2024

John F. Helliwell, Richard Layard, Jeffrey D. Sachs,
Jan-Emmanuel De Neve, Lara B. Aknin, and Shun Wang

This publication may be reproduced using thefollowing reference: Helliwell, J. F., Layard, R., Sachs,
J. D., De Neve, J.-E., Aknin, L. B., & Wang, S. (Eds.). (2024).
World Happiness Report 2024.
University of Oxford: Wellbeing Research Centre.
Full text and supporting documentation
can be downloaded from the website:
worldhappiness.report
ISBN 978-1-7348080-7-0
The World Happiness Report is a partnership of Gallup, the Oxford Wellbeing
Research Centre, the UN Sustainable Development Solutions Network, and
the WHR’s Editorial Board. The report is produced under the editorial controlof the WHR Editorial Board.
From 2024, the World Happiness Report is a publication of the Wellbeing
Research Centre at the University of Oxford, UK.
Any views expressed in this report do not necessarily reflect the viewsof any organization, agency or program of the United Nations.

Table of Contents

World Happiness Report2024
1	Happiness and Age: Summary .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 3Helliwell, Layard, Sachs, De Neve, Aknin, and Wang
2	Happiness of the Younger, the Older, andThose In Between.  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 9Helliwell, Huang, Shiplett, and Wang
3	Child and Adolescent Well-Being:
Global Trends, Challenges and Opportunities.  .  .  .  .  .  .  .  . 61
Marquez, Taylor, Boyle, Zhou, and De Neve4	Supporting the Well-being of an Aging Global
Population: Associations between Well-being
and Dementia. .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 103
Britton, Hill, and Willroth
5	Differences in Life Satisfaction Among Older Adults
in India .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 129
Paul, Pai, Thalil, and Srivastava

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 necessarilyreflect the views of any organization, agency, or program of the United Nations.

This version last updated March 8, 2024. Please see worldhappiness.report for latest data.

Photo Janaya Dasiuk on Unsplash

Chapter 1

Happiness and Age:
Summary
John F. Helliwell
Vancouver School of Economics,
University of British Columbia
Richard Layard
Wellbeing Programme, Centre for Economic Performance,
London School of Economics and Political ScienceJeffrey D. Sachs
Center for Sustainable Development at Columbia University
Jan-Emmanuel De Neve
Wellbeing Research Centre, University of Oxford
Lara B. Aknin
Department of Psychology, Simon Fraser University
Shun Wang
International Business School Suzhou,
Xi’an Jiaotong-Liverpool University

doi.org/10.18724/whr-kk3m-b586
This version last updated March 8, 2024. Please see worldhappiness.report for latest data.

In the seven ages of man in
Shakespeare’s As You Like It,
the later stages of life are
portrayed as deeply depressing.
But happiness research showsa more nuanced picture, andone that is changing over time.

Photo Yusron El Jihan on Unsplash1World Happiness Report 2024

In this issue of the World Happiness Report we
focus on the happiness of people at different
stages of life. In the seven ages of man in
Shakespeare’s As You Like It, the later stagesof life are portrayed as deeply depressing. But
happiness research shows a more nuanced
picture, and one that is changing over time.

environmental and behavioral strategies that
improve the lives of those living with dementia.
Finally, Chapter 5 focuses on India, the first suchchapter in the World Happiness Report. It stresses
that in India, the world’s most populous country,with a rapidly growing elder share, happinessrises into old age,more so for men than women.

In the West, the received wisdom was that the
young are the happiest and that happinessthereafter declines until middle age, followed bysubstantial recovery. But since 2006-10, as weshall see, happiness among the young (aged15-24) has fallen sharply in North America – to apoint where the young are less happy than theold. Youth happiness has also fallen (but lesssharply) in Western Europe.

In what follows, we give summaries of eachchapter, which will hopefully tempt readers toread further.

Chapter 2
Happiness of the Younger, the Older,
and Those In Between
Overall rankings

By contrast, happiness at every age has risen
sharply in Central and Eastern Europe, so thatyoung people are now equally happy in bothparts of Europe. In the former Soviet Union andEast Asia too there have been large increases inhappiness at every age, while in South Asia andthe Middle East and North Africa happiness has
fallen at every age.

•	The top 10 countries have remained muchthe same since before COVID. Finland is still
top, with Denmark now very close, and all five
Nordic countries in the top 10. But in the next
10, there is more change, with the transition
countries of Eastern Europe rising in happiness(especially Czechia, Lithuania and Slovenia).
Partly for this reason the United Statesand Germany have fallen to 23 and 24 in
the rankings.

It is of course an issue to what extent thesechanges reflect generational changes that can be
expected to persist as each generation gets older.
In pioneering work, Chapter 2 disentangles theeffect of which cohort you are in from that of age.
At the global level, it reveals a lower level ofhappiness among people born since 1980.

Happiness by age group•	In many but not all regions, the young arehappier than the old. But in North America
happiness has fallen so sharply for the youngthat they are now less happy than the old. By
contrast, in the transition countries of Central
and Eastern Europe, the young are muchhappier than the old. In Western Europe as awhole happiness is similar at all ages, while
elsewhere it tends to decline over the life cycle(with an occasional upturn for the old).

One thing is the average level of happiness,
another is its dispersion. Since 2006-10, theinequality of happiness has increased in every
region except Europe – another worrying trend.
As usual, all these trends are discussed in
Chapter 2, together with the country rankings.
The position of the young is discussed in finer
detail in Chapter 3. This draws on a wide rangeof data sources and also includes data for young
people aged 10-15. The rest of the report focuseson the old. As Chapter 4 stresses, the greatest
plague in old age is dementia. Fortunately, newand accumulating research demonstrates thathigher well-being is a protective factor against
future dementia. In addition, there are significant•	For these reasons, the ranking of countries byhappiness is very different for the young andfor the old. As between generations, after
taking into account age and life circumstances,
those born before 1965 have life evaluations
about one-quarter of a point higher than thoseborn after 1980.

5

World Happiness Report 2024

Benevolence by generation

Changes in happiness since 2006-2010:
by age group•	The COVID crisis led to a worldwide increasein the proportion of people who have helped
others in need. This increase in benevolence has
been large for all generations, but especially sofor those born since 1980, who are even more
likely than earlier generations to help othersin need.

•	The countries of Central and Eastern Europe
have had the largest increase in happiness – bysimilar amounts in all age groups. The gains in
the former Soviet Union were half as large.
In East Asia too there were large increases,
especially among the old.
•	By contrast, happiness fell in South Asia in
all age groups. It also fell in North America,
especially among the young. And it fell in the
Middle East and North Africa in all age groups.

Social support, loneliness and social interactions•	In almost every global region comparably
measured feelings of social support are morethan twice as prevalent as loneliness. Bothsocial support and loneliness affect happiness,with social support usually having the larger
effect. Social interactions of all kinds also add
to happiness, in addition to their effects flowing
through increases in social support andreductions in loneliness.

•	In Central and Eastern Europe, the young are
now as happy as in Western Europe, and among
the old the gap between East and West is one
half of what it was in 2006-10, though still large(one whole point on the scale of 0 to 10).
Inequality of happiness•	Since 2006-10 there has been a large increasein the inequality of happiness in every region
except Europe. And it has increased especiallyfor the old. The biggest increase is in
Sub-Saharan Africa.

Chapter 3
Child and Adolescent Well-being:
Global Trends, Challenges andOpportunities

Negative emotions•	In most countries life satisfaction drops
gradually from childhood through adolescenceand into adulthood. Globally, young people
aged 15-24 still report higher life satisfaction
than older adults. But this gap is narrowing inWestern Europe and recently reversed in North
America due to falling life satisfaction among
the young. Conversely, in Sub-Saharan Africa
life satisfaction has increased among the young.

•	Negative emotions are more frequent now
than in 2006-2010 everywhere except in East
Asia and in Europe. In fact in Central andEastern Europe, negative emotions are now
less frequent in all age groups than they werein 2006-2010.
•	In 2021-2023 negative emotions were in every
region more prevalent for females than males.
Almost everywhere the gender gap is larger atolder ages.

•	Overall, globally, young people aged 15-24
experienced improved life-satisfaction between
2006 and 2019, and stable life satisfaction sincethen. But the picture varied by region. Youthwellbeing fell in North America, Western
Europe, Middle East and North Africa, andSouth Asia. In the rest of the world it rose.

Positive emotions•	In all regions the frequency of positive emotions
has changed since 2006-2010 in the same
direction as life evaluations. But the age patterns
differ. The frequency of positive emotions inevery region is highest for those under 30,
thereafter steadily declining with age in every
region except North America, where positive
emotions are least frequent for those in the
middle age groups.

•	Turning to younger ages (10-15), evidence islimited. In high income countries, life satisfaction
has declined since 2019, especially for girls. For
East Asian countries, life satisfaction increased
in 2019. Before 2019, the evidence on trends
is mixed.
6World Happiness Report 2024

Chapter 5
Differences in Life Satisfaction amongOlder Adults in India•	Girls report lower life satisfaction than boys
by around the age of 12. This gap widens atages 13 and 15, and the pandemic has amplified
the difference. These points apply only tohigh-income countries since data on theseyoung ages is rarely gathered elsewhere. Forages 15-24, global data shows no global genderdifferences from 2006 until 2013. But from2014, females began reporting higher life
satisfaction than males, although the gap has
narrowed after the pandemic. This global
gender gap masks regional differences, and ismore pronounced in lower-income countries.
There are no gender differences in high-income
countries.

•	Older age is associated with higher life
satisfaction in India, refuting some claims thatthe positive association between age and life
satisfaction only exists in high-income nations.
However, older women in India report lower life
satisfaction than older men.
•	Older adults with secondary or higher
education and those of higher social castes
report higher life satisfaction than counterparts
without formal education and those fromscheduled castes and scheduled tribes.
•	Satisfaction with living arrangements, perceived
discrimination, and self-rated health emerged as
the top three predictors of life satisfaction.

Chapter 4
Supporting the Well-being of an
Aging Global Population:
Associations between Well-being
and Dementia

Acknowledgments
We have had a remarkable range of contributing
authors and expert reviewers to whom we are
deeply grateful for their willingness to share theirknowledge with our readers. Although the editors
and authors of the World Happiness Report arevolunteers, there are administrative and researchsupport costs covered by our partners: Glico, theKatsuiku Foundation, Blue Zones, illycaffè, andFondazione Ernesto Illy. We greatly value our
special relationship with Gallup who enable theearly access to the World Poll data that underpins
so much of the report. We very much appreciate
the continued work by Ryan Swaney andStislow Design for their skills in design and web
development. New this year, we are deeply
grateful to Jonathan Whitney and Leoni Boyle for
their extraordinary efforts on the production of
the report and media engagement. All of thesecontributions together are what makes the World
Happiness Report the go-to source for so manyaround the world wanting to find out the latest
evidence on the global state of happiness.

•	As the global population of older adults
increases, the number of worldwide dementia
cases is also expected to increase. Dementia isassociated with reduced quality of life andwell-being, and thus dementia prevention is
critical to maintaining the well-being of anaging global population.
•	Higher levels of prior well-being have been
robustly associated with lower risk for future
dementia, suggesting that increasing well-being
may be a promising non-pharmacologicalapproach to dementia prevention. Amongindividuals living with dementia, there areenvironmental changes and well-beingenhancing activities which have been shown
to improve well-being.

John Helliwell, Richard Layard, Jeffrey D. Sachs,
Jan-Emmanuel De Neve, Lara B. Aknin, andShun Wang.

7

Photo Nathan Dumlao on Unsplash

World Happiness Report 2024

8

Chapter 2

Happiness of theYounger, the Older, andThose In Between
John F. Helliwell
Vancouver School of Economics,
University of British Columbia
Haifang Huang
Department of Economics, University of Alberta
Hugh Shiplett
Faculty of Business, University of New Brunswick
Shun Wang
International Business School Suzhou,
Xi’an Jiaotong-Liverpool University

The authors are grateful for the financial support of the WHR sponsors and fordata from the Gallup World Poll and the Gallup/Meta State of Social Connections
study. For much helpful assistance and advice, we are grateful to Lara Aknin,
Chris Barrington-Leigh, Leoni Boyle, Felix Cheung, Jan-Emmanuel De Neve, Anat
Noa Fanti, Len Goff, Carol Graham, Richard Layard, Jessica Mahoney, Max Norton,
Andrew Oswald, Julie Ray, Laura Rosella, Marwan Saleh, Jonathan Whitney, Meik
Wiking, and Maggie Zhou.
doi.org/10.18724/whr-f1p2-qj33
This version last updated March 8, 2024. Please see worldhappiness.report for latest data.

