are negatively correlated with unemployment and divorce. The healthy, the married (as well as those in stable partnerships), and urban residents generally have high life satisfaction and happiness levels than their counterparts. SWB is also higher among the young and the old, with a dip occurring around the 40s or early 50s (Steptoe et al. 2015; Blanchflower and Oswald 2008). Additionally, both own income and the income of a reference group (i.e., relative income) matter for happiness and life satisfaction (e.g., Clark et al. 2008; FitzRoy and Nolan 2020; Senik 2009 and see “Wage Satisfaction and Reference Wages” chapter in this handbook). Studies generally find that women are happier and more life satisfied than men, except in places where gender rights are compromised (Graham and Chattopadhyay 2013). Stevenson and Wolfers (2009) find a trend of declining female happiness in several industrialized countries, including the United States, although later studies suggest that trend has since reversed, with men experiencing greater declines (Herbst 2011). The evidence on how having children is associated with life satisfaction and happiness remains mixed (MacKerron 2012).

Figure 3 shows the determinants of life evaluations (based on the Cantril ladder-of-life question, measured on a scale of 0–10) in the US sample of the Gallup World Poll for 2009–2018. Evidently, income is strongly positively associated with life evaluations, while unemployment has the greatest negative association with perceptions of best possible life, which is an evaluative well-being measure. Household size, living in a rural environment, being out of the labor force, and being Christian are unassociated with life evaluations, which is evident from the fact that the 95% confidence intervals are crossing the zero axis.
Fig. 3

The determinants of individual-level life evaluations in the United States, 2009–2018. (Source: Authors based on Gallup World Poll data for the United States, 2009–2018, N = 6,268. Notes: The bars represent the change in life evaluations (on a 0–10 scale) associated with a one-standard deviation change in the corresponding determinant. The whiskers are based on 95% confidence intervals. They were obtained from regressing evaluations of one’s best possible life on the variables identified above, along with year-fixed effects. The reference category for the employment status variables is “employed” (full time or part time). Income is per capita household income in international dollars and is log-transformed; the subsequent point estimate is used to calculate the association with a doubling of per capita income. Gallup calculates per capita annual income in using the World Bank’s individual consumption PPP conversion factor, making income estimates comparable across all countries. Life evaluations are based on the ladder-of-life question for the period 2009–2018, asking respondents to rate their current life circumstances on a ladder going from 0 to 10, where 0 is the worst possible life imaginable, and 10 is the best possible life imaginable)

Nevertheless, there are important differences in the patterns regarding the SWB determinants across levels of development (Bhuiyan and Szulga 2017; Graham and Nikolova 2015). For example, there is a life satisfaction and positive affect differential in favor of urban residents in low-income countries and in favor of rural residents in developed countries (Burger et al. 2020; Easterlin et al. 2011). At low levels of economic development, as captured by GDP per capita levels, living in an urban setting means higher incomes and higher opportunities than living in a rural setting. However, at high levels of economic development, the disappointments of urban life, such as congestion, inequality, and anomie may dominate, and residents of rural areas may experience higher happiness and life satisfaction than urban dwellers (Burger et al. 2020; Easterlin et al. 2011).

Moreover, the short-run relationship between income and subjective well-being at the individual level deserves some attention. Calculations from the Gallup World Poll individual-level sample for 2009–2018 show that the simple correlation coefficient between life evaluations and per capita household income is 0.33, and that between smiling the previous day and income is 0.05.

It is widely accepted in the literature that in a cross-section, rich individuals within a country have higher positive hedonic well-being and life satisfaction levels than their poorer compatriots. Nevertheless, while the positive relationship between household income and positive affect tapers off at a household income of about 75,000 USD, the relationship between income and life satisfaction continues in a linear fashion (Kahneman and Deaton 2010). In other words, while additional household income is unassociated with additional positive affect beyond a household income of 75,000 USD when it comes to life evaluations, additional income continuously brings additional life satisfaction, and there is no satiation point in the short run. Figure 4 below illustrates these points using estimations based on data for the United States from the Gallup World Poll, averaged for 2009–2018.
Fig. 4

Life evaluations, positive and negative hedonic well-being, United States, 2009–2018. (Source: Authors based on Gallup World Poll data for the United States for 2009–2018. Notes: Life evaluations are based on the ladder-of-life question, averaged for the period 2009–2018, asking respondents to rate their current life circumstances on a ladder going from 0 to 10, where 0 is the worst possible life imaginable, and 10 is the best possible life imaginable. Smile is based on the share of respondents reporting that they smiled or laughed a lot the day before, averaged for the period 2009–2018. Stress is based on the share of respondents reporting that they experienced a lot of stress the day before, averaged for the period 2009–2018. Gallup calculates per capita annual income in using the World Bank’s individual consumption PPP conversion factor, making income estimates comparable across all countries. The income variable is log-transformed and individuals are placed within four bins (income quartile groups) based on the overall individual income distribution in the United States during the 2009–2018 period)

Studying the causal relationship between income and subjective well-being is challenging because income increases (or decreases) are usually non-random and anticipated. For example, workers who have high unobserved ability and motivation are both more likely to be happy and satisfied with their lives and also more likely to put a lot of effort on the job and earn a higher income. Exogenous shocks in income are rare, though economists have used some clever identification strategies to study the effect of income on SWB.

For example, winning the lottery may be used as a viable identification strategy, mainly because it is independent of one’s happiness levels. One much-cited psychology study (Brickman et al. 1978) cross-sectionally compared the average happiness and mood levels of 22 lottery winners to those of a control group of 22 nonwinners. It concluded that winning the lottery is unassociated with happiness. Another study (Kuhn et al. 2011) with a robust research design and using Dutch data, which also controls statistically for the number of lottery tickets bought, also found no effect of winning the lottery on happiness. Nevertheless, a problem with the lottery tickets studies is that winning the lottery can only happen if individuals purchase lottery tickets. The more money lottery players spend on tickets, the greater the chance of winning. Therefore, results documenting happiness gains from lottery wins that do not control for the number of tickets purchased have a downward bias; once this bias is corrected for, the results seem to suggest that lottery wins are associated with both life satisfaction and happiness (Kim and Oswald 2020).

