The World Happiness Report,2016

8/19/2019 The World Happiness Report,2016

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TABLE OF CONTENTS

1. Setting the Stage 2

John Helliwell, Richard Layard and Jeffrey Sachs

2. The Distribution of World Happiness 8

John Helliwell, Haifang Huang and Shun Wang

3. Promoting Secular Ethics 50Richard Layard

4. Happiness and Sustainable Development:

Concepts and Evidence 56

Jeffrey Sachs

WORLD

HAPPINESS REPORT 2016Edited by John Helliwell, Richard Layard and Jeffrey Sachs

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

UpdateUpdate

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digs into a different aspect of life. The six factorsare GDP per capita, healthy years of life expec-tancy, social support (as measured by having

someone to count on in times of trouble), trust(as measured by a perceived absence of corrup-tion in government and business), perceivedfreedom to make life decisions, and generosity(as measured by recent donations). Differencesin social support, incomes and healthy lifeexpectancy are the three most important factors.International differences in positive and nega-tive emotions (affect) are much less fully ex-plained by these six factors. When affect mea-sures are used as additional elements in the

explanation of life evaluations, only positiveemotions contribute significantly, appearing toprovide an important channel for the effects ofboth perceived freedom and social support.

Analysis of changes in life evaluations from2005-2007 to 2013-2015 continue to show biginternational differences in the dynamics ofhappiness, with both the major gainers and themajor losers spread among several regions.

The main innovation in the World HappinessReport Update 2016 is our focus on inequality.We have previously argued that happiness, asmeasured by life evaluations, provides a broaderindicator of human welfare than do measures ofincome, poverty, health, education, and goodgovernment viewed separately. We now make aparallel suggestion for measuring and address-ing inequality. Thus we argue that inequality ofwell-being provides a better measure of thedistribution of welfare than is provided by

income and wealth, which have thus far heldcentre stage when the levels and trends ofinequality are being considered. First we showthat there is a wide variation among countriesand regions in their inequality of well-being,and in the extent to which these inequalitieschanged from 2005-2011 to 2012-2015. In theworld as a whole, in eight of the 10 globalregions, and in more than half of the countriessurveyed there was a significant increase in theinequality of happiness. By contrast, no global

region, and fewer than one in 10 countries,showed significant reductions in happinessinequality over that period.

Second, the chapter shows that people do careabout the happiness of others, and how it isdistributed. Beyond the six factors alreadydiscussed, new research suggests that people aresignificantly happier living in societies wherethere is less inequality of happiness.

Chapter 3: Promoting Secular Ethics(Richard Layard)

This chapter argues that the world needs an ethi-cal system that is both convincing and inspiring.To supplement what is seen as a global declinein the impact of religious ethics, the chapteroffers the principle of the greatest happiness asone that can inspire and unite people from allbackgrounds and cultures, and that is also inharmony with major religious traditions. But tosustain people in living good lives, more than aprinciple is needed. Living organisations areneeded, including those already provided bymany religions, in which people meet regularlyfor uplift and mutual support. To create secularorganisations of this type in addition to religiousinstitutions is an important opportunity topromote well-being in the 21st century. Themovement known as Action for Happiness isused as an example to show both the need forand the power of collaborative action to designand deliver better lives.

Chapter 4: Happiness and Sustainable Develop-ment: Concepts and Evidence (Jeffrey Sachs)

The year 2015 was a watershed for humanity,with the adoption of Sustainable DevelopmentGoals (SDGs) by heads of state at a specialsummit at the United Nations in September2015, on the 70th anniversary of the UN.

Sustainable development is a holistic approach towell-being that calls on societies to pursueeconomic, social, and environmental objectives

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W O R L D H A P P I N E S S R E P O R T 2 0 1 6 | U P D AT E

in an integrated manner. When countries sin-gle-mindedly pursue individual objectives, suchas economic development to the neglect of social

and environmental objectives, the results can behighly adverse for human well-being, evendangerous for survival. Many countries in recentyears have achieved economic growth at the costof sharply rising inequality, entrenched socialexclusion, and grave damage to the naturalenvironment. The SDGs are designed to helpcountries to achieve a more balanced approach,thereby leading to higher levels of well-being forthe present and future generations.

This chapter shows that measures of sustainabledevelopment, including a new SustainableDevelopment Index prepared by the SustainableDevelopment Solutions Network, help to accountfor cross-country variations in happiness, alongthe lines suggested by the analysis in Chapter 2 ofthis Report. In particular the SDG Index helps toaccount for cross-national patterns of happinesseven after controlling for GDP per capita andunemployment . A measure of Economic Free-dom, as proposed by libertarians, shows no such

explanatory weight. The evidence suggests thatindeed all three dimensions of sustainable devel-opment—economic, social, and environmental—are needed to account for the cross-countryvariation in happiness.

The UN Sustainable Development SolutionsNetwork has urged the inclusion of indicators ofSubjective Well-being to help guide and measurethe progress towards the SDGs. To this end, aletter from thirty global experts in well-being

research—plus national and global statisticianswith experience in collecting and using thesedata—has been sent to the UN Secretary Gener-al, and to the committees responsible for moni-toring the SDGs.

The 2016 Special Rome Edition

(Edited by Jeffrey Sachs, Leonardo

Becchetti and Anthony Annett)

As we have noted above, World Happiness Report2016—Special Rome Edition, separately selectedand edited, was prepared for the March 2016launch event in Rome. The papers all have strongRoman links: the paper by Anthony Annett linksCatholic social teaching with the work of otherphilosophers of well-being, while the other fourpapers are by Italian researchers dealing with avariety of issues in the analysis of well-being. Weare immensely grateful to our Roman hosts for

creating the launch event, and for contributing avariety of interesting papers. We provide below abrief description of each paper, and of its possi-ble implications for the future development ofglobal happiness research.

Chapter 1: Inside the Life Satisfaction Blackbox(Leonardo Becchetti, Luisa Corrado andPaola Sama)

The authors propose the use of a package ofdomain measures of the quality of life to supple-

ment or perhaps even replace the overall lifeevaluations central to the World Happiness Report .They find that their package measure is morefully explained by a typical set of individual-levelvariables, and prefer it for that reason. Theyrecommend, as do we, the collection of a broaderrange of variables that measure or arguablysupport various aspects of well-being. Only thuscan the science of well-being be broadened andstrengthened. However, to measure overallhappiness, we continue to attach more validity to

peoples’ own judgments of the quality of theirlives than to any index we might construct out ofpossible component measures.

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Chapter 2: Human Flourishing, the CommonGood, and Catholic Social Teaching(Anthony Annett)

This paper makes three claims. First, humanbeings are by their nature oriented towardbroader notions of happiness that are intimatelytied to the common good. Second, with the turntoward the individual, post-Enlightenment politi-cal and economic developments have strippedthe common good of all substantive content.Third, by restoring the centrality of the commongood, Catholic social teaching offers a coherentand internally consistent framework for humanflourishing that applies principles to particular

circumstances in a way that does not depend onagreeing with the confessional claims of theCatholic Church.

Chapter 3: The Challenges of Public Happiness:An Historical-Methodological Reconstruction(Luigino Bruni and Stefano Zemagni)

The central idea of this paper, drawn fromAristotle, is that there is an intrinsic value in

relational and civil life, without which humanlife does not fully flourish. They contrast thisbroader conception of a good life, for which theysee roots in the Italian civil economy, with whatthey see as narrower and more hedonisticapproaches. The central role they ascribe to thesocial context—what they refer to as relationalgoods—has echoes in the empirical findings inthe World Happiness Report , where the quality ofsocial support and the excellence of civil institu-tions are of primary importance, supplementednow by an apparent preference for equality ofhappiness.

Chapter 4: The Geography of Parenthood and Well-Being: Do Children Make Us Happy, Where, and Why? (Luca Stanca)

The author digs deeper into a frequent findingthat having children does not add to the happi-ness of their parents. The paper confirms anegative relationship between parenthood andlife satisfaction that is stronger for females thanmales, and turns positive only for older agegroups and for widowers. Looking across theworld, a negative relationship between parent-hood and life satisfaction is found in two-thirdsof the countries studied. The negative effect ofparenthood on life satisfaction is found to besignificantly stronger in countries with higherGDP per capita or higher unemployment rates.