Our happiness rankings are
based on life evaluations,as the more stable measureof the quality of people’s lives.

Photo Ben White on Unsplash2

World Happiness Report 2024

Key Insights
Ranking convergence continues between the two halves of Europe, with Czechia, Lithuania and Slovenia
at positions 18, 19 and 21, contributing to the fall of the United States and Germany from 15 and 16 lastyear to 23 and 24 this year.
Rankings differ a lot for the young and the old. In some cases these differences favour the old, as in
the United States and Canada, where the rankings for those aged 60 and older are 50 or more places
higher than for those under 30. In other cases, especially in Central and Eastern Europe, the reverse istrue, with many rankings being more than 40 places higher for the young than for the old.
From 2006-2010 to 2021-2023 changes in overall happiness varied greatly from country to country,
ranging from increases as large as 1.8 points in Serbia, (up 69 ranks from WHR2013 to WHR2024) and
1.6 points in Bulgaria (up 63 ranks from WHR2013 to WHR2024) to decreases as large as 2.6 points in
Afghanistan (13th from bottom in WHR2013 to unhappiest country in WHR2024).
Happiness changes also varied by global region. Central and Eastern Europe had the largest increases,of the same size for all age groups. Gains were half as large in the CIS countries. East Asia also had large
increases, especially for the older population. By contrast, life evaluations fell in South Asia in all age
groups, especially in the middle age groups. Happiness fell significantly in the country group including
the United States, Canada, Australia and New Zealand, by twice as much for the young as for the old.
Happiness has fallen from 2006-2010 to 2021-2023 in the Middle East and North Africa, with larger
declines for those in the middle age groups than for the old and the young.
For those under 30, happiness levels are now equal in both halves of Europe. For those ever 60, thegap between the two halves of Europe is about half of what it was in 2006-2010. But it is still very large,
more than a full point in 2021-2023.
In 2021-2023 negative emotions were in every region more prevalent for females than males, withalmost everywhere the gender gap being larger at higher ages.
Negative emotions are more frequent than in 2006-2010 everywhere except East Asia and both partsof Europe. In Central and Eastern Europe, in contrast to the rest of the world, but consistently with thehappiness convergence taking place within Europe, negative emotions are now less frequent in all age
groups than they were in 2006-2010.
Positive emotions have not changed much, while still remaining more frequent for the young than for
older age groups.
Global happiness inequality has increased by more than 20% over the past dozen years, in all regions
and age groups, to an extent that differs a lot by age and by region.
Post-COVID increases in benevolence, whether measured as shares of the population, or percentage
increases from pre-pandemic levels, are large for all generations, but especially so for the Millennials andGeneration Z, who are even more likely than their predecessors to help others in need.
New global social connections data show feelings of social support to have been more than twice as
prevalent as loneliness in 2022. Both social support and loneliness affect happiness, with social support
usually having the larger effect. Social interactions add to happiness, with their effects flowing throughincreases in social support and reductions in loneliness.
Age and generation both matter for happiness. As between generations, those born before 1965
(Boomers and their predecessors) have life evaluations about one-quarter of a point higher than thoseborn after 1980 (Millennials and Gen Z). Within each generation, life evaluations rise with age for those in
the older generations and fall with age for the younger ones, with little age effect for those in between.

11World Happiness Report 2024

split: those born before 1965, 1965-1980, andafter 1980. Although the best separation points
for generational differences will differ fromcountry to country, depending on their key
events, our separation does match some widelyused definitions,2 and also divides the sample
fairly evenly, with roughly 30% in each of the firsttwo groups, and 40% in the youngest cohort,which includes Millennials and their successors.

Photo S B Vonlanthen on Unsplash

We start by presenting our usual ranking andmodelling of national happiness of the population
as a whole. In Figure 2.1 we rank countries by theiraverage life evaluations over the three preceding
years, 2021-2023. We have two versions of
Figure 2.1. The first version presents actual life
evaluations alone on centre stage. We include
horizontal whiskers showing the 95% confidence
bands for our national estimates, supplemented
by a measure for each country of the range ofrankings within which its own ranking is likely to
be. The second version includes bars showing
how much each of the six variables explains eachcountry’s average life evaluation. We also present
the latest version, in Table 2.1, of the equation we
use to explain how and why life evaluations vary
among countries and over time.

This chapter is about happiness during different
life stages and of those in different generations. It
is not the first time we have looked at happinessby age and gender.1 But it is the first time wehave enough survey years to start separating thelife course from the ever-changing patterns ofhistory. Some important parts of life are tied
mainly to age, such as schooling, employment
and health. Others depend more on what is goingon in society and the world. These society-wide
factors range from violence, earthquakes andpandemics to how new technologies and changing
natural and social environments interact withalso-changing ways of seeing history, facing
inequalities, and connecting with each other.
While most of our analysis deals with life atdifferent ages, we bring in generational effects
where we find them most salient.

Subsequent sections look separately at the life
evaluations for the young, the old, and those inbetween, compare country rankings for each age
group, and show how life evaluations at different
ages have changed from a base period3 of 20062010 to the three most recent years, 2021-2023.
We then consider differences among age groups
in the levels and trends of positive and negative
emotions, proceeding from there to the important
topic of inequality. We show that inequality ofwell-being is generally greater at higher age(perhaps due to differences in health statusincreasing more among people as individualsage), and has been increasing in all age groups
in most global regions.
In the subsequent sections of the chapter, weconsider differences by generation as well as byage. In the first of these sections we return to oneof the most striking findings in our two previous
reports: the sharp increase, in every global region,of benevolent acts in 2020 and after, relative to

Our early sections relate to happiness as measured
by life evaluations and emotions, showing theirlevels and changes for the younger (<30), theolder (60+), and those in between divided into
two groups, aged 30-44 and 45-59. For our later
analysis by generation, we make a three-way

12World Happiness Report 2024

Finally, we return to international differences inlife evaluations at different ages and in different
generations. We assess the extent to which theoften-found U-shape in age is present or absent
across the globe, how these results have changed
between 2006-2010 and 2021-2023, and attempt
to separate the age-related changes fromgenerational ones.

their levels in the three pre-COVID years
2017-2019. This year we ask whether there
have been differences in the extent to which
different generations stepped to help others
during the pandemic.
We then use new evidence from the Gallup/Meta
global state of social connections survey includedin the 2022 round of the Gallup World Poll for 140
countries to show how generational differences infeelings of social support, loneliness, and beingsocially connected relate to six types of reported
social interactions and to overall life evaluations.

The concluding section highlights our key results.

Measuring and Explaining National
Differences in Life Evaluations
Box 2.1: 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 thereport as positive and negative affect).
Our happiness rankings are based on life
evaluations, as the more stable measure of
the quality of people’s lives.

about three emotions: laughter, enjoyment,
and interest (for details see Technical Box 2).

Life evaluations. The Gallup World Poll, which
remains the principal source of data in thisreport, asks respondents to evaluate theircurrent life as a whole using the image of aladder, 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 annuallyfor each country. Weights are used toconstruct population-representative national
averages for each year in each country.
We base our usual happiness rankings on athree-year average of these life evaluations,since the larger sample size enables more
precise estimates.

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

Negative emotions. Negative affect is givenby the average of individual yes or no answers
about three emotions: worry, sadness,
and anger.
Comparing life evaluations and emotions:•	Life evaluations vary more between countries
than do emotions and are better explainedby the diverse life experiences in different
countries. Emotions yesterday are well
explained by events of the day being asked
about, while life evaluations more closelyreflect the circumstances of life as a whole.
We show later in the chapter that emotions
are significant supports for life evaluations.
•	Positive emotions are still more than twice as
frequent as negative emotions, even during
the years since the onset of COVID.

Positive emotions. Positive affect is given by
the average of individual yes or no answers

13World Happiness Report 2024

Ranking of Happiness 2021-2023

life expectancy, freedom, generosity, andcorruption. As already noted, our happinessrankings are not based on any index of these six
factors. Rather, scores are based on individuals’
own assessments of their lives, in particular
their answers to the single-item Cantril ladderlife-evaluation question. We use observed dataon the six variables and estimates of theirassociations with life evaluations to help explain
the variation of life evaluations across countries,
much as epidemiologists estimate the extent towhich life expectancy is affected by factors suchas smoking, exercise, and diet.

Countries are ranked according to their selfassessed life evaluations (answers to the Cantril
ladder question in the Gallup World Poll),
averaged over the years 2021-2023.4 The overall
length of each country bar in Figure 2.1 represents
the average response to the ladder question. The
confidence intervals for each country’s average
life evaluation are shown by horizontal whiskers
at the right-hand end of each country bar.
Confidence intervals for the rank of a country
are shown in Figure 2.1 to the right of each country’s
bar.5 These ranking ranges are wider where there
are many countries with similar averages, and forcountries with smaller sample sizes.6

Scores are based on individuals’
own assessments of their lives,in particular their answers to
the single-item Cantril ladderlife-evaluation question.

Photo Alexander Grey on Unsplash

The online version Figure 2.1 also includes
colour-coded sub-bars in each country row,
representing the extent to which six key variables
contribute to explaining life evaluations. Thesevariables (described in more detail in TechnicalBox 2) are GDP per capita, social support, healthy14

95% c.i. for
rank:
95%
c.i.131–138
for rank: 80–93
95% c.i. for
rank:
95%
c.i.132–140
for rank: 80–93

133.
YemenMacedonia(3.561)
84. North(5.369)134. Algeria
Zambia (5.364)(3.502)85.

95%
c.i. forrank:
135.
Eswatini(3.502)95%
c.i.132–141
for rank: 81–94
86. Hong
Kong
S.A.R. of China (5.316)World Happiness
Report2024
95% c.i. forrank:
136. Albania
Malawi (3.421)
95%
c.i.132–141
for rank: 81–96
87.
(5.304)137.
Botswana (5.281)(3.383)
88. Tajikistan
138. Congo
Zimbabwe(3.341)
89.
(Brazzaville)(5.221)

95% c.i. forrank:
132–141
95%
c.i.
for rank: 82–96
95% c.i. for
rank:
95%
c.i.132–141
for rank: 82–98

139.
Congo (Kinshasa)(3.295)90. Mozambique(5.216)140. Sierra
Leone(3.245)91.
Georgia(5.185)95% c.i. forrank:
133–141
95%
c.i.
for rank: 82–98
95% c.i. forrank:
135–141
95%
c.i.
for rank: 84–98

95% c.i.in
for95%
rank:c.i.
134–141
Lesotho(3.186)for rank: 84–99
92.
(5.166)
Figure 141.
2.1:Iraq
Country
Rankings by Life Evaluations
2021-2023
95% c.i. for rank:
95%142–142
c.i. for rank: 84–99
95% c.i. for rank: 95%
143–143
c.i. for rank: 86–99

142.
(2.707)
93. Lebanon
Nepal (5.158)
143.
Afghanistan
94. Laos(5.139) (1.721)
95. Gabon (5.106)

95% c.i. for rank: 87–102

96.
Ivory
Coast
(5.080)1.
Finland(7.741)
97.
Guinea(5.023)
2. Denmark (7.583)

95% c.i. for 95%
rank:c.i.
88–105for rank: 1–1

98.
(4.975)
3. Turkiye
Iceland (7.525)
99.
4. Senegal
Sweden (4.969)(7.344)

0

1

2

3

4

100.
5. IranIsrael(4.923)(7.341)101.
Azerbaijan
6. Netherlands(4.893)(7.319)95% c.i. for rank:
95% 89–107
c.i. for rank: 2–3
95% c.i. for rank:
90–10795%
c.i. for rank: 2–3
5
6 7
8
95% c.i. for rank:
90–10795% c.i.
for rank: 4–7
95% c.i. for rank:
95–10795% c.i. for rank: 4–7
95% c.i. for rank:
95% 95–107
c.i. for rank: 4–7
95% c.i. for rank:
95% 96–107
c.i. for rank: 4–7

102.
7. Nigeria
Norway(4.881)(7.302)103.
of Palestine(4.879)8. State
Luxembourg(7.122)

95% c.i. for 95%
rank:c.i.
95–108for rank: 8–13
95% c.i. for95%
rank:c.i.
96–108for rank: 8–15

104.
9. Cameroon
Switzerland(4.874)(7.060)105.
Ukraine
10. Australia(4.873)(7.057)95% c.i. for95%
rank:c.i.
96–107for rank: 8–15
95% c.i. for95%
rank:c.i.
97–108for rank: 8–16