Another example of an unanticipated income shock comes from the sudden and unexpected income increases brought by the reunification of West and East Germany in 1990 following the Fall of the Berlin Wall, which presented a unique natural experiment (Frijters et al. 2004; Powdthavee 2010a). Specifically, using panel data tracing the same East Germans over an 11-year period from 1991 to 2001, Frijters et al. (2004) find that a 1 percentage point rise in real household income increased life satisfaction by 0.5 points (on a 0–10 scale). This effect is substantive and similar to the magnitude of the effect of escaping the misery of unemployment and gaining full-time salaried employment (Powdthavee 2010a).

A final example of identifying the causal effect of income on life satisfaction comes from Powdthavee (2010b), who relies on longitudinal information on the same UK respondents over time and an instrumental variables technique. The idea behind the instrumental variable technique is that the instrument should be correlated with the endogenous variable of interest (in this case, income) but not with the error term of regression equations such as (2). Powdthavee (2010b) exploits survey information about whether the respondent showed their payslip to the interviewer to instrument for income. The author assumes, however, that letting the interviewer see the payslip is unassociated with time-varying unobserved heterogeneity. The results show that a £1,000-increase in real household income leads to a small increase of 0.04 points in life satisfaction (measured on a scale of 1–7).

The main conclusion of the studies on subjective well-being and income is that there is a positive, and likely causal, short-term relationship between SWB and income, though the exact nature and magnitude of the relationship depend on the context studied, the SWB measure, and the methodology.

SWB and Society

The key societal-level determinants of SWB refer to macroeconomic and institutional factors. Studying how these factors affect SWB typically requires cross-country data analysis of Eq. (2), and the conclusions are often sensitive to which countries are included or excluded from the analyses. The availability of the Gallup World Poll since 2005/2006 has enabled cross-country (and within-country) analyses of SWB by allowing annual comparisons across more than 150 countries worldwide.

Since 2012, the World Happiness Report, a publication of the United Nations Sustainable Development Solutions Network, has been publishing SWB research and country rankings based on life evaluations and positive and negative affect. With some variation, Northern European countries often top the list, while developing countries and those plagued by war, such as Syria, often lurk at the bottom. One of the key findings of the researchers producing the World Happiness Report is that about three-quarters of the cross-country variation in life evaluations is due to six variables – GDP per capita, healthy life expectancy, freedom, generosity, trust, and social support – leaving only up to one-fifth of the cross-country variation unexplained. The unexplained differences in SWB outcomes could be due to four sources: unmeasured country circumstances (omitted variables), differences in appraisal styles (e.g., differences in optimism or pessimism), language differences, or cultural response styles of biases. The last bias relates to country-specific differences in how people answer SWB questions, regardless of their actual experiences. However, adding a geographic region of residence control (e.g., Latin America, Europe, etc.) does not seem to change the results (Helliwell et al. 2020), which suggests that the role of the cultural component in SWB may be limited.

One key question concerns the relationship between country-level income and individual – and country-level happiness and life satisfaction. The short-run cross-sectional evidence shows that there is a positive log-linear relationship between subjective well-being and income (GDP per capita), meaning that richer countries are, on average, more satisfied and happier than developing countries. The relationship between evaluative well-being and income is typically stronger than that between hedonic well-being and income, as illustrated by the steepness of the slopes in Fig. 5 versus Fig. 6 below. However, in the long-run, the evidence is mixed on whether there is a long-term relationship between growth and happiness/life satisfaction (see “The Easterlin Paradox” chapter in this Handbook). This remains a topic of ongoing research and debate. New research suggests that part of the debate may be because studies had not distinguished between negative and positive economic growth – at least in the short-run, individuals’ life satisfaction and happiness react more strongly to macroeconomic declines than they do to upswings (De Neve et al. 2018).
Fig. 5

Life evaluations and individual income, country-level evidence, 2009–2018. (Source: Authors based on Gallup World Poll data for 2009–2018, all available countries in the GWP except Somalia. Notes: Life evaluations are based on each country’s average score on the ladder-of-life question for the period 2009–2018, asking respondents to rate their current life circumstances on a ladder going from 0 to 10, where 0 is the worst possible life imaginable, and 10 is the best possible life imaginable. Gallup calculates per capita annual income in using the World Bank’s individual consumption PPP conversion factor, making income estimates comparable across all countries)

Fig. 6

Positive hedonic well-being and individual income, country-level evidence, 2009–2018. (Source: Authors based on Gallup World Poll data for 2009–2018, all available countries in the GWP except Somalia. Notes: The variable on the y-axis is based on the share of respondents in each country reporting that they smiled or laughed a lot the day before for the period 2009–2018. Gallup calculates per capita annual income in using the World Bank’s individual consumption PPP conversion factor, making income estimates comparable across all countries)

Furthermore, inflation and unemployment both negatively influence happiness and life satisfaction at both the individual and country levels (Di Tella et al. 2003; Wolfers 2003). This research finds that unemployment has a much higher psychic (i.e., subjective well-being) cost, compared to inflation, which has implications for policymakers and central banks. Business cycle volatility, as captured by the standard deviation of unemployment and inflation, imposes additional subjective well-being costs (Wolfers 2003).

The “Institutions and Life Satisfaction” chapter in this handbook furnishes a comprehensive overview of the relationship of political (e.g., the degree of democracy), legal (e.g., the degree of the rule of law), and economic institutions (e.g., economic freedom). Functioning institutions mostly have a positive relationship with life satisfaction and happiness, though some studies find no relationship. Similarly, the relationship between inequality and SWB is surveyed in the chapter Inequality and Happiness in this handbook. The evidence on that topic is varied, with some contributions finding a positive and others reporting a negative relationship. It seems that individuals generally dislike inequality, yet, to some, inequality may signal the opportunity to make it to the top of the income distribution through hard work.

Beyond the topics related to macroeconomics and intuitions, the literature has examined the subjective well-being implications of tax policy (Gruber and Mullainathan 2005), pollution and climate change (Levinson 2012; Rehdanz and Maddison 2005), terrorism (Akay et al. 2020), international trade (Colantone et al. 2019), and migration flows (e.g., O’Connor 2019; see also “Happiness and Migration” chapter in this handbook). Finally, while most of the studies have mixed individual- with microlevel data information without making any econometric adjustments, researchers increasingly realize the need to use multilevel modeling when studying macro- and microlevel determinants of subjective well-being at the same time (see “The Economic Geography of Happiness” chapter in this handbook).

