See Cantril (1965). ↩︎

See Zadeh (2015) for a review of the concepts of “fuzzy” sets, boundaries, and logic. ↩︎

In terms of emotions, balance/harmony are invoked in numerous constructs. Following work by Bradburn (1969), “affect balance” is understood as pertaining to the ratio of positive to negative emotions experienced by a person, whereby well-being is generally viewed as the former outweighing the latter to some extent. Parks et al. (2012), for instance, conclude that high well-being involves a ratio of positive to negative emotions of at least 2.15:1. However, work on such ratios has been critiqued by Brown et al. (2013), and their precise dynamics are yet to be ascertained (Nickerson, 2018). In slightly different conceptual territory are constructs like “emotional equanimity” (Desbordes et al., 2015) and “emotional equilibrium” (Labouvie-Vief et al., 2010), which pertain more to low arousal emotional states (e.g., calmness, peace, tranquillity). These two have subtle differences though, in that equanimity often implies synchronous balance (e.g., emotional neutrality at a given moment), while equilibrium can describe a diachronous process that averages out over time (e.g., a capacity to return relatively swiftly from negative or positive affect to a neutral baseline). In that respect, the latter relates to notions such as “emotional homeostasis” (Rinomhota & Cooper, 1996), which describes a complex system’s ability to self-regulate around a desired set-point. ↩︎

Motivational balance is another form of mental balance identified by Wallace and Shapiro (2006), who refer to it as “conative balance” (which also encompasses phenomena such as intention and volition). Situated in this space are numerous relevant constructs and related research. One example is Vallerand’s (2008) dualistic model of passion, which differentiates “harmonious” forms (i.e., accommodating to other aspects of life, and conducive to well-being overall) from “obsessive” forms (i.e., all-consuming, and hindering well-being). Another example is Block and Block’s (2006) notions of ego control and ego resiliency. Ego control refers to whether people characteristically express affect and impulse (under-control) versus inhibit such tendencies (over-control). Ego resiliency is then the ability to strike an optimal balance between under- and over-control, skilfully adapting according to one’s situational dynamics (Seaton & Beaumont, 2015). ↩︎

In terms of character, recognition of the relevance of balance/harmony is often traced specifically to Aristotle (2000). In articulating his ideas on virtue, for instance, his principle of the “golden mean” held that one should judiciously tread a middle line between opposing vices of excess and deficiency (courage, for example, involves avoiding both cowardice and recklessness). Such ideas have been embraced by contemporary researchers. For instance, Rashid (2015) and Niemiec (2017) have pioneered an approach to understanding mental illness and health based on under- and over-use of character strengths. From this perspective, strengths (e.g., perseverance) are not positive in themselves, but only insofar as one finds a middle ground between under-use (e.g., laziness) and over-use (e.g., stubbornness). Such ideas have been applied vis-a-vis conditions, including social anxiety (Freidlin et al., 2017) and obsessive-compulsive disorder (Littman-Ovadia & Freidlin, 2019). ↩︎

Sleep/rest are another important category of body maintenance activities to which balance/harmony apply. With sleep, one should ideally strike an optimal balance between insufficient and excessive sleep, both of which can be detrimental to well-being (Yang et al., 2015). Similar principles apply to rest/activity in general. In the workplace, for instance, while over-exertion can be problematic (e.g., necessitating remedial actions, such as regulations to limit working hours), so too is under-exertion (e.g., leading to interventions to limit sedentary behaviours, such as active workstations) (Dupont et al., 2019). ↩︎

Work-life balance is the most widely recognized and cited aspect of balance/harmony in academia, with the largest literature devoted to it (e.g., a Google Scholar search for “work-life balance” returns 273,000 results). The relevant research is now so extensive that there are numerous systematic reviews or meta-analyses focusing just on specific aspects and outcomes, such as organisational performance (Wong et al., 2020), or on particular contexts and populations, such as Asia (Le et al., 2020). ↩︎

Many relationship scholars and therapists emphasise the importance of balance/harmony in some way. This includes, for example, acknowledging the value to successful partnerships of principles such as reciprocity and fairness, which can be interpreted as being about striking a balance between the needs and goals of the various partners (Pillemer et al., 2008). The importance of reciprocity is partly a question of people wanting fair treatment, as elucidated by game theory (Debove et al., 2016). However, people also tend to value treating others fairly, and are often reluctant to “over-benefit” from the relationship at their conspecific’s expense (McPherson et al., 2010). ↩︎

Regarding politics, it is conventional to analyse and situate political views on a left-right spectrum. In that respect, democratic governments usually try to win and maintain power by striking an optimal balance between these poles, one that is appealing to a majority of people (Lomas, 2017). For example, one manifestation of this left-right polarity is taxation, with the left and right generally favouring higher and lower taxation respectively. Rather than cleaving to either extreme (i.e., a 100% versus 0% tax rate), most governments try to find some optimal point between them (i.e., one that is practical, sustainable, and supported or at least tolerated by a majority of the population). ↩︎

Balance/harmony apply to humans’ relationship with the natural world, as elucidated by Kjell (2011). Indeed, it is increasingly recognized that finding such balance/harmony is necessary for the prosperity and even the very survival of humankind. Notions of living in harmony with nature have previously tended to be somewhat niche concerns in industrialised nations. Less industrialised cultures — particularly indigenous ones — are often seen as having more successfully developed and/or maintained philosophies of such harmony, which includes balancing humans’ needs with those of the natural world (Izquierdo, 2005; Lomas, 2019). By contrast, more industrialised countries have been dominated by disconnected, instrumentalist modes of relationship which view nature more as a resource to be exploited. But growing recognition of the climate crisis has brought environmentalism to the fore worldwide (Pihkala, 2018), including realising that aspirations for economic growth must be balanced against the earth’s capacity to sustain it (Schumacher, 2011). ↩︎

See Li (2008, 2012) and Lomas (2021). ↩︎

As developed in Aristotle’s (2000) Nicomachean Ethics; see Niemiec (2017) for a contemporary exposition and adaptation. ↩︎

Each has an extensive literature: a search on Google Scholar in January 2022, for example, returned approximately 273,000 hits for the specific phrase “work-life balance” and 115,000 for “balanced diet.” ↩︎

See Delle Fave et al. (2011), who conducted a mixed-methods study with 666 participants in Australia, Croatia, Germany, Italy, Portugal, Spain, and South Africa (although the status of the latter as Western is potentially ambiguous and disputed). Delle Fave et al. (2016) then also conducted a follow-up study with 2,799 participants in Argentina, Brazil, Croatia, Hungary, India, Italy, Mexico, New Zealand, Norway, Portugal, South Africa, and the United States. ↩︎

See Ragnarsdottir (1996). ↩︎

See Lomas (2021) for a review of the concepts of balance and harmony and their application across various life domains. ↩︎

See Li (2008) for a review of ideals of harmony in classical Chinese and Greek philosophy. ↩︎

See Delle Fave et al. (2016). ↩︎

See e.g., Diener et al. (1999). ↩︎

See McManus et al. (2019) for commentary on the tendency of research on positive emotions to focus on high arousal forms, and also for a review of the predictive value of low arousal positive emotions. ↩︎

See Kjell and Diener (2021). ↩︎

See Henrich et al. (2010). ↩︎

See Arnett (2008). ↩︎

See Lomas (2018) for a theoretical review of the impact of language in particular on the way people experience and understand the world (an extensive body of research sometimes referred to broadly as the “linguistic relativity hypothesis”). ↩︎

Although the WEIRD framework has been very impactful and necessary, Ghai (2021) suggests that classifying places in a binary way as WEIRD or non-WEIRD may be unhelpful, and it may be better to view each element of the acronym as a spectrum upon which countries may be variously situated. See also Muthukrishna et al. (2020), who have created a tool for mapping degrees of WEIRDness (and more generally measuring the psychological and cultural distance between societies). ↩︎

Analysing well-being scholarship over the past 150 years, Lomas (2022) suggests we are now seeing an emergent wave of “global wellbeing scholarship,” featuring a concerted effort to engage with cross-cultural populations and ideas. Although there is a long tradition of cross-cultural research dating back over a century, it has been fairly niche in fields like psychology as a whole. However, in the wake of critiques like Henrich et al. (2010), there is an increasingly widespread recognition of the need for research to become less Western-centric, and indeed positive steps towards that goal. Hendricks et al. (2019) conducted a bibliometric analysis of randomised controlled trials of “positive psychology interventions”, for example, and of 188 studies identified, 78.2% were conducted in Western countries. However, the authors note “a strong and steady increase in publications from non-Western countries since 2012”, indicating an encouraging “trend towards globalization” of happiness research (p.489). ↩︎

Tsai (2007) described such preferences as “ideal affect” (i.e., “the affective states that people strive for or ideally want to feel”; p.243). Tsai has been at the forefront of work indicating different forms of ideal affect in Eastern and Western cultures, observing overall that Eastern cultures lean towards valuing low arousal forms of affect (see e.g., Tsai et al., 2000, 2006a, 2006b, 2007a, 2007b, 2007c, Tsai & Levenson, 1997, Sims et al., 2015). ↩︎

The individualism-collectivism distinction was first brought to attention by Hofstede (1980), who developed it initially as a societal identifier (i.e., a way of identifying and differentiating cultural contexts). It was then developed further by Markus and Kitayama (1991), who shifted the emphasis by viewing it more in terms of self-construal (i.e., how people in different cultures view themselves). ↩︎

This literature is now so substantial that there are many meta-analyses, not only of the individualism-collectivism distinction in general, but of specific facets of it, including its relationship to: subjective well-being (Yu et al., 2018); self-concepts (Oyserman et al., 2002); conformity (Bond & Smith, 1996); social media use (Cheng et al., 2021); ethnicity (Vargas & Kemmelmeier, 2013); socio-economic development (Santos et al., 2017); cultural products (Morling & Lamoreaux, 2008); cultural change (Taras et al., 2012); and justice (Sama & Papamarcos, 2000). ↩︎

Santos et al. (2017), for example, examined 51 years of data on individualist practices and values across 78 countries, and found that individualism appears to be rising in most (with the exceptions being Cameroon, Malawi, Malaysia, and Mali in terms of “cultural practices,” and Armenia, China, Croatia, Ukraine, and Uruguay in terms of “cultural values”). ↩︎

Nisbett et al. (2001) presented an initial case for this distinction, drawing on various empirical literature. It has since been explored and corroborated in numerous studies. For instance, Han and Ma (2014) found different patterns of neural activation in Western versus Eastern participants based on these modes. ↩︎

As with the individualism-collectivism distinction, the literature is now so extensive that meta-analyses of East versus West differences have been conducted in relation to various specific phenomena, including: neural activity (Han & Ma, 2014); locus of control (Cheng et al., 2013); moral viewpoints (Forsyth et al., 2008); social anxiety (Woody et al., 2015); grit (Lam & Zhou, 2021); social capital (Zhang et al., 2019); gender differences (Shan et al., 2019); bullying/victimisation (Yuchang et al., 2019); corporate governance (Cao et al., 2019); organisational justice (Li & Cropanzano, 2009); and attitudes towards ageing (North & Fiske, 2015). ↩︎

See Joshanloo (2014) for a review of how various Eastern traditions have shaped cultural views around happiness in the region. ↩︎

See Li (2012), p.845, and also Li (2008). ↩︎

This analysis derives from a qualitative analysis of college students (95 American and 73 Japanese) by Uchida and Kitayama (2009). ↩︎

Hitokoto and Uchida (2015) developed their nine item Interdependent Happiness Scale over several studies. In study 1, interdependent happiness correlated with both subjective well-being and interdependent self-construal among Japanese students. Study 2 then found that these students’ subjective well-being was more likely to be explained by the Interdependent Happiness Scale than that of American students. In study 3, the Interdependent Happiness Scale explained the subjective well-being of working adults in the US, Germany, Japan, and Korea. Likewise in study 4 it explained the subjective well-being of Japanese adults and elders from more collectivist regions of the country. ↩︎

Besides the work by Tsai (see endnote 32), these studies include: a survey of college students (597 Chinese and Taiwanese and 91 European American) by Lee et al. (2013) in the development of their Peace of Mind Scale; a survey of college students (330 European-American, 156 immigrant Asian, and 147 Asian American) by Leu et al. (2011); a survey of college students (439 Taiwanese and 344 British) by Lu et al. (2001); a survey of college students (482 Belgian/Dutch, 223 Spanish, 535 Canadian, 487 Chinese/Hong Kong, 450 Japanese, and 365 Korean) by Kuppens et al. (2017); an analysis of survey data collected in Hong Kong (n = 2002) and China (n not reported) by Ip (2014); and a longitudinal survey of 107 Chinese workers by Xi et al. (2021). ↩︎

See e.g., Leu et al. (2011) and Uchida and Kitayama (2009). ↩︎

See e.g., Lee et al. (2012). ↩︎

Said (1979) showed that notions of East versus West were not merely generalisations but moreover were potent discourses that could be harnessed in harmful ways. He coined the term “Orientalism” to denote the process by which 19th Century thinkers in the West came to understand themselves and their society by contrasting it with the “Other” of the East in various ways. More benevolent, albeit still problematic, were forms of “Romantic Orientalism,” in which the East was viewed through a utopian lens as superior to the West in some manner, such as wiser, less materialistic, and more spiritual. More pernicious disparaging were Orientalist discourses that were used in attempts to justify imperialism and colonialism, for instance presenting the East as apparently inefficient and badly-run and therefore “in need” of intervention. ↩︎

See Delle Fave et al. (2011). ↩︎

See Delle Fave et al. (2016). ↩︎

See Lambert et al. (2020) for an introduction to the Global Wellbeing Initiative, and for a discussion of initial topics of interest. ↩︎

The Gallup World Poll divides the countries of the world into 10 regional groups. For the WEIRD countries we combined region 0 (Western Europe) and region 7 (comprising the United States, Canada, New Zealand, and Australia). The East Asian group includes all those in region 5 for which data are available (Japan, South Korea, China, Hong Kong, Taiwan, and Mongolia). ↩︎

In these calculations, the WEIRD sample includes the countries of Western Europe (Gallup’s region 0) and the countries in Gallup’s region 7 (United States, Canada, Australia and New Zealand). ↩︎

Of the top ten countries for balance, their rankings on GDP per capita are: Finland 15th; Malta 23rd; Switzerland 2nd; Romania 37th; Portugal 33rd; Lithuania 29th; Norway 5th; Slovenia 28th; Denmark 6th; and the Netherlands 8th. Of the bottom countries for balance, their rankings on GDP per capita are: Cambodia 100th; Cameroon 103rd; Congo Brazzaville 104th; Gabon 59th; Zambia 107th; Benin 105th; Uganda 113th; Lebanon 70th; Mali 112th; and Zimbabwe 108th. ↩︎

Although calmness is an exemplar of a low arousal positive emotion, excitement is a more complex and even ambiguous construct. Excitement is usually coded as positive in various ways, including in terms of physiology, valence, and desirability (Machizawa et al., 2020). However, it can also be read, to an extent, as an “ambivalent” or “mixed” emotion, since it can include affective dimensions or elements that may be more negatively coded, such as fear or anxiety (Brooks, 2014). People may be drawn towards risk-taking activities, for instance, because they find these exciting, but inherent in that experience is a certain degree of danger, which is precisely what helps make it exciting. Indeed, research on “edgework” suggests that people pursue self-transcendence through a wide variety of risky activities that can threaten the very existence or integrity of the self, which some observers might evaluate quite negatively (Lyng, 1990). So, excitement is not an unambiguously positive emotion. Nevertheless, it is a close enough proxy for high arousal positive emotions. ↩︎

The item does not map onto the individualism-collectivism distinction in its entirety. After all, the distinction itself is multifaceted, with different interpretations and applications. As noted in endnote 33, for instance, Hofstede (1980) developed it initially as a societal identifier, while Markus and Kitayama (1991) shifted the emphasis by viewing it more in terms of self-construal. This item is primarily about a judgement or belief that is, (a) normative (i.e., asking what respondents think should be the case, rather than necessarily is the case), and (b) more about others (i.e., asking how respondents think people in general should act, rather than how they themselves should act, although respondents are likely to include themselves within the answer, since they are among the general “people” referred to). Nevertheless, even in its partiality, this item can be regarded as a decent proxy for the individualism-collectivism distinction. ↩︎

Correlations were calculated by pooling individual-level data across countries. ↩︎

The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by the Gallup World Poll data.