106.
(4.832)
11. Namibia
New Zealand(7.029)107.
(4.795)
12. Morocco
Costa Rica
(6.955)95% c.i. for95%
rank:c.i.
97–109for rank: 8–19
95% c.i. for 95%
rank: c.i.
104–114
for rank: 8–19

108.
(4.657)
13. Pakistan
Kuwait (6.951)
109.
Niger
(4.556)
14. Austria (6.905)

95% c.i. for rank:
107–120
95% c.i.
for rank: 11–20
95% c.i. for 95%
rank: c.i.
108–120
for rank: 9–22

110.
Faso (4.548)
15. Burkina
Canada (6.900)
111.
(4.505)
16. Mauritania
Belgium (6.894)

95% c.i. for rank:
95% 108–121
c.i. for rank: 11–21
95% c.i. for rank:
108–12195% c.i.
for rank: 12–25

112.
(4.485)
17. Gambia
Ireland (6.838)
113.
Chad
(4.471)
18. Czechia (6.822)

95% c.i. for rank:
108–123
95% c.i.
for rank: 12–25
95% c.i. for rank:
108–12295% c.i.
for rank: 12–25

114.
(4.470)19. Kenya
Lithuania(6.818)115.
(4.422) (6.749)
20. Tunisia
United Kingdom

95% c.i. for 95%
rank: c.i.
109–123
for rank: 15–28
95% c.i. for rank:
109–125
95% c.i. for rank: 16–28

116.
(4.377)
21. Benin
Slovenia(6.743)
117.
Uganda(4.372)
22. United ArabEmirates (6.733)

95% c.i. for rank:
109–125
95% c.i.
for rank: 14–29
95% c.i. for rank:
95% 109–125
c.i. for rank: 17–29

118.
(4.354)
23. Myanmar
United States(6.725)119.
(4.341)
24. Cambodia
Germany (6.719)

95% c.i. for rank:
95% 109–125
c.i. for rank: 17–29
95% c.i. for rank:
95% 111–125
c.i. for rank: 17–33

120.
25. Ghana
Mexico(4.289)(6.678)121.
Liberia(4.269)26. Uruguay(6.611)

95% c.i. for 95%
rank: c.i.
109–126
for rank: 20–33
95% c.i. for rank:
95% 114–126
c.i. for rank: 20–33

122.
(4.232)
27. MaliFrance(6.609)123.
28. Madagascar
Saudi Arabia(4.228)(6.594)95% c.i. for rank:
114–126
95% c.i.
for rank: 20–36
95% c.i. for 95%
rank: c.i.
116–126
for rank: 22–38

124.
(4.214)
29. Togo
Kosovo(6.561)125.
Jordan(4.186)
30. Singapore(6.523)

95% c.i. for 95%
rank: c.i.
116–126
for rank: 25–40
95% c.i. for rank:
95% 121–130
c.i. for rank: 25–42

126.
(4.054)31. India
TaiwanProvince of China (6.503)
127.
(3.977)
32. Egypt
Romania(6.491)

95% c.i. for rank:
95%124–131
c.i. for rank: 25–43
95% c.i. for rank:
95%126–131
c.i. for rank: 26–44

128.
Lanka (3.898)
33. Sri
El Salvador(6.469)129.
Bangladesh(3.886)
34. Estonia (6.448)

95% c.i. for rank:
95%126–131
c.i. for rank: 27–44
95% c.i. for rank:
95%127–131
c.i. for rank: 28–44

130.
(3.861)35. Ethiopia
Poland (6.442)
131.
(3.781)
36. Tanzania
Spain (6.421)

95% c.i. for rank:
95%127–133
c.i. for rank: 28–44
95% c.i. for rank:
131–13995%
c.i. for rank: 28–45

132.
(3.566)
37. Comoros
Serbia (6.411)
133.
Yemen
(3.561)
38. Chile (6.360)

95% c.i. for rank:
131–138
95%
c.i. for rank: 31–48
95% c.i. for rank:
132–14095%c.i. for rank: 29–49

134.
39. Zambia
Panama (3.502)(6.358)135.
(3.502)
40. Eswatini
Malta (6.346)

95% c.i. for rank:
132–14195%
c.i. for rank: 31–49
95% c.i. for rank:
132–141
95% c.i. for rank: 32–49

136.
(3.421)
41. Malawi
Italy (6.324)
137.
Botswana
42. Guatemala(3.383)(6.287)95% c.i. for rank:
132–14195%
c.i. for rank: 32–50
95% c.i. for rank:
132–14195%
c.i. for rank: 32–49

138.
(3.341)
43. Zimbabwe
Nicaragua (6.284)
139.
(Kinshasa) (3.295)
44. Congo
Brazil (6.272)

95% c.i. for rank:
133–14195%
c.i. for rank: 33–49
95% c.i. for rank:
135–141
95% c.i. for rank: 37–49

140.
Leone
(3.245)
45. Sierra
Slovakia(6.257)141.
Lesotho(3.186)
46. Latvia (6.234)

95% c.i. for rank:
134–14195%
c.i. for rank: 38–52
95% c.i. for rank: 95%
142–142c.i. for rank: 38–58

142.
(2.707)47. Lebanon
Uzbekistan(6.195)143.
(1.721)
48. Afghanistan
Argentina (6.188)

95% c.i. for rank: 143–143
95% c.i. for rank: 38–59

49. Kazakhstan (6.188)

95% c.i. for rank: 39–57

50. Cyprus (6.068)

95% c.i. for rank: 45–66

51.

95% c.i. for rank: 46–66

Japan (6.060)

Average Life Evaluation

52. South Korea (6.058)
95% confidence interval

0

1

2

3

4

53. Philippines (6.048)

5

6 95%
7 c.i.
8 for rank: 46–67
95% c.i. for rank: 46–69

54. Vietnam (6.043)

95% c.i. for rank: 47–68

55. Portugal (6.030)

95% c.i. for rank: 47–69

56. Hungary (6.017)
57. Paraguay (5.977)

1595% c.i. for rank: 47–69
95% c.i. for rank: 50–72

58. Thailand (5.976)

95% c.i. for rank: 50–72

59. Malaysia (5.975)

95% c.i. for rank: 50–71

36. Spain (6.421)
95% c.i. for rank: 28–44
95% c.i. forrank:
133. North
YemenMacedonia(3.561)95%
c.i.131–138
for rank: 80–93
84.
(5.369)37. Serbia (6.411)
95% c.i. for rank: 28–45
95%
c.i.
for
rank:
134.
(3.502)
95% c.i.132–140
for rank: 80–93
85. Zambia
Algeria (5.364)
38. Chile (6.360)
95% c.i. for rank: 31–48
95%
c.i. forrank:
135.
Eswatini(3.502)95%
c.i.132–141
for rank: 81–94
86. Hong
Kong
S.A.R. of China (5.316)World Happiness
Report2024
39. Panama (6.358)
95%
c.i. for rank: 29–49
95% c.i. forrank:
136. Albania
Malawi (3.421)
95%
c.i.132–141
for rank: 81–96
87.
(5.304)40. Malta (6.346)
95% c.i. for rank: 31–49
95% c.i. forrank:
132–141
137.
Botswana (5.281)(3.383)95%
c.i.
for rank: 82–96
88. Tajikistan
41. Italy (6.324)
95% c.i. for rank: 32–49
95% c.i. forrank:
138. Congo
Zimbabwe(3.341)95%
c.i.132–141
for rank: 82–98
89.
(Brazzaville)(5.221)
42. Guatemala (6.287)
95% c.i. for rank: 32–50
95% c.i. forrank:
133–141
139.
Congo (Kinshasa)(3.295)95%c.i.
for rank: 82–98
90. Mozambique(5.216)
43. Nicaragua (6.284)
95% c.i. for rank: 32–49
95% c.i. forrank:
135–141140. Sierra
Leone(3.245)95%c.i.
for rank: 84–98
91.
Georgia(5.185)
44. Brazil (6.272)
95% c.i. for rank: 33–49
95% c.i.in
for95%
rank:c.i.
134–141
Lesotho(3.186)for rank: 84–99
92.
(5.166)
Figure 141.
2.1:Iraq
Country
Rankings by Life Evaluations
2021-2023(continued)
45. Slovakia (6.257)
95% c.i. for rank: 37–49
95%
c.i.
for
rank:
142–142142. Nepal
Lebanon(2.707)95% c.i. for rank: 84–99
93.
(5.158)46. Latvia (6.234)
95% c.i. for rank: 38–52
95% c.i. for rank: 95%
143–143
143.
Afghanistan
c.i. for rank: 86–99
94. Laos(5.139) (1.721)
47. Uzbekistan (6.195)
95% c.i. for rank: 38–58
95% c.i. for rank: 87–102
95. Gabon (5.106)
48. Argentina (6.188)
95% c.i. for rank: 38–59
95% c.i. for rank: 88–105
96. Ivory Coast (5.080)
95% c.i. for rank: 39–57
49. Kazakhstan (6.188)
95% c.i. for rank: 89–107
97. Guinea (5.023)
95% c.i. for rank: 45–66
50. Cyprus (6.068)
95% c.i. for rank: 90–107
98. Turkiye (4.975)
0 1
2
3
4 5
6 95%
7 c.i.
8 for rank: 46–66
51. Japan (6.060)
95% c.i. for rank: 90–107
99. Senegal (4.969)
95% c.i. for rank: 46–67
52. South Korea (6.058)
95% c.i. for rank: 95–107
100. Iran (4.923)
95% c.i. for rank: 46–69
53. Philippines (6.048)
95% c.i. for rank: 95–107
101. Azerbaijan (4.893)
95% c.i. for rank: 47–68
54. Vietnam (6.043)
95% c.i. for rank: 96–107
102. Nigeria (4.881)
95% c.i. for rank: 47–69
55. Portugal (6.030)
95% c.i. for rank: 95–108
103. State of Palestine (4.879)
95% c.i. for rank: 47–69
56. Hungary (6.017)
95% c.i. for rank: 96–108
104. Cameroon (4.874)
95% c.i. for rank: 50–72
57. Paraguay (5.977)
95% c.i. for rank: 96–107
105. Ukraine (4.873)
95% c.i. for rank: 50–72
58. Thailand (5.976)
95% c.i. for rank: 97–108
106. Namibia (4.832)
95% c.i. for rank: 50–71
59. Malaysia (5.975)
95% c.i. for rank: 97–109
107. Morocco (4.795)
95% c.i. for rank: 50–71
60. China (5.973)
95% c.i. for rank: 104–114
108. Pakistan (4.657)
95% c.i. for rank: 48–73
61. Honduras (5.968)
95% c.i. for rank: 107–120
109. Niger (4.556)
95% c.i. for rank: 47–78
62. Bahrain (5.959)
95% c.i. for rank: 108–120
110. Burkina Faso (4.548)
95% c.i. for rank: 50–72
63. Croatia (5.942)
95% c.i. for rank: 108–121
111. Mauritania (4.505)
95% c.i. for rank: 50–73
64. Greece (5.934)
95% c.i. for rank: 108–121
112. Gambia (4.485)
95% c.i. for rank: 52–78
65. Bosnia and Herzegovina (5.877)
95% c.i. for rank: 108–123
113. Chad (4.471)
95% c.i. for rank: 50–78
66. Libya (5.866)
95% c.i. for rank: 108–122
114. Kenya (4.470)
95% c.i. for rank: 50–80
67. Jamaica (5.842)
95% c.i. for rank: 109–123
115. Tunisia (4.422)
95% c.i. for rank: 54–78
68. Peru (5.841)
95% c.i. for rank: 109–125
116. Benin (4.377)
95% c.i. for rank: 54–79
69. Dominican Republic (5.823)
95% c.i. for rank: 109–125
117. Uganda (4.372)
95% c.i. for rank: 57–78
70. Mauritius (5.816)
95% c.i. for rank: 109–125
118. Myanmar (4.354)
95% c.i. for rank: 57–79
71. Moldova (5.816)
95% c.i. for rank: 109–125
119. Cambodia (4.341)
95% c.i. for rank: 60–79
72. Russia (5.785)
95% c.i. for rank: 111–125
120. Ghana (4.289)
95% c.i. for rank: 58–79
73. Bolivia (5.784)
95% c.i. for rank: 109–126
121. Liberia (4.269)
95% c.i. for rank: 64–80
74. Ecuador (5.725)
95% c.i. for rank: 114–126
122. Mali (4.232)
95% c.i. for rank: 64–80
75. Kyrgyzstan (5.714)
95% c.i. for rank: 114–126
123. Madagascar (4.228)
95% c.i. for rank: 64–80
76. Montenegro (5.707)
95% c.i. for rank: 116–126
124. Togo (4.214)
95% c.i. for rank: 64–80
77. Mongolia (5.696)
95% c.i. for rank: 116–126
125. Jordan (4.186)
95% c.i. for rank: 64–80
78. Colombia (5.695)
95% c.i. for rank: 121–130
126. India (4.054)
95% c.i. for rank: 70–83
79. Venezuela (5.607)
95% c.i. for rank: 124–131
127. Egypt (3.977)
95% c.i. for rank: 73–83
80. Indonesia (5.568)
95% c.i. for rank: 126–131
128. Sri Lanka (3.898)
95% c.i. for rank: 79–89
81. Bulgaria (5.463)
95% c.i. for rank: 126–131
129. Bangladesh (3.886)
95% c.i. for rank: 79–89
82. Armenia (5.455)
95% c.i. for rank: 127–131
130. Ethiopia (3.861)
95% c.i. for rank: 79–90
83. South Africa (5.422)
95% c.i. for rank: 127–133
131. Tanzania (3.781)
95% c.i. for rank: 80–93
84. North Macedonia (5.369)
95% c.i. for rank: 131–139
132. Comoros (3.566)
95% c.i. for rank: 80–93
85. Algeria (5.364)
95% c.i. for rank: 131–138
133. Yemen (3.561)
95% c.i. for rank: 81–94
86. Hong Kong S.A.R. of China (5.316)
95% c.i. for rank: 132–140
134. Zambia (3.502)
95% c.i. for rank: 81–96
87. Albania (5.304)
95% c.i. for rank: 132–141
135. Eswatini (3.502)
95% c.i. for rank: 82–96
88. Tajikistan (5.281)
95% c.i. for rank: 132–141
136. Malawi (3.421)
95% c.i. for rank: 82–98
89. Congo (Brazzaville) (5.221)
95% c.i. for rank: 132–141
137. Botswana (3.383)
95% c.i. for rank: 82–98
90. Mozambique (5.216)
95% c.i. for rank: 132–141
138. Zimbabwe (3.341)
95% c.i. for rank: 84–98
91. Georgia (5.185)
95% c.i. for rank: 133–141
139. Congo (Kinshasa) (3.295)
95% c.i. for rank: 84–99
92. Iraq (5.166)
95% c.i. for rank: 135–141
140. Sierra Leone (3.245)
95% c.i. for rank: 84–99
93. Nepal (5.158)
95% c.i. for rank: 134–141
141. Lesotho (3.186)
95% c.i. for rank: 86–99
94. Laos (5.139)
95% c.i. for rank: 142–142
142. Lebanon (2.707)
95% c.i. for rank: 87–102
95. Gabon (5.106)
95% c.i. for rank: 143–143
143. Afghanistan (1.721)
95% c.i. for rank: 88–105
96. Ivory Coast (5.080)
97. Guinea (5.023)