Subjective Well-Being and Public Policy

Despite the emerging consensus regarding the measurement and validity of SWB, there is still a debate on how to best use these measures for public policy. Most of the policy efforts are in the first stages of systematically collecting SWB data according to the OECD guidelines. The second stage will be to use the insights of the SWB literature to design and evaluate policies. Following the OECD Guidelines (Durand and Smith 2013), governments worldwide, ranging from the United Kingdom, the Netherlands, to New Zealand, are broadening the range of welfare information they collect. They now increasingly use several well-being indicators, including objective and self-reported measures, as the basis for understanding differences in well-being.

This multidimensional approach to welfare measurement, advocated by the Stiglitz-Sen-Fitoussi Commission and the OECD Better Life Initiative, has included SWB measures as part of a broad dashboard comprising many well-being indicators, such as income and resources, environment, health, and education, among others (Durand 2015). Such use of SWB metrics can be informative when assessing the complex ramifications of policies and understanding who benefits and who loses and in what aspects of welfare. For example, a dashboard approach can help policymakers better understand and balance the consequences of interventions that increase welfare in some dimensions but not in others.

A second example of using SWB in policy relates to cost-benefit analyses. Policymakers have limited resources and must set priorities for which problems they will take on. They typically compare options by conducting cost-benefit analyses, which essentially add up the economic benefits of an existing or proposed action and weigh these against the costs. To know which policy decisions are best for society in general or for specific cohorts, then, policymakers can be helped by translating well-being metrics into terms that can be fed into cost-benefit and cost-effectiveness equations. That is, they may want to put a monetary value on different experiences and life events, such as by calculating how much a person would be willing to pay to avoid having a health condition or work arrangement. Another advantage is that people are not prompted to directly think about the source of their well-being changes as these calculations are done by researchers ex-post.

For example, using the life satisfaction valuation approach, Nikolova and Ayhan (2019) calculate that the life satisfaction loss due to involuntary unemployment of one spouse “requires” a monetary compensation of about 50,000 Euros for the other spouse. In another example, Powdthavee and van den Berg (2011) calculate the “shadow prices” of having different health conditions, which allows an ex ante comparison of different costs and benefits of different health treatments. The authors find that depression and anxiety appear to be among the most debilitating health problems, requiring compensation of £455 million per year to offset the life satisfaction dip associated with having this condition. Such calculations can be made for many life events and circumstances, including social relationships, marriage, social interactions, death of a loved one, airport noise, pollution, and others (e.g., see “The Price of What Money can’t Buy: Part II” in Powdthavee 2010a). This method makes it easy to compare the relative impact of such life events and circumstances using the same metric, which underscores the relevance for policy and cost-benefit analyses that can be used by judges, practitioners, environmental scientists, and others (Powdthavee 2010a).

Subjective well-being evidence can influence cost-benefit analyses in three important ways. First, the inclusion of subjective well-being assessments adds to the list of types of costs and benefits that can be quantified and included in such analysis. In addition, subjective well-being evidence can demonstrate that the impacts (benefits or costs) on individuals may be larger or smaller than those observed through individuals’ behavior or through market prices. Last, subjective well-being evidence demonstrates that a subjective well-being gain associated with an additional increment of income may be higher for a low-income recipient than for a high- income recipient. Because money is used as the common factor in cost-benefit analysis, benefits and costs can also be weighted to increase the monetary value of benefits or costs that accrue to lower-income individuals or households to reflect this principle (Graham and MacLennan 2020).

SWB measures can be useful to policymakers. Yet, some caveats related to using SWB in policymaking apply. Governments should communicate to the public why SWB data are collected and how they are being used. They should not promote happiness or life satisfaction as the only societal goal and should not establish ministries to achieve that (Graham and MacLennan 2020). Currently, authoritarian governments, such as those of the United Arab Emirates or Venezuela, have proclaimed a focus on happiness and have established ministries to promote well-being. While this may sound benign, as Graham and MacLennan (2020, p. 9) argue, “Such moves can make the public suspicious of the government’s motives and the data that are released.” Moreover, a sole focus on SWB may make ordinary citizens automatic polling stations and their subjective well-being reports subject to misuse and misrepresentation by politicians (Frey and Stutzer 2010; Nikolova 2019). Besides, utilitarian policies that seek to maximize happiness as a government goal could be dangerous and lead to unethical decisions. For example, following the principle of maximizing total happiness could justify sacrificing the happiness of many people with low individual happiness to increase the happiness of few people with high individual utilities (MacKerron 2012). Nevertheless, empirically, this last concern may not be very serious: Given the law of diminishing marginal returns, which should also apply to SWB, it seems easier to make unhappy people happier than to make happy people happier.

However, the “new utilitarian” view on maximizing total happiness could, for example, justify ethnic cleansing. The expulsion of Nepalese minorities out of Bhutan, a country that seeks to promote Gross National Happiness, has been cited as one example of such misuse of SWB measures in a utilitarian fashion (Keating 2010; Schmidt 2017). Such considerations are less relevant for policy approaches that use SWB alongside a dashboard of different well-being indicators as the basis for welfare measurement and policy design and evaluation. Specifically, dashboard approaches examine in a disaggregated fashion many different well-being measures, such as income, subjective well-being, environmental quality, and education, which allow an understanding of whether and how, for example, certain policies may increase well-being in some dimensions but decrease it in others.

Finally, this Chapter cautions against the increasing practice of merging income, happiness, and other data into composite “mashup” indices, which mix and match different metrics, scales, and conceptual bases into composite metrics that tell little novel information, and are rift with measurement error and bias.

The Future of SWB Research: Open Questions and New Directions

The research on SWB is continuously developing not just in terms of methods and data used but also in terms of topics. The research frontier in happiness economics has now moved way past looking at the determinants of different subjective well-being dimensions. In short, it is now well-known what makes people happy or satisfied with their lives. This overview offers some insights into the open questions and new directions in the field. For further discussion and alternative views, see Clark (2018) and Frey (2020).