The Report is supported by The Ernesto Illy Foundation, illycaffè, Davines Group, Unilever’s largest ice cream brand Wall’s, The Blue Chip Foundation, The William, Jeff, and Jennifer Gross Family Foundation, The Happier Way Foundation, and The Regenerative Society Foundation.

The World Happiness Report was written by a group of independent experts acting in their personal capacities. Any views expressed in this report do not necessarily reflect the views of any organization, agency or program of the United Nations.

WHR 2020 | Chapter 2 Social Environments for World Happiness

Introduction

This is the eighth World Happiness Report. Its central purpose remains as it was for the first Report, to review the science of measuring and understanding subjective well-being, and to use survey measures of life satisfaction to track the quality of lives as they are being lived in more than 150 countries. In addition to presenting updated rankings and analysis of life evaluations throughout the world, each World Happiness Report has a variety of topic chapters, often dealing with an underlying theme for the report as a whole. Our special focus for World Happiness Report 2020 is environments for happiness. This chapter focuses more specifically on social environments for happiness, as reflected by the quality of personal social connections and social institutions.

Before presenting fresh evidence on the links between social environments and how people evaluate their lives, we first present our analysis and rankings of national average life evaluations based on data from 2017-2019.

Our rankings of national average life evaluations are accompanied by our latest attempts to show how six key variables contribute to explaining the full sample of national annual averages from 2005-2019. Note that we do not construct our happiness measure in each country using these six factors – the scores are instead based on individuals’ own assessments of their subjective well-being, as indicated by their survey responses in the Gallup World Poll. Rather, we use the six variables to help us to understand the sources of variations in happiness among countries and over time. We also show how measures of experienced well-being, especially positive emotions, supplement life circumstances and the social environments in supporting high life evaluations. We will then consider a range of data showing how life evaluations and emotions have changed over the years covered by the Gallup World Poll. [1]

We next turn to consider social environments for happiness, in two stages. We first update and extend our previous work showing how national average life evaluations are affected by inequality, and especially the inequality of well-being. Then we turn to an expanded analysis of the social context of well-being, showing for the first time how a more supportive social environment not only raises life evaluations directly, but also indirectly, by providing the greatest gains for those most in misery. To do this, we consider two main aspects of the social environment. The first is represented by the general climate of interpersonal trust, and the extent and quality of personal contacts. The second is covered by a variety of measures of how much people trust the quality of public institutions that set the stage on which personal and community-level interactions take place.

We find that individuals with higher levels of interpersonal and institutional trust fare significantly better than others in several negative situations, including ill-health, unemployment, low incomes, discrimination, family breakdown, and fears about the safety of the streets. Living in a trusting social environment helps not only to support all individual lives directly, but also reduces the well-being costs of adversity. This provides the greatest gains to those in the most difficult circumstances, and thereby reduces well-being inequality. As our new evidence shows, to reduce well-being inequality also improves average life evaluations. We estimate the possible size of these effects later in the chapter.

Measuring and Explaining National Differences in Life Evaluations

In this section we present our usual rankings for national life evaluations, this year covering the 2017-2019 period, accompanied by our latest attempts to show how six key variables contribute to explaining the full sample of national annual average scores over the whole period 2005-2019. These variables are GDP per capita, social support, healthy life expectancy, freedom, generosity, and absence of corruption. As already noted, our happiness rankings are not based on any index of these six factors – the scores are instead based on individuals’ own assessments of their lives, as revealed by their answers to the Cantril ladder question that invites survey participants to imagine their current position on a ladder with steps numbered from 0 to 10, where the top represents the best possible and the bottom the worst possible life for themselves. We use the six variables to explain the variation of happiness across countries, and also to show how measures of experienced well-being, especially positive affect, are themselves affected by the six factors and in turn contribute to the explanation of higher life evaluations.

In Table 2.1 we present our latest modeling of national average life evaluations and measures of positive and negative affect (emotion) by country and year. [2] For ease of comparison, the table has the same basic structure as Table 2.1 in several previous editions of the World Happiness Report. We can now include 2019 data for many countries. The addition of these new data slightly improves the fit of the equation, while leaving the coefficients largely unchanged. [3] There are four equations in Table 2.1. The first equation provides the basis for constructing the sub-bars shown in Figure 2.1.

The results in the first column of Table 2.1 explain national average life evaluations in terms of six key variables: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and freedom from corruption. [4] Taken together, these six variables explain three-quarters of the variation in national annual average ladder scores among countries, using data from the years 2005 to 2019. The model’s predictive power is little changed if the year fixed effects in the model are removed, falling from 0.751 to 0.745 in terms of the adjusted R-squared.

The second and third columns of Table 2.1 use the same six variables to estimate equations for national averages of positive and negative affect, where both are based on answers about yesterday’s emotional experiences (see Technical Box 1 for how the affect measures are constructed). In general, emotional measures, and especially negative ones, are differently and much less fully explained by the six variables than are life evaluations. Per-capita income and healthy life expectancy have significant effects on life evaluations, but not, in these national average data, on either positive or negative affect. The situation changes when we consider social variables. Bearing in mind that positive and negative affect are measured on a 0 to 1 scale, while life evaluations are on a 0 to 10 scale, social support can be seen to have similar proportionate effects on positive and negative emotions as on life evaluations. Freedom and generosity have even larger influences on positive affect than on the Cantril ladder. Negative affect is significantly reduced by social support, freedom, and absence of corruption.

In the fourth column we re-estimate the life evaluation equation from column 1, adding both positive and negative affect to partially implement the Aristotelian presumption that sustained positive emotions are important supports for a good life. [5] The most striking feature is the extent to which the results buttress a finding in psychology that the existence of positive emotions matters much more than the absence of negative ones when predicting either longevity [6] or resistance to the common cold. [7] Consistent with this evidence we find that positive affect has a large and highly significant impact in the final equation of Table 2.1, while negative affect has none.

As for the coefficients on the other variables in the fourth column, the changes are substantial only on those variables – especially freedom and generosity – that have the largest impacts on positive affect. Thus, we infer that positive emotions play a strong role in support of life evaluations, and that much of the impact of freedom and generosity on life evaluations is channeled through their influence on positive emotions. That is, freedom and generosity have large impacts on positive affect, which in turn has a major impact on life evaluations. The Gallup World Poll does not have a widely available measure of life purpose to test whether it too would play a strong role in support of high life evaluations.

Table 2.1: Regressions to Explain Average Happiness across Countries (Pooled OLS)

Technical Box 1: Detailed information about each of the predictors in Table 2.1

Our country rankings in Figure 2.1 show life evaluations (answers to the Cantril ladder question) for each country, averaged over the years 2017-2019. Not every country has surveys in every year; the total sample sizes are reported in Statistical Appendix 1, and are reflected in Figure 2.1 by the horizontal lines showing the 95% confidence intervals. The confidence intervals are tighter for countries with larger samples.

The overall length of each country bar represents the average ladder score, which is also shown in numerals. The rankings in Figure 2.1 depend only on the average Cantril ladder scores reported by the respondents, and not on the values of the six variables that we use to help account for the large differences we find.

Each of these bars is divided into seven segments, showing our research efforts to find possible sources for the ladder levels. The first six sub-bars show how much each of the six key variables is calculated to contribute to that country’s ladder score, relative to that in a hypothetical country called “Dystopia”, so named because it has values equal to the world’s lowest national averages for 2017-2019 for each of the six key variables used in Table 2.1. We use Dystopia as a benchmark against which to compare contributions from each of the six factors. The choice of Dystopia as a benchmark permits every real country to have a positive (or at least zero) contribution from each of the six factors. We calculate, based on the estimates in the first column of Table 2.1, that Dystopia had a 2017-2019 ladder score equal to 1.97 on the 0 to 10 scale. The final sub-bar is the sum of two components: the calculated average 2017-2019 life evaluation in Dystopia (=1.97) and each country’s own prediction error, which measures the extent to which life evaluations are higher or lower than predicted by our equation in the first column of Table 2.1. These residuals are as likely to be negative as positive. [8]

How do we calculate each factor’s contribution to average life evaluations? Taking the example of healthy life expectancy, the sub-bar in the case of Tanzania is equal to the number of years by which healthy life expectancy in Tanzania exceeds the world’s lowest value, multiplied by the Table 2.1 coefficient for the influence of healthy life expectancy on life evaluations. The width of each sub-bar then shows, country-by-country, how much each of the six variables contributes to the international ladder differences. These calculations are illustrative rather than conclusive, for several reasons. First, the selection of candidate variables is restricted by what is available for all these countries. Traditional variables like GDP per capita and healthy life expectancy are widely available. But measures of the quality of the social context, which have been shown in experiments and national surveys to have strong links to life evaluations and emotions, have not been sufficiently surveyed in the Gallup or other global polls, or otherwise measured in statistics available for all countries. Even with this limited choice, we find that four variables covering different aspects of the social and institutional context – having someone to count on, generosity, freedom to make life choices, and absence of corruption – are together responsible for more than half of the average difference between each country’s predicted ladder score and that of Dystopia in the 2017-2019 period. As shown in Statistical Appendix 1, the average country has a 2017-2019 ladder score that is 3.50 points above the Dystopia ladder score of 1.97. Of the 3.50 points, the largest single part (33%) comes from social support, followed by GDP per capita (25%) and healthy life expectancy (20%), and then freedom (13%), generosity (5%), and corruption (4%). [9]

The variables we use may be taking credit properly due to other variables, or to unmeasured factors. There are also likely to be vicious or virtuous circles, with two-way linkages among the variables. For example, there is much evidence that those who have happier lives are likely to live longer, and be more trusting, more cooperative, and generally better able to meet life’s demands. [10] This will feed back to improve health, income, generosity, corruption, and sense of freedom. In addition, some of the variables are derived from the same respondents as the life evaluations and hence possibly determined by common factors. There is less risk when using national averages, because individual differences in personality and many life circumstances tend to average out at the national level.

To provide more assurance that our results are not significantly biased because we are using the same respondents to report life evaluations, social support, freedom, generosity, and corruption, we tested the robustness of our procedure (see Table 10 of Statistical Appendix 1of World Happiness Report 2018 for more detail) by splitting each country’s respondents randomly into two groups. We then used the average values from one half the sample for social support, freedom, generosity, and absence of corruption to explain average life evaluations in the other half. The coefficients on each of the four variables fell slightly, just as we expected. [11] But the changes were reassuringly small (ranging from 1% to 5%) and were not statistically significant. [12]

The seventh and final segment in each bar is the sum of two components. The first component is a fixed number representing our calculation of the 2017-2019 ladder score for Dystopia (=1.97). The second component is the average 2017-2019 residual for each country. The sum of these two components comprises the right-hand sub-bar for each country; it varies from one country to the next because some countries have life evaluations above their predicted values, and others lower. The residual simply represents that part of the national average ladder score that is not explained by our model; with the residual included, the sum of all the sub-bars adds up to the actual average life evaluations on which the rankings are based.

Figure 2.1: Ranking of Happiness 2017–2019 (Part 1)

Figure 2.1: Ranking of Happiness 2017–2019 (Part 2)

Figure 2.1: Ranking of Happiness 2017–2019 (Part 3)

What do the latest data show for the 2017-2019 country rankings? Two features carry over from previous editions of the World Happiness Report. First, there is still a lot of year-to-year consistency in the way people rate their lives in different countries, and since we do our ranking on a three-year average, there is information carried forward from one year to the next. Nonetheless, there are interesting changes. Finland reported a modest increase in happiness from 2015 to 2017, and has remained roughly at that higher level since then (See Figure 1 of Statistical Appendix 1 for individual country trajectories). As a result, dropping 2016 and adding 2019 further boosts Finland’s world-leading average score. It continues to occupy the top spot for the third year in a row, and with a score that is now significantly ahead of other countries in the top ten.

Denmark and Switzerland have also increased their average scores from last year’s rankings. Denmark continues to occupy second place. Switzerland, with its larger increase, jumps from 6th place to 3rd. Last year’s third ranking country, Norway, is now in 5th place with a modest decline in average score, most of which occurred around between 2017 and 2018. Iceland is in 4th place; its new survey in 2019 does little to change its 3-year average score. The Netherlands slipped into 6th place, one spot lower than in last year’s ranking. The next two countries in the ranking are the same as last year, Sweden and New Zealand in 7th and 8th places, respectively, both with little change in their average scores. In 9th and 10th place are Austria and Luxembourg, respectively. The former is one spot higher than last year. For Luxembourg, this year’s ranking represents a substantial upward movement; it was in 14th place last year. Luxembourg’s 2019 score is its highest ever since Gallup started polling the country in 2009.

Canada slipped out of the top ten, from 9th place last year to 11th this year. Its 2019 score is the lowest since the Gallup poll begins for Canada in 2005. [13] Right after Canada is Australia in 12th, followed by United Kingdom in 13th, two spots higher than last year, and five positions higher than in the first World Happiness Report in 2012. [14] Israel and Costa Rica are the 14th and 15th ranking countries. The rest of the top 20 include four European countries: Ireland in 16th, Germany in 17th, Czech Republic in 19th and Belgium in 20th. The U.S. is in 18th place, one spot higher than last year, although still well below its 11th place ranking in the first World Happiness Report. Overall the top 20 are all the same as last year’s top 20, albeit with some changes in rankings. Throughout the top 20 positions, and indeed at most places in the rankings, the three-year average scores are close enough to one another that significant differences are found only between country pairs that are several positions apart in the rankings. This can be seen by inspecting the whisker lines showing the 95% confidence intervals for the average scores.

There remains a large gap between the top and bottom countries. Within these groups, the top countries are more tightly grouped than are the bottom countries. Within the top group, national life evaluation scores have a gap of 0.32 between the 1st and 5th position, and another 0.25 between 5th and 10th positions. Thus, there is a gap of about 0.6 points between the 1st and 10th positions. There is a bigger range of scores covered by the bottom ten countries, where the range of scores covers almost an entire point. Tanzania, Rwanda and Botswana still have anomalous scores, in the sense that their predicted values, based on their performance on the six key variables, would suggest much higher rankings than those shown in Figure 2.1. India now joins the group sharing the same feature. India is a new entrant to the bottom-ten group. Its large and steady decline in life evaluation scores since 2015 means that its annual score in 2019 is now 1.2 points lower than in 2015.

Despite the general consistency among the top country scores, there have been many significant changes among the rest of the countries. Looking at changes over the longer term, many countries have exhibited substantial changes in average scores, and hence in country rankings, between 2008-2012 and 2017-2019, as will be shown in more detail in Figure 2.4.