95% c.i. for rank: 89–107

98. Turkiye (4.975)

95% c.i. for rank: 90–107
95% c.i. for rank: 90–107

99. Senegal (4.969)

Average Life Evaluation

100.interval
Iran (4.923)
95% confidence

0

1

2

3

4

101. Azerbaijan (4.893)

for rank:
5 95%
6 c.i. 7
8 95–107
95% c.i. for rank: 95–107

102. Nigeria (4.881)

95% c.i. for rank: 96–107

103. State of Palestine (4.879)

95% c.i. for rank: 95–108

104. Cameroon (4.874)
105. Ukraine (4.873)

16

95% c.i. for rank: 96–108
95% c.i. for rank: 96–107

106. Namibia (4.832)

95% c.i. for rank: 97–108

107. Morocco (4.795)

95% c.i. for rank: 97–109

95% c.i. for rank: 80–93
84. North Macedonia (5.369)
95% c.i. for
rank:
133.
YemenMacedonia(3.561)95%
c.i.131–138
for rank: 80–93
84. North(5.369)95% c.i. for rank: 80–93
85. Algeria (5.364)
95%
c.i.
for
rank:
134. Algeria
Zambia (5.364)(3.502)95%
c.i.132–140
for rank: 80–93
85.
95% c.i. for rank: 81–94
86. Hong Kong S.A.R. of China (5.316)
95%
c.i. forrank:
135.
Eswatini(3.502)95%
c.i.132–141
for rank: 81–94
86. Hong
Kong
S.A.R. of China (5.316)World Happiness
Report95% 2024
c.i. for rank: 81–96
87. Albania (5.304)
95% c.i. forrank:
136. Albania
Malawi (3.421)
95%
c.i.132–141
for rank: 81–96
87.
(5.304)95% c.i. for rank: 82–96
88. Tajikistan (5.281)
95% c.i. forrank:
132–141
137.
Botswana (5.281)(3.383)95%
c.i.
for rank: 82–96
88. Tajikistan
95% c.i. for rank: 82–98
89. Congo (Brazzaville) (5.221)
95%
c.i.
for
rank:
138. Congo
Zimbabwe(3.341)95%
c.i.132–141
for rank: 82–98
89.
(Brazzaville)(5.221)
95% c.i. for rank: 82–98
90. Mozambique (5.216)
95% c.i. forrank:
133–141
139.
Congo (Kinshasa)(3.295)95%c.i.
for rank: 82–98
90. Mozambique(5.216)95% c.i. for rank: 84–98
91. Georgia (5.185)
95% c.i. forrank:
135–141140. Sierra
Leone(3.245)95%c.i.
for rank: 84–98
91.
Georgia(5.185)95% c.i. for rank: 84–99
92. Iraq (5.166)
95% c.i.in
for95%
rank:c.i.
134–141
Lesotho(3.186)for rank: 84–99
92.
(5.166)
Figure 141.
2.1:Iraq
Country
Rankings by Life Evaluations
2021-2023(continued)
95% c.i. for rank: 84–99
93. Nepal (5.158)
95%
c.i.
for
rank:
142. Nepal
Lebanon(2.707)
95%142–142
c.i. for rank: 84–99
93.
(5.158)95% c.i. for rank: 86–99
94. Laos (5.139)
95% c.i. for rank: 95%
143–143
143.
Afghanistan
c.i. for rank: 86–99
94. Laos(5.139) (1.721)
95% c.i. for rank: 87–102
95. Gabon (5.106)
95% c.i. for rank: 87–102
95. Gabon (5.106)
95% c.i. for rank: 88–105
96. Ivory Coast (5.080)
95% c.i. for rank: 88–105
96. Ivory Coast (5.080)
95% c.i. for rank: 89–107
97. Guinea (5.023)
95% c.i. for rank: 89–107
97. Guinea (5.023)¨
95% c.i. for rank: 90–107
98. Turkiye(4.975)95% c.i. for rank: 90–107
98. Turkiye (4.975)
0 1
2
3
4 5 95%
6 c.i.7for rank:
8 90–107
99. Senegal (4.969)
95% c.i. for rank: 90–107
99. Senegal (4.969)
95% c.i. for rank: 95–107
100. Iran (4.923)
95% c.i. for rank: 95–107
100. Iran (4.923)
95% c.i. for rank: 95–107
101. Azerbaijan (4.893)
95% c.i. for rank: 95–107
101. Azerbaijan (4.893)
95% c.i. for rank: 96–107
102. Nigeria (4.881)
95% c.i. for rank: 96–107
102. Nigeria (4.881)
95% c.i. for rank: 95–108
103. State of Palestine (4.879)
95% c.i. for rank: 95–108
103. State of Palestine (4.879)
95% c.i. for rank: 96–108
104. Cameroon (4.874)
95% c.i. for rank: 96–108
104. Cameroon (4.874)
95% c.i. for rank: 96–107
105. Ukraine (4.873)
95% c.i. for rank: 96–107
105. Ukraine (4.873)
95% c.i. for rank: 97–108
106. Namibia (4.832)
95% c.i. for rank: 97–108
106. Namibia (4.832)
95% c.i. for rank: 97–109
107. Morocco (4.795)
95% c.i. for rank: 97–109
107. Morocco (4.795)
95% c.i. for rank: 104–114
108. Pakistan (4.657)
95% c.i. for rank: 104–114
108. Pakistan (4.657)
95% c.i. for rank: 107–120
109. Niger (4.556)
95% c.i. for rank: 107–120
109. Niger (4.556)
95% c.i. for rank: 108–120
110. Burkina Faso (4.548)
95% c.i. for rank: 108–120
110. Burkina Faso (4.548)
95% c.i. for rank: 108–121
111. Mauritania (4.505)
95% c.i. for rank: 108–121
111. Mauritania (4.505)
95% c.i. for rank: 108–121
112. Gambia (4.485)
95% c.i. for rank: 108–121
112. Gambia (4.485)
95% c.i. for rank: 108–123
113. Chad (4.471)
95% c.i. for rank: 108–123
113. Chad (4.471)
95% c.i. for rank: 108–122
114. Kenya (4.470)
95% c.i. for rank: 108–122
114. Kenya (4.470)
95% c.i. for rank: 109–123
115. Tunisia (4.422)
95% c.i. for rank: 109–123
115. Tunisia (4.422)
95% c.i. for rank: 109–125
116. Benin (4.377)
95% c.i. for rank: 109–125
116. Benin (4.377)
95% c.i. for rank: 109–125
117. Uganda (4.372)
95% c.i. for rank: 109–125
117. Uganda (4.372)
95% c.i. for rank: 109–125
118. Myanmar (4.354)
95% c.i. for rank: 109–125
118. Myanmar (4.354)
95% c.i. for rank: 109–125
119. Cambodia (4.341)
95% c.i. for rank: 109–125
119. Cambodia (4.341)
95% c.i. for rank: 111–125
120. Ghana (4.289)
95% c.i. for rank: 111–125
120. Ghana (4.289)
95% c.i. for rank: 109–126
121. Liberia (4.269)
95% c.i. for rank: 109–126
121. Liberia (4.269)
95% c.i. for rank: 114–126
122. Mali (4.232)
95% c.i. for rank: 114–126
122. Mali (4.232)
95% c.i. for rank: 114–126
123. Madagascar (4.228)
95% c.i. for rank: 114–126
123. Madagascar (4.228)
95% c.i. for rank: 116–126
124. Togo (4.214)
95% c.i. for rank: 116–126
124. Togo (4.214)
95% c.i. for rank: 116–126
125. Jordan (4.186)
95% c.i. for rank: 116–126
125. Jordan (4.186)
95% c.i. for rank: 121–130
126. India (4.054)
95% c.i. for rank: 121–130
126. India (4.054)
95% c.i. for rank: 124–131
127. Egypt (3.977)
95% c.i. for rank: 124–131
127. Egypt (3.977)
95% c.i. for rank: 126–131
128. Sri Lanka (3.898)
95% c.i. for rank: 126–131
128. Sri Lanka (3.898)
95% c.i. for rank: 126–131
129. Bangladesh (3.886)
95% c.i. for rank: 126–131
129. Bangladesh (3.886)
95% c.i. for rank: 127–131
130. Ethiopia (3.861)
95% c.i. for rank: 127–131
130. Ethiopia (3.861)
95% c.i. for rank: 127–133
131. Tanzania (3.781)
95% c.i. for rank: 127–133
131. Tanzania (3.781)
95% c.i. for rank: 131–139
132. Comoros (3.566)
95% c.i. for rank: 131–139
132. Comoros (3.566)
95% c.i. for rank: 131–138
133. Yemen (3.561)
95% c.i. for rank: 131–138
133. Yemen (3.561)
95% c.i. for rank: 132–140
134. Zambia (3.502)
95% c.i. for rank: 132–140
134. Zambia (3.502)
95% c.i. for rank: 132–141
135. Eswatini (3.502)
95% c.i. for rank: 132–141
135. Eswatini (3.502)
95% c.i. for rank: 132–141
136. Malawi (3.421)
95% c.i. for rank: 132–141
136. Malawi (3.421)
95% c.i. for rank: 132–141
137. Botswana (3.383)
95% c.i. for rank: 132–141
137. Botswana (3.383)
95% c.i. for rank: 132–141
138. Zimbabwe (3.341)
95% c.i. for rank: 132–141
138. Zimbabwe (3.341)
95% c.i. for rank: 133–141
139. Congo (Kinshasa) (3.295)
95% c.i. for rank: 133–141
139. Congo (Kinshasa) (3.295)
95% c.i. for rank: 135–141
140. Sierra Leone (3.245)
95% c.i. for rank: 135–141
140. Sierra Leone (3.245)
95% c.i. for rank: 134–141
141. Lesotho (3.186)
95% c.i. for rank: 134–141
141. Lesotho (3.186)
95% c.i. for rank: 142–142
142. Lebanon (2.707)
95% c.i. for rank: 142–142
142. Lebanon (2.707)
95% c.i. for rank: 143–143
143. Afghanistan (1.721)
95% c.i. for rank: 143–143
143. Afghanistan (1.721)

Average Life Evaluation
95% confidence interval

0
0

1
1

2
2

3
3

4
4

17

5
5

6
6

7
7

8
8

World Happiness Report 2024

While the top ten countries
remain largely unchanged,
there has been much moreaction in the top twenty.