Chapter 5: Multidimensional Well-Being inContemporary Europe: Analysis of the Use of aSelf-Organizing Map Applied to SHARE Data(Mario Lucchini, Luca Crivelli and Sara della Bella).

The authors use a network-based mechanicaldata-reduction process to look for common and

divergent features of 38 different well-beingindicators collected from the same survey ofolder European adults that provided the data forthe paper by Becchetti et al. They find that themeasures of positive emotions tend to clustertogether, as do the measures of negative emo-tions. Overall life evaluations show a moreumbrella-like character, with somewhat morekinship to the positive emotions. This seems tobe consistent with the World Happiness Report2016 Update finding that positive and negativeaffect have quite different apparent impacts oflife evaluations, being strongly positive forpositive affect but only very slightly negative fornegative affect.

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Conclusion

In light of the limited time since the last report,

the 2016 Update is shorter than usual. This year,as detailed in Chapter 2 of the Update, weprovide a fuller accounting of the distribution ofhappiness among people within each countryand region. Just as happiness provides a broadermeasure of well-being than separate accountingsof income, health status, and the quality of thesocial context, we find that inequality of well-be-ing provides a broader measure of inequalitythan measures focusing on the distribution ofincome and wealth. After documenting a general

rise in the inequality of happiness, we presentpreliminary evidence that countries with moreequal distributions of well-being have higheraverage life evaluations. This in turn invitesbroader discussions about the policies that mightimprove the levels and distribution of well-beingwithin and among countries.

We also present in Chapter 4 some preliminaryevidence that sustainable development is condu-cive to happiness. We find that happiness is

higher in countries closer to realizing the Sus-tainable Development Goals, as approved by thenations of the world in September 2015.

To supplement our short World Happiness Report2016 Update, and to fuel the discussions at thethree-day series of launch events in Rome, wehave also issued the companion Volume 2—theWorld Happiness Report 2016 Special RomeEdition. This separately-edited volume compris-es more technical papers, mainly prepared by

our Roman hosts.

We are also in the midst of planning the nextfull report, the World Happiness Report 2017, which will include special chapters on happinessin Africa and in China, as well as analyses ofhappiness in the workplace and over the courseof life. We also plan to extend our analysis of theinequality of happiness, and to dig deeper into thehappiness consequences of international migration.

The cause of happiness as a primary goal forpublic policy continues to make good progress.So far, four national governments—Bhutan,

Ecuador, United Arab Emirates and Venezuela—have appointed ministers of happiness responsi-ble for coordinating their national efforts. Thereare many more sub-national governments—from large states like Jalisco in Mexico to manycities and communities around the world—thatare now committed to designing policies en-abling people to live happier lives. Experimenta-tion is easier at the sub-national level, and this iswhere we expect to find the most progress.These local efforts are often supported by more

encompassing organizations—such as theHappiness Research Institute based in Copenha-gen and the Action for Happiness in the UnitedKingdom—designed to foster and transmitlocally-inspired and delivered innovations.

In these interconnected ways, we see increasingevidence that the emerging science of well-beingis combining with growing policy interest at alllevels of government to enable people to livesustainably happier lives. Our data show what

needs to be done to improve the level and distri-bution of happiness. We are encouraged thatprogress can and will be made.

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Our reason for paying more attention to thedistribution of life evaluations is quite simple. Ifit is appropriate to use life evaluations as an

umbrella measure of the quality of life, to supple-ment and consolidate the benefits available fromincome, health, family and friends, and thebroader institutional and social context, then it isequally important to broaden the measurementof inequalities beyond those for income andwealth. Whether people are more concerned withequality of opportunities or equality of outcomes,the data and analysis should embrace the avail-ability of and access to sustainable and livablecities and communities as much as to income

and wealth. We will make the case that thedistribution of life evaluations provides anover-arching measure of inequality in just thesame way as the average life evaluations providean umbrella measure of well-being.

The structure of the chapter is as follows. Weshall start with a review of how and why we uselife evaluations as our central measure of subjec-tive well-being within and among nations. Weshall then present data for average levels of life

evaluations within and among countries andglobal regions. This will include our latestefforts to explain the differences in nationalaverage evaluations, across countries and overthe years. After that we present the latest data onchanges between 2005-2007 and 2013-2015 inaverage national life evaluations.

We shall then turn to consider inequality andwell-being. We first provide a country ranking ofthe inequality of life evaluations based on data

from 2012-2015, followed by a country rankingbased on the size of the changes in inequalitythat have taken place between 2005-2011 and2012-2015. We then attempt to assess the possibleconsequences for average levels of well-being,and for what might be done to address well-beinginequalities. We conclude with a summary of ourlatest evidence and its implications.

Measuring and Understanding

Happiness

Chapter 2 of the first World Happiness Report explained the strides that had been made duringthe preceding 30 years, mainly within psychology,in the development and validation of a variety ofmeasures of subjective well-being. Progress sincethen has moved faster, as the number of scientificpapers on the topic has continued to growrapidly,1 and as the measurement of subjectivewell-being has been taken up by more nationaland international statistical agencies, guided bytechnical advice from experts in the field.

By the time of the first report there was alreadya clear distinction to be made among three mainclasses of subjective measures: life evaluations,positive emotional experiences (positive affect)and negative emotional experiences (negativeaffect); see Technical Box 1. The Organizationfor Economic Co-operation and Development(OECD) subsequently released Guidelines onMeasuring Subjective Well-being ,2 which includedboth short and longer recommended modules of

subjective well-being questions.3

The centerpieceof the OECD short module was a life evaluationquestion, asking respondents to assess theirsatisfaction with their current lives on a 0 to 10scale. This was to be accompanied by two orthree affect questions and a question about theextent to which the respondents felt they hada purpose or meaning in their lives. The latterquestion, which we treat as an important sup-port for subjective well-being, rather than adirect measure of it, is of a type4 that has cometo be called “eudaimonic,” in honor of Aristotle,who believed that having such a purpose wouldbe central to any reflective individual’s assess-ment of the quality of his or her own life.

Chapter 2 of World Happiness Report 2015 re-viewed evidence from many countries andseveral different surveys about the types ofinformation available from different measuresof subjective well-being.8 What were the mainmessages? First, all three of the commonly used

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life evaluations (specifically Cantril ladder,satisfaction with life, and happiness with life ingeneral) tell almost identical stories about thenature and relative importance of the variousfactors influencing subjective well-being. Forexample, for several years it was thought (and isstill sometimes reported in the literature) that

respondents’ answers to the Cantril ladderquestion, with its use of a ladder as a framingdevice, were more dependent on their incomesthan were answers to questions about satisfac-tion with life. The evidence for this came fromcomparing modeling using the Cantril ladder inthe Gallup World Poll (GWP) with modeling

Technical Box 1: Measuring Subjective Well-being

The OECD (2013) Guidelines on Measuring Sub- jective Well-being , quotes in its introduction thefollowing definition and recommendation fromthe earlier Commission on the Measurement ofEconomic and Social Progress:

“Subjective well-being encompasses three dif-ferent aspects: cognitive evaluations of one’slife, positive emotions (joy, pride), and nega-tive ones (pain, anger, worry). While these as-pects of subjective well-being have differentdeterminants, in all cases these determinants

go well beyond people’s income and materialconditions... All these aspects of subjectivewell-being should be measured separately toderive a more comprehensive measure of peo-ple’s quality of life and to allow a better under-standing of its determinants (including peo-ple’s objective conditions). National statisticalagencies should incorporate questions on sub-jective well-being in their standard surveys tocapture people’s life evaluations, hedonic expe-riences and life priorities.”5

The OECD Guidelines go on to recommend acore module of questions to be used by nationalstatistical agencies in their household surveys:

“There are two elements to the core measuresmodule.

The first is a primary measure of life evaluation.This represents the absolute minimum re-quired to measure subjective well-being, and itis recommended that all national statisticalagencies include this measure in one of their

annual household surveys.