When looking at average ladder scores, it is also important to note the horizontal whisker lines at the right-hand end of the main bar for each country. These lines denote the 95% confidence regions for the estimates, so that countries with overlapping error bars have scores that do not significantly differ from each other. The scores are based on the resident populations in each country, rather than their citizenship or place of birth. In World Happiness Report 2018 we split the responses between the locally and foreign-born populations in each country, and found the happiness rankings to be essentially the same for the two groups, although with some footprint effect after migration, and some tendency for migrants to move to happier countries, so that among 20 happiest countries in that report, the average happiness for the locally born was about 0.2 points higher than for the foreign-born. [15]

Average life evaluations in the top ten countries are more than twice as high as in the bottom ten. If we use the first equation of Table 2.1 to look for possible reasons for these very different life evaluations, it suggests that of the 4.16 points difference, 2.96 points can be traced to differences in the six key factors: 0.94 points from the GDP per capita gap, 0.79 due to differences in social support, 0.62 to differences in healthy life expectancy, 0.27 to differences in freedom, 0.25 to differences in corruption perceptions, and 0.09 to differences in generosity. [16] Income differences are the single largest contributing factor, at one-third of the total, because of the six factors, income is by far the most unequally distributed among countries. GDP per capita is 20 times higher in the top ten than in the bottom ten countries. [17]

Overall, the model explains average life evaluation levels quite well within regions, among regions, and for the world as a whole. [18] On average, the countries of Latin America still have mean life evaluations that are higher (by about 0.6 on the 0 to 10 scale) than predicted by the model. This difference has been attributed to a variety of factors, including some unique features of family and social life in Latin American countries. To explain what is special about social life in Latin America, Chapter 6 of World Happiness Report 2018 by Mariano Rojas presented a range of new data and results showing how a generation-spanning social environment supports Latin American happiness beyond what is captured by the variables available in the Gallup World Poll. In partial contrast, the countries of East Asia have average life evaluations below those predicted by the model, a finding that has been thought to reflect, at least in part, cultural differences in the way people answer questions. [19] It is reassuring that our findings about the relative importance of the six factors are generally unaffected by whether or not we make explicit allowance for these regional differences. [20]

Our main country rankings are based on the average answers to the Cantril ladder life evaluation question in the Gallup World Poll. The other two happiness measures, for positive and negative affect, are themselves of independent importance and interest, as well as being contributors to overall life evaluations, especially in the case of positive affect. Measures of positive affect also play important roles in other chapters of this report, in large part because most lab experiments, being of relatively small size and duration, can be expected to affect current emotions but not life evaluations, which tend to be more stable in response to small or temporary disturbances. Various attempts to use big data to measure happiness using word analysis of Twitter feeds, or other similar sources, are likely to capture mood changes rather than overall life evaluations. In World Happiness Report 2019 we presented comparable rankings for all three of the measures of subjective well-being that we track: the Cantril ladder, positive affect, and negative affect, accompanied by country rankings for the six variables we use in Table 2.1 to explain our measures of subjective well-being. Comparable data for 2017-2019 are reported in Figures 19 to 42 of Statistical Appendix 1.

Changes in World Happiness

As in Chapter 2 of World Happiness Report 2019, we start by showing the global and regional trajectories for life evaluations, positive affect, and negative affect between 2006 and 2019. This is done in the four panels of Figure 2.2. [21] The first panel shows the evolution of global life evaluations measured three different ways. Among the three lines, two lines cover the whole world population (age 15+), with one of the two weighting the country averages by each country’s share of the world population, and the other being an unweighted average of the individual national averages. The unweighted average is often above the weighted average, especially after 2015, when the weighted average starts to drop significantly, while the unweighted average starts to rise equally sharply. This suggests that the recent trends have not favoured the largest countries, as confirmed by the third line, which shows a population-weighted average for all countries in the world except the five countries with the largest populations – China, India, the United States, Indonesia, and Brazil. Even with the five largest countries removed, the population-weighted average does not rise as fast as the unweighted average, suggesting that smaller countries have had greater happiness growth since 2015 than have the larger countries. To expose the different trends in different parts of the world, the second panel of Figure 2.2 shows the dynamics of life evaluations in each to ten global regions, with population weights used to construct the regional averages.

The regions with the highest average evaluations are Northern American plus Australasian region, Western Europe, and the Latin America Caribbean region. Northern America plus Australasia, though they always have the highest life evaluations, show an overall declining trend since 2007. The level in 2019 was 0.5 points lower than that in 2007. Western Europe shows a U-shape, with a flat bottom spanning from 2008 to 2015. The Latin America Caribbean region shows an inverted U-shape with the peak in 2013. Since then, the level of life evaluations has fallen by about 0.6 points. All other regions except Sub-Saharan Africa were almost in the same cluster before 2010. Large divergences have emerged since. Central and Eastern Europe’s life evaluations achieved a continuous and remarkable increase (by over 0.8 points), and caught up with Latin American and Caribbean region in the most recent two years. South Asia, by contrast, has continued to show falling life evaluations, amounting to a cumulative decrease of more than 1.3 points, by far the largest regional change. The country data in Figure 1 of Statistical Appendix 1 shows the South Asian trend to be dominated by India, with its large population and sharply declining life evaluations. The Middle East and North Africa (MENA) also shows a long-term declining trend, though with a rebound in 2014. Comparing 2019 to 2009, the decrease in life evaluations in MENA is over 0.5 points.

East Asia, Southeast Asia, and the Commonwealth of Independent States (CIS) remain largely stable since 2011. The key difference is that East Asia and the CIS suffered significantly in the 2008 financial crisis, while life evaluations in Southeast Asia were largely unaffected. Sub-Saharan Africa has significantly lower level of life evaluations than any other region, particularly before 2016. Its level has remained fairly stable since, though with some decrease in 2013 and then a recovery until 2018. In the meantime, South Asia’s life evaluations worsened dramatically so that its average life evaluations since 2017 are significantly below those in Sub-Saharan Africa, with no sign of recovery.

We next examine the global pattern of positive and negative affect in the third and fourth panels of Figure 2.2. Each figure has the same structure for life evaluations as in the first panel. There is no striking trend in the evolution of positive affect, except that the population-weighted series excluding the five largest countries declined mildly since 2010. The population-weighted series show slightly, but significantly, more positive affect than does the unweighted series, showing that positive affect is on average higher in the larger countries.

In contrast to the relative stability of positive affect over the study period, there has been a rapid increase in negative affect, as shown in the last panel of Figure 2.2. All three lines consistently show a generally increasing trend since 2010 or 2011, indicating that citizens in both large and small countries have experienced increasing negative affect. The increase is sizable. In 2011, about 22% of world adult population reported negative affect, increasing to 29.3% in 2019. In other words, the share of adults reporting negative affect increased by almost 1% per year during this period. Seen in the context of political polarization, civil and religions conflicts, and unrest in many countries, these results created considerable interest when first revealed in World Happiness Report 2019. Readers were curious to know in particular which negative emotions were responsible for this increase. We have therefore unpacked the changes in negative affect into their three components: worry, sadness, and anger.

Figure 2.2: World Dynamics of Happiness

Figure 2.3 illustrates the global trends for worry, sadness, and anger, while the changes for each individual country are shown in Tables 16 to 18 of Statistical Appendix 1. Figure 2.3, like Figure 2.2, shows three lines for each emotion, representing a population-weighted average, a population-weighted average excluding the five most populous countries, and an unweighted average. The first panel shows the trends for worry. The three lines move in the same direction, starting to increase about 2010. People reporting worry yesterday increased by around 8

10% in the 9 years span. Sadness is much less frequent than worry, although the trend is very similar. The share of respondents reporting sadness yesterday increases by around 7

9% since 2010 or 2011. Anger yesterday in the third panel also shows an upward trend in recent years, but contributes very little to the rising trend for negative affect. The rise is almost entirely due to sadness and worry, with the latter being a slightly bigger contributor. Comparable data for other emotions, including stress, are shown in Statistical Appendix 2.

Figure 2.3: World Dynamics of Components of Negative Affect

We now turn to our country-by-country ranking of changes in life evaluations. The year-by-year data for each country are shown, as always, in Figure 1 of online Statistical Appendix 1, and are also available in the online data appendix. Here we present a ranking of the country-by-country changes from a five-year starting base of 2008-2012 to the most recent three-year sample period, 2017-2019. We use a five-year average to provide a more stable base from which to measure changes. In Figure 2.4 we show the changes in happiness levels for all 149 countries that have sufficient numbers of observations for both 2008-2012 and 2017-2019.

Figure 2.4: Changes in Happiness from 2008-2012 to 2017-2019 (Part 1)

Figure 2.4: Changes in Happiness from 2008-2012 to 2017-2019 (Part 2)

Figure 2.4: Changes in Happiness from 2008-2012 to 2017-2019 (Part 3)

Among the 20 top gainers, all of which showed average ladder scores increasing by more than 0.75 points, ten are in the Commonwealth of Independent States or Central and Eastern Europe, and six are in Sub-Saharan Africa. The other four are Bahrain, Malta, Nepal and the Philippines. Among the 20 largest losers, all of which show ladder reductions exceeding 0.45 points, seven are in Sub-Saharan Africa, five in the Latin America and Caribbean region with Venezuela at the very bottom, three in the Middle East and North Africa including Yemen, and two in the Commonwealth of Independent States including Ukraine. The remaining three are Afghanistan, Albania, and India.

These changes are very large, especially for the ten most affected gainers and losers. For each of the ten top gainers, the average life evaluation gains were more than would be expected from a tenfold increase of per capita incomes. For each of the ten countries with the biggest drops in average life evaluations, the losses were more than four times as large as would be expected from a halving of GDP per capita.

On the gaining side of the ledger, the inclusion of a substantial number of transition countries among the top gainers reflects rising life evaluations for the transition countries taken as a group. The appearance of Sub-Saharan African countries among the biggest gainers and the biggest losers reflects the variety and volatility of experiences among the Sub-Saharan countries for which changes are shown in Figure 2.8, and whose experiences were analyzed in more detail in Chapter 4 of World Happiness Report 2017. Benin, the largest gainer over the period, by more than 1.6 points, ranked 4th from last in the first World Happiness Report and has since risen close to the middle of the ranking (86 out of 153 countries this year).

The ten countries with the largest declines in average life evaluations typically suffered some combination of economic, political, and social stresses. The five largest drops since 2008-2012 were in Venezuela, Afghanistan, Lesotho, Zambia, and India, with drops over one point in each case, the largest fall being almost two points in Venezuela. In previous rankings using the base period 2005-2008, Greece was one of the biggest losers, presumably because of the impact of the financial crisis. Now with the base period shifted to the post-crisis years from 2008 to 2012, there has been little net gain or loss for Greece. But the annual data for Greece in Figure 1 of Statistical Appendix 1 do show a U-shape recovery from a low point in 2013 and 2014.

Inequality and Happiness

Previous reports have emphasized the importance of studying the distribution of happiness as well as its average levels. We did this using bar charts showing for the world as a whole and for each of ten global regions the distribution of answers to the Cantril ladder question asking respondents to value their lives today on a scale of 0 to 10, with 0 representing the worst possible life, and 10 representing the best possible life. This gave us a chance to compare happiness levels and inequality in different parts of the world. Population-weighted average life evaluations differed significantly among regions from the highest evaluations in Northern America and Oceania, followed by Western Europe, Latin America and the Caribbean, Central and Eastern Europe, the Commonwealth of Independent States, East Asia, Southeast Asia, The Middle East and North Africa, Sub-Saharan Africa, and South Asia, in that order. We found that well-being inequality, as measured by the standard deviation of the distributions of individual life evaluations, was lowest in Western Europe, Northern America and Oceania, and South Asia, and greatest in Latin America, Sub-Saharan Africa, and the Middle East and North Africa. [22] What about changes in well-being inequality? Since 2012, well-being inequality has increased significantly in most regions, including especially South Asia, Southeast Asia, Sub-Saharan Africa, the Middle East and North Africa, and the CIS (with Russia dominating the population total), while falling insignificantly in Western Europe and Central and Eastern Europe.

In this section we assess how national changes in the distribution of happiness might influence the average national level of happiness. Although most studies of inequality have focused on inequality in the distribution of income and wealth, [23] we argued in Chapter 2 of World Happiness Report 2016 Update that just as income is too limited an indicator for the overall quality of life, income inequality is too limited a measure of overall inequality. [24] For example, inequalities in the distribution of health [25] have effects on life satisfaction above and beyond those flowing through their effects on income. We and others have found that the effects of happiness inequality are often larger and more systematic than those of income inequality. [26] For example, social trust, often found to be lower where income inequality is greater, is more closely connected to the inequality of subjective well-being than it is to income inequality. [27]

To extend our earlier analysis of the effects of well-being inequality we now consider a broader range of measures of well-being inequality. In our previous work we mainly measured the inequality of well-being in terms of its standard deviation. Since then we have found evidence [28] that the shape of the well-being distribution is better and more flexibly captured by a ratio of percentiles, for example, the average life evaluation at the 80th percentile divided by that at the 20th percentile. Using this and other new ways of measuring the distribution of well-being we continue to find that well-being inequality is consistently stronger than income inequality as a predictor of life evaluations. Statistical Appendix 3 provides a full set of our estimation results; here we shall report only a limited set. Table 2.2 shows an alternative version of Table 2.1 of World Happiness Report 2019 in which we have added a variable equal to the ratio of the 80th and 20th percentiles of a distribution of predicted values for individual life evaluations. As explained in detail in Statistical Appendix 3, we use the 80/20 ratio because it provides marginally the best fit of the alternatives tested, and we use its predicted value in order to provide a more continuous ranking across countries. Our use of the predicted values also helps to avoid any risk that our measure is contaminated by being derived directly from the same data as the life evaluations themselves. [29] The calculated 80/20 ratio adds to the explanation provided by the six-factor explanation of Table 2.1. The left-hand columns of Table 2.2 use national aggregate panel data for comparability with Table 2.1, while the right-hand columns are based on individual responses.

Table 2.2: Estimating the effects of well-being inequality on average life evaluations

Inequality matters, such that increasing well-being inequality by two standard deviations (covering about two thirds of the countries) in the country panel regressions would be associated with life evaluations about 0.2 points lower on the 0 to 10 scale used for life evaluations. This result helps to motivate the next section, wherein we consider how a higher quality of social environment not only raises the average quality of lives directly, but also reduces their inequality. [30]

Assessing the Social Environments Supporting World Happiness

In World Happiness Report 2017, we made a special review of the social foundations of happiness. In this report we return to dig deeper into several aspects of the social environments for happiness. The social environments influencing happiness are diverse and interwoven, and likely to differ within and among communities, nations and cultures. We have already seen in earlier World Happiness Reports that different aspects of the social environment, as represented by the combined impact of the four social environment variables—having someone to count on, trust (as measured by the absence of corruption), a sense of freedom to make key life decisions, and generosity—together account for as much as the combined effects of income and healthy life expectancy in explaining the life evaluation gap between the ten happiest and the ten least happy countries in World Happiness Report 2019. [31] In this section we dig deeper in an attempt to show how the social environment, as reflected in the quality of neighbourhood and community life as well as in the quality of various public institutions, enables people to live better lives. We will also show that strong social environments, by buffering individuals and communities against the well-being consequences of adverse events, are predicted to reduce well-being inequality. As we will show, this happens because those who gain most from positive social environments are those most subject to adversity, and are hence likely to fall at the lower end of the distribution of life evaluations within a community or nation.

We consider individual and community-level measures of social capital, and people’s trust in various aspects of the quality of government services and institutions as separate sources of happiness. Both types of trust affect life evaluations directly and also indirectly, as protective buffers against adversity and as substitutes for income as means of achieving better lives.

Government institutions and policies deserve to be treated as part of the social environment, as they set the stages on which lives are lived. These stages differ from country to country, from community to community, and even from year to year. The importance of international differences in the social environment was shown forcefully in World Happiness Report 2018, which presented separate happiness rankings for immigrants and the locally-born, and found them to be almost identical (a correlation of +0.96 for the 117 countries with a sufficient number of immigrants in their sampled populations). This was the case even for migrants coming from source countries with life evaluations less than half as high as in the destination country. This evidence from the happiness of immigrants and the locally-born suggests strongly that the large international differences in average national happiness documented in each World Happiness Report depend primarily on the circumstances of life in each country. [32]

In Chapter 2 of World Happiness Report 2017 we dealt in detail with the social foundations of happiness, while in Chapter 2 of World Happiness Report 2019 we presented much evidence on how the quality of government affects life evaluations. In this chapter, we combine these two strands of research with our analysis of the effects of inequality. In this new research we are able to show that social connections and the quality of social institutions have primary direct effects on life evaluations, and also provide buffers to reduce happiness losses from several life challenges. These indirect or protective effects are of special value to people most at risk, so that happiness increases more for those with the lowest levels of well-being, thereby reducing inequality. A strong social environment thus allows people to be more resilient in the face of life’s hardships.