What do the latest data show for the2021-2023 country rankings?7
Two features carry over from previous editions of
the World Happiness Report. First, there is still alot of year-to-year consistency in the way people
rate their lives in different countries, and since ourrankings are based on a three-year average there
is information carried forward from one year
to the next. In the case of cataclysmic events
happening during a particular year, their effecton the rankings will depend on when the survey
took place, and will be muted by the three-year
averaging. In the case of the October 7th attack
on Israel and the subsequent war between Israeland Hamas, the survey in Palestine took placeearlier in the year and the Israel survey after the
hostage taking but before much of the subsequent
warfare. Life evaluations fell sharply in Israel, by
0.9 on the 10-point scale, only one-third of which
will enter the three-year averages discussed
below. (See the Statistical Appendix for individual
country trajectories on an annual basis, plotted
separately by age group and by generation).8born was about 0.2 points higher than for theforeign-born.
How have the rankings changed since last year?
While the top ten countries remain largely
unchanged, there has been much more action in
the top twenty. Costa Rica and Kuwait are both
new entrants10 to the top 20, at positions 12 and13. The continuing convergence in happinesslevels between the two sides of Europe led lastyear to Czechia and Lithuania being in the top
twenty, nearly joined now by Slovenia in 21stplace. The new entrants are matched by thedepartures of the United States and Germany
from the top 20, dropping from 15 and 16 lastyear to 23 and 24 this year.
The top countries no longer include any of thelargest countries. In the top ten countries only
the Netherlands and Australia have populations
over 15 million. In the whole of the top twenty,
only Canada and the United Kingdom have
populations over 30 million.

Second, there remains a large gap between the
top and bottom countries, a full six points (onthe 0 to 10 scale) between Finland at the top andAfghanistan at the bottom. The top countries are
more tightly grouped than the bottom ones. The
top twenty countries all fall within 1 point of eachother, compared with a 2.5 point spread among
the bottom twenty. The remaining 100-odd
countries cover the remaining 2.5 points of thetotal range. This means that relatively modestchanges in a national average can lead to a large
shift in ranks, as illustrated by the 95% confidence
region exceeding 25 ranks for several countries
in the middle of the global list.

Why do happiness levels differ?
In Table 2.1 we present our latest modelling of
national average life evaluations and measuresof positive and negative emotions (affect) bycountry and year.11 The results in the first column
explain national average life evaluations in termsof six key variables: GDP per capita, healthy life
expectancy, having someone to count on,
freedom to make life choices, generosity, andfreedom from corruption.12 Taken together, thesesix variables explain more than three-quarters ofthe variation in national annual average ladderscores across countries and years, using datafrom 2005 through 2023.13 The six variables wereoriginally chosen as the best available measuresof factors established in both experimental andsurvey data as having significant links to subjective
well-being, and especially to life evaluations.14 The

Happiness scores are based on the resident
populations in each country, rather than theircitizenship or place of birth. In World Happiness
Report 2018 we split the responses between thelocally and foreign-born populations in eachcountry and found the happiness rankings to be
essentially the same for the two groups.9 Therewas some footprint effect after migration, andsome tendency for migrants to move to happiercountries, so that among 20 happiest countries inthat report, the average happiness for the locally18

World Happiness Report 2024

larger than the effects of loneliness in all age
groups and generations.

explanatory power of the unchanged model has
gradually increased as we have added more years
to the sample, which is now almost three times aslarge as when the equation was first introduced in
World Happiness Report 2013. We keep looking
for possible improvements when and if new
evidence becomes available.15

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

The second and third columns of Table 2.1 use thesame six variables to estimate equations fornational 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, and especiallynegative ones, are differently and much lessfully explained by the six variables than are life
evaluations. Per-capita income and healthy life
expectancy have significant effects on life
evaluations,16 but not, in these national averagedata, on positive emotions.17 But the social
variables do have significant effects on both
positive and negative emotions. Bearing in mind
that positive and negative emotions are measured
on a 0 to 1 scale, while life evaluations are on a
0 to 10 scale, having someone to count on canbe seen to have similar proportionate effects
on positive and negative emotions as on life
evaluations. Freedom and generosity have evenlarger associations with positive emotions thanwith the Cantril ladder. Negative emotions are
significantly reduced by social support, a senseof freedom, and the absence of corruption.

Photo Francis Odeyemi on Unsplash

In the fourth column, we re-estimate the life
evaluation equation from column 1, adding
both positive and negative emotions to partially
implement the Aristotelian presumption thatsustained positive emotions are important
supports for a good life.18 The results continue
to buttress a finding in psychology that theexistence of positive emotions matters more than
the absence of negative ones when predicting
either longevity19 or resistance to the common
cold.20 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. In a parallel way, weshow in a later section of this chapter that theeffects of a positive social environment are19

World Happiness Report 2024

Table 2.1: Regressions to Explain Average Happiness across Countries (Pooled OLS)
Dependent Variable
Independent Variable
Log GDP per capita

Social support

Healthy life expectancy at birth

Freedom to make life choices

Generosity

Perceptions of corruption

Cantril Ladder

Positive Affect

Negative Affect

Cantril Ladder

0.349

-.015

-.002

0.382

(0.068)***(0.009)(0.007)(0.066)***

2.563

0.315

-.342

1.936

(0.349)***(0.056)***(0.045)***(0.349)***0.028-.0007

0.003

0.029

(0.011)***(0.001)(0.001)***(0.011)***

1.378

0.376

-.090

0.571

(0.295)***(0.044)***(0.039)**(0.273)**

0.487

0.084

0.029

0.296

(0.252)*(0.032)***(0.027)(0.241)

-.733

-.012

0.093

-.724

(0.256)***(0.027)(0.022)***(0.243)***

Positive affect2.206(0.33)***

Negative affect

0.193(0.381)

Year fixed effects

Included

Included

Included

Included

155

155

155

155

Number of obs.

2103

2098

2102

2097

Adjusted R-squared

0.757

0.43

0.343

0.781

Number of countries

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 2023. See Technical Box 2 for detailed information about each of the predictors. Coefficientsare reported with robust standard errors clustered by country (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent levels respectively.

20

World Happiness Report 2024

Box 2.2: Detailed information about each of the predictors in Table 2.1

4.	Freedom to make life choices is the national
average of binary responses to the GWP
question “Are you satisfied or dissatisfied
with your freedom to choose what you dowith your life?”

1.	GDP per capita is in terms of Purchasing
Power Parity (PPP) adjusted to constant2017 international dollars, taken from theWorld Development Indicators (WDI) by
the World Bank (version 23, metadata last
updated on September 27, 2023). See
Statistical Appendix for more details. GDPdata for 2023 are not yet available, so we
extend the GDP time series from 2022 to2023 using country-specific forecasts ofreal GDP growth from the OECD Economic
Outlook No. 113 (June 2023) or, if missing,from the World Bank’s Global Economic
Prospects (last updated: June 6, 2023),
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.

5.	Generosity is the residual of regressing
the national average of GWP responses tothe donation question “Have you donatedmoney to a charity in the past month?”
on log GDP per capita.
6.	Perceptions of corruption are the averageof binary answers to two GWP questions:“Is corruption widespread throughout the
government or not?” and “Is corruption
widespread within businesses or not?”Where data for government corruption
are missing, the perception of businesscorruption is used as the overall
corruption-perception measure.

2.	The time series for healthy life expectancy
at birth are constructed based on data fromthe World Health Organization (WHO)Global Health Observatory data repository,
with data available for 2005, 2010, 2015,2016, and 2019. To match this report’s
sample period (2005-2023), interpolationand extrapolation are used. See Statistical
Appendix for more details.

7.	Positive affect is defined as the average ofprevious-day affect measures for laughter,enjoyment, and doing interesting things. The
inclusion of doing interesting things (first
added for World Happiness Report 2022),
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? See Statistical
Appendix 1 for more details.

3.	Social support is the national average of thebinary responses (0=no, 1=yes) to the Gallup
World Poll (GWP) question “If you were introuble, do you have relatives or friends you
can count on to help you whenever you
need them, or not?”8.	Negative affect is defined as the average ofprevious-day affect measures for worry,sadness, and anger.

21

World Happiness Report 2024

although only slightly and insignificantly so in ourlatest results.27 This may reflect, at least in part,
cultural differences in the way people think about
and report on the quality of their lives.28 It isreassuring 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.29

The variables we use in our Table 2.1 modelling
may be taking credit properly due to othervariables, or to unmeasured factors. There arealso likely to be vicious or virtuous circles, with
two-way linkages among the variables. For
example, there is much evidence that those whohave happier lives are likely to live longer,21 andbe more trusting, more cooperative, and generally
better able to meet life’s demands.22 This will
double back to improve health, income, generosity,
corruption, and a sense of freedom. Collectively,
these possibilities suggest that we should interpretthe observed relationships with some caution.

We once again used the model of Table 2.1 toassess the overall effects of COVID-19 on life
evaluations. If we add an indicator for the four
COVID years 2020-2023 to our Table 2.1 equation,
we find no net increase or decrease in life evaluations.30 This suggests, in a preliminary way, thatthe undoubted pains of living through a pandemic
were offset by increases in countervailing forces,
such as the extent to which respondents had
been able to discover and share the capacity tocare for each other in difficult times.

Another possible reason for a cautious interpretation of our results is that some of the data comefrom the same respondents as the life evaluations
and are thus possibly determined by common
factors. This is less likely when comparing national
averages because individual differences inpersonality and individual life circumstances tend
to average out at the national level. To provide
even more assurance that our results are notsignificantly biassed because we are using thesame respondents to report life evaluations, social
support, freedom, generosity, and corruption, we
tested the robustness of our procedure by splitting each country’s respondents randomly into
two groups (see Table 10 of Statistical Appendix 1of World Happiness Report 2018 for more detail).
We then examined whether the average values ofsocial support, freedom, generosity, and absenceof corruption from one half of the sample
explained average life evaluations in the otherhalf of the sample. The coefficients on each of thefour variables fell slightly, just as we expected.23
But the changes were reassuringly small (ranging
from 1% to 5%) and were not statistically significant.24

How do happiness rankings varyby age group?
Figure 2.2 shows the happiness rankings for the
young (under 30), and Figure 2.3 does the samefor those over 60.31
As shown by Figures 2.2 and 2.3, country rankingsfor the young and the old are quite different, andsystematically so. For example, Lithuania, a recent
entrant to the overall top twenty, ranks number 1
for those under 30 compared to 44 for those over
60, underscoring the fact that convergence
between the two halves of Europe has been
driven mainly by the rising happiness of theyoung. Countries ranking highest for the old are
generally countries with high overall rankings, but
include several where the young have recently
fared very poorly.

Overall, the model explains average life evaluationlevels quite well within regions, among regions,and for the world as a whole.25 On average, thecountries 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 themodel. This difference has been attributed to avariety of factors, including some unique featuresof family and social life in Latin American countries.26
In partial contrast, countries in East Asia haveaverage life evaluations below predictions,

Countries ranking highest for theold are generally countries withhigh overall rankings, but include
several where the young haverecently fared very poorly.
22

133. Ecuador
Sierra Leone(3.471)84.
(4.927)134. Congo
Uganda(Brazzaville)(3.403)85.
(4.918)135. Moldova
Comoros(4.896)(3.305)86.
136. Tajikistan
Eswatini (3.075)
87.
(4.888)World Happiness Report 2024

137. Armenia
Zimbabwe(3.021)88.
(4.865)138. Mozambique
Lesotho (2.808)
89.
(4.804)139. Bulgaria
Congo (Kinshasa)
90.
(4.775) (2.703)
140. Georgia
Botswana(2.528)91.
(4.719)141.
Lebanon(2.490)(4.694)
Figure 92.
2.2:Turkiye
Rankingof Happiness - the Young (Age below 30): 2021-2023
142. Mauritania
Zambia (2.484)
93.
(4.691)143. Chad
Afghanistan
94.
(4.689)(1.456)
95. Iraq (4.684)
96. Ivory Coast (4.682)
1.
Lithuania (7.759)
97. Israel
Montenegro
2.
(7.667)(4.674)
98. Serbia
North Macedonia(4.658)3.
(7.658)99. Iceland
State of(7.598)
Palestine (4.643)
4.