The second element consists of a short series ofaffect questions and an experimental eudaimon-ic question (a question about life meaning orpurpose). The inclusion of these measures com-plements the primary evaluative measure bothbecause they capture different aspects of subjec-tive well-being (with a different set of drivers)and because the difference in the nature of themeasures means that they are affected in differ-ent ways by cultural and other sources of mea-surement error. While it is highly desirable thatthese questions are collected along with the pri-

mary measure as part of the core, these ques-tions should be considered a lower priority thanthe primary measure.”6

Almost all OECD countries7 now contain a lifeevaluation question, usually about life satisfac-tion, on a 0 to 10 rating scale, in one or more oftheir surveys. However, it will be many years be-fore the accumulated efforts of national statisti-cal offices will produce as large a number ofcomparable country surveys as is now availablethrough the Gallup World Poll (GWP), which

has been surveying an increasing number ofcountries since 2005, and now includes almostall of the world’s population. The GWP containsone life evaluation as well as a range of positiveand negative experiential questions, includingseveral measures of positive and negative affect,mainly asked with respect to the previous day.In this chapter, we make primary use of the lifeevaluations, since they are, as we show in Table2.1, more international in their variation and aremore readily explained by life circumstances.

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based on life satisfaction answers in the WorldValues Survey (WVS). But this conclusion, basedon comparing two different surveys, unfortu-

nately combines survey and method differenceswith the effects of question wording. When itsubsequently became possible to ask bothquestions9 of the same respondents on thesame scales, as was the case in the GallupWorld Poll in 2007, it was shown that theestimated income effects and almost all otherstructural influences were identical, and a morepowerful explanation was obtained by using anaverage of the two answers.10

It was also believed at one time that whenquestions included the word “happiness” theyelicited answers that were less dependent onincome than were answers to life satisfactionquestions or the Cantril ladder. Evidence for thatview was based on comparing World ValuesSurvey happiness and life satisfaction answers,11 and by comparing the Cantril ladder with happi-ness yesterday (and other emotions yesterday).Both types of comparison showed the effects ofincome on the happiness answers to be less

significant than on satisfaction with life or theCantril ladder. Both conclusions were based onthe use of non-comparable data. The first com-parison, using WVS data, involved differentscales and a question about happiness thatmight have combined emotional and evaluativecomponents. The second strand of literature,based on GWP data, compared happinessyesterday, which is an experiential/emotionalresponse, with the Cantril ladder, which isequally clearly an evaluative measure. In thatcontext, the finding that income has morepurchase on life evaluations than on emotionsseems to have general applicability, and standsas an established result.12

But what if happiness is used as part of a lifeevaluation? That is, if respondents are askedhow happy, rather than how satisfied, they arewith their life as a whole? Would the use of“happiness” rather than “satisfaction” affect theinfluence of income and other factors on the

answers? For this important question, no defini-tive answer was available until the EuropeanSocial Survey (ESS) asked the same respondents

“satisfaction with life” and “happy with life”questions, wisely using the same 0 to 10 re-sponse scales. The answers showed that incomeand other key variables all have the same effectson the “happy with life” answers as on the“satisfied with life” answers, so much so thatonce again more powerful explanations comefrom averaging the two answers.

Another previously common view was thatchanges in life evaluations at the individual level

were largely transitory, returning to their base-line as people rapidly adapt to their circumstanc-es. This view has been rejected by four indepen-dent lines of evidence. First, average lifeevaluations differ significantly and systematical-ly among countries, and these differences aresubstantially explained by life circumstances.This implies that rapid and complete adaptationto different life circumstances does not takeplace. Second, there is evidence of long-standingtrends in the life evaluations of sub-populations

within the same country, further demonstratingthat life evaluations can be changed withinpolicy-relevant time scales.13 Third, even thoughindividual-level partial adaptation to major lifeevents is a normal human response, there isvery strong evidence of continuing influence onwell-being from major disabilities and unem-ployment, among other life events.14 The case ofmarriage is still under debate. Some recentresults using panel data from the UK havesuggested that people return to baseline levels oflife satisfaction several years after marriage, aresult that has been argued to support the moregeneral applicability of set points.15 However,subsequent research using the same data hasshown that marriage does indeed have long-last-ing well-being benefits, especially in protectingthe married from as large a decline in themiddle-age years that in many countries repre-sent a low-point in life evaluations.16 Fourth, andespecially relevant in the global context, arestudies of migration showing migrants to have

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average levels and distributions of life evalua-tions that resemble those of other residents oftheir new countries more than of comparable

residents in the countries from which they haveemigrated.17 This confirms that life evaluationsdo depend on life circumstances, and are notdestined to return to baseline levels as requiredby the set point hypothesis.

Why Use Life Evaluations for

International Comparisons of

the Quality of Life?

In each of the three previous World HappinessReport s we presented different ranges of datacovering most of the experiences and life evalua-tions that were available for a large number ofcountries. We were grateful for the breadth ofavailable information, and used it to deepen ourunderstanding of the ways in which experientialand evaluative reports are connected. Ourconclusion is that while experiential and evalua-tive measures differ from each other in waysthat help to understand and validate both, life

evaluations provide the most informative mea-sures for international comparisons becausethey capture the overall quality of life as a whole.

For example, experiential reports about happi-ness yesterday are well explained by events ofthe day being asked about, while life evaluationsmore closely reflect the circumstances of life as awhole. Most Americans sampled daily in theGallup-Healthways Well-Being Index Survey feelhappier on weekends, to an extent that depends

on the social context on and off the job. Theweekend effect disappears for those employed ina high trust workplace, who regard their superi-or more as a partner than a boss, and maintaintheir social life during weekdays.18

By contrast, life evaluations by the same respon-dents in that same survey show no weekendeffects.19 This means that when they are answer-ing the evaluative question about life as a whole,

people see through the day-to-day and hour-to-hour fluctuations, so that the answers they giveon weekdays and weekends do not differ.

On the other hand, although life evaluations donot vary by the day of week, they are much moreresponsive than emotional reports to differencesin life circumstances. This is true whether thecomparison is among national averages20 oramong individuals.21

Furthermore, life evaluations vary more betweencountries than do emotions. Thus almostone-quarter of the global variation in life evalua-tions is among countries, compared tothree-quarters among individuals in the samecountry. This one-quarter share for life evalua-tions is far more than for either positive affect(7 percent) or negative affect (4 percent). Thisdifference is partly due to the role of income,which plays a stronger role in life evaluationsthan in emotions, and is also very unequallyspread among countries. For example, morethan 40 percent of the global variation amonghousehold incomes is among nations rather

than among individuals within nations.22

These twin facts – that life evaluations varymuch more than do emotions across countries,and that these life evaluations are much morefully explained by life circumstances than areemotional reports– provide for us a sufficientreason for using life evaluations as our centralmeasure for making international compari-sons.23 But there is more. To give a central roleto life evaluations does not mean we need to

either ignore or downplay the important infor-mation provided by experiential measures. Onthe contrary, we see every reason to keep experi-ential measures of well-being, as well as mea-sures of life purpose, as important elements inour attempts to measure and understand subjec-tive well-being. This is easy to achieve, at least inprinciple, because our evidence continues tosuggest that experienced well-being and a senseof life purpose are both important influences on

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life evaluations, above and beyond the criticalrole of life circumstances. We shall providedirect evidence of this, and especially of the

importance of positive emotions, in Table 2.1.Furthermore, in Chapter 3 of World HappinessReport 2015 we gave experiential reports a centralrole in our analysis of variations of subjectivewell-being across genders, age groups, andglobal regions.

We would also like to be able to compare in-equality measures for life evaluations with thosefor emotions, but unfortunately that is notcurrently possible, since the Gallup World Poll

emotion questions all offer only yes and noresponses. Thus nothing can be said about theirdistribution beyond the national average sharesof yes and no answers. For life evaluations,however, there are 11 response categories, so weare able to contrast distribution shapes for eachcountry and region, and see how these evolve astime passes. We start by looking at the popula-tion-weighted global and regional distributionsof life evaluations, based on how respondentsrate their lives24.

In the rest of this report, Cantril ladder is theonly measure of life evaluations to be used, and“happiness” and “subjective well-being” are usedexchangeably. All the analysis on the levels orchanges of subjective well-being refers only tolife evaluations, specifically the Cantril ladder.

The Distribution of Happiness

around the WorldThe various panels of Figure 2.1 contain barcharts showing for the world as a whole, and foreach of 10 global regions, the distribution of the2012-2015 answers to the Cantril ladder questionasking respondents to value their lives today ona 0 to 10 scale, with the worst possible life as a 0and the best possible life as a 10.

In Table 2.1 we present our latest modeling ofnational average life evaluations and measuresof positive and negative affect (emotion) by

country and year. For ease of comparison, theTable has the same basic structure as Table 2.1 inthe World Happiness Report 2015 . The majordifference comes from the inclusion of data forlate 2014 and 2015, which increases by 144 (orabout 15 percent) the number of country-yearobservations.25 The resulting changes to theestimated equation are very slight.26 There arefour equations in Table 2.1. The first equationprovides the basis for constructing the sub-barsshown in Figure 2.2.