Strong social environments provide buffers against adversity

To test the possibility that strong social environments can provide buffers against life challenges, we estimate the extent to which a strong social environment lowers the happiness loss that would otherwise be triggered by adverse circumstances. Table 2.3 shows results from a life satisfaction equation based on nine waves of the European Social Survey, covering 2002-2018. We use that survey for our illustration, even though it has fewer countries than some other surveys because it has a larger range of trust variables, all measured on a 0 to 10 scale giving them more explanatory power than is provided by variables with 0 and 1 as the only possible answers. The equation is estimated using data from approximately 375,000 respondents in 35 countries. [33] We use fixed effects for survey waves and for countries, thereby helping to ensure that our results are based on what is happening within each country.

Table 2.3: Interaction of social environment with risks and supports for life evaluations in the ESS

The top part of Table 2.3 shows the effects of risks to life evaluations. These risks include a variety of different challenges to well-being, including discrimination, ill-health, unemployment, low income, loss of family support (through separation, divorce or spousal death), or lack of perceived night-time safety, for respondents with relatively low trust in other people and in public institutions. For example, respondents who describe themselves as belonging to a group that is discriminated against in their country have life evaluations that are on average lower by half a point on the 0 to 10 scale. Life evaluations are almost a full point lower for those in poor rather than good health. [34] Unemployment has a negative life evaluation effect of three-quarters of a point. To have low income, as defined here as being in the bottom quintile of the income distribution, with the middle three quintiles as the basis for comparison, has a negative impact of almost half a point, similar to the impact of separation, divorce, or widowhood. The final risk to the social environment is faced by those who are afraid to be in the streets after dark, for whom life evaluations are lower by one-quarter of a point. These impacts are all estimated in the same equation so that their effects can be added up to apply to any individual who is in more than one of the categories. The sub-total shows that someone in a low trust environment who faces all of these circumstances is estimated to have a life evaluation almost 3.5 points lower than someone who face none of these challenges. Statistical Appendix 3 contains the full results for this equation. The Appendix also shows results estimated separately for males and females. The coefficients are similar, with a few interesting differences. [35]

The next columns show the extent to which those who judge themselves to live in high-trust environments are buffered against some of the well-being costs of misfortune. This is done separately for inter-personal trust, average confidence in a range of state institutions, and trust in police, where the latter is considered to be of independent importance for those who describe themselves as being afraid in the streets after dark. The effects estimated are known as interaction effects, since they estimate the offsetting change in well-being for someone who is subject to the hardship in question, but lives in a high-trust environment. [36] The interaction effects are usually assumed to be zero, implying, for example, that being in a high-trust environment has the same well-being effects for the unemployed as for the employed, and so on. Once we started to investigate these interactions, we discovered them to be highly significant in statistical, economic, and social terms, and hence demanding of more of our attention. [37]

For this chapter we have expanded our earlier analysis to cover the buffering effects of two types of trust (social and institutional) in reducing the well-being costs of six types of adversity: discrimination, [38] ill-health, [39] unemployment, low income, [40] [41] loss of marital partner (through separation, divorce, or death), and fear of being in the streets after dark. The total number of risk interactions tested rises to 13 because we surmised, and found, that trust in police might mitigate the well-being costs of unsafe streets. Of these 13 interaction terms tested in the upper part of Table 2.3, nine are estimated to have a very high degree of statistical significance (p [42]

We then ask, in the subsequent columns, whether the well-being benefits of frequent social meetings, of having intimates available for the discussion of personal matters, and having a high income (as indicated by being in the top income quintile, relative to those in the three middle quintiles) are of equal value for those in high and low trust social environments. The theory supporting the risk results reported above would suggest that the benefits of closer personal networks and high incomes are both likely to be less for those who are living in broader social networks that are more supportive. For those without confidence in the broader social environment, there is more need for, and benefit from, more immediate social networks. Similarly, higher income can be used to purchase some substitute for the benefits of a more trustworthy environment, e.g. defensive expenditures of the sort symbolized by gated communities.

The interaction effects for the well-being supports, as shown in Table 2.4, are as predicted above. The high-trust offsets have the expected signs, ranging from 31% to half (in the case of social meetings) of the well-being advantages of having the support in question, totaling 0.54 points, or 26% of the main effects plus the three supports.

Bringing the top and bottom halves of Table 2.3 together, two results are clear. First, there are large estimated well-being differences between those in differing life circumstances, and these effects differ by type of risk and by the extent to which there is a buffering social environment. Ignoring for a moment the buffers provided by a positive social environment, someone living in a low trust environment suffering from all six risks is estimated to have a life evaluation that is lower by almost 3.5 points on the 10-point scale when compared to someone facing none of those risks. On the support side of the ledger, someone in the top income quintile with a close confidante and at-least weekly social meetings, and has high social and institutional trust has life evaluations higher by more than two points compared to someone in the middle income quintiles, without a close friend, with infrequent social meetings, and with low social and institutional trust. Of this difference, about half comes from the two personal social connection variables, one-third from higher social and institutional trust, and one-sixth from the higher income.

Secondly, as shown in the last column of Table 2.3, we have found large direct and interaction effects when the social environment is considered in the calculations. To get some idea of the direct effects of a good social environment, we consider not just trust, but also those aspects of the social environment that affect well-being directly, but do not have estimated interaction effects. In our table, these additional variables include intimates and social meetings, [43] which have a combined effect of almost a full point. We can add this to the direct effects of the three trust measures, for a total direct social environment effect of over 1.7 points, twice as large as the effect from moving from the bottom to the top quintile of the income distribution. This does not yet include consideration of the all-important interaction effects.

We must also take into account the indirect effects coming from the interaction terms in Table 2.3. If we compare the effects of both risks and advantages for those living in high and low trust social environments, the well-being gap is 1.9 points smaller in the high trust than the low trust environment, as shown by the bottom line of Table 2.3. This is of course in addition to the direct effects of social and institutional trust. These interaction effects are especially relevant for well-being inequality. The 1.9 points calculated above represents the total interaction effects for someone suffering from all of the risks with none of the supports, so that it overestimates the benefits for more typical respondents. To get a suitable population-wide measure, we need to consider how risks and supports are distributed in the population at large. We shall do this after first presenting some parallel results from the Gallup World Poll. The European Social Survey was selected for special treatment because of its fuller coverage of the social environment. To make sure that our results are applicable on a world-wide basis, we have used a very similar model to explain the effects of the social environment using individual-level Gallup World Poll data from about a million respondents from 143 countries. The results from this estimated equation are shown in Table 2.4 below, and in detail in Statistical Appendix 1.

Table 2.4: Interaction of social environment with risks and supports for life evaluations in the Gallup World Poll

The results from the Gallup World Poll (GWP) show a very similar pattern to what we have already seen from the European Social Survey (ESS). [44] There is no social trust variable generally available in the Gallup World Poll, but a system trust variable has been generated that is analogous to the one used for the ESS analysis. The GWP results show a smaller direct health effect that is nonetheless significantly buffered for respondents who have more confidence in the quality of their public institutions. [45] We find in the GWP, as we did in the ESS, that the negative effects of low income and the positive effects of high income are of a similar magnitude in the two surveys, and are significantly buffered in both cases by the climate of institutional trust. Divorce, separation, and widowhood have negative effects in both surveys, and in both cases these effects are significantly buffered by institutional trust. Unemployment has a lower estimated life evaluation effect in the Gallup World Poll, and this effect is less significantly buffered by institutional trust. Overall, the two large international surveys both find that trust provides a significant offset to the negative well-being consequences of adverse events and circumstances. [46]

To get an overall measure of the importance of the social environment, we return to the ESS data, since it covers a larger range of social capital measures. Finding a realistic answer requires us to estimate how the social environment affects the level and distribution of life evaluations of the population taken as a whole. We do this by calculating for each ESS respondent what their life satisfaction would be, given their actual health, employment, income, personal social supports, and marital circumstances, under two different assumptions about the climate for social and institutional trust. One assumption is that everyone has trust levels equal to the average value from all those who report relatively low trust on a 0 to 10 scale. [47] The alternative is that everyone has the same levels of social and institutional trust as currently held by the more trusting 30% of the population. The calculations thus take into account the actual distributions of life circumstances, but different levels of trust. These trust differences alter each person’s life satisfaction both directly and indirectly (via the interaction effects in Table 2.3). The distributions are significantly different, reflecting the fact that the interactions are especially helpful for those under difficult circumstances. Living in a higher trust environment gives an average life satisfaction of 7.72, compared to 6.76 in the lower trust environment. These results take into account all of the effects reported in Table 2.3, and also now reflect the prevalence and distribution of the various individual-level risks and supports shown in Table 2.3. Distributions based on the details of individual lives enables us to calculate the consequences of different trust levels for the distribution of well-being. The effects of trust on inequality of well-being are very substantial. The dispersion of life satisfaction about its population average, as measured by the standard deviation, is more than 40% larger in the low trust environment. [48] As can be seen in Panel A of Figure 2.5, the high-trust distribution is not only less widely dispersed, but also the bulk of the changes have come at the bottom end of the distribution, improving especially the lives of those worst off.

Trust, as we have seen, is very important both directly and indirectly, for life evaluations. But there are more personal aspects of social capital that are important to the quality of life. In the case we have examined in Table 2.3, these include the frequency of social meetings and whether a respondent has one or more intimate friend. We can then use the distribution of these social connections to create a pair of happiness distributions that differ according to social connections. The fortunate group has one or more friends or relatives available for intimate discussions and has weekly or more frequent social meetings. The unfortunate group has neither of these forms of social support. We know that those with more supportive personal social connections and activity are more satisfied with their lives, but the reductions in inequality are expected to be less than in the trust case, since separate interaction effects are not estimated. This is confirmed by the results shown in Panel B of Figure 2.5 in which the well-connected population has life evaluations averaging 0.86 points higher than the group with weaker social connections. There is also a reduction in the dispersion of the distribution, but only by one-quarter as much as in the trust case.

Figure 2.5: Predicted life evaluations in differing social environments

Next, as shown in Panel C of Figure 2.5, we can combine the estimated effects of trust and personal social connections as aspects of the social environment. One distribution covers people with low trust and weaker social connections, while the other gives everyone higher average trust and social connections. As before, the actual circumstances for all other aspects of their lives are unchanged. This provides the most comprehensive estimate of the total effects of the social environment on the levels and distribution of life satisfaction. The life evaluation difference provided by higher trust and closer social connections amounts to 1.8 points on the 10-point scale. While the reduction in inequality is very large in the combined case shown in Panel C, the reduction is slightly less than in the trust case on its own. This is because the primary inequality-reducing power of a better social environment comes from the interaction effects that enable higher trust to buffer the well-being effects of a variety of risks.

Finally, to provide a more realistic example that starts from existing levels of trust and social connections, we show in Panel D of Figure 2.5 a comparison of the predicted results in a high-trust strong-connection world with predicted values based on everyone’s actual reported trust and personal social connections. The differences are smaller than those in Panel C, since we are now comparing the high-trust case not with a low-trust environment, but with the actual circumstances of the surveyed populations. This is a more interesting comparison, since it starts with the current situation and asks how much better that reality might be if those who have low trust and social connections were to have the same levels as respondents in the more trusting and socially connected part of the population. This is in principle an achievable result, since the gains of trust and social connections do not need to come at the expense of those already living in more supportive social environments. It is apparent from Panel D that there are large potential gains for raising average well-being and reducing inequality at the same time. For example, the median respondent stands to gain 0.71 points, compared to an average gain of more than twice as much (1.51 points) for someone at the 10th percentile of the happiness distribution. [49] Conversely, the gains for those already at the 90th percentile of the distribution are much smaller (0.25 points). There are two reasons for the much smaller gains at the top. The main reason is that almost all those at the top of the happiness distribution are already living in trusting and connected social environments. The second reason is that they are individually less likely to be suffering from the risks shown in Table 2.4 and hence less likely to receive the buffering gains delivered by high social capital to those most in need.

These results may underestimate the total effects of better social environments, as they are calculated holding constant the existing levels of income and health, both of which have frequently been shown to be improved when trust and social connections are more supportive. There is also evidence that communities and nations with higher levels of social trust and connections are more resilient in the face of natural disasters and economic crises. [53] Fixing rather than fighting becomes the order of the day, and people are happy to find themselves willing and able to help each other in times of need.

But there are also possibilities that our primary evidence, which comes from 35 countries in Europe, may not be so readily applied to the world as a whole. Our parallel research with the Gallup World Poll in Table 2.4 gave somewhat smaller estimates, and showed effects that were somewhat larger in Europe than in the rest of the world. It is also appropriate to ask whether the trust answers reflect reality. Fortunately, experiments have shown that social trust measures are a strong predictor of international differences in the likelihood of lost wallets being returned. [54] There is also evidence that people are too pessimistic about the extent to which their fellow citizens will go out of their way to help return a lost wallet. [55] To the extent that trust levels are falsely low, better information in itself would help to increase trust levels. But there is clearly much more research needed about the creation and maintenance of a stronger social environment.

Conclusions

The rankings of country happiness are based this year on the pooled results from Gallup World Poll surveys from 2017-2019 and continue to show both change and stability. The top countries tend to have high values for most of the key variables that have been found to support well-being, including income, healthy life expectancy, social support, freedom, trust, and generosity, to such a degree that year to year changes in the top rankings are to be expected. The top 20 countries are the same as last year, although there have been ranking changes within the group. Over the eight editions of the Report, four different countries have held the top position: Denmark in 2012, 2013 and 2016, Switzerland in 2015, Norway in 2017, and now Finland in 2018, 2019 and 2020. With its continuing upward trend in average scores, Finland consolidated its hold on first place, now significantly ahead of an also-rising Denmark in second place, and an even faster-rising Switzerland in 3rd, followed by Iceland in 4th and Norway 5th. All previous holders of the top spot are still among the top five. The remaining countries in the top ten are the Netherlands, Sweden, New Zealand, and Austria in 6th, 7th, 8th, and 9th followed this year by a top-ten newcomer Luxembourg, which pushes Canada and Australia to 11th and 12th, followed by the United Kingdom in 13th, five places higher than in the first World Happiness Report. The rest of the top 20 include, in order, Israel, Costa Rica, Ireland, Germany, the United States, the Czech Republic, and Belgium.

At a global level, population-weighted life evaluations fell sharply during the financial crisis, recovered almost completely by 2011, and then fell fairly steadily to a 2019 value about the same level as its post-crisis low. These global movements mask a greater variety of experiences among and within global regions. The most remarkable regional dynamics include the continued rise of life evaluations in Central and Eastern Europe, and their decline in South Asia. More modest changes have brought Western Europe up and Northern America plus Australia and New Zealand down, with roughly equal averages for the two regions in 2019. As for affect measures, positive emotions show no significant trends, while negative emotions have risen significantly, mostly driven by worry and sadness rather than anger.

At the national level, most countries showed significant changes from 2008-2012 to 2017-2019, with slightly more gainers than losers. The biggest gainer was Benin, up 1.64 points and moving from the bottom of the ranking to near the middle. The biggest life evaluation drops were in Venezuela and Afghanistan, down by about 1.8 and 1.5 points respectively. India, with close to a fifth of global population, saw a 1.2-point decline.

We next consider how well-being inequality affects the average level of well-being, before turning to the main focus for this year’s chapter: how different features of the social environment affect the level and distribution of happiness. Using a variety of different measures for the inequality of well-being, we find a consistent picture wherein countries with a broader spread of well-being outcomes have lower average life evaluations. The effect is substantial, despite being measured with considerable uncertainty. This suggests that people do care about the well-being of others, so that efforts to reduce the inequality of happiness are likely to raise happiness for all, especially those at the bottom end of the well-being distribution. Second, as we showed in our analysis of the buffering effects of trust, anything that can increase social and institutional trust produces especially large benefits for those subject to various forms of hardship.