First, one of the key questions in the SWB literature is what kind of tradeoffs people make between daily feelings and long-term life or job satisfaction. For example, it is unclear whether sacrificing daily happiness underlies human progress or whether the two can go hand-in-hand. That said, achieving extreme happiness and life satisfaction and avoiding negative feelings may neither be possible nor desirable (Kashdan and Biswas-Diener 2014). For instance, respondents with the highest life evaluation levels are less likely to become reemployed after losing their job (Krause 2013) and are less likely to value different capabilities and means, such as income, health, and education (Graham and Nikolova 2015). While positive affect and life evaluations have many benefits, research shows that there could be too much of a good thing. Both negative affect and more moderate levels of positive affect and life satisfaction may indeed be optimal for creativity and avoiding risky or unhealthy behaviors (Diener et al. 2018; Gruber et al. 2011).

Second, while interest in eudaimonic well-being is long-standing (Ryff 1989), the progress in measurement and analysis has been uneven, owing to the concept’s theoretical complexity. As a result, some scholars remain unconvinced of empirical research on eudaimonia. Further research is urgently needed to better understand how to measure and interpret this SWB dimension and how to link its insights to economic theory and analysis (Nikolova and Cnossen 2020).

Third, little is known about whether and how geography matters when it comes to happiness and life satisfaction (Ballas and Dorling 2013; Ballas and Tranmer 2012). For example, to what extent does where people live affect how they feel and why (not)? Future research should prioritize explorations of how SWB is related to group and place identity, feelings of affiliation and belonging, and the ability to feel solidarity with other people and places. Adding a geographical dimension to the debate could involve a consideration of interdependencies between people in different neighborhoods, cities, regions, and countries (see “The Economic Geography of Happiness” chapter in this handbook).

Fourth, researchers are exploring new dimensions of SWB, including hope, optimism, and meaningfulness. Carol Graham, for example, has been studying the relationship between hope and future-oriented investments and outcomes. Some of her early work in this area, based on longitudinal data for Russians and isolating residual happiness, found that happier people did better over time in the labor market and health arenas, with the channel being optimism for their futures (Graham et al. 2004). Subsequently, papers by Guven (2011), Krause (2013) and O’Connor (2020) find residual life satisfaction, which is interpreted as positive cognitive bias (i.e., optimism or hope), affects future outcomes including social capital and the likelihood of reemployment.

Recently, based on panel data for the United States, O’Connor and Graham (2019) found that optimists live longer and are more likely to have higher levels of education (which may be in part due to belief in their futures). Graham and Pinto (2019) also document that despair and lack of hope are robustly associated with the trends in premature mortality among less than college-educated whites in the United States (i.e., the so-called deaths of despair), at the level of individuals, races, and counties (based on Gallup data). Hope, much more than life satisfaction, seems to display a link to behaviors that determine better futures.

In another example of new SWB dimensions, Nikolova and Cnossen (2020) examine what matters for deriving meaningfulness from work and how that determines effort in the workplace. The authors propose that work meaningfulness is a eudaimonic dimension of well-being at work. They find that autonomy, competence, and relatedness explain about 60% of the variation in work meaningfulness perceptions. Meanwhile, extrinsic factors, such as income, benefits, and performance pay, are relatively unimportant for work meaningfulness.

In addition, a new line of research (e.g., Binder and Broekel 2012; Cordero et al. 2017; Nikolova and Popova 2020) has investigated relative happiness. While the determinants of absolute subjective well-being levels are well documented, much less is known about how individuals and countries use their resources and endowments to reach given subjective well-being levels, i.e., about their “happiness efficiency” (Binder and Broekel 2012). The central question of such analyses is how wastefully or productively nations and persons utilize their available resources, such as income, education, and health, to reach certain subjective well-being levels relative to peers with similar or lower resources. A measure of relative subjective well-being also contributes to debates in ecological economics, according to which achieving well-being and progress cannot hinge on continued GDP growth. While GDP growth is instrumental for satisfying basic consumption needs, it may not necessarily contribute to subjective well-being in the long-run (see “The Easterlin Paradox” chapter in this Handbook). Therefore, by utilizing resources more efficiently or equitably, well-being can be achieved without excessive use of resources and endangering the planet’s carrying capacity. The growing consensus that human well-being, poverty reduction, and development must go hand-in-hand with preserving the health of the environment and embracing sustainability will likely make such analyses of relative happiness critical inputs in public policy decision-making in the future.

Finally, a growing body of literature has examined how different life events spill over within the family (e.g., Nikolova and Ayhan 2019). Other studies have specifically looked into how childhood circumstances matter for life satisfaction and well-being later in life, in part due to the greater availability of cohort data (e.g., Clark and Lee 2017; Flèche et al. 2019a, b; Nikolova and Nikolaev 2018; Powdthavee and Vernoit 2013 and see “Childhood Circumstances and Well-being Later in Life chapter in this handbook).

Other emerging topics in the happiness economics literature relate to the perceived well-being consequences of automation (e.g., Hinks 2020) and routine work, as well as those brought by aspects of globalization, such as migration, trade, and offshoring. One promising area has been the application of big data and machine learning algorithms to the study of SWB (see the “Big Data and Happiness” chapter in this handbook). Other novel topics, related to SWB research and future directions in the field, are provided in the rest of the chapters in this Section of the handbook.


In sum, the inclusion of subjective well-being and the associated metrics into economic analysis has provided new insights into social science and policy. Like all metrics – and empirical data – there are biases and errors that need to be accounted for, and methodological best practices that are necessary to ensure robust analysis. The field has developed a great deal in just several decades and has gone from the use of happiness data in large-scale surveys to assess the nonincome dimensions of welfare, to a much more complex approach that robustly identifies distinct dimensions of well-being, which can identify patterns stemming from innate traits and those that come from the environment people navigate throughout their lives (and how the two interact), to the causal properties of these dimensions, among other things. The assumptions underlying this approach, and the resulting data and analysis, provide important complements to what standard economic models and analysis find, and serve to deepen our understanding of human experience and behavior.