The equation explains national average lifeevaluations in terms of six key variables: GDPper capita, social support, healthy life expectan-cy, freedom to make life choices, generosity andfreedom from corruption.27 Taken together,these six variables explain almost three-quartersof the variation in national annual averageladder scores among countries, using data fromthe years 2005 to 2015. The model’s predictivepower is little changed if the year fixed effects in

the model are removed, falling from 74.1% to73.6% in terms of the adjusted r-squared.

Figure 2.1: Population-Weighted Distributions ofHappiness, 2012-2015 (Part 1)

.25

.15

0 1 2 3 4 5 6 7 8 9 10

.05

.2

.1

Mean = 5.353

SD = 2.243

World

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.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 7.125

SD = 2.016

Northern America & ANZ

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 6.578

SD = 2.329

Latin America & Caribbean

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 6.575

SD = 1.944

Western Europe

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 5.554

SD = 2.152

Central and Eastern Europe

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 5.502

SD = 2.073

Commonwealth of Independent States

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 5.363

SD = 1.963

Southeast Asia

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 5.288

SD = 2.000

East Asia

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 4.999

SD = 2.452

Middle East & North Africa

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 4.589

SD = 2.087

South Asia

.25

.1

.05

.3

.15

.35

.2

0 1 2 3 4 5 6 7 8 9 10

Mean = 4.370

SD = 2.115

Sub-Saharan Africa

Figure 2.1: Population-Weighted Distributions of Happiness, 2012-2015 (Part 2)

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The second and third columns of Table 2.1 usethe same six variables to estimate equations fornational averages of positive and negative affect,

where both are based on averages for answersabout yesterday’s emotional experiences. Ingeneral, the emotional measures, and especiallynegative emotions, are much less fully explainedby the six variables than are life evaluations. Butthe differences vary a lot from one circumstanceto another. Per-capita income and healthy lifeexpectancy have significant effects on life evalua-tions, but not, in these national average data, oneither positive or negative affect. The situationchanges when we consider social variables.

Bearing in mind that positive and negative affectare measured on a 0 to 1 scale, while life evalua-tions are on a 0 to 10 scale, social support can be

seen to have a similar proportionate effect onpositive and negative emotions as on life evalua-tions. Freedom and generosity have even largerinfluences on positive affect than on the ladder.Negative affect is significantly reduced by socialsupport, freedom, and absence of corruption.

In the fourth column we re-estimate the lifeevaluation equation from column 1, adding bothpositive and negative affect to partially imple-

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

Notes: This is a pooled OLS regression for a tattered panel explaining annual national average Cantril ladderresponses from all available surveys from 2005 to 2015. See Technical Box 2 for detailed information about eachof 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.

Dependent Variable

Independent Variable Cantril Ladder Positive Affect Negative Affect Cantril LadderLog GDP per capita 0.338 -0.002 0.011 0.341

(0.059)*** (0.009) (0.008) (0.058)***

Social support 2.334 0.253 -0.238 1.768(0.429)*** (0.052)*** (0.046)*** (0.417)***

Healthy life expectancy at birth 0.029 0.0002 0.002 0.028(0.008)*** (0.001) (0.001)* (0.008)***

Freedom to make life choices 1.056 0.328 -0.089 0.315(0.319)*** (0.039)*** (0.045)** (0.316)

Generosity 0.820 0.171 -0.011 0.429(0.276)*** (0.032)*** (0.030) (0.277)

Perceptions of corruption -0.579 0.033 0.092 -0.657(0.282)** (0.030) (0.025)*** (0.271)**

Positive affect 2.297(0.443)***

Negative affect 0.050(0.506)

Year fixed effects Included Included Included Included

Number of countries 156 156 156 156

Number of observations 1,118 1,115 1,117 1,114

Adjusted R-squared 0.741 0.497 0.226 0.765

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

1. GDP per capita is in terms of PurchasingPower Parity (PPP) adjusted to constant 2011international dollars, taken from the WorldDevelopment Indicators (WDI) released bythe World Bank in December 2015. See theappendix for more details. GDP data for 2015are not yet available, so we extend the GDPtime series from 2014 to 2015 using coun-try-specific forecasts of real GDP growth fromthe OECD Economic Outlook No. 98 (Edition2015/2) and World Bank’s Global EconomicProspects (December 2014 release), after ad-justment for population growth. The equa-tion uses the natural log of GDP per capita,since that form fits the data significantly bet-ter than does GDP per capita.

2. The time series of healthy life expectancy atbirth are constructed based on data from theWorld Health Organization (WHO) and theWorld Development Indicators (WDI). WHOpublishes the data on healthy life expectancyfor the year 2012. The time series of life ex-pectancies, with no adjustment for health,are available in WDI. We adopt the followingstrategy to construct the time series of healthylife expectancy at birth: first we generate theratios of healthy life expectancy to life expec-tancy in 2012 for countries with both data.We then apply the country-specific ratios toother years to generate the healthy life expec-tancy data. See the appendix for more details.

3. Social support (or having someone to counton in times of trouble) is the national averageof the binary responses (either 0 or 1) to theGallup World Poll (GWP) question “If youwere in trouble, do you have relatives orfriends you can count on to help you whenev-er you need them, or not?”

4. Freedom to make life choices is the nationalaverage of binary responses to the GWPquestion “Are you satisfied or dissatisfiedwith your freedom to choose what you dowith your life?”

5. Generosity is the residual of regressing thenational average of GWP responses to thequestion “Have you donated money to a char-ity in the past month?” on GDP per capita.

6. Perceptions of corruption are the average ofbinary answers to two GWP questions: “Iscorruption widespread throughout the gov-ernment or not” and “Is corruption wide-spread within businesses or not?” Where datafor government corruption are missing, theperception of business corruption is used asthe overall corruption-perception measure.

7. Positive affect is defined as the average of pre-vious-day affect measures for happiness,laughter and enjoyment for GWP waves 3-7(years 2008 to 2012, and some in 2013). It isdefined as the average of laughter and enjoy-ment for other waves where the happinessquestion was not asked.

8. Negative affect is defined as the average ofprevious-day affect measures for worry, sad-ness and anger for all waves. See the appen-dix for more details.

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ment the Aristotelian presumption that sus-tained positive emotions are important supportsfor a good life.28 The most striking feature is the

extent to which the results buttress a finding inpsychology, that the existence of positive emo-tions matters much more than the absence ofnegative ones. Positive affect has a large andhighly significant impact in the final equation ofTable 2.1, while negative affect has none.

As for the coefficients on the other variables inthe final equation, the changes are material onlyon those variables – especially freedom andgenerosity – that have the largest impacts on

positive affect. Thus we can infer first thatpositive emotions play a strong role in supportof life evaluations, and second that most of theimpact of freedom and generosity on life evalua-tions is mediated by their influence on positiveemotions. That is, freedom and generosity havea large impact on positive affect, which in turnhas an impact on life evaluations. The GallupWorld Poll does not have a widely availablemeasure of life purpose to test whether it toowould play a strong role in support of high life

evaluations. However, data from the largesamples of UK data now available does suggestthat life purpose plays a strongly supportive role,independent of the roles of life circumstancesand positive emotions.

Ranking of Happiness by Country

Figure 2.2 (below) shows the average ladderscore (the average answer to the Cantril ladder

question, asking people to evaluate the quality oftheir current lives on a scale of 0 to 10) for eachcountry, averaged over the years 2013-2015. Notevery country has surveys in every year; the totalsample sizes are reported in the statisticalappendix, and are reflected in Figure 2.2 by thehorizontal lines showing the 95 percent confi-dence regions. The confidence regions aretighter for countries with larger samples. Toincrease the number of countries ranked, wealso include four countries that had no 2013-

2015 surveys, but did have a survey in 2012. Thisbrings the number of countries shown in Figure2.2 to 157.

The length of each overall bar represents theaverage score, which is also shown in numerals.The rankings in Figure 2.2 depend only onthe average Cantril ladder scores reported bythe respondents.