0

1

2

3

4

5

6

7

8

5

6

7

8

100. Denmark
Albania (4.643)
5.
(7.329)101.
Niger (4.634)(7.301)
6. Luxembourg
102. Finland
Myanmar(4.626)7.
(7.300)103. Romania
Iran (4.596)
8.
(7.284)

Average Life Evaluation

104. Netherlands
Liberia (4.534)
9.
(7.248)
105.
Burkina Faso
(4.505)
10. Czechia(7.198)95% confidence interval

106. Costa
GabonRica(4.457)11.
(7.150)107. Austria
Cameroon(4.428)12.
(7.142)108. Switzerland
Azerbaijan (4.417)
13.
(7.138)109.
Madagascar(4.416)
14. Croatia (7.116)
110. Slovenia
Cambodia(4.401)15.
(7.111)111. Kuwait
Senegal(7.104)(4.366)16.
112. El
Gambia(4.346)
17.
Salvador(7.057)113.
Morocco(4.293)18. Sweden (7.026)
114. Australia
Namibia (4.285)
19.
(7.013)115. Norway
Ukraine (4.279)
20.
(6.995)116. Ireland
Mali (4.211)
21.
(6.954)117.
Benin (4.206)
22. Mexico(6.954)118. Kosovo
Tunisia (4.167)
23.
(6.949)119. Belgium
Kenya (4.134)
24.
(6.947)120. Taiwan
Bangladesh(4.124)25.
Provinceof China (6.908)
121.
India
(4.095)
26. Panama (6.883)
122. New
Pakistan(4.030)27.
Zealand(6.859)123. Nicaragua
Jordan (4.024)
28.
(6.789)124. Moldova
Egypt (3.969)
29.
(6.786)125.
Ghana (3.839)
30. Uruguay(6.775)126. Latvia
Tanzania(3.826)31.
(6.766)
127. UnitedTogo (3.790)
32.
Kingdom (6.754)
128. Bosnia
Sri Lanka(3.772)33.
andHerzegovina (6.746)
129.
Yemen
(3.740)34. Argentina (6.746)
130. United
Nigeria Arab
(3.720)35.
Emirates (6.732)
131. Hungary
Ethiopia (3.563)
36.
(6.720)132. Paraguay
Malawi (3.498)
37.
(6.715)133.
Sierra Leone(3.471)
38. Slovakia(6.674)134. Chile
Uganda(3.403)39.
(6.662)135. Bulgaria
Comoros(6.621)(3.305)40.
136. Italy
Eswatini(3.075)41.
(6.618)137.
Zimbabwe(3.021)
42. Saudi Arabia(6.617)138. Poland
Lesotho(6.605)(2.808)43.
139. Estonia
Congo (Kinshasa)(2.703)44.
(6.599)140. Thailand
Botswana(6.597)(2.528)45.
141.
Lebanon(2.490)46. Portugal (6.588)
142. Germany
Zambia (2.484)
47.
(6.578)143. France
Afghanistan(1.456)48.
(6.561)
49. Guatemala (6.548)
50. Montenegro (6.536)
51.

Cyprus (6.525)

Average Life Evaluation

52. South Korea (6.503)
95% confidence interval

0

1

2

3

4

53. Greece (6.502)
54. Singapore (6.484)
55. Spain (6.463)
56. Honduras (6.462)
57. Malta (6.452)
58. Canada (6.439)
59. Ecuador (6.437)

23
Average
Life Evaluation

95% confidence interval

36. Hungary (6.720)
133. Sierra Leone(3.471)84.
37. Ecuador
Paraguay(4.927)(6.715)134. Congo
Uganda(Brazzaville)(3.403)85.
(4.918)38. Slovakia (6.674)
135. Comoros(4.896)(3.305)86.
World Happiness Report 2024
39. Moldova
Chile (6.662)
136. Eswatini (3.075)
87.
(4.888)
40. Tajikistan
Bulgaria (6.621)
137. Zimbabwe(3.021)88.
(4.865)
41. Armenia
Italy (6.618)
138. Mozambique
Lesotho (2.808)
89.
42. Saudi Arabia (4.804)(6.617)139. Congo (Kinshasa)
90.
(4.775) (2.703)
43. Bulgaria
Poland (6.605)
140. Botswana(2.528)91.
(4.719)44. Georgia
Estonia (6.599)
141.
Lebanon(2.490)Turkiye(4.694)
Figure 92.
2.2:
Rankingof Happiness - the Young (Age below 30): 2021-2023 (continued)
45. Thailand (6.597)
142. Mauritania
Zambia (2.484)
93.
(4.691)
46. Portugal (6.588)
143. Afghanistan(1.456)94.
(4.689)47. Chad
Germany(6.578)95.
Iraq(4.684)
48. France (6.561)
96.
Coast(6.548)(4.682)
49. Ivory
Guatemala97.
Montenegro(4.674)50. Montenegro (6.536)
98. Cyprus
North Macedonia
51.
(6.525) (4.658)
99. South
State of
Palestine(4.643)52.
Korea(6.503)

0

1

2

3

4

5

6

7

8

5

6

7

8

100. Greece
Albania (6.502)(4.643)
53.
101.
Niger
(4.634)
54. Singapore (6.484)
102. Spain
Myanmar(4.626)55.
(6.463)103. Honduras
Iran (4.596)
56.
(6.462)Average Life Evaluation

104. Malta
Liberia(6.452)(4.534)
57.
105.
Burkina
Faso (4.505)
58. Canada (6.439)

95% confidence interval

106. Ecuador
Gabon (4.457)
59.
(6.437)107. Brazil
Cameroon(4.428)60.
(6.436)108. Dominican
Azerbaijan Republic(4.417) (6.407)
61.
109.
Madagascar
62. United States(4.416)(6.392)110. Peru
Cambodia(4.401)63.
(6.382)111. Malaysia
Senegal (4.366)
64.
(6.372)112. Vietnam
Gambia (4.346)
65.
(6.363)113.
Morocco(6.358)(4.293)
66. Albania
114. North
Namibia(4.285) (6.329)
67.
Macedonia
115. Russia
Ukraine(6.328)(4.279)68.
116. Kazakhstan
Mali (4.211) (6.324)
69.
117.
Benin (4.206)
70. Philippines(6.305)118. Uzbekistan
Tunisia (4.167)
71.
(6.283)119. Armenia
Kenya (4.134)
72.
(6.245)120. Japan
Bangladesh(4.124)73.
(6.232)121.
India(4.095)74. Bolivia (6.157)
122. Indonesia
Pakistan (4.030)
75.
(6.089)123. Colombia
Jordan (4.024)
76.
(6.035)124. Bahrain
Egypt (3.969)
77.
(6.034)125.
Ghana (3.839)
78. Georgia(6.031)126. China
Tanzania(3.826)79.
(5.949)127. Libya
Togo (3.790)
80.
(5.937)128. Kyrgyzstan
Sri Lanka (3.772)
81.
(5.935)
129.
Yemen (3.740)
82. Ukraine(5.907)130. Venezuela
Nigeria (3.720)
83.
(5.896)131. Jamaica
Ethiopia (5.826)(3.563)84.
132. Mauritius
Malawi (3.498)
85.
(5.791)
133.
Sierra Leone(3.471)
86. Mongolia(5.758)134. South
Uganda(3.403)87.
Africa(5.650)135. Congo
Comoros(3.305)88.
(Brazzaville)(5.574)136. Tajikistan
Eswatini (3.075)
89.
(5.500)137.
Zimbabwe
90. Iraq(5.486)(3.021)
138. Gabon
Lesotho(5.477)(2.808)91.
139. Nepal
Congo(5.467)(Kinshasa) (2.703)
92.
140. Algeria
Botswana(2.528)93.
(5.379)141.
Lebanon(2.490)94. Mozambique (5.352)
142. Azerbaijan
Zambia (2.484)
95.
(5.352)143. IranAfghanistan
96.
(5.331) (1.456)
97. Hong Kong S.A.R. of China (5.329)
98. Morocco (5.281)
99. Senegal (5.266)
Average Life Evaluation

100. Ivory Coast (5.251)

95% confidence interval

0

1

2

3

4

101. Turkiye (5.173)
102. State of Palestine (5.120)
103. Guinea (5.106)
104. Laos (5.091)
105. Namibia (5.089)
106. Cameroon (4.996)
107. Pakistan (4.949)

24
Average
Life Evaluation

95% confidence interval

84. Jamaica (5.826)
133. Ecuador
Sierra Leone(3.471)84.
(4.927)85.
Mauritius(5.791)134. Congo
Uganda(Brazzaville)(3.403)85.
(4.918)86.
Mongolia(5.758)135. Moldova
Comoros(3.305)86.
(4.896)87.
South
Africa(5.650)136.
Eswatini(3.075)87.
Tajikistan(4.888)88. Congo (Brazzaville)(5.574)

World Happiness Report 2024

137. Armenia
Zimbabwe(3.021)88.
(4.865)89.
Tajikistan(5.500)138. Mozambique
Lesotho(2.808)89.
(4.804)90.
Iraq(5.486)139. Bulgaria
Congo (5.477)(Kinshasa)
90.
(4.775) (2.703)
91.
Gabon
140.
Botswana(2.528)91.
Georgia(4.719)92. Nepal (5.467)

141.
Lebanon(2.490)(4.694)
Figure 92.
2.2:Turkiye
Rankingof Happiness - the Young (Age below 30): 2021-2023 (continued)
93.
Algeria(5.379)142. Mauritania
Zambia (2.484)
93.
(4.691)
94.
Mozambique(5.352)143. Chad
Afghanistan(1.456)94.
(4.689)95.
Azerbaijan(5.352)95.
96. Iraq
Iran (4.684)(5.331)
96.
Ivory
97. Hong Coast
Kong (4.682)
S.A.R. of China (5.329)
97. Morocco
Montenegro(4.674)98.
(5.281)
98. Senegal
North Macedonia
99.
(5.266) (4.658)
99. Ivory
State Coastof Palestine100.
(5.251)(4.643)
100.
Albania(4.643)¨
101. Turkiye (5.173)

01101. State
Niger of(4.634)102.
Palestine (5.120)
102. Guinea
Myanmar(4.626)103.
(5.106)

2

3

4

5

6

7

8

5
5

6
6

7
7

8
8

Average Life Evaluation

103. Laos
Iran (4.596)
104.
(5.091)
104.
Liberia105. Namibia(4.534)(5.089)95% confidence interval

105. Cameroon
Burkina Faso
(4.505)106.
(4.996)106. Pakistan
Gabon (4.457)
107.
(4.949)107. Nigeria
Cameroon(4.428)108.
(4.906)108.
Azerbaijan(4.417)109. Kenya (4.906)
109. Gambia
Madagascar(4.416)110.
(4.735)110. Uganda
Cambodia(4.401)111.
(4.718)111. Cambodia
Senegal (4.366)
112.
(4.699)112.
Gambia(4.346)113. Liberia (4.670)
113. Jordan
Morocco(4.293)114.
(4.667)114. Benin
Namibia(4.285)115.
(4.665)115. Niger
Ukraine(4.279)116.
(4.616)116.
Mali(4.211)117. Burkina Faso (4.601)
117. Tunisia
Benin (4.206)
118.
(4.560)118. Mauritania
Tunisia (4.167)
119.
(4.517)119. Chad
Kenya(4.462)(4.134)120.
120.
Bangladesh(4.124)121. Ghana (4.426)
121. Myanmar
India (4.095)
122.
(4.354)122. SriPakistan123.
Lanka(4.030)(4.339)123. Madagascar
Jordan (4.024)
124.
(4.334)124.
Egypt(3.969)125. Mali(4.332)125. Togo
Ghana(4.323)(3.839)126.
126. India
Tanzania(3.826)127.
(4.281)127. Bangladesh
Togo (3.790)
128.
(4.200)128.
Sri Lanka (4.161)(3.772)129. Tanzania
129. Egypt
Yemen(4.126)(3.740)130.
130. Ethiopia
Nigeria (3.720)
131.
(4.125)131. Comoros
Ethiopia (3.563)
132.
(4.111)
132.
Malawi(3.498)133. Botswana(4.012)133. Eswatini
Sierra Leone(3.471)134.
(3.894)134. Yemen
Uganda(3.822)(3.403)135.
135. Zambia
Comoros(3.305)136.
(3.794)136.
Eswatini(3.075)137. Malawi (3.710)
137. Lesotho
Zimbabwe(3.021)138.
(3.700)138. Zimbabwe
Lesotho (2.808)
139.
(3.661)139. Congo
Congo (Kinshasa)
140.
(Kinshasa)(2.703)(3.441)
140.
Botswana(2.528)141. Sierra Leone(3.225)141. Lebanon
Lebanon (2.997)(2.490)142.
142. Afghanistan
Zambia (2.484)
143.
(1.827)143. Afghanistan (1.456)

Average Life Evaluation
95% confidence interval

0
0

1
1

2
2

3
3

4
4

25
Average
Life Evaluation

95% confidence interval

133. Ecuador
Sierra Leone(3.471)84.
(4.927)134. Congo
Uganda(Brazzaville)(3.403)85.
(4.918)135. Moldova
Comoros(4.896)(3.305)86.
136. Tajikistan
Eswatini (3.075)
87.
(4.888)World Happiness Report 2024

137. Armenia
Zimbabwe(3.021)88.
(4.865)138. Mozambique
Lesotho (2.808)
89.
(4.804)139. Bulgaria
Congo (Kinshasa)
90.
(4.775) (2.703)
140. Georgia
Botswana(2.528)91.
(4.719)141.
Lebanon(2.490)(4.694)
Figure 92.
2.3:Turkiye
Rankingof Happiness - the Old (age 60 and above): 2021-2023
142. Mauritania
Zambia (2.484)
93.
(4.691)143. Chad
Afghanistan
94.
(4.689)(1.456)
95. Iraq (4.684)
96.
Ivory
Coast
(4.682)
1.
Denmark(7.916)97.
Montenegro(4.674)
2. Finland (7.912)
98. Norway
North Macedonia
3.
(7.660) (4.658)
99. Sweden
State of (7.588)
Palestine (4.643)
4.