The primary result from our empirical analysis of the social environment is that several kinds of individual and social trust and social connections have large direct and indirect impacts on life evaluations. The indirect impacts, which are measured by allowing the effects of trust to buffer the estimated well-being effects of bad times, show that both social trust and institutional trust reduce the inequality of well-being by increasing the resilience of individual well-being to various types of adversity, including perceived discrimination, ill-health, unemployment, low income, and fear when walking the streets at night. Average life satisfaction is estimated to be almost one point higher (0.96 points) in a high trust environment than in a low trust environment.

The total effects of the social environment are even greater when we add in the well-being benefits of personal social connections, which provide an additional 0.87 points, for a total of 1.83 points, as shown in Panel C of Figure 2.5. This is considerably more than double the 0.8 point estimated life satisfaction gains from moving from the bottom to the top quintile of the income distribution.

To measure the possible gains from improving current trust and connection levels, we can compare the distribution of life evaluations under actual trust and social connections with what would be feasible if all respondents had the same average trust and social connections as enjoyed already by the more trusting and connected share of the population. The results are shown in Panel D of Figure 2.5. Average life evaluations are higher by more than 0.8 points, and the gains are concentrated among those who are currently the least happy. For example, those who are currently at the 10th percentile of the happiness distribution gain more than 1.5 points, compared to less than 0.3 points for those at the 90th percentile. The stronger social environment thereby leads to a significant reduction in the inequality of well-being (by about 13%), which then adds a further boost (about 0.06 points) to average life satisfaction. Moving from current levels of trust and social connections in Europe to a situation of high trust and good social connections is therefore estimated to raise average life evaluations by almost 0.9 on the 0 to 10 scale. Favourable social environments not only raise the level of well-being but also improve its distribution. We conclude that social environments are of first-order importance for the quality of life.

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Endnotes

The evidence and reasoning supporting our choice of a central role for life evaluations, with supporting roles for affect measures, have been explained in Chapter 2 of several World Happiness Reports, and have been updated and presented more fully in Helliwell (2019). ↩︎

The statistical appendix contains alternative forms without year effects (Table 12 of Appendix 1), and a repeat version of the Table 2.1 equation showing the estimated year effects (Table 11 of Appendix 1). These results confirm, as we would hope, that inclusion of the year effects makes no significant difference to any of the coefficients. ↩︎

As shown by the comparative analysis in Table 10 of Appendix 1. ↩︎

The definitions of the variables are shown in Technical Box 1, with additional detail in the online data appendix. ↩︎

This influence may be direct, as many have found, e.g. De Neve et al. (2013). It may also embody the idea, as made explicit in Fredrickson’s broaden-and-build theory (Fredrickson, 2001), that good moods help to induce the sorts of positive connections that eventually provide the basis for better life circumstances. ↩︎

See, for example, the well-known study of the longevity of nuns, Danner, Snowdon, and Friesen (2001). ↩︎

See Cohen et al. (2003), and Doyle et al. (2006). ↩︎

We put the contributions of the six factors as the first elements in the overall country bars because this makes it easier to see that the length of the overall bar depends only on the average answers given to the life evaluation question. In World Happiness Report 2013 we adopted a different ordering, putting the combined Dystopia+residual elements on the left of each bar to make it easier to compare the sizes of residuals across countries. To make that comparison equally possible in subsequent World Happiness Reports, we include the alternative form of the figure in the online Statistical Appendix 1 (Appendix Figures 7-9). ↩︎

These calculations are shown in detail in Table 20 of online Statistical Appendix 1. ↩︎

The prevalence of these feedbacks was documented in Chapter 4 of World Happiness Report 2013, De Neve et al. (2013). ↩︎

We expect the coefficients on these variables (but not on the variables based on non-survey sources) to be reduced to the extent that idiosyncratic differences among respondents tend to produce a positive correlation between the four survey-based factors and the life evaluations given by the same respondents. This line of possible influence is cut when the life evaluations are coming from an entirely different set of respondents than are the four social variables. The fact that the coefficients are reduced only very slightly suggests that the common-source link is real but very limited in its impact. ↩︎

The coefficients on GDP per capita and healthy life expectancy were affected even less, and in the opposite direction in the case of the income measure, being increased rather than reduced, once again just as expected. The changes were very small because the data come from other sources, and are unaffected by our experiment. However, the income coefficient does increase slightly, since income is positively correlated with the other four variables being tested, so that income is now able to pick up a fraction of the drop in influence from the other four variables. We also performed an alternative robustness test, using the previous year’s values for the four survey-based variables. This also avoided using the same respondent’s answers on both sides of the equation, and produced similar results, as shown in Table 13 of Statistical Appendix 1 in World Happiness Report 2018. The Table 13 results are very similar to the split-sample results shown in Tables 11 and 12, and all three tables give effect sizes very similar to those in Table 2.1 in reported in the main text. Because the samples change only slightly from year to year, there was no need to repeat these tests with this year’s sample. ↩︎

There has been a corresponding drop in Canada’s ranking, from 4th in 2012 to 11th in 2020. Average Cantril ladder scores for Canada fell from 7.42 in 2017 to 7.17 in 2018 and 7.11 in 2019. The large-scale official surveys measure life satisfaction every year, so some cross-checking is possible. The data for 2019 are not yet available, but for the larger Canadian Community Health Survey there is no drop from 2017 to 2018. The smaller General Social Survey shows a drop from 2017 to 2018, although survey cycle effects make the magnitude hard to establish. ↩︎

This footprint affects average scores by more for those countries with the largest immigrant shares. The extreme outlier is the United Arab Emirates (UAE), with a foreign-born share exceeding 85%. The UAE also makes a distinction between nationality and place of birth, and oversamples the national population to obtain larger sample sizes. Thus it is possible in their case to calculate separate average scores 2017-2019 for nationals (6.98), the locally born (6.85), and the foreign-born (6.76). The difference between their foreign-born and locally-born scores is very similar to that found on average for the top 20 countries in the 2018 rankings. ↩︎

These calculations come from Table 21 in Statistical Appendix 1. ↩︎

Actual and predicted national and regional average 2017-2019 life evaluations are plotted in Figure 43 of Statistical Appendix 1. The 45-degree line in each part of the Figure shows a situation where the actual and predicted values are equal. A predominance of country dots below the 45-degree line shows a region where actual values are below those predicted by the model, and vice versa. East Asia provides an example of the former case, and Latin America of the latter. ↩︎

For example, see Chen et al. (1995). ↩︎

One slight exception is that the negative effect of corruption is estimated to be slightly larger, although not significantly so, if we include a separate regional effect variable for Latin America. This is because perceived corruption is worse than average in Latin America, and its happiness effects there are offset by stronger close-knit social networks, as described in Rojas (2018). The inclusion of a special Latin American variable thereby permits the corruption coefficient to take a higher value. ↩︎

Some countries do not have data in all years over the duration of the study period (2006–2019). We impute the missing data by using the neighboring year’s data. The first wave of Gallup World Poll was collected in 2005 and 2006. We treat them all as 2006 observations in the trend analysis. ↩︎

These results may all be found in Figure 2.1 of World Happiness Report 2018. ↩︎

See, for example, Atkinson (2015), Atkinson and Bourguignon (2014), Kennedy et al. (1997), Keeley (2015), OECD (2015), Neckerman and Torche (2007), and Piketty (2014). ↩︎

See Helliwell, Huang, and Wang (2016). See also Goff et al. (2018), Gandelman and Porzekanski (2013), and Kalmijn and Veenhoven (2005). ↩︎

See, for example, Evans et al. (1997), Marmot et al. (1994), and Marmot (2005). ↩︎

See Goff et al. (2018) for estimates using individual responses from several surveys, including the Gallup World Poll, the European Social Survey, and the World Values Survey. ↩︎

See Goff et al. (2018), Table 6. ↩︎

Following the example of Nichols and Reinhart (2019). ↩︎

The predicted values are obtained by estimating a life evaluation equation from the entire micro sample of GWP data, based on a version of the Table 2.1 equation suitable for this application, and then using the results to create predicted values for each individual in every year and country. These values are then used to build predicted distributions for each year and country, and these distributions are in turn used to construct percentile ratios for each country and year. ↩︎

See Goff et al. (2018), Table 6. ↩︎

See Table 17 in the online Statistical Appendix 1 of World Happiness Report 2019. ↩︎

The importance of local environments is emphasized by more recent research showing that the happiness of immigrants to different regions of Canada and the United Kingdom approaches the happiness of other residents of those regions (Helliwell et al., 2020). This is a striking finding, especially in the light of the fact, illustrated by the city rankings of Chapter 3, that life evaluation differences among cities in a country are far smaller than differences between countries. ↩︎

The adjusted R-squared is 0.350. Without country fixed effects, the adjusted R-squared is 0.318. ↩︎

This move is measured by the difference, in points, between the averages of the good and very good responses and of the fair, poor and very poor responses. The poor-health group comprises 35% of the ESS respondents. ↩︎

The effects of unemployment on happiness are roughly one-third greater for males than females, while the effect of feeling unsafe on the street is more than 60% greater for males. Weekly or more frequent social meetings add 25% more happiness for females than for males. The sample frequencies of circumstances can also differ by gender, with males 25% more likely to be unemployed, and 15% less likely to see the streets as unsafe. The frequency of weekly or more social meetings is the same for male and female respondents. Full results may be found in Statistical Appendix 3. ↩︎

For social trust, the value of 7 is the lower bound of the high trust group, since that provides the same share of high trusters, about 30%, that is provided in the same countries when people are asked a binary question on social trust. We use the same lower bound for trust in police. For institutional trust, where assessments are generally lower, we adopt a lower bound of 5.5, since that puts about 30% of respondents into the high-trust group. ↩︎

See Helliwell et al. (2018) and Helliwell, Aknin et al. (2018). ↩︎

Yanagisawa et al. (2011) provide experimental evidence that social trust reduces the psychosocial costs of social exclusion, while Branscombe et al. (2016) show that a sense of community belonging buffers the life satisfaction effects of perceived discrimination felt by disabled youth. ↩︎

Although there have been many studies showing links between trust and actual or perceived ill-health (See Kawachi (2018) for a recent review), there has not been corresponding analysis of whether and how trust might affect the links running between actual or perceived health and life evaluations. ↩︎

Akaeda (2019), using data from the European Quality of Life Survey, also finds that higher social trust (in his case using national averages for social trust) significantly reduces the effects on income on life evaluations. Akaeda assumes symmetric effects from top and bottom incomes, while we estimate the two effects separately and find them to be of roughly equivalent size. ↩︎

Our findings on this score are consistent with those of Annick et al. (2016), who find that high social trust reduces the estimated losses of subjective well-being caused by perceived financial hardship among self-employed respondents to two waves of the European Social Survey. ↩︎

The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by the Gallup World Poll data.

The Report is supported by The Ernesto Illy Foundation, illycaffè, Davines Group, Unilever’s largest ice cream brand Wall’s, The Blue Chip Foundation, The William, Jeff, and Jennifer Gross Family Foundation, The Happier Way Foundation, and The Regenerative Society Foundation.

The World Happiness Report was written by a group of independent experts acting in their personal capacities. Any views expressed in this report do not necessarily reflect the views of any organization, agency or program of the United Nations.

WHR 2022 | Chapter 2 Happiness, Benevolence, and Trust During COVID-19 and Beyond

Introduction

This year marks the tenth anniversary of the World Happiness Report, thus inviting us to look back and forward while maintaining our reporting of current well-being and broadening our analysis of the far-ranging effects of COVID-19. Our first section presents our usual ranking and modelling of national happiness based on data covering 2019 through 2021.

In our second section, we look back at the evolution of life evaluations and a number of emotions since the Gallup World Poll data first became available in 2005-2006. Using a wider range of the emotional and other supports for life evaluations enables us to distinguish a greater variety of global and regional trends. It also sets the stage for the third section of the chapter, where we use individual-level data from 2017 through 2021 to examine how life under COVID-19 has changed for people in different circumstances.

In our fourth section, we briefly update our analysis of how different features of national demographic, social, and political structures have combined with the consequences of policy strategies and disease exposure to help explain international differences in 2020 and 2021 COVID-19 death rates. A central finding continues to be the extent to which the quality of the social context, especially the extent to which people trust their governments and have trust in the benevolence of others, supports their happiness before, during, and likely after the pandemic. Countries where people trusted their governments and each other experienced lower COVID-19 death tolls and set the stage for maintaining or rebuilding a sense of common purpose to deliver happier, healthier and more sustainable lives. This forward-looking part permits an optimistic tinge based on the remarkable growth in prosocial activities during 2021.

Our results are summarised in a short concluding section.

Measuring and Explaining National Differences in Life Evaluations

Technical Box 1: Measuring Subjective Well-Being.

Our measurement of subjective well-being continues to rely on three main well-being indicators: life evaluations, positive emotions, and negative emotions (described in the report as positive and negative affect). Happiness rankings are based on life evaluations as the more stable measure of the quality of people’s lives. In World Happiness Report 2022, we pay special attention, as we did in World Happiness Report 2021, to specific daily emotions (the components of positive and negative affect) to better track how COVID-19 has altered different aspects of life.

Life evaluations. The Gallup World Poll, which remains the principal source of data in this report, asks respondents to evaluate their current life as a whole using the mental image of a ladder, with the best possible life for them as a 10 and worst possible as a 0. Each respondent provides a numerical response on this scale, referred to as the Cantril ladder. Typically, around 1,000 responses are gathered annually for each country. Weights are used to construct population-representative national averages for each year in each country. We base our national happiness rankings on a three-year average, thereby increasing the sample size to provide more precise estimates.

Positive emotions. Positive affect is given by the average of individual yes or no answers for three questions about emotions experienced or not on the previous day: laughter, enjoyment, and learning or doing something interesting (for details, see Technical Box 2).

Negative emotions. Negative affect is given by the average of individual yes or no answers about three emotions experienced on the previous day: worry, sadness, and anger.

Comparing life evaluations and emotions:

Life evaluations provide the most informative measure for international comparisons because they capture quality of life in a more complete and stable way than emotional reports based on daily experiences.

Life evaluations differ more between countries than emotions and are better explained by the widely differing life experiences in different countries. Emotions experienced the previous day are well explained by events of the day being asked about, while life evaluations more closely reflect the circumstances of life as a whole. We show later in the chapter that emotions are significant supports for life evaluations and provide essential insights into how the quality of life has changed during COVID-19 for people in different life circumstances. [1]

Positive emotions are more than twice as frequent as negative emotions. Looking at last year’s data, the global average of positive emotions was 0.66 (i.e., the average respondent experienced 2 of the 3 positive emotions the previous day) compared to the global average of 0.29 for negative emotions.

Ranking of Happiness 2019-2021

Our country rankings in Figure 2.1 show life evaluations (answers to the Cantril ladder question) for each country, averaged over 2019-2021. Not every country has surveys every year. The total sample sizes are reported in Statistical Appendix 1 and are reflected in Figure 2.1 by the horizontal lines showing the 95% confidence intervals. The confidence intervals are tighter for countries with larger samples.

The overall length of each country bar represents the average ladder score, also shown in numerals next to the country names. The rankings in Figure 2.1 depend only on the respondents’ average Cantril ladder scores, not on the values of the six variables that we use to help account for the large differences we find.

Figure 2.1: Ranking of Happiness 2019-2021

Note: Those with a * do not have survey information in 2020 or 2021. Their averages are based on the 2019 survey.