Each of these bars is divided into seven seg-ments, showing our research efforts to findpossible sources for the ladder levels. The firstsix sub-bars show how much each of the six keyvariables is calculated to contribute to thatcountry’s ladder score, relative to that in ahypothetical country called Dystopia, so namedbecause it has values equal to the world’slowest national averages for 2013-2015 for eachof the six key variables used in Table 2.1. Weuse Dystopia as a benchmark against which tocompare each other country’s performance interms of each of the six factors. This choice ofbenchmark permits every real country to have anon-negative contribution from each of the six

factors. We calculate, based on estimates inTable 2.1, a 2013–2015 ladder score in Dystopiato have been 2.33 on the 10-point scale. Thefinal sub-bar is the sum of two components: thecalculated average 2013-2015 life evaluation inDystopia (=2.33) and each country’s own predic-tion error, which measures the extent to whichlife evaluations are higher or lower than pre-dicted by our equation in the first column ofTable 2.1. The residuals are as likely to benegative as positive.29

Returning to the six sub-bars showing thecontribution of each factor to each country’saverage life evaluation, it might help to show inmore detail how this is done. Taking the exam-ple of healthy life expectancy, the sub-bar forthis factor in the case of India is equal to theamount by which healthy life expectancy inIndia exceeds the world’s lowest value, multi-plied by the Table 2.1 coefficient for the influ-

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ence of healthy life expectancy on life evalua-tions. The width of these different sub-bars thenshows, country-by-country, how much each of

the six variables is estimated to contribute toexplaining the international ladder differences.These calculations are illustrative rather thanconclusive, for several reasons. First, the selec-tion of candidate variables was restricted bywhat is available for all these countries. Tradi-tional variables like GDP per capita and healthylife expectancy are widely available. But mea-sures of the quality of the social context, whichhave been shown in experiments and nationalsurveys to have strong links to life evaluations,

have not been sufficiently surveyed in theGallup or other global polls, or otherwise mea-sured in statistics available for all countries.Even with this limited choice, we find that fourvariables covering different aspects of the socialand institutional context – having someone tocount on, generosity, freedom to make lifechoices and absence of corruption – are togeth-er responsible for 50 percent of the averagedifferences between each country’s predictedladder score and that in Dystopia in the 2013-2015 period. As shown in Table 13 of the Statisti-cal Appendix, the average country has a 2013-2015 ladder score that is 3.05 points above theDystopia ladder score of 2.33. Of the 3.05 points,the largest single part (31 percent) comes fromGDP per capita, followed by social support (26percent) and healthy life expectancy (18 per-cent), and then by freedom (12 percent), gener-osity (8 percent) and corruption (5 percent).30

Our limited choice means that the variables weuse may be taking credit properly due to otherbetter variables, or to un-measurable otherfactors. There are also likely to be vicious orvirtuous circles, with two-way linkages amongthe variables. For example, there is much evi-dence that those who have happier lives arelikely to live longer, to be most trusting, morecooperative, and generally better able to meetlife’s demands.31 This will feed back to influencehealth, GDP, generosity, corruption, and thesense of freedom. Finally, some of the variables

are derived from the same respondents as thelife evaluations, and hence possibly determinedby common factors. This risk is less using

national averages, because individual differencesin personality and many life circumstances tendto average out at the national level.

The seventh and final segment is the sum of twocomponents. The first is a fixed baseline num-ber representing our calculation of the ladderscore for Dystopia (=2.33). The second compo-nent is the average 2013-2015 residual for eachcountry. The sum of these two componentscomprises the right-hand sub-bar for each

country; it varies from one country to the nextbecause some countries have life evaluationsabove their predicted values, and others lower.The residual simply represents that part of thenational average ladder score that is not ex-plained by our model; with the residual includ-ed, the sum of all the sub-bars adds up to theactual average life evaluations on which therankings are based.

What do the latest data show for the 2013-2015

country rankings? Two main facts carry overfrom the previous editions of the World Happi-ness Report . First, there is a lot of year-to-yearconsistency in the way people rate their lives indifferent countries. Thus there remains a four-point gap between the 10 top-ranked and the 10bottom-ranked countries. The top 10 countriesin Figure 2.2 are the same countries that weretop-ranked in World Happiness Report 2015 ,although there has been some swapping ofplaces, as is to be expected among countries so

closely grouped in average scores. Denmark, forexample, was ranked first in World HappinessReport 2013, third in World Happiness Report 2015 ,and now first again in World Happiness Report2016 Update. In Figure 2.2, the average ladderscore differs only by 0.24 points between the topcountry and the 10th country. The 10 countrieswith the lowest average life evaluations arelargely the same countries as in the 2015 rank-ing (identical in the case of the bottom 6).Compared to the top 10 countries in the current

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Figure 2.2: Ranking of Happiness 2013-2015 (Part 2)

0 1 2 3 4 5 6 7 8

54. Kazakhstan (5.919)

55. Moldova (5.897)56. Russia (5.856)

57. Poland (5.835)

58. South Korea (5.835)

59. Bolivia (5.822)

60. Lithuania (5.813)

61. Belarus (5.802)

62. North Cyprus (5.771)

63. Slovenia (5.768)

64. Peru (5.743)

65. Turkmenistan (5.658)

66. Mauritius (5.648)

67. Libya (5.615)

68. Latvia (5.560)

69. Cyprus (5.546)

70. Paraguay (5.538)71. Romania (5.528)

72. Estonia (5.517)

73. Jamaica (5.510)

74. Croatia (5.488)

75. Hong Kong (5.458)

76. Somalia (5.440)

77. Kosovo (5.401)

78. Turkey (5.389)

79. Indonesia (5.314)

80. Jordan (5.303)

81. Azerbaijan (5.291)

82. Philippines (5.279)

83. China (5.245)

84. Bhutan (5.196)

85. Kyrgyzstan (5.185)

86. Serbia (5.177)

87. Bosnia and Herzegovina (5.163)

88. Montenegro (5.161)

89. Dominican Republic (5.155)

90. Morocco (5.151)

91. Hungary (5.145)

92. Pakistan (5.132)

93. Lebanon (5.129)

94. Portugal (5.123)

95. Macedonia (5.121)

96. Vietnam (5.061)

97. Somaliland region (5.057)

98. Tunisia (5.045)

99. Greece (5.033)

100. Tajikistan (4.996)

101. Mongolia (4.907)102. Laos (4.876)

103. Nigeria (4.875)

104. Honduras (4.871)

105. Iran (4.813)

106. Zambia (4.795)

Explained by: GDP per capita

Explained by: social support

Explained by: healthy life expectancy

Explained by: freedom to make life choices

Explained by: generosity

Explained by: perceptions of corruption

Dystopia (2.33) + residual

95% confidence interval

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Figure 2.2: Ranking of Happiness 2013-2015 (Part 3)

0 1 2 3 4 5 6 7 8

Explained by: GDP per capita

Explained by: social support

Explained by: healthy life expectancy

Explained by: freedom to make life choices

Explained by: generosity

Explained by: perceptions of corruption

Dystopia (2.33) + residual

95% confidence interval

107. Nepal (4.793)

108. Palestinian Territories (4.754)109. Albania (4.655)

110. Bangladesh (4.643)

111. Sierra Leone (4.635)

112. Iraq (4.575)

113. Namibia (4.574)

114. Cameroon (4.513)

115. Ethiopia (4.508)

116. South Africa (4.459)

117. Sri Lanka (4.415)

118. India (4.404)

119. Myanmar (4.395)

120. Egypt (4.362)

121. Armenia (4.360)

122. Kenya (4.356)

123. Ukraine (4.324)124. Ghana (4.276)

125. Congo (Kinshasa) (4.272)

126. Georgia (4.252)

127. Congo (Brazzaville) (4.236)

128. Senegal (4.219)

129. Bulgaria (4.217)

130. Mauritania (4.201)

131. Zimbabwe (4.193)

132. Malawi (4.156)

133. Sudan (4.139)

134. Gabon (4.121)

135. Mali (4.073)

136. Haiti (4.028)

137. Botswana (3.974)

138. Comoros (3.956)

139. Ivory Coast (3.916)

140. Cambodia (3.907)

141. Angola (3.866)

142. Niger (3.856)

143. South Sudan (3.832)

144. Chad (3.763)

145. Burkina Faso (3.739)

146. Uganda (3.739)

147. Yemen (3.724)

148. Madagascar (3.695)

149. Tanzania (3.666)

150. Liberia (3.622)

151. Guinea (3.607)

152. Rwanda (3.515)

153. Benin (3.484)

154. Afghanistan (3.360)155. Togo (3.303)

156. Syria (3.069)

157. Burundi (2.905)

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ranking, there is a much bigger range of scorescovered by the bottom 10 countries. Within thisgroup, average scores differ by as much as 0.8points, or 24 percent of the average nationalscore in the group. Second, despite this generalconsistency and stability, many countries havehad, as we shall show later in more detail,substantial changes in average scores, and hencein country rankings, between 2005-2007 and2013-2015.