Where does all of this leave us? Subjective well-being research and the happiness economics approach have slowly carved an important field in mainstream economics because they reveal important insights about human well-being that income and labor market outcomes alone cannot convey. These measures have much promise in informing public policy debates about the consequences of different interventions. They can also be useful in terms of serving as diagnostic tools to identify misery and ill-being, understand their causes, and design policy interventions to alleviate suffering. Accordingly, well-trained academics and members of the general public who can correctly and credibly assess the data using theory, robust empirical methods, and a range of metrics as a guide will be essential for ensuring the wider and responsible use of such measures in the policy arena.

In terms of their broader meaning in the social sciences, by being interdisciplinary in nature, subjective well-being measures have arguably brought together different academic fields. This cross-disciplinary exchange can only enrich the theoretical and empirical space in which economists are working.




The authors would like to thank Mark Fabian, Caspar Kaiser, Anthony Lepinteur, Kelsey O’Connor, Olga Popova, and Nick Powdthavee for helpful comments and insights. All errors are our own.


  1. Akay A, Bargain O, Elsayed A (2020) Global terror, well-being and political attitudes. Eur Econ Rev 123:103394. Scholar
  2. Angelini V, Cavapozzi D, Corazzini L, Paccagnella O (2014) Do Danes and Italians rate life satisfaction in the same way? Using vignettes to correct for individual-specific scale biases. Oxf Bull Econ Stat 76(5):643–666CrossRefGoogle Scholar
  3. Ballas D, Dorling D (2013) The geography of happiness. In: David S, Boniwell I, Ayers A (eds) The Oxford handbook of happiness. Oxford University Press, Oxford, pp 465–481Google Scholar
  4. Ballas D, Tranmer M (2012) Happy people or happy places? A multilevel modeling approach to the analysis of happiness and well-being. Int Reg Sci Rev 35(1):70–102CrossRefGoogle Scholar
  5. Bhuiyan MF, Szulga RS (2017) Extreme bounds of subjective well-being: economic development and micro determinants of life satisfaction. Appl Econ 49(14):1351–1378CrossRefGoogle Scholar
  6. Binder M, Broekel T (2012) Happiness no matter the cost? An examination on how efficiently individuals reach their happiness levels. J Happiness Stud 13(4):621–645CrossRefGoogle Scholar
  7. Bjørnskov C (2010) How comparable are the Gallup World Poll life satisfaction data? J Happiness Stud 11(1):41–60CrossRefGoogle Scholar
  8. Blanchflower D, Oswald A (2004) Well-being over time in Britain and the U.S.A. J Public Econ 88:1359–1386Google Scholar
  9. Blanchflower D, Oswald A (2008) Is well-being U-shaped over the life cycle? Soc Sci Med 66:1733–1749CrossRefGoogle Scholar
  10. Bond TN, Lang K (2019) The sad truth about happiness scales. J Polit Econ 127(4):1629–1640CrossRefGoogle Scholar
  11. Brickman P, Coates D, Janoff-Bulman R (1978) Lottery winners and accident victims: is happiness relative? J Pers Soc Psychol 36(8):917CrossRefGoogle Scholar
  12. Burger MJ, Morrison PS, Hendriks M, Hoogerbrugge MM (2020) Urban-rural happiness differentials across the world. In: Helliwell JF, Layard R, Sachs JD, Neve J-ED (eds). World Happiness Report. Sustainable Development Solutions Network, New York, pp 66–93Google Scholar
  13. Cantril H (1965) Pattern of human concerns. Rutgers University Press, New BrunswickGoogle Scholar
  14. Chen LY, Oparina E, Powdthavee N, Srisuma S (2019) Have econometric analyses of happiness data been futile? A simple truth about happiness scales. IZA discussion paper 12152. Institute for Labor Economics (IZA), BonnGoogle Scholar
  15. Clark AE (2001) What really matters in a job? Hedonic measurement using quit data. Labour Econ 8(2):223–242CrossRefGoogle Scholar
  16. Clark AE (2016a) SWB as a measure of individual well-being. In: Adler MD, Fleurbaey M (eds) The Oxford handbook of well-being and public policy. Oxford University Press, Oxford, pp 1–47Google Scholar
  17. Clark AE (2016b) Adaptation and the Easterlin paradox. In: Tachibanaki T (ed) Advances in happiness research. Springer, New York, pp 75–94CrossRefGoogle Scholar
  18. Clark AE (2018) Four decades of the economics of happiness: where next? Rev Income Wealth 64(2):245–269CrossRefGoogle Scholar
  19. Clark AE, Lee T (2017) Early-life correlates of later-life well-being: evidence from the Wisconsin longitudinal study. J Econ Behav Organ.
  20. Clark AE, Lepinteur A (2019) The causes and consequences of early-adult unemployment: evidence from cohort data. J Econ Behav Organ 166:107–124CrossRefGoogle Scholar
  21. Clark AE, Frijters P, Shields MA (2008a) Relative income, happiness, and utility: an explanation for the Easterlin Paradox and other puzzles. J Econ Lit 46(1):95–144CrossRefGoogle Scholar
  22. Clark AE, Diener E, Georgellis Y, Lucas RE (2008b) Lags and leads in life satisfaction: a test of the baseline hypothesis. Econ J 118(529):F222–F243CrossRefGoogle Scholar
  23. Clark AE, d’Ambrosio C, Ghislandi S (2016) Adaptation to poverty in long-run panel data. Rev Econ Stat 98(3):591–600CrossRefGoogle Scholar
  24. Colander D (2007) Retrospectives: Edgeworth’s hedonimeter and the quest to measure utility. J Econ Perspect 21(2):215–226CrossRefGoogle Scholar
  25. Colantone I, Crinò R, Ogliari L (2019) Globalization and mental distress. J Int Econ 119:181–207CrossRefGoogle Scholar
  26. Conti G, Pudney S (2011) Survey design and the analysis of satisfaction. Rev Econ Stat 93(3):1087–1093CrossRefGoogle Scholar