The colour-coded sub-bars in each country row represent the extent to which six key variables contribute to explaining life evaluations. These variables (shown in Table 2.1) are GDP per capita, social support, healthy life expectancy, freedom, generosity, and corruption. As already noted, our happiness rankings are not based on any index of these six factors—the scores are instead based on individuals’ own assessments of their lives, as revealed by their answers to the single-item Cantril ladder life-evaluation question. We use observed data on the six variables and estimates of their associations with life evaluations to explain the observed variation of life evaluations across countries, much as epidemiologists estimate the extent to which life expectancy is affected by factors such as smoking, exercise and diet. As will be explained in more detail later, and in the online FAQ, the value for Dystopia (1.83) is the predicted Cantril ladder for a hypothetical country with the world’s lowest values for each of the six variables. This permits the calculated contributions from the six factors to be zero or positive for every actual country. We also show how measures of experienced well-being, especially positive affect, are predicted by the six factors and how the affect measures contribute to the explanation [2] of higher life evaluations.

In Table 2.1, we present our latest modelling of national average life evaluations and measures of positive and negative affect (emotion) by country and year. [3] For ease of comparison, the table has the same basic structure as Table 2.1 did in several previous editions, most recently in World Happiness Report 2020. We now include data for both 2020 and 2021. Despite difficulties COVID-19 posed for the Gallup World Poll’s operations, our sample now includes data from 116 countries and territories in 2020 and 119 in 2021. Adding the data from 2020 and 2021 slightly improves the model’s overall fit while leaving the coefficients largely unchanged. There are four equations in Table 2.1. The first equation provides the basis for constructing the sub-bars shown in Figure 2.1.

The results in the first column of Table 2.1 explain national average life evaluations in terms of six key variables: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and freedom from corruption. [4] Taken together, the six variables explain more than three-quarters of the variation in national annual average ladder scores among countries, using data from the years 2005 to 2021. [5]

Notes: This is a pooled OLS regression for a tattered panel explaining annual national average Cantril ladder responses from all available surveys from 2005 through 2021. See Technical Box 2 for detailed information about each of the predictors. Coefficients are reported with robust standard errors clustered by country in parentheses. ***, **, and * indicate significance at the 1, 5 and 10 percent levels respectively.

The second and third columns of Table 2.1 use the same six variables to estimate equations for national averages of positive and negative affect, where both are based on answers about yesterday’s emotional experiences (see Technical Box 2 for how the affect measures are constructed). In general, emotional measures, especially negative ones, are differently and much less fully explained by the six variables than life evaluations. Per-capita income and healthy life expectancy have significant effects on life evaluations, but not, in these national average data, on affect. [6] The situation changes when we consider social variables. Bearing in mind that positive and negative affect are measured on a 0 to 1 scale, while life evaluations are on a 0 to 10 scale, social support can be seen to have similar proportionate effects on positive and negative emotions as on life evaluations. Freedom and generosity have even larger associations with positive affect than with the Cantril ladder. Negative affect is significantly reduced by social support, freedom, and the absence of corruption.

In the fourth column, we re-estimate the life evaluation equation from column 1, adding both positive and negative affect to partially implement the Aristotelian presumption that sustained positive emotions are important supports for a good life. [7] The most striking feature is the extent to which the results continue to buttress a finding in psychology that the existence of positive emotions matters much more than the absence of negative ones when predicting either longevity [8] or resistance to the common cold. [9] Consistent with this evidence, we find that positive affect has a large and highly significant impact in the final equation of Table 2.1, while negative affect has none. This finding of national differences does not carry forward into our later modelling of differences among individuals within the same country, where we find positive and negative affect to have almost equal impacts at the individual level.

As for the other coefficients in the fourth column, the differences are only substantial on variables that have the largest impacts on positive affect: social support, freedom, and generosity. Thus, we infer that positive emotions play a strong role in support of life evaluations. Much of the impact of social support, freedom, and generosity on life evaluations is channelled through their influence on positive emotions. That is, these three variables have large impacts on positive affect, which in turn has a major impact on life evaluations.

Technical Box 2: Detailed information about each of the predictors in Table 2.1

GDP per capita is in terms of Purchasing Power Parity (PPP) adjusted to constant 2017 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on December 16, 2021. See Statistical Appendix 1 for more details. GDP data for 2021 are not yet available, so we extend the GDP time series from 2020 to 2021 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 110 (Edition December 2021) or, if missing, the World Bank’s Global Economic Prospects (Last Updated: 01/11/2022), after adjustment for population growth. The equation uses the natural log of GDP per capita, as this form fits the data significantly better than GDP per capita.

The time series for healthy life expectancy at birth is constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2000, 2010, 2015, and 2019. Interpolation and extrapolation are used to match this report’s sample period (2005-2021). See Statistical Appendix 1 for more details.

Social support is the national average of the binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”

Freedom to make life choices is the national average of binary responses (0=no, 1=yes) to the GWP question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”

Generosity is the residual of regressing the national average of GWP responses to the donation question “Have you donated money to a charity in the past month?” on log GDP per capita.

Perceptions of corruption are the average of binary answers to two GWP questions: “Is corruption widespread throughout the government in this country or not?” and “Is corruption widespread within businesses in this country or not?” Where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure.

Positive affect is defined as the average of previous-day affect measures for laughter, enjoyment, and doing or learning something interesting. This marks a change from recent years, where only laughter and enjoyment were included. The inclusion of interest gives us three components in each of positive and negative affect and slightly improves the equation fit in column 4. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? Only the interest question is phrased differently: Did you learn or do something interesting yesterday? See Statistical Appendix 1 for more details.

Negative affect is defined as the average of previous-day affect measures for worry, sadness, and anger.

In Figure 2.1, each country’s bar is divided into seven segments, showing our research efforts to associate the ladder levels with possible sources. The first six sub-bars show how much each of the six key variables is calculated to contribute to that country’s ladder score, relative to a hypothetical country called “Dystopia”—named because it has values equal to the world’s lowest national averages for 2019-2021 for each of the six key variables used in Table 2.1. We use Dystopia as a benchmark against which to compare contributions from each of the six factors. The choice of Dystopia as a benchmark permits every real country to have a positive (or at least zero) contribution from each of the six factors. Based on the estimates in the first column of Table 2.1, we calculate that Dystopia had a 2019-2021 life evaluation equal to 1.83 on the 0 to 10 scale. The final sub-bar is the sum of two components: the calculated average 2017-2019 life evaluation in Dystopia (=1.83) plus each country’s own prediction error, which measures the extent to which life evaluations are higher or lower than those predicted by our equation in the first column of Table 2.1. These residuals are as likely to be negative as positive. [10]

How do we calculate each factor’s contribution to average life evaluations? Taking the example of healthy life expectancy, the sub-bar in the case of Tanzania is equal to the number of years by which healthy life expectancy in Tanzania exceeds the world’s lowest value, multiplied by the Table 2.1 coefficient for the influence of healthy life expectancy on life evaluations. The width of each sub-bar then shows, country-by-country, how much each of the six variables contributes to the international ladder differences.

These calculations are illustrative rather than conclusive for several reasons. One important limitation is that our selection of candidate variables is restricted to what is available for all these countries. Traditional variables like GDP per capita and healthy life expectancy are widely available. But measures of the quality of the social context, including a variety of indicators of social trust, engagement, and belonging, are not yet available for all countries. The variables we use may be properly taking credit due to other variables or unmeasured factors. There are also likely to be vicious or virtuous circles, with two-way linkages among the variables. For example, there is much evidence that those who have happier lives are likely to live longer, and be more trusting, more cooperative, and generally better able to meet life’s demands. [11] This will feed back to improve health, income, generosity, corruption, and a sense of freedom. Additionally, some of the variables are derived from the same respondents as the life evaluations, and hence possibly determined by common factors. There is less risk when using national averages because individual differences in personality and many life circumstances tend to average out at the national level.

We developed robustness tests to ensure that our results are not significantly biased because we use the same individuals to report life evaluations, social support, freedom, generosity, and corruption. We first split each country’s respondents (see Table 10 of Statistical Appendix 1 of World Happiness Report 2018 for more detail) randomly into two groups. We then used the average values for social support, freedom, generosity, and absence of corruption taken from one half of the sample to explain average life evaluations in the other half. As expected, the coefficients on each of the four variables fell slightly. [12] But the changes were reassuringly small (ranging from 1% to 5%) and were not statistically significant, thus giving additional confidence in the estimates shown in Table 2.1. [13]

The seventh and final segment in each bar is the sum of two components. The first component is a fixed number representing our calculation of the 2017-2019 ladder score for Dystopia (=1.83). The second component is the average 2017-2019 residual for each country. The sum of these two components comprises the right-hand sub-bar (in violet) for each country. It varies from one country to the next because some countries have life evaluations above their predicted values, and others lower. The residual simply represents the part of the national average ladder score not explained by our six variables. With the residual included, the sum of all the sub-bars adds up to the actual average life evaluation response. This actual average life evaluation is what is used for our country rankings.

What do the data show for the 2019-2021 country rankings?

Two features carry over from previous editions of the World Happiness Report. First, there is still a lot of year-to-year consistency in the way people rate their lives in different countries. Since we do our ranking on a three-year average, information is carried forward from one year to the next (See Figure 1 of Statistical Appendix 1 for individual country trajectories). For the fifth year in a row, Finland continues to occupy the top spot, with a score significantly ahead of other countries in the top ten. Denmark continues to occupy second place, with Iceland up from 4th place last year to 3rd this year. Switzerland is 4th, followed by the Netherlands and Luxembourg. The top ten are rounded out by Sweden, Norway, Israel and New Zealand. The following five are Austria, Australia, Ireland, Germany, and Canada. This marks a substantial fall for Canada, which was 5th in the first World Happiness Report. The rest of the top 20 include the United States at 16th (up from 19th last year), the United Kingdom and Czechia still in 17th and 18th, followed by Belgium at 19th, and France at 20th, its highest ranking yet.

Finland continues to occupy the top spot, one of five Nordic countries in the top ten.

When looking at average ladder scores, it is also important to note the horizontal whisker lines at the right-hand end of the main bar for each country. These lines denote the 95% confidence regions for the estimates, so that countries with overlapping error bars have scores that do not significantly differ from each other. [14]

Second, there remains a large gap between the top and bottom countries. Within these groups, the top countries are more tightly grouped than are the bottom countries. Within the top group, national life evaluation scores have a gap of 0.40 between the 1st and 5th positions and another 0.21 between the 5th and 10th positions. Thus, there is a gap of about 0.6 points between the first and 10th positions. The bottom ten countries have a much bigger range of scores, covering almost 1.4 points.

Despite the general consistency among the top country scores, there have been many significant changes among the other countries. Looking at changes over the longer term, many countries have exhibited substantial changes in average scores, and hence in country rankings, as shown in more detail in Figures 13 to 15 in the Statistical Appendix.

Scores and confidence regions are based on resident populations in each country rather than their citizenship or place of birth. In World Happiness Report 2018, we split the responses between the locally and foreign-born populations in each country. We found the happiness rankings to be essentially the same for the two groups. There is, in some cases, some continuing influence from source-country happiness and some tendency for migrants to move to happier countries. Among the 20 happiest countries in that report, the average happiness for the locally born was about 0.2 points higher than for the foreign-born.

Overall, the model explains average life evaluation levels quite well within regions, among regions, and for the world as a whole. On average, the countries of Latin America still have mean life evaluations that are significantly higher (by about 0.5 on the 0 to 10 scale) than predicted by the model. This difference has been attributed to a variety of factors, including some unique features of family and social life in Latin American countries. To explain what is special about social life in Latin America, Chapter 6 of World Happiness Report 2018 by Mariano Rojas presented a range of new data and results showing how a multigenerational social environment supports Latin American happiness beyond what is captured by the variables available in the Gallup World Poll. In partial contrast, the countries of East Asia have average life evaluations below predictions, although only slightly and insignificantly so in our latest results. [15] This has been thought to reflect, at least in part, cultural differences in the way people think about and report on the quality of their lives. [16] Our findings of the relative importance of the six factors are generally unaffected by whether or not we make explicit allowance for these regional differences. [17] Chapter 6 contains data (only available for 2020) from several new variables sometimes thought to be more prevalent in East Asia than elsewhere, including life balance, feeling at peace with life, and a focus on others rather than oneself. As shown in Chapter 6, these variables are important to life evaluations everywhere and are, in fact, most prevalent in the top-ranked Nordic countries. Thus, taking those data into account when explaining life evaluations does not materially change the relative importance of the other variables and does not change the relative predicted rankings, and hence the average residuals, in East Asia and the Nordic Countries. [18]

Our main country rankings are not based on the predicted values from our equations but rather, and by our deliberate choice, on the national averages of answers to the Cantril ladder life evaluation question. The other two happiness measures for positive and negative affect are themselves of independent importance and interest and contribute to overall life evaluations, especially in the case of positive affect. Measures of emotions play an even greater role in our analysis of life under COVID-19. This is partly because COVID-19 has affected various emotions differently and partly because emotions based on yesterday’s experiences tend to be more volatile than life evaluations, which are more stable in response to temporary disturbances. Various attempts to use big data to measure happiness using word analysis of Twitter feeds, as in Chapter 4 of this report, are more likely to capture mood changes rather than changes in overall life evaluations. In World Happiness Report 2019, we presented comparable rankings for all three subjective well-being measures that we track: the Cantril ladder (and its standard deviation, which provides a measure of happiness inequality [19] ), positive affect and negative affect, along with country rankings for the six variables we use in Table 2.1 to explain our measures of subjective well-being. Comparable data for 2019-2021 are reported in Figures 16 to 39 of Statistical Appendix 1.

Tracking happiness since 2005-2006

As shown in Chapter 3, there has been in this century a surge of interest in happiness. This has been to a significant extent enabled by the data available in the Gallup World Poll since 2005-2006 and analysed in the World Happiness Report since 2012. Looking back over these years, what has happened to happiness? The availability of fifteen years of data covering more than 150 countries provides a unique stock-taking opportunity. In this section, we consider how life evaluations, emotions and many of their supports have evolved for the world as a whole, and more importantly, by global region and country. [20] Country-by-country analysis can be found in Figures 13-15 in the online Statistical Appendix for this chapter. We show the difference for each country between their average Cantril ladder 2008-2012 with the corresponding average for 2019-2021. The latter is the same average used in the rankings shown in Figure 2.1. As shown in the Appendix, and also in this link, life evaluations rose by more than a full point on the 0 to 10 scale in 15 countries and fell by that amount or more in eight countries. The ten countries with the largest gains from 2008-2012 to 2019-2021 were, in order, Serbia, Bulgaria, Romania, Hungary, Togo, Bahrain, Latvia, Benin, Guinea and Armenia. The ten countries with the largest drops were Lebanon, Venezuela, Afghanistan, Lesotho, Zimbabwe, Jordan, Zambia, India, Mexico and Botswana.

Over the past ten years, life evaluations rose by more than a full point on the 0 to 10 scale in 15 countries and fell by that amount or more in eight countries.

The second panel shows positive affect in total and also its three components. Smiling or laughing a lot during the previous day is the most common of all the components of either positive or negative affect, and has been on a slightly rising trend over the past 15 years, slipping slightly during the pandemic years 2020 and 2021. Enjoyment started at the same frequency as laughter, but by 2021 it was significantly less common. Doing or learning something interesting fell over the first five years of the survey but has been on a generally rising trend since 2011. Positive affect, as the average of the three measures, has been more stable than any of the components, with no discernable trend in its average value of about 0.66 on the scale from 0 to 1.

The third panel shows negative affect, its three components separately (worry, sadness and anger), and stress, all referring to a person’s feelings on the day preceding the survey. The levels and patterns are quite different from positive affect, and their average levels are less than half as high. After five reasonably stable years (2005/06 through 2010), worry and sadness have been rising over the past ten years, especially during 2020, the first year of COVID-19, before improving somewhat in 2021. Anger remains much less frequent, with no significant trend changes. The average for negative affect was about 0.25 for the first five years and followed a fairly steady upward trend since, with a jump in 2020 and mostly returning to the underlying trend in 2021. Stress, which is not a component of our negative affect measure, was also fairly constant for the first five years but has increased steadily ever since, faster than worry or sadness, with its steepest increase in 2020.