When looking at the average ladder scores, it isimportant to note also the horizontal whiskerlines at the right hand end of the main bar foreach country. These lines denote the 95 percentconfidence regions for the estimates, and coun-tries with overlapping errors bars have scoresthat do not significantly differ from each other.Thus it can be seen that the four top-rankedcountries (Denmark, Switzerland, Iceland, andNorway) have overlapping confidence regions,

and all have national average ladder scores of 7.5or slightly above. The next five countries (Fin-land, Canada, Netherlands, New Zealand andAustralia) all have overlapping confidenceregions and average ladder scores between 7.3and 7.4, while the next two (Sweden and Israel)have almost identical averages just below 7.3.

The 10 countries with the lowest ladder scores2013-2015 all have averages below 3.7. They spana range more than twice as large as do the 10 topcountries, with the two lowest countries havingaverages of 3.1 or lower. Eight of the 10 are insub-Saharan Africa, while the remaining two arewar-torn countries in other regions (Syria in theMiddle East and Afghanistan in South Asia).

Average life evaluations in the top 10 countriesare more than twice as high as in the bottom 10,7.4 compared to 3.4. If we use the first equationof Table 2.1 to look for possible reasons for these

Technical Box 3: Changes in Gallup World Poll research methods

As part of Gallup’s effort to continue to improveits research methods and global coverage, therehave been changes to the World Poll’s methodsover time that may have an impact on the happi-ness data.

In 2013, Gallup changed from face-to-face inter-viewing to telephone surveying (both cell phoneand landline) in Malaysia, the United ArabEmirates, Saudi Arabia, Qatar, Kuwait, Bahrain,and Iraq. In addition, Gallup added interviewsin English as a language of interview in additionto Arabic in the United Arab Emirates, SaudiArabia, Qatar, Kuwait and Bahrain in an effortto reach the large, non-Arab expatriate popula-tion. Due to the three-year rolling average, thisis the first report to no longer include face-to-face data from those countries. In addition, Gal-lup switched from face-to-face interviewing totelephone interviewing in Turkey in 2014. Cau-

tion should be used when comparing these dataacross time periods.

The United Arab Emirates was especially affect-ed by the changes in survey methods, in part be-cause of its newly sampled non-Emirati popula-tion. This has caused its ranking to drop fortechnical reasons unrelated to life in the UAE.Where the expatriate population is very large, itcomes to dominate the overall averages based onthe total resident population. The UAE providesa good example case, as it has the largest popula-tion share of expatriates among the Gallup coun-tries, and has sample sizes large enough to makea meaningful comparison. Splitting the UAEsample into two groups would give a 2013-2015Emirati ladder average of 7.06 (ranking 15th inFigure 2.2), and a non-Emirati average 6.48(ranking 31st), very close to the overall average of6.57 (ranking 28th.)

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very different life evaluations, it suggests that ofthe 4 point difference, 3 points can be traced todifferences in the six key factors: 1.13 points

from the GDP per capita gap, 0.8 due to differ-ences in social support, 0.5 to differences inhealthy life expectancy, 0.3 to differences infreedom, 0.2 to differences in corruption, and0.13 to differences in generosity. Income differ-ences are more than one-third of the totalexplanation because, of the six factors, income isthe most unequally distributed among countries.GDP per capita is 25 times higher in the top 10than in the bottom 10 countries.32

Overall, the model explains quite well the lifeevaluation differences within as well as betweenregions and for the world as a whole.33 However,on average the countries of Latin America haveaverage life evaluations that are higher (by about0.6 on the 10 point scale) than predicted by themodel. This difference has been found in earlierwork, and variously been considered to repre-sent systematic personality differences, someunique features of family and social life in Latincountries, or some other cultural differences.34

In partial contrast, the countries of East Asiahave average life evaluations below those pre-dicted by the model, a finding that has beenthought to reflect, at least in part, culturaldifferences in response style. It is also possiblethat both differences are in substantial measuredue to the existence of important excludedfeatures of life that are more prevalent in thosecountries than elsewhere.35 It is reassuring thatour findings about the relative importance of thesix factors are generally unaffected by whetheror not we make explicit allowance for theseregional differences.36

Changes in the Levels of Happiness

In this section we consider how life evaluationshave changed. For life evaluations, we considerthe changes from 2005-2007, before the onsetof the global recession, to 2013-2015, the mostrecent three-year period for which data from the

Gallup World Poll are available. We present firstthe changes in average life evaluations.

In Figure 2.3 we show the changes in happinesslevels for all 126 countries having sufficientnumbers of observations for both 2005-2007and 2013-2015.37

Of the 126 countries with data for 2005-2007and 2013-2015, 55 had significant increases,ranging from 0.13 to 1.29 points on the 0 to 10scale, while 45 showed significant decreases,ranging from -0.12 to -1.29 points, with theremaining 26 countries showing no significantchange. Among the 20 top gainers, all of whichshowed average ladder scores increasing by 0.50or more, eight are in the Commonwealth ofIndependent States and Eastern Europe, sevenin Latin America, two in sub-Saharan Africa,Thailand and China in Asia, and Macedonia inWestern Europe. Among the 20 largest losers,all of which showed ladder reductions of 0.44 ormore, five were in the Middle East and NorthAfrica, five were in sub-Saharan Africa, fourwere in Western Europe, three in Latin America

and the Caribbean, two in Asia and one in theCommonwealth of Independent States.

These gains and losses are very large, especiallyfor the 10 most affected gainers and losers. Foreach of the 10 top gainers, the average lifeevaluation gains exceeded those that would beexpected from a doubling of per capita incomes.For each of the 10 countries with the biggestdrops in average life evaluations, the losses weremore than would be expected from a halving of

GDP per capita. Thus the changes are far morethan would be expected from income losses orgains flowing from macroeconomic changes,even in the wake of an economic crisis as largeas that following 2007.

On the gaining side of the ledger, the inclusionof four Latin American countries among the top10 gainers is emblematic of broader LatinAmerican experience. The analysis in Figure

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3.10 of Chapter 3 of World Happiness Report 2015showed that Latin Americans in all age groupsreported substantial and continuing increases in

life evaluations between 2007 and 2013. Fivetransition countries are also among the top 10gainers, matching the rising average life evalua-tions for the transition countries taken as agroup. The appearance of sub-Saharan Africancountries among the biggest gainers and the big-gest losers reflects the variety and volatility ofexperiences among the 25 sub-Saharan coun-tries for which changes are shown in Figure 2.3.

The 10 countries with the largest declines in

average life evaluations typically suffered somecombination of economic, political and socialstresses. Three of the countries (Greece, Italyand Spain) were among the four hard-hit euro-zone countries whose post-crisis experience wasanalyzed in detail in World Happiness Report2013. A series of recent annual declines has nowpushed Ukraine into the group of 10 largesthappiness declines, joining India, Venezuela,Saudi Arabia, two North African countries,Egypt and Yemen, and Botswana.

Looking at the list as a whole, and not just at thelargest gainers and losers, what were the circum-stances and policies that enabled some countriesto navigate the recession, in terms of happiness,better than others? The argument was made inWorld Happiness Report 2013 and World HappinessReport 2015 that the strength of the underlyingsocial fabric, as represented by levels of trust andinstitutional quality, affects a society’s resiliencein response to economic and social crises. We

gave Greece, which remains the biggest happi-ness loser in Figure 2.3 (improved from WorldHappiness Report 2015 , but still almost 1.3 pointsdown from 2005-2007 to 2013-2015), specialattention, because the well-being losses were somuch greater than could be explained directly byeconomic outcomes. The report provided evi-dence of an interaction between social capitaland economic or other crises, with the crisisproviding a test of the quality of the underlyingsocial fabric.38 If the fabric is sufficiently strong,

then the crisis may even lead to higher subjec-tive well-being, in part by giving people a chanceto work together towards good purpose, and to

realize and appreciate the strength of theirmutual social support; and in part because thecrisis will be better handled and the underlyingsocial capital improved in use.