  27. Cordero JM, Salinas-Jiménez J, Salinas-Jiménez MM (2017) Exploring factors affecting the level of happiness across countries: a conditional robust nonparametric frontier analysis. Eur J Oper Res 256(2):663–672CrossRefGoogle Scholar
  28. De Neve J-E, Diener E, Tay L, Xuereb C (2013) The objective benefits of subjective Well-being. In: Helliwell JF, Layard R, Sachs J (eds) World happiness report. Sustainable Development Solutions Network, New York, pp 54–79Google Scholar
  29. De Neve J-E, Ward G, De Keulenaer F, Van Landeghem B, Kavetsos G, Norton MI (2018) The asymmetric experience of positive and negative economic growth: global evidence using subjective well-being data. Rev Econ Stat 100(2):362–375CrossRefGoogle Scholar
  30. Deaton A, Stone AA (2016) Understanding context effects for a measure of life evaluation: how responses matter. Oxf Econ Pap 68(4):861–870CrossRefGoogle Scholar
  31. Di Tella R, MacCulloch R (2006) Some uses of happiness data in economics. J Econ Perspect 20(1):25–46Google Scholar
  32. Di Tella R, MacCulloch RJ, Oswald AJ (2003) The macroeconomics of happiness. Rev Econ 85(4):809–825Google Scholar
  33. Diener E, Lucas RE, Oishi S (2018) Advances and open questions in the science of subjective well-being. Collabra Psychol 4(1):1–49. Scholar
  34. Dolan P, Kavetsos G (2016) Happy talk: mode of administration effects on subjective well-being. J Happiness Stud 17(3):1273–1291CrossRefGoogle Scholar
  35. Durand M (2015) The OECD better life initiative: How’s life? And the measurement of well-being. Rev Income Wealth 61(1):4–17CrossRefGoogle Scholar
  36. Durand M, Smith C (2013) The OECD approach to measuring subjective well-being. In: Helliwell J, Layard R, Sachs J (eds) World happiness report. Sustainable Development Solutions Network, New York, pp 112–137Google Scholar
  37. Easterlin RA, Angelescu L, Zweig JS (2011) The impact of modern economic growth on urban–rural differences in subjective well-being. World Dev 39(12):2187–2198CrossRefGoogle Scholar
  38. Easterlin RA, Wang F, Wang S (2017) Growth and happiness in China, 1990–2015. In: Helliwell RLJ, Sachs J (eds) World happiness report 2017. Sustainable Development Solutions Network, New York, pp 48–83Google Scholar
  39. Ehrhardt JJ, Saris WE, Veenhoven R (2000) Stability of life-satisfaction over time. J Happiness Stud 1(2):177–205CrossRefGoogle Scholar
  40. Exton C, Smith C, Vandendriessche D (2015) Comparing happiness across the world. OECD Statistics Directorate working paper no. 62. OECD Publishing, Paris, FranceGoogle Scholar
  41. Fabian M (2020) The coalescence of being: a model of the self-actualisation process. J Happiness Stud.
  42. Ferrer-i-Carbonell A, Frijters P (2004) How important is methodology for the estimates of the determinants of happiness? Econ J 114(497):641–659CrossRefGoogle Scholar
  43. FitzRoy FR, Nolan MA (2020) Education, income and happiness: panel evidence for the UK. Empir Econ 58(5):2573–2592.
  44. Flèche S, Lekfuangfu WN, Clark AE (2019a) The long-lasting effects of family and childhood on adult wellbeing: evidence from British cohort data. J Econ Behav Organ.
  45. Flèche S, Clark AE, Layard R, Powdthavee N, Ward G (2019b) The origins of happiness: the science of well-being over the life course. Princeton University Press, PrincetonGoogle Scholar
  46. Frey BS (2020) What are the opportunities for future happiness research? Int Rev Econ 67(1):5–12.
  47. Frey BS, Stutzer A (2010) Happiness and public choice. Public Choice 144(3–4):557–573CrossRefGoogle Scholar
  48. Frijters P, Haisken-DeNew JP, Shields MA (2004) Money does matter! Evidence from increasing real income and life satisfaction in East Germany following reunification. Am Econ Rev 94(3):730–740CrossRefGoogle Scholar
  49. Gielen AC, Van Ours JC (2014) Unhappiness and job finding. Economica 81(323):544–565CrossRefGoogle Scholar
  50. Graham C (2009) Happiness around the world: the paradox of happy peasants and miserable millionaires. Oxford University Press, OxfordGoogle Scholar
  51. Graham C (2011) Adaptation amidst prosperity and adversity: insights from happiness studies from around the world. World Bank Res Obs 26(1):105–137CrossRefGoogle Scholar
  52. Graham C (2017) Happiness for all?: unequal hopes and lives in pursuit of the American dream. Princeton University Press, Princeton/OxfordCrossRefGoogle Scholar
  53. Graham C, Chattopadhyay S (2013) Gender and well-being around the world. Int J Happin Develop 1(2):212–232Google Scholar
  54. Graham C, MacLennan S (2020) Policy insights from the new science of well-being. Behav Sci Policy 6(1):1–20CrossRefGoogle Scholar
  55. Graham C, Markowitz J (2011) Aspirations and happiness of potential Latin American immigrants. J Soc Res Policy 2:9–25Google Scholar
  56. Graham C, Nikolova M (2013) Does access to information technology make people happier? Insights from well-being surveys from around the world. J Socio-Econ 44:126–139CrossRefGoogle Scholar
  57. Graham C, Nikolova M (2015) Bentham or Aristotle in the development process? An empirical investigation of capabilities and subjective well-being. World Dev 68:163–179CrossRefGoogle Scholar
  58. Graham C, Nikolova M (2018) Chapter 5: happiness and international migration in Latin America. In: John RL, Helliwell F, Sachs JD (eds) World happiness report 2018. Sustainable Development Solutions Network, New York, pp 88–114Google Scholar
  59. Graham C, Pinto S (2019) Unequal hopes and lives in the USA: optimism, race, place, and premature mortality. J Popul Econ 32:665–733.
  60. Graham C, Eggers A, Sukhtankar S (2004) Does happiness pay? Insights from panel data from Russia. J Econ Behav Organ 55:319–342Google Scholar
  61. Graham C, Laffan K, Pinto S (2018) Well-being in metrics and policy. Science 362(6412):287–288CrossRefGoogle Scholar