The following panels show the corresponding time paths for the main variables to explain happiness in Figure 2.1. There has been growth in both real GDP per capita and healthy life expectancy, [23] fairly constant levels of social support, declines in perceived corruption, and substantial average growth in the extent to which people feel they have the freedom to make key life choices and in helping strangers and other forms of benevolence. [24]

Finally, we show that average levels of trust in public institutions have generally grown slightly since 2012.

Fig. 2.2: Global trends from 2006 through 2021

These global patterns mask considerable variety among global regions, as shown by Figures 2.3 to 2.5. As shown by the Cantril ladder, life evaluations have continued their 15-year convergence between Western and Eastern Europe, with three Balkan countries, Bulgaria, Romania and Serbia, as already noted, having the largest increases in life evaluations from 2008-2012 to 2019-2021. The current gap in life evaluations between Western and Eastern Europe is now less than half what it was ten years ago. The Commonwealth of Independent States (CIS) countries shared this convergence at first but not in later years. Life evaluations in Asia show some growth in East and Southeast Asia and drops since 2010 in South (S) Asia. Ladder evaluations grew until 2012 in Latin America while falling slightly, especially in 2020. Ladder scores have generally fallen in the MENA (the Middle East and North Africa) region while being fairly constant for Sub-Saharan Africa (SSA). The NA+ANZ group of countries (North America, Australia, and New Zealand) had higher life evaluations than Western Europe at the beginning of the period, but that gap has mostly disappeared. Within Western Europe, the Nordic countries have especially high life evaluations and generally better performance in handling COVID-19, as shown later in the chapter.

The remaining panels of Figure 2.3 show positive affect and its components for each of the ten global regions. Over the survey period, the average for positive affect has been highest in the Americas, but on a generally falling trend. It has been rising fastest in Eastern Europe, Southeast Asia and the CIS, and low and falling in South Asia and the MENA countries. There have been no significant trends for positive affect in Sub Saharan Africa and East Asia.

There are interesting regional differences in the components of positive affect, with enjoyment highest in the NA+ANZ group and lowest in MENA but falling on the same downward trend in both. Enjoyment was initially much higher in Western than Eastern Europe until 2012 but had been falling in the west and rising in the east since reaching full convergence in 2020 before rising in both parts of Europe in 2021.

Smiling and laughing started high and have since risen further in Southeast Asia while starting low and falling since in South Asia. By 2020 and 2021, these two parts of Asia were the world’s top and bottom regions, respectively. Smiling and laughing were least frequent, and equally so, in Eastern Europe and the CIS at the beginning of the Gallup World Poll in 2005-2006. They have since been rising in lockstep to exceed those in South Asia and MENA. Laughing and smiling were initially most frequent in Latin America and the NA+ANZ group and have been fairly constant there since then. Nine of the ten regions have seen less laughter during both of the COVID-19 years, with Eastern Europe providing the sole exception.

Doing or learning something of interest has large inter-regional differences in levels but fewer trends than for the other components of positive affect. Interest was lowest in South Asia throughout the survey period, but generally rising rather than falling. Interest grew equally, from initially low levels, in the CIS and Eastern Europe. It was highest and fairly constant in Latin America and NA+ANZ, and slightly lower but converging upwards in Western Europe, following a similar path as in Sub-Saharan Africa.

Figure 2.4 shows the regional averages for negative affect and its components and stress. Negative affect as a whole was highest and rising in MENA and South Asia, with the increase greatest in South Asia. All regions have more negative affect now than ten years ago, except for Eastern Europe. This is best explained by looking at the components separately.

Sadness in East Asia has throughout the period been less than in any other region, declining until 2010 and rising thereafter, still less than half as prevalent as elsewhere in the world. The fastest increases in sadness and the highest eventual levels were in South Asia, MENA, Latin America, and Sub-Saharan Africa. There were mid-range levels and no clear trends in the other regions. There was increased sadness in 2020 in every region except South Asia and Sub-Saharan Africa, followed in 2021 by reductions in sadness in every region except South Asia, which has also seen by far the largest increases in worry over the past ten years. The patterns for worry and sadness thus share many similarities.

Worry ten years ago was lowest in East Asia and the CIS and since has risen less fast there than elsewhere. Worry was much more frequent in Eastern than Western Europe in 2010, growing in the west and declining in the east to converge in 2019 before both rose in 2020 and fell in 2021. The 2021 decline in worry was shared by all other regions but South Asia, with the largest increases over the past ten years.

Fig. 2.3: Regional Trends of Life Evaluations and Positive Affect

Fig. 2.4: Regional Trends of Negative Affect and Stress

Although anger has low global levels and no trend, the regional differences are striking. Anger is far more prevalent in MENA than in the rest of the world, at a fairly constant level. Anger has risen most dramatically in South Asia, approaching MENA levels in 2020 and 2021. There have been longer-term drops in the prevalence of anger in Western and Eastern Europe, especially in Eastern Europe, and also in NA+ANZ. There was a rising trend of anger in Sub-Saharan Africa until 2018, with reductions since. Anger in Southeast Asia is fairly stable, currently just below the middle of the large gap between the high level in South Asia and the low level in East Asia.

Stress, also shown in Figure 2.4, is higher now than ten years ago in every global region. Unusually, all three parts of Asia had similar levels and growth rates, staying in the middle of the global range throughout the period. Nonetheless, among the three regions, South Asia was the least stressed at the outset and the most stressed at the end. Stress started and finished at the top of the range in both NA+ANZ and MENA. Stress rose faster in Eastern than Western Europe, almost converging by the end of the period. Stress started lowest in the CIS and grew fairly slowly, ending the period with stress half as frequent as in the rest of the world.

Three measures of prosocial behaviour—donations, volunteering, and helping strangers—all
showed increases in 2021 in every global region.

Figure 2.5 presents regional differences in levels and trends for the six main variables from Table 2.1, plus other variables of special interest for this chapter. GDP per capita and healthy life expectancy, for which the national data come from international agencies, show trend growth over the 15 years, with both levels and growth differing among the regions. Real GDP per capita grew fastest in Asia, followed by Africa, Eastern Europe and the CIS, and slowest in Latin America, MENA, Western Europe, and NA+ANZ. Healthy life expectancy grew fastest in Sub Saharan Africa, followed by South Asia. It grew most slowly in MENA and NA+ANZ.

Social support, as measured by having someone to count on in times of trouble, was least (and not growing) in South Asia and Sub-Saharan Africa. It was slightly above average and growing in both the CIS and Eastern Europe, declining in MENA, globally high but slightly declining in Western Europe and NA+ANZ, and fairly constant elsewhere.

Having a sense of freedom to make key life decisions grew substantially in most regions. It had the lowest initial levels but the fastest subsequent growth in Eastern Europe, sharing its recent path with the CIS. Within Asia, it started high and grew fast in Southeast Asia, starting low and grew even faster in South Asia. It started fairly low and grew very little in MENA and Sub-Saharan Africa, leaving those regions with the lowest regional levels in 2021. Freedom to make life choices started high in Western Europe but did not grow, so the two parts of Europe had mostly converged by 2020. Freedom was initially highest in NA+ANZ but did not share in the general global growth.

Perceived levels of corruption fell since 2010 in all regions except for Latin America (where it remained higher than anywhere else but Eastern Europe) and NA+ANZ (where it remained unchanged at the globally lowest levels). Both Western and Eastern Europe had favourable corruption trends, but at a far higher level in Eastern Europe. All three parts of Asia reported high but slightly falling corruption. Western Europe had the biggest drop in perceived corruption between 2012 and the most recent years.

Three measures of prosocial behaviour—donations, volunteering and helping strangers—had differing levels and trends. Still, all showed increases in 2021 in every global region, often at remarkable rates not seen for any of the variables we have tracked before and during the pandemic. We shall discuss this more fully in the final section of this chapter.

Regional averages of well-being inequality remained fairly stable until about 2012 and have risen thereafter. The biggest increases in inequality have been in Sub Saharan Africa and MENA. Southeast Asia started with the least inequality but has since passed through that in East Asia and converged to that in South Asia, which has also been on a sharply rising trend over the past decade. Well-being inequality in Eastern Europe was initially greater than in the CIS, but the two have since converged to a level significantly higher than in Western Europe and the NA+ANZ groups, where inequality has shown no increase over the 15 years. Well-being inequality in East Asia has remained in the middle of the range, following the same increase as the global average.

Fig. 2.5: Regional Trends of Happiness-Supporting Factors and Inequality

How has well-being under COVID-19 varied among population subgroups in 2020 and 2021?

We turn now from long-run trends to changes during the last two years. There have been numerous studies of how the effects of COVID-19, whether in terms of illness and death or living conditions for the uninfected, have differed among population sub-groups. [25] The fact that the virus is more easily transmitted in close living and working arrangements partly explains the higher incidence of disease among those in elder care, prisons, hospitals, housing for migrant and temporary workers, and other forms of group living. Similarly, risks are higher for those employed in essential services, especially for front-line health care workers and others who deal with many members of the public or work in crowded conditions. Age has been the main factor separating those with differing risks of serious or fatal consequences, although this association is complicated by the preponderance of fatalities in elder-care settings where lower immune responses of the elderly are compounded by comorbidities. [26] Those with lower incomes are also thought to be more at risk, being perhaps more likely to be in high-risk workplaces, with fewer opportunities to work from home and fewer resources to support the isolation required for those infected.

The Gallup World Poll data are not sufficiently fine-grained to separate respondents by their living or working arrangements. Still, they provide several ways of testing for different patterns of consequences. In particular, we can separate respondents by age, gender, migrant status, income, unemployment, and general health status. Previous well-being research by ourselves and many others have shown subjective life evaluations to be lower for the unemployed, poor in health, and in the lowest income categories. In World Happiness Report 2015, we examined the distribution of life evaluations and emotions by age and gender, finding a widespread but not universal U-shape in age for life evaluations, with those under 30 and over 60 happier than those in between. Female life evaluations, and frequency of negative affect, were generally slightly higher than for males. For immigrants, we found in World Happiness Report 2018 that life evaluations of international migrants tend to move fairly quickly toward the levels of respondents born in the destination country.

In this section, we shall first confirm these general findings using all individual-level data from the years 2017 through 2021, testing if these effects have become larger or smaller during 2020 or 2021. We use the 2020 and 2021 effects as proxies for the effects of COVID-19 and all related changes to economic and social circumstances, a simplification not easily avoided.

Table 2.2 shows the results of individual-level estimation of a version of the model used in Table 2.1 to explain differences at the national level. At the individual level, all of the variables except the log of household income are either 0 or 1 according to whether each respondent was in that category or felt the emotion in question the previous day. We use the same column structure as in our usual Table 2.1 while adding more rows to introduce variables that help to explain differences among individuals but average out at the national level. The first three columns show separate equations for life evaluations, positive affect and negative affect. The fourth column is a repeat of the life evaluation equation with several positive and negative emotions as additional independent variables, reflecting their power to influence how people rate the lives they are leading.

Fourth, there is also the emerging evidence of increasing levels of prosocial activity during COVID-19, emerging initially in 2020 with increased help to strangers, but now including donations and volunteering, with large increases in all activities in 2021. This evidence will be discussed later in our forward-looking section but is worth mentioning here as evidence of changes in feelings and behaviour likely to be providing support for life evaluations during the COVID-19 years.

The equations in Table 2.2 produce the same general patterns of results as Table 2.1. Income, health, having someone to count on, having a sense of freedom to make key life decisions, generosity, and the absence of corruption all play strong roles in supporting life evaluations. Confidence in public institutions also plays an important role.

These large samples of individual responses can also be used to show how average life evaluations, and the factors that support them, have varied among different sub-groups of the population. What do the results show? We start by reporting (in Table 2.3) how the 2020 and 2021 levels of key variables differ from those in the base period 2017-2019 and then see (in Table 2.4) whether the well-being effects of these conditions have become greater or less under COVID-19.

On average, there were no significant changes in the sense of freedom, perceived corruption and institutional trust during 2020 and 2021. Confidence in government rose in 2020 and then returned to baseline in 2021.

By far, the largest changes were in three types of benevolent actions, especially in 2021. As shown later in Figure 2.6, in 2020, there was a substantial increase in help given to strangers but no substantial change in donations and volunteering. In 2021, all three types of activity were much higher than in 2017-2019, having an increase averaging about 25% of baseline activity. We shall return to this in the next section of the chapter.

What about emotions in 2020 and 2021? Worry and sadness were both significantly higher than baseline in 2020, with about 3% more of the population feeling each of these emotions. [30] This is equal to about 10% of people feeling these emotions pre-pandemic. The increases in 2021 were about half their 2020 size, remaining statistically significant only for sadness. Anger remained stable and infrequent at its 20% baseline level in both years. Negative affect as a whole was about 8% above its pre-pandemic value in 2020, falling almost completely back to baseline in 2021 (as shown below in Figure 2.6). Similarly, perceived stress was higher by 8% of its pre-pandemic frequency in 2020 but has also fallen back to baseline in 2021.

In the base period 2017-2019, worry, sadness, and stress were about 10% more prevalent among females than males, while anger was 10% less frequent among females. The same patterns continued during 2020 and 2021, with males and females having similar proportionate increases in worry, sadness and stress, with the female increases being slightly higher than those for males. For example, worry grew in frequency, relative to its base value, by 5.7% for females and 4.7% for males. [31] Anger was unchanged for both males and females.

Positive emotions as a whole remained more than twice as frequent as negative ones.

Positive emotions as a whole remained more than twice as frequent as negative ones, and their average frequency did not change during 2020 and 2021. Positive affect in the baseline was 13% more frequent for the young than the old (72% frequency for the young vs 59% for the old), with that initial gap reducing to about 8.5% in 2020 and 2021, with gains for the old being offset by losses for the young. These patterns were similar for both laughter and enjoyment while doing something of interest did not change for the young but increased for the other two groups. The gains were twice as large for the old as for those in middle age, reducing an initial gap of 9% to 7%, about equally in both years. These patterns for positive emotions and their changes were very similar for females and males.

For negative emotions, there are some interactions of gender and age. Among those over 60, there were reductions rather than increases in negative emotions, to the same extent for females and males. In the youngest age group, baseline values were lower for worry, sadness and stress and were quite similar for females and males. Anger was the exception, taking its highest average value (.22) for young males. In the young age group, negative affect was increased more than for other age groups, and equally so for females and males.

Table 2.4 repeats the basic equation for life evaluations in Table 2.2 but now fits separate equations for 2017-2019 and 2020-2021. This permits us to see to what extent the happiness impacts of COVID-19 have varied among population sub-groups.

Note: Regressions in columns 1 and 2 include a constant, country fixed effects, and controls for country-years with missing questions. Column 3 reports changes in the absolute value of the coefficients from 2017-2019 to 2020-2021. See appendix note on calculation of standard errors in column 3. Standard errors are clustered by country. * p [32]

As for institutional trust, Table 2.4 shows that it remains a highly important determinant of life evaluations. We shall now explore how it also enables societies to deal effectively with crises, especially in limiting deaths from COVID-19.