For this argument to be convincing requiresexamples on both sides of the ledger. It is onething to show cases where the happiness losseswere very big and where the erosion of the socialfabric appeared to be a part of the story. But whatexamples are there on the other side? With

respect to the post-2007 economic crisis, thebest examples of happiness maintenance in theface of large external shocks are Ireland andespecially Iceland. Both suffered decimation oftheir banking systems as extreme as anywhere,and yet have suffered incommensurately smallhappiness losses. In the Icelandic case, thepost-shock recovery in life evaluations has beengreat enough to put Iceland third in the globalrankings for 2013-2015. That there is a continu-ing high degree of social support in both coun-

tries is indicated by the fact that of all the coun-tries surveyed by the Gallup World Poll, thepercentage of people who report that they havesomeone to count on in times of crisis is excep-tionally high in Iceland and Ireland.39

If the social context is important for happi-ness-supporting resilience under crisis, it islikely to be equally applicable for non-economiccrises. There is now research showing that levelsof trust and social capital in the Fukushima

region of Japan were sufficient that the GreatEast Japan Earthquake of 2011 actually led toincreased trust and happiness in the region.40 The happiness effects of crisis response mayalso be mediated through generosity triggeredby a large natural disaster, with the additionalgenerosity adding to happiness.41

What can be learned by using the six-variableexplanation of Table 2.1 to explain happiness

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changes between 2005-2007 and 2013-2015 incountries and global regions? We have per-formed this exercise on a population-weighted

basis to compare actual and predicted regionalchanges in happiness, and find that the equationprovides a significant part of the story, whileleaving lots of remaining puzzles. As shown inTable 31 of the Statistical Appendix, the modeldoes best in explaining the average increase of0.4 points in the Commonwealth of Indepen-dent States, and the average decreases of 0.23points in Western Europe and North America &ANZ countries. For the Commonwealth ofIndependent States, the gains arise from im-

provements in all six variables. For WesternEurope, meanwhile, expected gains from im-provements in healthy life expectancy andcorruption combined with no GDP growth anddeclines in the other three variables to explainmore than half of the actual change of 0.23points. The largest regional drop (-0.6 points)was in South Asia, in which India has by far thelargest population share, and is unexplained bythe model, which shows an expected gain basedon improvements in five of the six variables,offset by a drop in social support.

The same framework can be used to try toexplain the changes for the two groups of 10countries, the biggest gainers and the biggestlosers. For the group of 10 countries with thelargest gains, on average they had increases inall six variables, to give an expected gain of 0.29points, compared to the actual average increaseof 0.9 points.42 For the group of 10 countrieswith the largest drops, GDP per capita was onaverage flat, expected gains in healthy lifeexpectancy (which are driven by long termtrends not responsive to current life circum-stances) were offset by worsening in each of thefour social variables, with the biggest predicteddrops coming from lower social support andlosses in perceived freedom to make life choices.Of the average loss equal to 0.8 points, 0.17 waspredicted by the partially offsetting effects fromchanges in the six variables.

The World Happiness Report 2015 also consideredevidence that good governance has enabledcountries to sustain or improve happiness

during the economic crisis. Results presentedthere suggested not just that people are moresatisfied with their lives in countries with bettergovernance, but also that actual changes ingovernance quality since 2005 have led tosignificant changes in the quality of life.43 Forthis report we have updated that analysis usingan extended version of the model that includescountry fixed effects, and hence tries to explainthe changes going on from year to year in eachcountry. Our new results, as shown in Table 11 of

the Statistical Appendix, show GDP per capitaand changes in governmental quality to haveboth contributed significantly to changes in lifeevaluations over the 2005 to 2015 period.

Inequality and Happiness

The basic argument in this section is that in-equality is best measured by looking at thedistribution of life evaluations across those with

very low, medium and high evaluations. If it istrue, as we have argued before, that subjectivewell-being provides a broader and more inclu-sive measure of the quality of life than doesincome, then so should the inequality of subjec-tive well-being provide a more inclusive andmeaningful measure of the distribution ofwell-being among individuals within a society.

However, although there has been increasingand welcome attention in recent years to ques-

tions of distribution and inequality, that atten-tion has been almost entirely focused on thenature and consequences of economic equality,especially the distribution of income andwealth. The United Nations,44 the World Bank,45 and the OECD46 have produced reports recentlyon the risks of rising economic inequality, andseveral prominent researchers have publishedrecent books.47 All have concentrated on thesources and consequences of economic inequal-ity, principally relating to the distribution of

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income and wealth. There have also beenstudies of inequality of health care and out-comes48, access to education, and equality of

opportunity49 more generally.

Much has and can be learned from these studiesof inequality in different aspects of life. Butwould it not be helpful to have a measure ofdistribution that has some capacity to bring thedifferent facets of inequality together, and toassess their joint consequences? Just as we haveargued that subjective well-being provides abroader and more appropriate measure ofhuman progress, so does the distribution of

happiness provide a parallel and better measureof the consequences of any inequalities in thedistribution of key variables, e.g. incomes, health,education, freedom and justice, that underpinthe levels and distribution of human happiness.

In the middle of the 20th Century, Simon Kuznetssurveyed data from economic history over thepreceding decades to expose a pattern wherebyeconomic inequality would increase in the earlystages of industrialization, principally driven by

the transfer of some workers from lower-paidrural to higher paid urban industrial jobs.50 Hehypothesized that when this transfer was largelyaccomplished, attention would turn, as it did inmany industrial countries in the middle decadesof the 20th century, to the design of social safetynets, and more widely available health care andeducation, intended to spread the benefits ofeconomic growth more evenly among the popula-tion. Thus the so-called Kuznets curve, witheconomic inequality at first growing and then

declining as economic growth proceeds. Amongthe industrial countries of the OECD, that patternwas largely in evidence for the first three-quartersof the 20th Century. But then, for reasons that arevaried and still much debated,51 the inequality ofincomes and wealth has grown significantly inmost of these same countries. The OECD esti-mates that during the period from the mid-1980sto 2013, income inequality grew significantly in 17of 22 countries studied, with only one countryshowing a significant decrease.52

For the majority of the world’s population livingoutside the OECD countries, economic growthand industrialization has happened much later.

This might suggest, if the Kuznets analysis werestill to hold, that income inequality would havekept growing for longer before turning around.This appears to have been the case, with theUnited Nations reporting that for most countriesin the world income inequality rose from 1980to 2000 and then fell between then and 2010.53 World Bank data for subsequent changes inwithin-nation income inequality are still ratherpatchy, and show a mixed picture from which itis too early to construct a meaningful average.54

What are the consequences of inequality forsubjective well-being? There are argumentsboth ethical and empirical suggesting thathumans are or at least ought to be happier tolive where there is more equality of opportuni-ties and generally of outcomes as well. Beyondsuch direct links between inequality and subjec-tive well-being, income inequalities have beenargued to be responsible for damage to otherkey supports for well-being, including social

trust, safety, good governance, and both theaverage quality of and equal access to healthand education, - important, in turn, as supportsfor future generations to have more equalopportunities. Others have paid more directattention to inequalities in the distribution ofvarious non-income supports to well-being,without arguing that these inequalities weredriven by income inequality.

If we are right to argue that broadening the

policy focus from GDP to happiness should alsoentail broader measures of inequality, and if it istrue that people are happier living in more equalsocieties, then we should expect to find thatwell-being inequality is a better predictor ofaverage well-being levels than is the inequality ofincome. Comparative evidence on the relativeinformation content of different measures ofinequality is relatively scarce. For internationalcomparison of the prevalence of poverty, animportant channel though which inequality

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affects well-being, it has been argued thatpeople’s own subjective assessments of thequality of their lives, including access to food

and other essential supports, should supplementand may even be preferable to the constructionof poverty estimates based on the comparison ofmoney incomes.55

Thus the broader availability and possibly morerelevant measurement of well-being inequalitiesshould help them to perform better as factorsexplaining life evaluations. There is, however,only a short span of historical data available forsuch comparisons. One recent study, based on

data from the World Values Survey and paneldata from several industrial countries, reportedevidence of a ‘great moderation’ in the inequali-ty of well-being, with downward trends evidentin most countries.56 That was argued to repre-sent a favorable outcome, on the assumptionthat most people would prefer more equality.The data we shall present later on recent trendsin well-being inequality suggest a less sanguineview. Countries with significantly greater in-equality of life evaluations in the 2012-2015

period, compared to the 2005-2011 base period,are five times more numerous than countrieswith downward trends.