0

1

2

3

4

5

6

7

8

5

6

7

8

100. Iceland
Albania(7.585)(4.643)
5.
101.
Niger
(4.634)
6. New Zealand (7.390)
102. Netherlands
Myanmar (4.626)
7.
(7.360)103. Canada
Iran (4.596)
8.
(7.343)

Average Life Evaluation

104. Australia
Liberia (4.534)
9.
(7.304)
105.
BurkinaStates
Faso (4.505)
10. United(7.258)95% confidence interval

106. United
Gabon Arab
(4.457)
11.
Emirates (7.248)
107. Luxembourg
Cameroon (4.428)
12.
(7.214)108. Kuwait
Azerbaijan(4.417)13.
(7.154)
109.
Madagascar(4.416)
14. Switzerland (7.084)
110. Austria
Cambodia(4.401)15.
(6.939)111. Ireland
Senegal(6.932)(4.366)16.
112. Costa
Gambia(4.346)17.
Rica(6.932)
113.
Morocco(4.293)
18. Israel (6.854)
114. Belgium
Namibia (6.842)(4.285)19.
115. United
UkraineKingdom(4.279) (6.812)
20.
116. Germany
Mali (4.211)
21.
(6.734)117.
Benin (4.206)
22. Uzbekistan(6.633)118. Czechia
Tunisia (4.167)
23.
(6.591)119. Uruguay
Kenya (4.134)
24.
(6.561)120. France
Bangladesh(4.124)25.
(6.524)121.
India(4.095)
26. Singapore (6.477)
122. Saudi
Pakistan(4.030)27.
Arabia(6.431)123. Mauritius
Jordan (4.024)
28.
(6.388)124. Spain
Egypt(6.363)(3.969)
29.
125.
Ghana
(3.839)30. China (6.359)
126. Malta
Tanzania(3.826)31.
(6.353)127. Slovenia
Togo (3.790)
32.
(6.310)128. Mexico
Sri Lanka(3.772)33.
(6.287)129.
Yemen(3.740)34. Taiwan Province of China (6.284)
130. Estonia
Nigeria (6.164)(3.720)35.
131. Japan
Ethiopia(3.563)36.
(6.146)132. Brazil
Malawi(6.124)(3.498)37.
133.
SierraLeone
38. Italy (6.119) (3.471)
134. Kosovo
Uganda (6.096)(3.403)39.
135. Poland
Comoros(3.305)40.
(6.051)136. Thailand
Eswatini (6.001)(3.075)41.
137.
Zimbabwe
42. Kazakhstan(3.021)(6.000)138. Philippines
Lesotho (2.808)
43.
(5.976)139. Lithuania
Congo (Kinshasa)
44.
(5.965) (2.703)
140. Argentina
Botswana (5.948)(2.528)45.
141.
Lebanon(2.490)46. Chile (5.946)
142. Nicaragua
Zambia (2.484)
47.
(5.904)143. Romania
Afghanistan(1.456)48.
(5.902)
49. Guatemala (5.887)
50. Libya (5.835)
51.

Latvia (5.811)

Average Life Evaluation

52. El Salvador (5.716)
95% confidence interval

0

1

2

3

4

53. Mongolia (5.701)
54. Serbia (5.696)
55. Kyrgyzstan (5.687)
56. Panama (5.687)
57. Cyprus (5.665)
58. Honduras (5.645)
59. South Korea (5.642)

26
Average
Life Evaluation

95% confidence interval

36. Japan (6.146)
133. Ecuador
Sierra Leone(3.471)84.
(4.927)
37.
Brazil (6.124)
134. Congo
Uganda(Brazzaville)(3.403)85.
(4.918)38.
Italy (6.119)
135. Moldova
Comoros(4.896)(3.305)86.
World Happiness Report 2024
39.
Kosovo (6.096)
136. Tajikistan
Eswatini (3.075)
87.
(4.888)40.
Poland (6.051)
137. Armenia
Zimbabwe(3.021)88.
(4.865)
41.
Thailand (6.001)
138. Mozambique
Lesotho (2.808)
89.
(4.804)42.
Kazakhstan (6.000)
139. Bulgaria
Congo (Kinshasa)(2.703)90.
(4.775)43.
Philippines(5.976)140. Georgia
Botswana(2.528)91.
(4.719)44. Lithuania (5.965)
141.
Lebanon(2.490)92.
Turkiye(4.694)
Figure 45.
2.3:Argentina
Rankingof Happiness - the Old (age 60 and above): 2021-2023 (continued)(5.948)142. Mauritania
Zambia (2.484)
93.
(4.691)46.
Chile (5.946)
143. Chad
Afghanistan(1.456)94.
(4.689)47.
Nicaragua(5.904)95.
Iraq(4.684)
48. Romania (5.902)
96.
Ivory Coast(5.887)(4.682)
49. Guatemala
97.
Montenegro
50. Libya (5.835)(4.674)
98. Latvia
North Macedonia(4.658)51.
(5.811)
99. ElStateof Palestine
52.
Salvador(5.716)(4.643)

0

1

2

3

4

5

6

7

8

5

6

7

8

100. Mongolia
Albania (4.643)
53.
(5.701)
101.
Niger (4.634)
54. Serbia(5.696)102. Kyrgyzstan
Myanmar (4.626)
55.
(5.687)103. Panama
Iran (4.596)
56.
(5.687)

Average Life Evaluation

104. Cyprus
Liberia (4.534)
57.
(5.665)
105.
Burkina Faso
(4.505)58. Honduras(5.645)95% confidence interval

106. South
GabonKorea(4.457)59.
(5.642)107. Slovakia
Cameroon(4.428)60.
(5.641)108. Bahrain
Azerbaijan(4.417)61.
(5.640)109.
Madagascar(4.416)
62. Algeria (5.631)
110. Portugal
Cambodia(4.401)63.
(5.571)111. Venezuela
Senegal (4.366)
64.
(5.570)112. Bolivia
Gambia(5.565)(4.346)65.
113.
Morocco(4.293)
66. Russia (5.544)
114. Greece
Namibia(5.534)(4.285)67.
115. Jamaica
Ukraine (4.279)
68.
(5.529)116. Vietnam
Mali (4.211)
69.
(5.521)
117.
Benin (4.206)
70. Hungary(5.474)118. Malaysia
Tunisia (4.167)
71.
(5.418)119. Colombia
Kenya (4.134)
72.
(5.393)120. Peru
Bangladesh
73.
(5.313) (4.124)
121.
India
74. Hong (4.095)
Kong S.A.R. of China (5.297)
122. Dominican
Pakistan (4.030)
75.
Republic (5.269)
123. Nepal
Jordan(5.259)(4.024)76.
124. Laos
Egypt(5.256)(3.969)
77.
125.
Ghana
78. Bosnia (3.839)
and Herzegovina (5.241)
126. Indonesia
Tanzania (3.826)
79.
(5.159)127. Croatia
Togo (3.790)
80.
(5.137)128. Guinea
Sri Lanka(3.772)81.
(5.128)129.
Yemen(3.740)82. South Africa (5.083)
130. Paraguay
Nigeria (3.720)
83.
(5.013)131. Ecuador
Ethiopia (4.927)(3.563)84.
132. Congo
Malawi (Brazzaville)(3.498)85.
(4.918)133.
SierraLeone(3.471)
86. Moldova (4.896)
134. Tajikistan
Uganda (3.403)
87.
(4.888)135. Armenia
Comoros(4.865)(3.305)88.
136. Mozambique
Eswatini (3.075)
89.
(4.804)137.
Zimbabwe(3.021)90. Bulgaria(4.775)138. Georgia
Lesotho (4.719)(2.808)91.
139. Turkiye
Congo(Kinshasa)¨
92.
(4.694) (2.703)
140. Mauritania
Botswana (2.528)
93.
(4.691)
141.
Lebanon(2.490)94. Chad(4.689)142. Iraq
Zambia(2.484)95.
(4.684)143. Ivory
Afghanistan(1.456)96.
Coast (4.682)
97. Montenegro (4.674)
98. North Macedonia (4.658)
99. State of Palestine (4.643)
Average Life Evaluation

100. Albania (4.643)

95% confidence interval

0

1

2

3

4

101. Niger (4.634)
102. Myanmar (4.626)
103. Iran (4.596)
104. Liberia (4.534)
105. Burkina Faso (4.505)
106. Gabon (4.457)
107. Cameroon (4.428)

27
Average
Life Evaluation

95% confidence interval

84. Ecuador (4.927)
133. Ecuador
Sierra Leone(3.471) (4.918)
84.
(4.927)85.
Congo(Brazzaville)
134. Congo
Uganda(Brazzaville)(3.403)85.
(4.918)86.
Moldova(4.896)135. Tajikistan
Comoros(4.896)(3.305)86.
Moldova87.
(4.888)136. Armenia
Eswatini (4.865)(3.075)87.
Tajikistan(4.888)88.

World Happiness Report 2024

137. Armenia
Zimbabwe(3.021)88.
(4.865)
89.
Mozambique(4.804)138. Bulgaria
Lesotho (4.775)(2.808)89.
Mozambique(4.804)90.
139. Bulgaria
Congo (Kinshasa)
90.
(4.775) (2.703)
91.
Georgia(4.719)140.
Botswana(2.528)91.
Georgia(4.719)92. Turkiye (4.694)

141.
Lebanon(2.490)(4.694)
Figure 92.
2.3:Turkiye
Rankingof Happiness - the Old (age 60 and above): 2021-2023 (continued)
93.
Mauritania(4.691)142. Chad
Zambia(2.484)93.
Mauritania(4.691)94.
(4.689)143. Chad
Afghanistan
94.
(4.689)(1.456)
95.
Iraq(4.684)95.
96. Iraq
Ivory(4.684)
Coast (4.682)
96.
Coast (4.682)
97. Ivory
Montenegro(4.674)
97. NorthMontenegro(4.674)98.
Macedonia(4.658)98.
North of
Macedonia
99. State
Palestine (4.658)(4.643)99.
State of (4.643)
Palestine (4.643)
100. Albania
100.
Albania(4.643)101. Niger (4.634)