  62. Green F (2010) Well-being, job satisfaction and labour mobility. Labour Econ 17(6):897–903. Scholar
  63. Gruber JH, Mullainathan S (2005) Do cigarette taxes make smokers happier? BE J Econ Anal Policy 5(1):1–43Google Scholar
  64. Gruber J, Mauss IB, Tamir M (2011) A dark side of happiness? How, when, and why happiness is not always good. Perspect Psychol Sci 6(3):222–233CrossRefGoogle Scholar
  65. Guriev S, Melnikov N (2018) Happiness convergence in transition countries. J Comp Econ 46(3):683–707CrossRefGoogle Scholar
  66. Guven C (2011) Are happier people better citizens? Kyklos 64(2):178–192CrossRefGoogle Scholar
  67. Guven C (2012) Reversing the question: does happiness affect consumption and savings behavior? J Econ Psychol 33(4):701–717CrossRefGoogle Scholar
  68. Headey B, Wearing A (1991) Subjective well-being: a stocks and flows framework. Subject Well-being Interdisciplin Perspect 21:49–73Google Scholar
  69. Helliwell JF, Barrington-Leigh CP (2010) Measuring and understanding subjective well-being. Can J Econ/Revue Can D’écon 43(3):729–753Google Scholar
  70. Helliwell JF, Huang H, Wang S, Norton M (2020) Social environments for world happiness. In: Helliwell JF, Layard R, Sachs J, De Neve J-E (eds) World happiness report 2020. Sustainable Development Solutions Network, New York, pp 12–45Google Scholar
  71. Herbst C (2011) Paradoxical decline? Another look at the relative decline in female happiness. J Econ Psychol 32:773–788CrossRefGoogle Scholar
  72. Hinks T (2020) Fear of robots and life satisfaction. Int J Soc Robot.
  73. Ivlevs A (2014) Happiness and the emigration decision. IZA World of Labor. Retrieved from
  74. Kahneman D, Deaton A (2010) High income improves evaluation of life but not emotional well-being. Proc Natl Acad Sci 107(38):16489–16493CrossRefGoogle Scholar
  75. Kahneman D, Krueger AB, Schkade D, Schwarz N, Stone AA (2006) Would you be happier if you were richer? A focusing illusion. Science 312(5782):1908–1910CrossRefGoogle Scholar
  76. Kaiser C, Vendrik MC (2019) How threatening are transformations of reported happiness to subjective wellbeing research?. Working paper.
  77. Kapteyn A (2020) Income growth is unlikely to help, but we can learn from international comparisons. Behaviour Public Policy 4(2):188–197.
  78. Kapteyn A, Smith JP, Van Soest A (2013) Are Americans really less happy with their incomes? Rev Income Wealth 59(1):44–65CrossRefGoogle Scholar
  79. Kapteyn A, Lee J, Tassot C, Vonkova H, Zamarro G (2015) Dimensions of subjective well-being. Soc Indic Res 123(3):625–660CrossRefGoogle Scholar
  80. Kashdan T, Biswas-Diener R (2014) The upside of your dark side: why being your whole self–not just your “good” self–drives success and fulfillment. Penguin Random House, New YorkGoogle Scholar
  81. Keating J (2010) Ethnic cleansing in the kingdom of happiness. Foreign Policy. Retrieved from
  82. Kim S, Oswald AJ (2020) Happy lottery winners and LT bias. Rev Income Wealth.
  83. Knabe A, Rätzel S, Schöb R, Weimann J (2010) Dissatisfied with life but having a good day: time-use and well-being of the unemployed. Econ J 120(547):867–889Google Scholar
  84. Krause A (2013) Don’t worry, be happy? Happiness and reemployment. J Econ Behav Organ 96:1–20CrossRefGoogle Scholar
  85. Kristensen N, Johansson E (2008) New evidence on cross-country differences in job satisfaction using anchoring vignettes. Labour Econ 15(1):96–117CrossRefGoogle Scholar
  86. Krueger AB, Schkade DA (2008) The reliability of subjective well-being measures. J Public Econ 92(8/9):1833–1845CrossRefGoogle Scholar
  87. Kuhn P, Kooreman P, Soetevent A, Kapteyn A (2011) The effects of lottery prizes on winners and their neighbors: evidence from the Dutch postcode lottery. Am Econ Rev 101(5):2226–2247CrossRefGoogle Scholar
  88. Lee S, McClain C, Webster N, Han S (2016) Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated health, and subjective life expectancy in survey instruments. Qual Life Res 25(10):2497–2510CrossRefGoogle Scholar
  89. Levinson A (2012) Valuing public goods using happiness data: the case of air quality. J Public Econ 96(9–10):869–880CrossRefGoogle Scholar
  90. Liberini F, Redoano M, Proto E (2017) Happy voters. J Public Econ 146:41–57CrossRefGoogle Scholar
  91. MacKerron G (2012) Happiness economics from 35 000 feet. J Econ Surv 26(4):705–735CrossRefGoogle Scholar
  92. Marsh H, Huppert F, Donald J, Horwood M, Sahdra B (2020) The well-being profile (WB-Pro): creating a theoretically based multidimensional measure of well-being to advance theory, research, policy, and practice. Psychol Assess 32(3):294–313CrossRefGoogle Scholar
  93. Nikolova M (2019) Happiness: using subjective well-being metrics to gauge development. In: Drydyk J, Keleher L (eds) Routledge handbook of development ethics. Routledge, New York, pp 84–92Google Scholar
  94. Nikolova M, Ayhan SH (2019) Your spouse is fired! How much do you care? J Popul Econ 32(3):799–844CrossRefGoogle Scholar
  95. Nikolova M, Cnossen F (2020) What makes work meaningful and why economists should care about it. Labour Econ 65:101847. Scholar
  96. Nikolova M, Graham C (2015) In transit: the well-being of migrants from transition and post-transition countries. J Econ Behav Organ 112:164–186Google Scholar
  97. Nikolova M, Nikolaev BN (2018) Family matters: the effects of parental unemployment in early childhood and adolescence on subjective well-being later in life. J Econ Behav Organ.
  98. Nikolova M, Popova O (2020) Sometimes your best just ain’t good enough: the worldwide evidence on subjective well-being efficiency. BE J Econ Anal Policy.