A companion research paper57 compares incomeinequality (as measured by the Gini coefficient)with well-being inequality (measured by thestandard deviation of the distribution of lifeevaluations), as predictors of life evaluations,making use of three international surveys andone large domestic US survey. In each case

well-being inequality is estimated to have astronger negative impact of life evaluations thandoes the inequality of income. To buttress thisevidence, which is subject to the possibilities ofmeasurement bias arising from the limitednumber of response categories, two ancillarytests were run. First, it was confirmed that theestimated effects of well-being inequality aregreater for those individuals who said they wishto see inequalities reduced. 58 A second testmade use of the established indirect linkage run-

ning from inequality to reduced social trust,with subsequent implications for well-being. Ifwell-being inequality is a better umbrella mea-

sure of inequality than income inequality, thenit might also be expected to be a better predictorof social trust. This is an especially appropriatetest since the inequality of income has been along-established explanation for internationaldifferences in social trust, 59 and several formsof trust have been found to provide strongsupport for subjective well-being. 60 In all threeinternational surveys, trust was better predictedby a country’s inequality of life evaluations thanby its inequality of incomes.61 These auxiliary

tests provide assurance that there are likely tobe real effects running, both directly and indi-rectly, from well-being inequality to the level ofwell-being.

We have also tested the inequality of lifeevaluations and the inequality of income in thecontext of the equation of Table 2.1, and find asignificant negative effect running from theinequality of well-being to average life evalua-tions.62 The effects from income inequality are

mixed, depending on which measure is used.63

The strongest equations come from using theinequality of life evaluations along with theinequality of incomes varying each year basedon the income data provided by the respon-dents to the Gallup World Poll. Both inequalitymeasures are associated with lower average lifeevaluations.64

Having presented evidence that the inequality ofwell-being deserves more attention, we turn now

to consider first the levels and then changes inthe standard deviation of life evaluations.65 Forthe levels, Figure 2.4 shows population-weightedregional estimates, and Figure 2.5 the nationalestimates for each country’s standard deviationsof ladder answers based on all available surveysfrom 2012-2015. In part because we combinedata from four years, to increase the samplesize, we are able to identify significant in-ter-country differences.66 The standard devia-tions are negatively correlated with the average

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inequality created in part by the banking boomand bust was erased in the subsequent recoveryof well-being, suggesting a high degree of social

resilience in Iceland.

The 10 countries with the largest increases inwell-being inequality have all been undergoingsignificant political, social and economic diffi-culties. To what extent these inequality increasescan be explained by changes in the underlyinginequalities of income, social supports, health,generosity, corruption, freedom cannot beestimated on the basis of data currently avail-able. This is because many of the key variables

are not yet measured using scales with sufficientnumbers of categories to permit measures oftheir inequality to be computed. Thus thereremains much to be learned. It is perhapsenough, at this stage, to have made the case fortaking well-being inequality seriously, and tohave provided evidence on its levels and trendsin nations, regions, and the world.

Summary and Conclusions

In presenting and explaining the national-leveldata in this chapter, we make primary use ofpeople’s own reports of the quality of their lives,as measured on a scale with 10 representing thebest possible life and 0 the worst. We averagetheir reports for the years 2013 to 2015, provid-ing a typical national sample size of 3,000. Wethen rank these data for 157 countries, as shownin Figure 2.2. The 10 top countries are onceagain all small or medium-sized western indus-

trial countries, of which seven are in WesternEurope. Beyond the first ten, the geographyimmediately becomes more varied, with thesecond 10 including countries from four of the10 global regions.

In the top 10 countries, life evaluations average7.4 on the 0 to 10 scale, while for the bottom 10the average is less than half that, at 3.4. Thelowest countries are typically marked by lowvalues on all of the six variables used here to

explain international differences – GDP percapita, healthy life expectancy, social support,freedom, generosity and absence of corruption –

and often subject in addition to violence anddisease. Of the 4-point gap between the 10 topand 10 bottom countries, more than three-quar-ters is accounted for by differences in the sixvariables, with GDP per capita, social support andhealthy life expectancy the largest contributors.

When we turn to consider life evaluation chang-es for 126 countries between 2005-2007 and2013-2015, we see lots of evidence of movement,including 55 significant gainers and 45 signifi-

cant losers. Gainers especially outnumber losersin Latin America, the Commonwealth of Inde-pendent States and Central and Eastern Europe.Losers outnumber gainers in Western Europeand to a lesser extent in sub-Saharan Africa,Middle East and North Africa. Changes in thesix key variables explain a significant proportionof these changes, although the magnitude andnatures of the crises facing nations since 2005have been such as to move some countries intopoorly charted waters. We continue to see

evidence that major crises have the potential toalter life evaluations in quite different waysaccording to the quality of the social and institu-tional infrastructure. In particular, as shown inWorld Happiness Report 2013 and World HappinessReport 2015 , there is evidence that a crisis im-posed on a weak institutional structure canactually further damage the quality of the sup-porting social fabric if the crisis triggers blameand strife rather than co-operation and repair.On the other hand, economic crises and naturaldisasters can, if the underlying institutions areof sufficient quality, lead to improvements ratherthan damage to the social fabric.71 These im-provements not only ensure better responses tothe crisis, but also have substantial additionalhappiness returns, since people place real valueto feeling that they belong to a caring andeffective community.

With respect to the inequality of well-being, asmeasured by the standard deviation of life

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evaluations within each country, we find that itvaries among countries quite differently fromaverage happiness, and from the inequality of

income. We have argued that just as subjectivewell-being provides a broader and more inclu-sive measure of the quality of life than doesincome, then so should the inequality of subjec-tive well-being provide a more inclusive andmeaningful measure of the distribution ofwell-being among individuals within a society.We then measured changes since the 2005-2011averages reported in the first World HappinessReport . We find, in contrast to some earlierevidence of global convergence in happiness

equality, that from the first to the second half ofour data there has been increased inequality ofhappiness within most countries, almost allregions, and for the world as a whole. Onlyone-tenth of countries showed a significantreduction in happiness inequality, while morethan half showed a significant increase. Theworld as a whole and 8 of 10 global regionsshowed significant increases in well-beinginequality from 2005-2011 to 2012-2015. We alsofound evidence that greater inequality of well-be-ing contributes to lower average well-being.

Discussions about the inequality of income andwealth, and what to do about them, typicallyinclude reference to the transfer of resourcesfrom richer to poorer to achieve greater equality.Increasing the equality of happiness does not ingeneral require transfer, since building happi-ness for some does not require reduction in thehappiness of others. Indeed, one of the sidebenefits of broadening the focus of policy atten-tion from income and wealth to subjectivewell-being is that there are many more optionsfor improving average happiness, and increasingequality by improving the lot of those at thebottom, without others being worse off.

Targeting the non-material sources of well-be-ing, which is encouraged by considering abroader measure of well-being, opens possibili-ties for increasing happiness while simultane-ously reducing stress on scarce material resourc-

es. Much more research is needed to fullyunderstand the interplay of factors that deter-mine the inequality of well-being, but there is

every hope that simply changing the focus fromincome inequality to well-being inequality willspeed the arrival of a time when the distributionof well-being can be improved, for the benefit ofcurrent and future generations in all countries.

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1 Diener, Lucas, & Oishi (2016) estimate the number of newscientific articles on subjective well-being to have grown byabout two orders of magnitude in the past 25 years, fromabout 130 per year in 1980 to almost 15,000 in 2014.

2 See OECD (2013).

3 As foreshadowed by an OECD case study in the first WHR,and more fully explained in the OECD Chapter in WHR2013. See Durand & Smith (2013).

4 See Ryff & Singer (2008). The first use of a question aboutlife meaning or purpose in a large-scale international surveywas in the Gallup World Poll waves of 2006 and 2007. Itwas also introduced in the third round of the EuropeanSocial Survey (Huppert et al. 2009). It has since becomeone of the four key well-being questions asked by the UKOffice for National Statistics (Hicks, Tinkler, & Allin, 2013).

5 Stiglitz, Sen, & Fitoussi (2009, p. 216).

6 OECD (2013, p. 164).

7 The latest OECD list

Documents

The World Happiness Report,2016