Trust and benevolence during and after COVID-19

Many studies of the effects of COVID-19 have emphasised the importance of public trust as support for successful pandemic responses. [33] We have studied similar linkages in earlier reports dealing with other national and personal crises. In World Happiness Report 2020, we found that individuals with high social and institutional trust levels were happier than those living in less trusting and trustworthy environments. [34] The benefits of high trust were especially great for those in conditions of adversity, including ill-health, unemployment, low income, discrimination and unsafe streets. [35] In World Happiness Report 2013, we found that the happiness consequences of the financial crisis of 2007-2008 were smaller in those countries with greater levels of mutual trust. These findings are consistent with a broad range of studies showing that communities with high levels of trust are generally much more resilient in the face of a wide range of crises, including tsunamis, [36] earthquakes, [37] accidents, storms, and floods. Trust and cooperative social norms facilitate rapid and cooperative responses, which themselves improve the happiness of citizens and demonstrate to people the extent to which others are prepared to do benevolent acts for them and the community in general. Since this sometimes comes as a surprise, there is a happiness bonus when people get a chance to see the goodness of others in action and to be of service themselves. Seeing trust in action has been found to lead to post-disaster increases in trust, [38] especially where government responses are considered to be sufficiently timely and effective. [39]

World Happiness Report 2021 presented new evidence using the return of lost wallets as a powerful measure of trust and benevolence. We compared the life satisfaction effects of the likelihood of a Gallup World Poll respondent’s lost wallet being returned with the comparably measured likelihood of negative events, such as illness or violent crime. The results were striking, with the expected likely return of a lost wallet being associated with a life evaluation more than one point higher on the 0 to 10 scale, far higher than the association with any of the negative events assessed by the same respondents. [40]

COVID-19, as the biggest health crisis in more than a century, with unmatched global reach and duration, has provided a correspondingly important test of the power of trust and prosocial behaviour to provide resilience and save lives and livelihoods. Now that we have two years of evidence, we can assess the importance of benevolence and trust and see how they have fared during the pandemic. Many have seen the pandemic as creating social and political divisions above and beyond those created by the need to maintain physical distance from loved ones for many months. Some of the evidence noted above shows that large crises can lead to improvements in trust, benevolence and well-being if it leads people to reach out to help others, especially if seeing that benevolence comes as a welcome surprise to their neighbours more used to reading of acts of ill-will. Looking to the future, it is important to know whether trust and benevolence have been fostered or destroyed by two years of the pandemic. We have not found significant changes in our measures of institutional trust during the pandemic but did find, especially in 2021, very large increases in the reported frequency of benevolent acts.

The increasing importance of trust in limiting deaths from COVID-19

At the core of our interest in investigating international differences in death rates from COVID-19 is to see what links there may be between the variables that support high life evaluations and those that are related to success in keeping death rates low. We found in World Happiness Report 2021 that social and institutional trust are the only main determinants of subjective well-being that showed a strong carry-forward into success in fighting COVID-19. This section updates our analysis to include data from both 2020 and 2021 to see whether these results also appeared in 2021.

We find continuing evidence that the quality of the social context, which we have previously found so important to explaining life evaluations within and across societies, has also affected progress in fighting COVID-19. Several studies within nations have found that regions with high social capital have been more successful in reducing rates of infection and deaths. [41] Others have argued that different elements of the social context might have opposite effects in the fight against COVID-19. [42] In particular, it has been suggested that the close personal relations within families and communities sparked and fed by frequent in-person meetings might provide a good transmission climate for the virus. On the other hand, those aspects of social capital relating to prosocial behaviour, trust in others, and especially trust in institutions might be expected to foster behaviours that would help a society follow physical distancing and other rules designed to stop the spread of the virus. Our 2020 finding that trust is an important determinant of international differences in COVID-19 has since been confirmed independently for cumulative COVID-19 infection rates extending to September 30, 2021, [43] and we show below that this finding also holds for the whole of 2021.

We capture these vital trust linkages in two ways. We have a direct measure of trust in public institutions, described below. We do not have a measure of general trust in others for our large sample of countries, so we make use instead of a measure of the inequality of income distribution, which has often been found to be a robust predictor of the level of social trust. [44]

Our attempts to explain international differences in COVID-19 death rates divide the explanatory variables into two sets, both of which refer to circumstances that are likely to have affected a country’s success in battling COVID-19. The first set of variables covers demographic, geographic and disease exposure circumstances at the beginning of the pandemic. The second set of variables covers several aspects of economic and social structure, also measured before the pandemic, that help to explain the differential success rates of national COVID-19 strategies.

The first set comprises a variable combining the age distribution of each country’s population with the age-specific mortality risks [45] for COVID-19, whether the country is an island, and an exposure index measuring how close a country was, in the very early stages of the pandemic (March 31, 2020), to infections in other countries. In World Happiness Report 2021, we used a pair of measures of the extent to which a country could remember and apply the epidemic control strategies learned during the SARS epidemic of 2003. These include membership in the World Health Organisation’s Western Pacific Region (WHOWPR) and distance from countries with the most direct experience of the SARS epidemic. These two variables are highly correlated, so in our current modelling, we make use only of the WHOWPR variable. Countries in the WHO Western Pacific Region have been building on SARS experiences to develop fast and maintained virus suppression strategies. [46] Hence membership in that region is used as a proxy measure of the likelihood of a country adopting a virus elimination strategy. [47] The trust-related variables include a measure of institutional trust and the Gini coefficient measuring each country’s income inequality. An earlier version of this model was explained more fully and first applied in chapter 2 of World Happiness Report 2021, while further developments are reported elsewhere. [48]

Note: Robust standard errors reported in parentheses. *p [49] There was very early evidence that COVID-19 was highly infectious, spread by asymptomatic [50] and pre-symptomatic [51] carriers, and subject to aerosol transmission. [52] These characteristics require masks [53] and physical distancing to slow transmission, rapid and widespread testing [54] to identify and eliminate community [55] outbreaks, and effective testing and isolation for those needing to move from one community or country to another. Countries that quickly adopted all these pillar policies were able to drive community transmission to zero. By doing so, and then using widespread testing and targeted lockdowns when faced with fresh outbreaks, those countries were able to avoid the high levels of community exposure that led to subsequent waves that were in most countries even more deadly than the first. Countries that did not try to drive their community transmission to zero almost always found themselves with insufficient testing, tracking and tracing capacities to suppress subsequent waves of infection, requiring them eventually to have higher average levels of stringency than in countries that chose to eliminate community transmission. [56] They also made the infection risks worse for everyone by providing large community pools of infection that provided opportunities for mutations to develop and spread.

The results for 2020 and 2021 are most appropriately compared by looking at the standardised beta coefficients, which adjust for the fact that average COVID-19 death rates across our 154-country sample were twice as high in 2021 as in 2020. Comparing the standardised coefficients, the two equations are very consistent. The only significant differences are for the early exposure variable, which shows, as expected, a weaker association during the second year, and the institutional trust variable, which is of even greater importance in 2021 than in 2020. If the associations between institutional trust and COVID-19 deaths in 2021 could be regarded as causal, they suggest that an increase of 0.12 in institutional trust [57] would have reduced average deaths per 100,000 population by 6.4 in 2020 (21% of average deaths) and by 19.7 in 2021 (representing 28% of average deaths). The death reduction is greater in 2021 mainly because average deaths were more than twice as great [58] in 2021, plus an even greater role for trust in explaining 2021 death rates. This does not reflect possible increases in trust triggered by the pandemic because the measure used reflects average confidence levels during 2017-2019. The results for income inequality, which we treat here as partially representing interpersonal trust, [59] suggest that to move from a country with a Gini coefficient of 0.27 (like Denmark or Sweden) to 0.47 (like Mexico or the United States) is associated with COVID-19 death rates per 100,000 population that are higher by 25 in 2020 and 41 in 2021. Our results for both institutional trust and income inequality suggest important associations in both years, even larger in 2021 than in 2020.

The Nordic countries merit special attention in the light of their generally high levels of personal and institutional trust. They have also had COVID-19 death rates only one-third as high as elsewhere in Western Europe during 2020 and 2021, 27 per 100,000 in the Nordic countries compared to 80 in the rest of Western Europe. There is an equally great divide when Sweden is compared with the other Nordic countries as death rates were five times higher in Sweden, with 2020-2021 COVID-19 death rates of 75 per 100,000 compared to 15 in the other Nordic countries. This difference shows the importance of a chosen pandemic strategy. Sweden, at the outset, chose [60] not to suppress community transmission, while the other Nordic countries aimed to contain it. As a result, Sweden had much higher death rates than the other Nordic countries, while in the end being forced to adopt stringency measures that were on average stricter [61] than in the other Nordic countries. High trust helps, but it requires an appropriate strategy to deliver better results.

Growth of benevolence during 2020 and 2021

A primary message from the 2020 data analysed in World Happiness Report 2021 was of significant increases in negative emotions accompanied by an even larger increase in the extent to which people helped strangers, with the comparison in both cases being to the average values in 2017-2019. As shown in Figures 2.5 and 2.6, a striking feature of our new evidence is that the size of the increase since 2017-2019 in the helping of strangers has doubled from 2020 to 2021 and is now accompanied by significant increases in donations and volunteering. While benevolence has increased in 2021 relative to both 2017-2019 and 2020, negative affect in 2021 has fallen back towards the 2017-2019 baseline. Hence, relative to 2020, the second year of COVID-19 has seen global growth of prosocial activities of all three types combined, while negative affect is now only slightly above baseline.

Giving help to strangers in 2021 was above baseline in all global regions and by more than 10% of the population in six of the ten. Moreover, everywhere, it was also above its 2020 value. The prosociality average is also higher in 2021 in every region than in the 2017-2019 baseline, also showing in all regions an increase from 2020 to 2021.

The variable ‘prosocial’ is an average of the measures for donations, volunteering and helping strangers. In 2021 this combined measure of benevolence was above its pre-pandemic level by 8% as a share of the total population of responders, 25% of the pre-pandemic frequency of these prosocial acts.

Figure 2.6: Percentage of population performing benevolent acts in 2020 and 2021 compared to 2017-2019

Among the regions, some interesting patterns appear. Before the pandemic, prosociality was significantly higher in Western than in Eastern Europe, averaging 38% in Western Europe and 24% in Eastern Europe. In 2021, prosociality was up by 2% in Western Europe and 16% in Eastern Europe, erasing the pre-pandemic gap. At the global level, a somewhat similar comparison can be made. In 2017-2019 the percentage of the population involved in the selected prosocial acts was 40% in the western industrial countries [62] and 30% in the rest of the world. This gap was substantially closed in 2020 and especially in 2021. Prosociality in 2021 was greater than baseline in both groups of countries, by 2.5% of the population in the western industrial countries and by 9.5% in all other regions, thus removing two-thirds of the 2017-2019 gap.

Looking at these regional differences over the long term, as shown earlier in Figure 2.5, shows that the universally significant increases in 2021 were a stable continuation of an established upward trend in MENA and South Asia, an accelerated upward trend in Latin America, Southeast Asia, Eastern Europe and the CIS, and a reversal of previous downward trends in Western Europe and NA+ANZ.

It is too early to tell whether the increased benevolence in 2021 will carry forward as a welcome addition to global well-being. In research at the individual level, benevolence has been found to contribute to a positive feedback loop with happiness, with the benevolent more likely to be happy and the happy more likely to act benevolently. [63] But there are counter forces at work, with pandemic fatigue possibly fuelling a loss of public trust and perhaps private benevolence. The reported averages for the fraction of the population expressing trust in government is globally the same in 2020 and 2021 as before the pandemic began. However, many countries have evident signs of discontent and political polarisation as the pandemic enters its third year.

Life evaluations continue to be strikingly resilient in the face of COVID-19, supported by a 2021 pandemic of benevolence.

Summary

Overall levels of life evaluations have been fairly stable during two years of COVID-19, matched by modest changes in the global rankings. Finland remains in the top position for the fifth year running, followed by Denmark in 2^nd^ and all five Nordic countries among the top eight countries, joined by Switzerland, the Netherlands and Luxembourg. France reached its highest ranking to date, at 20^th^, while Canada slipped to its lowest ranking ever, at 15^th^, just behind Germany at 14th and followed closely by the United States and the United Kingdom at 16^th^ and 17^th^.

Trends over the past 15 years show slight growth in life evaluations for the typical country until 2011 and reductions since. The largest trend increases were in Central and Eastern Europe, East Asia and the CIS. Consistent with trend convergence in happiness between Eastern and Western Europe, the three countries with the greatest growth in average life evaluations over the past 10 years were Serbia, Bulgaria and Romania, with gains averaging 1.4 points on the 0 to 10 scale, or more than 20% of their levels in the 2008-2012 period.

Among the six variables used to explain these levels, there has been general growth in real GDP per capita and healthy life expectancy, generally declining perceptions of corruption and freedom, declining generosity (until 2020), and fairly constant overall levels of social support.

Well-being inequality has generally grown since 2011, especially in Sub Saharan Africa, MENA, Latin America, and South and Southeast Asia.

Positive emotions have generally been twice as prevalent as negative ones. That gap has been narrowing over the past ten years, with enjoyment and laughter on a negative trend in most regions and worry and sadness on rising trends (with the general exception of Central and Eastern Europe). Over the past decade, the trend growth in worry and sadness has been greatest in South Asia, Latin America, MENA, and Sub-Saharan Africa.

Anger has remained low and stable in the global average, with large increases in South Asia and Sub-Saharan Africa offset by trend declines elsewhere.

There have been trend increases in national-average stress levels in all ten global regions.

Individual-level data for emotions and life evaluations reveal that COVID-19 has worsened the well-being costs of unemployment and ill health. The pandemic has also exposed, but not increased, pre-existing differences between males and females and between those with low and high incomes.

Fuelled by worry and sadness, but not by anger, negative affect as a whole was about 8% above its pre-pandemic value in 2020, falling to 3% above baseline in 2021.

Over the five most recent years, positive emotions as a whole remained more than twice as frequent as negative ones and greater for the young than the old. Their average frequency did not change during 2020 and 2021, with losses among the young offset by increases for the old, partially closing the initial gap favouring the young age group.

Trust and benevolence have, if anything, become more important. Higher institutional trust continues to be linked to lower death rates from COVID-19 to a greater extent in 2021 than in 2020.

Although our three measures of prosocial behaviour – donations, volunteering and helping strangers – had differing levels and trends, all showed increases in 2021 in every global region, often at remarkable rates not seen for any of the variables we have tracked before and during the pandemic.

Global benevolence, as measured by the average of the three measures of prosocial behaviour, has increased remarkably in 2021, up by almost 25% of its pre-pandemic level, led by the helping of strangers, but with strong growth also in donations and volunteering. The COVID-19 pandemic starting in 2020 has led to a 2021 pandemic of benevolence with equally global spread. All must hope that the pandemic of benevolence will live far beyond COVID-19. If sustainable, this outpouring of kindness provides grounds for hope and optimism in a world needing more of both.

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Endnotes

For a recent review of alternative ways of measuring well-being, see the various chapters of Lee, Kubzansky and VanderWeele, eds. (2021). ↩︎

Because of the presence of two-way linkages and the inability to formally define a causal structure, our results are based on correlations that do not in themselves imply causality. Our use of the term ‘explanation’ should thus be interpreted to imply correlation but not necessarily causation. ↩︎

The statistical appendix contains alternative forms without year effects (Table 9), and a repeat version of the Table 2.1 equation showing the estimated year effects (Table 8). These results confirm, as we would hope, that inclusion of the year effects makes no significant difference to any of the coefficients. ↩︎

The definitions of the variables are shown in Technical Box 2, with additional detail in the online data appendix. ↩︎

The model’s predictive power is little changed if the year fixed effects in the model are removed, falling from 0.753 to 0.748 in terms of the adjusted R-squared. ↩︎

The exception to this is the newly significant positive coefficient on healthy life expectancy in the equation for negative affect. This is likely reflecting the fact that negative affect within countries is lowest among the young (age Back to the 2022 report

The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by the Gallup World Poll data.

The Report is supported by The Ernesto Illy Foundation, illycaffè, Davines Group, Unilever’s largest ice cream brand Wall’s, The Blue Chip Foundation, The William, Jeff, and Jennifer Gross Family Foundation, The Happier Way Foundation, and The Regenerative Society Foundation.

The World Happiness Report was written by a group of independent experts acting in their personal capacities. Any views expressed in this report do not necessarily reflect the views of any organization, agency or program of the United Nations.

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