  99. Nikolova E, Sanfey P (2016) How much should we trust life satisfaction data? Evidence from the Life in Transition Survey. J Comp Econ 44(3):720–731CrossRefGoogle Scholar
  100. Nikolova M, Nikolaev B, Popova O (2020) The perceived well-being and health costs of exiting self-employment. Small Bus Econ.
  101. O’Connor KJ (2019) The effect of immigration on natives’ wellbeing in the European Union, GLO discussion paper, no. 352. Global Labor Organization (GLO), EssenGoogle Scholar
  102. O’Connor KJ (2020) Life satisfaction and noncognitive skills: effects on the likelihood of unemployment. Kyklos.
  103. O’Connor KJ, Graham C (2019) Longer, more optimistic lives: historic optimism and life expectancy in the United States. J Econ Behav Organ 168:374–392CrossRefGoogle Scholar
  104. O’Donnell G, Oswald AJ (2015) National well-being policy and a weighted approach to human feelings. Ecol Econ 120:59–70CrossRefGoogle Scholar
  105. OECD (2011) “Subjective Well-Being”, in how’s life?: measuring well-being. OECD Publishing, Paris.
  106. OECD (2013) OECD guidelines on measuring subjective well-being. OECD Publishing, ParisGoogle Scholar
  107. Oswald AJ, Powdthavee N (2008) Does happiness adapt? A longitudinal study of disability with implications for economists and judges. J Public Econ 92(5–6):1061–1077CrossRefGoogle Scholar
  108. Oswald AJ, Proto E, Sgroi D (2015) Happiness and productivity. J Labor Econ 33(4):789–822CrossRefGoogle Scholar
  109. Otrachshenko V, Popova O (2014) Life (dis)satisfaction and the intention to migrate: evidence from Central and Eastern Europe. J Socio-Econ 48:40–49CrossRefGoogle Scholar
  110. Powdthavee N (2010a) The happiness equation: the surprising economics of our most valuable asset. Icon Books Ltd, LondonGoogle Scholar
  111. Powdthavee N (2010b) How much does money really matter? Estimating the causal effects of income on happiness. Empir Econ 39(1):77–92CrossRefGoogle Scholar
  112. Powdthavee N, van den Berg B (2011) Putting different price tags on the same health condition: re-evaluating the well-being valuation approach. J Health Econ 30(5):1032–1043CrossRefGoogle Scholar
  113. Powdthavee N, Vernoit J (2013) Parental unemployment and children’s happiness: a longitudinal study of young people’s well-being in unemployed households. Labour Econ 24:253–263CrossRefGoogle Scholar
  114. Rehdanz K, Maddison D (2005) Climate and happiness. Ecol Econ 52(1):111–125CrossRefGoogle Scholar
  115. Riedl M, Geishecker I (2014) Keep it simple: estimation strategies for ordered response models with fixed effects. J Appl Stat 41(11):2358–2374CrossRefGoogle Scholar
  116. Ryff CD (1989) Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J Pers Soc Psychol 57(6):1069CrossRefGoogle Scholar
  117. Ryff CD (2014) Self realization and meaning making in the face of adversity: a eudaimonic approach to human resilience. J Psychol Africa (south of the Sahara, the Caribbean, and Afro-Latin America) 24(1):1–12Google Scholar
  118. Sarracino F (2019) When does economic growth improve well-being? In: Rojas M (ed) The economics of happiness: how the Easterlin Paradox transformed our understanding of well-being and progress. Springer, Cham, Switzerland, pp 355–370Google Scholar
  119. Schmidt JD (2017) Development challenges in Bhutan: perspectives on inequality and gross national happiness. In: Schmidt JD (ed) Development challenges in Bhutan: perspectives on inequality and gross national happiness. Springer International Publishing, Cham, pp 1–16CrossRefGoogle Scholar
  120. Schröder C, Yitzhaki C (2017) Revisiting the evidence for cardinal treatment of ordinal variables. Eur Econ Rev 92:337–358CrossRefGoogle Scholar
  121. Senik C (2009) Direct evidence on income comparisons and their welfare effects. J Econ Behav Organ 72(1):408–424CrossRefGoogle Scholar
  122. Steptoe A, Deaton A, Stone AA (2015) Subjective wellbeing, health, and ageing. Lancet 385(9968):640–648CrossRefGoogle Scholar
  123. Stevenson B, Wolfers J (2009) The paradox of declining female happiness. Am Econ J Econ Pol 1(2):190–225CrossRefGoogle Scholar
  124. Stiglitz JE, Sen A, Fitoussi J-P (2009) Report by the commission on the measurement of economic performance and social progress. Available at:
  125. Stone AA, Krueger AB (2018) Understanding subjective well-being. In: Stiglitz J, Fitoussi J, Durand M (eds) For good measure: advancing research on well-being metrics beyond GDP. OECD Publishing, Paris. pp 163–201.
  126. Stone AA, Mackie CE (2013) Subjective well-being: measuring happiness, suffering, and other dimensions of experience. National Academies Press, Washington, DCGoogle Scholar
  127. Ubel PA, Peeters Y, Smith D (2010) Abandoning the language of “response shift”: a plea for conceptual clarity in distinguishing scale recalibration from true changes in quality of life. Qual Life Res 19(4):465–471CrossRefGoogle Scholar
  128. Van Praag BMS, Ferrer-i-Carbonell A (2008) Happiness quantified: a satisfaction calculus approach, Revised edition. Oxford University Press, Oxford/New YorkGoogle Scholar
  129. Ward G (2020) Happiness and voting: evidence from four decades of elections in Europe. Am J Polit Sci 64(3):504–518Google Scholar
  130. Wolfers J (2003) Is business cycle volatility costly? Evidence from surveys of subjective well-being. Int Finance 6(1):1–26CrossRefGoogle Scholar

Authors and Affiliations

  1. 1.Faculty of Economics and Business, Global Economics and ManagementUniversity of GroningenGroningenThe Netherlands
  2. 2.The Brookings Institution/University of MarylandWashingtonUSA

Section editors and affiliations

  • Milena Nikolova
    • 1
  1. 1.Faculty of Economics and BusinessUniversity of GroningenGroningenThe Netherlands

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