01101. Myanmar
Niger (4.634)
102.
(4.626)102. Iran
Myanmar(4.626)103.
(4.596)

2

3

4

5

6

7

8

5
5

6
6

7
7

8
8

Average Life Evaluation

103.
Iran (4.596)
104. Liberia(4.534)104.
Liberia105. Burkina(4.534)
Faso (4.505)

95% confidence interval

105. Gabon
Burkina(4.457)
Faso (4.505)
106.
106. Gabon(4.457)
107.
Cameroon(4.428)
107.
Cameroon (4.428)
108. Azerbaijan(4.417)108.
Azerbaijan
109. Madagascar(4.417)(4.416)109. Madagascar(4.416)
110.
Cambodia (4.401)
110. Cambodia(4.401)
111.
Senegal (4.366)
111.
Senegal (4.346)(4.366)112. Gambia
112.
Gambia(4.346)113. Morocco (4.293)
113. Namibia
Morocco (4.285)(4.293)114.
114. Namibia(4.285)115.
Ukraine (4.279)
115.
(4.279)116. Ukraine
Mali (4.211)
116.
Mali
(4.211)117. Benin (4.206)
117. Tunisia
Benin (4.206)
118.
(4.167)118. Kenya
Tunisia(4.134)(4.167)
119.
119.
Kenya (4.134)
120. Bangladesh(4.124)120.
Bangladesh
121. India (4.095)(4.124)
121. Pakistan
India (4.095)
122.
(4.030)122. Jordan
Pakistan(4.024)(4.030)123.
123.
(4.024)124. Jordan
Egypt (3.969)
124.
Egypt
(3.969)125. Ghana (3.839)
125. Tanzania
Ghana (3.839)
126.
(3.826)126. Togo
Tanzania(3.826)127.
(3.790)127.
(3.790)128. Togo
Sri Lanka(3.772)
128.
Sri
Lanka
(3.772)129. Yemen (3.740)
129. Nigeria
Yemen (3.740)
130.
(3.720)130. Nigeria
131.
Ethiopia(3.720)(3.563)131.
Ethiopia(3.498)(3.563)132. Malawi
132.
Malawi(3.498)133. Sierra Leone (3.471)
133. Uganda
Sierra Leone(3.471)134.
(3.403)134. Uganda
135.
Comoros(3.403)(3.305)135.
(3.305)136. Comoros
Eswatini (3.075)
136.
Eswatini(3.075)137. Zimbabwe (3.021)
137. Lesotho
Zimbabwe(3.021)138.
(2.808)138. Congo
Lesotho(Kinshasa)(2.808) (2.703)
139.
139.
Congo (Kinshasa)
140. Botswana(2.528) (2.703)
140.
Botswana(2.528)141. Lebanon (2.490)
141. Zambia
Lebanon(2.484)(2.490)142.
142. Afghanistan
Zambia (2.484)
143.
(1.456)143. Afghanistan (1.456)

Average Life Evaluation
95% confidence interval

0
0

1
1

2
2

3
3

4
4

Average Life Evaluation
28
Average
Life Evaluation
95% confidence interval
95% confidence interval

World Happiness Report 2024

see in the following sections, these gaps havemainly arisen since 2010, and probably involvesome mix of generational and age effects.

To better illustrate the overall patterns of international differences in happiness at different ages,
Table 2.2 shows for each country the ranking of
its life evaluations for the whole population (inthe first column) and then four age groups- under30, 30-44, 45-59, and 60+. The two columns at
the right hand side of the table show for eachcountry the happiest and least happy ages. The
countries are listed in order of 2021-2023 average
life evaluations for the whole population, thesame order as is used for Figure 2.1. Countries
with very different rankings at different ages
reflect something unusual, relative to the world
average experience for each age group. For
example, the four countries in the NANZ group the United States, Canada, Australia and New
Zealand - all have rankings for the young thatare much lower than for the old, with the biggest
discrepancies in the United States and Canada
where the gap is 50 places or more. As we shall

Photo Philip White on Unsplash

There are many more countries where the rankingsfor the young are more than 40 places higher
than for the old, mainly in Central and Eastern
Europe and Latin America. The biggest gap isin Croatia, where the ranking for the young is
66 places higher than for the old. There are gapsof 50 or more places for Bulgaria, Moldova, andSerbia, and between 40 and 50 places inRomania, Bosnia and Herzegovina, Montenegro,
and Paraguay. There are clearly generational aswell as age effects at play here as well, as theolder populations of Bosnia, Serbia, Croatia, andMontenegro bear the most scars from the early
1990s wars and genocide following the breakupof the former Yugoslavia.32

29

World Happiness Report 2024

Table 2.2: Ranking of life evaluations by age group, 2021- 2023
Country

All Ages

The Young

Lower Middle

Upper Middle

The Old

Happiest

Finland

1

7

1

1

2

Old

Least Happy
Young

Denmark

2

5

3

4

1

Old

Young

Iceland

3

4

4

2

5

Young

LowerMiddle

Sweden

4

18

8

3

4

Old

Young

Israel

5

2

2

7

18

Young

Old

Netherlands

6

9

5

5

7

Old

Young

Norway

7

20

6

6

3

Old

Young

Luxembourg

8

6

11

8

12

Young

LowerMiddle

Switzerland

9

13

9

11

14

Young

UpperMiddle

Australia

10

19

14

10

9

Old

LowerMiddle

New Zealand

11

27

18

13

6

Old

LowerMiddle

Costa Rica

12

11

15

23

17

Young

UpperMiddle

Kuwait

13

16

20

9

13

Old

LowerMiddle

Austria

14

12

17

18

15

Young

UpperMiddle

Canada

15

58

28

12

8

Old

Young

Belgium

16

24

13

15

19

LowerMiddle

Old

Ireland

17

21

21

21

16

Young

UpperMiddle

Czechia

18

10

12

22

23

Young

Old

Lithuania

19

1

7

20

44

Young

Old

United Kingdom

20

32

27

19

20

Old

LowerMiddle

Slovenia

21

15

10

27

32

Young

Old

United Arab Emirates

22

35

25

16

11

Old

LowerMiddle

United States

23

62

42

17

10

Old

LowerMiddle

Germany

24

47

16

28

21

LowerMiddle

Young

Mexico

25

22

19

32

33

Young

Old

Uruguay

26

30

22

34

24

Young

UpperMiddle

France

27

48

23

26

25

LowerMiddle

Old

Saudi Arabia

28

42

39

14

27

UpperMiddle

LowerMiddle

Kosovo

29

23

37

33

39

Young

Old

Singapore

30

54

36

25

26

UpperMiddle

Old

Taiwan Province of China

31

25

35

31

34

Young

Old

Romania

32

8

26

35

48

Young

Old

El Salvador

33

17

38

45

52

Young

Old

Estonia

34

44

24

30

35

LowerMiddle

Old

Poland

35

43

34

24

40

UpperMiddle

Old

Spain

36

55

40

29

29

UpperMiddle

Old

Serbia

37

3

29

44

54

Young

Old

Chile

38

39

32

42

46

Young

Old

Panama

39

26

43

41

56

Young

Old

Malta

40

57

41

38

31

Young

UpperMiddle
Old

Italy

41

41

31

39

38

Young

Guatemala

42

49

46

54

49

Young

Old

Nicaragua

43

28

53

61

47

Young

UpperMiddle

Brazil

44

60

44

40

37

Young

Old

Slovakia

45

38

33

37

60

Young

Old

Latvia

46

31

30

49

51

Young

Old

Uzbekistan

47

71

62

36

22

Old

LowerMiddle

Argentina

48

34

52

64

45

Young

UpperMiddle

Kazakhstan

49

69

48

43

42

Young

Old

30

World Happiness Report 2024

Table 2.2: Ranking of life evaluations by age group, 2021- 2023 (continued)
Country

All Ages

The Young

Lower Middle

Upper Middle

The Old

Happiest

Cyprus

50

51

49

62

57

Young

Least Happy
Old

Japan

51

73

63

52

36

Young

LowerMiddle

South Korea

52

52

45

55

59

Young

Old

Philippines

53

70

68

58

43

Young

LowerMiddle

Vietnam

54

65

54

53

69

Young

Old

Portugal

55

46

50

46

63

Young

Old

Hungary

56

36

51

48

70

Young

Old

Paraguay

57

37

59

75

83

Young

Old

Thailand

58

45

69

69

41

Young

UpperMiddle

Malaysia

59

64

66

60

71

Young

Old

China

60

79

67

57

30

Old

LowerMiddle

Honduras

61

56

72

73

58

Young

UpperMiddle

Bahrain

62

77

60

50

61

Young

Old

Croatia

63

14

47

59

80

Young

Old

Greece

64

53

58

56

67

Young

Old

Bosnia and Herzegovina

65

33

65

67

78

Young

Old

Libya

66

80

73

51

50

UpperMiddle

LowerMiddle

Jamaica

67

84

61

47

68

UpperMiddle

Old

Peru

68

63

64

80

73

Young

UpperMiddle

Dominican Republic

69

61

70

79

75

Young

Old

Mauritius

70

85

77

63

28

Old

LowerMiddle

Moldova

71

29

55

66

86

Young

Old

Russia

72

68

57

78

66

Young

UpperMiddle
UpperMiddle

Bolivia

73

74

75

77

65

Young

Ecuador

74

59

79

89

84

Young

Old

Kyrgyzstan

75

81

81

68

55

Young

LowerMiddle

Montenegro

76

50

56

70

97

Young

Old

Mongolia

77

86

74

65

53

Young

LowerMiddle

Colombia

78

76

78

71

72

Young

Old

Venezuela

79

83

80

83

64

Young

UpperMiddle

Indonesia

80

75

82

84

79

Young

Old

Bulgaria

81

40

71

74

90

Young

Old

Armenia

82

72

83

88

88

Young

Old

South Africa

83

87

84

81

82

Young

Old

North Macedonia

84

67

76

85

98

Young

Old

Algeria

85

93

85

82

62

Old

UpperMiddle

Hong Kong S.A.R. of China

86

97

89

72

74

UpperMiddle

LowerMiddle

Albania

87

66

86

97

100

Young

Old

Tajikistan

88

89

88

86

87

Young

Old

Congo (Brazzaville)

89

88

97

90

85

Young

Old

Mozambique

90

94

87

96

89

Young

UpperMiddle
Old

Georgia

91

78

91

91

91

Young

Iraq

92

90

96

94

95

Young

Old

Nepal

93

92

101

93

76

Young

UpperMiddle

Laos

94

104

93

76

77

UpperMiddle

LowerMiddle

Gabon

95

91

99

100

106

Young

Old

Ivory Coast

96

100

92

95

96

Young

Old

Guinea

97

103

94

99

81

Old

UpperMiddle

Türkiye

98

101

98

92

92

Young

Old

31

World Happiness Report 2024

Table 2.2: Ranking of life evaluations by age group, 2021- 2023 (continued)
Country

All Ages

The Young

Lower Middle

Upper Middle

The Old

Happiest

Senegal

99

99

104

102

111

Young

Least Happy
Old

Iran

100

96

100

104

103

Young

UpperMiddle
Old

Azerbaijan

101

95

103

103

108

Young

Nigeria

102

108

95

87

130

UpperMiddle

Old

State of Palestine

103

102

105

109

99

Young

UpperMiddle

Cameroon

104

106

102

98

107

Young

Old

Ukraine

105

82

90

110

115

Young

Old

Namibia

106

105

106

101

114

Young

Old

Morocco

107

98

108

107

113

Young

Old

Pakistan

108

107

109

113

122

Young

Old

Niger

109

116

110

114

101

Old

UpperMiddle

Burkina Faso

110

117

107

116

105

LowerMiddle

UpperMiddle

Mauritania

111

119

112

106

93

Old

LowerMiddle

Gambia

112

110

116

115

112

Young

LowerMiddle

Chad

113

120

111

111

94

Old

UpperMiddle

Kenya

114

109

119

123

119

Young

UpperMiddle

Tunisia

115

118

113

108

118

Young

Old

Benin

116

115

117

122

117

Young

UpperMiddle

Uganda

117

111

118

124

134

Young

Old

Myanmar

118

122

115

105

102

Old

LowerMiddle
LowerMiddle

Cambodia

119

112

122

120

110

Young

Ghana

120

121

114

119

125

Young

Old

Liberia

121

113

126

127

104

Young

UpperMiddle

Mali

122

125

120

118

116

Young

LowerMiddle

Madagascar

123

124

123

117

109

Old

LowerMiddle

Togo

124

126

121

112

127

UpperMiddle

Old

Jordan

125

114

124

130

123

Young

UpperMiddle

India

126

127

127

121

121

Young

LowerMiddle

Egypt

127

130

125

126

124

Young

UpperMiddle

Sri Lanka

128

123

128

128

128

Young

UpperMiddle

Bangladesh

129

128

129

129

120

Young

UpperMiddle

Ethiopia

130

131

130

125

131

Young

LowerMiddle

Tanzania

131

129

132

131

126

Young

UpperMiddle

Comoros

132

132

139

133

135

Young

LowerMiddle
UpperMiddle

Yemen

133

135

135

136

129

Young

Zambia

134

136

131

138

142

Young

Old

Eswatini

135

134

134

137

136

Young

UpperMiddle

Malawi

136

137

140

135

132

Young

LowerMiddle

Botswana

137

133

133

140

140

Young

Old

Zimbabwe

138

139

138

139

137

Young

UpperMiddle

Congo (Kinshasa)

139

140

137

134

139

Young

Old

Sierra Leone

140

141

136

132

133

Old

LowerMiddle

Lesotho

141

138

141

142

138

Young

UpperMiddle

Lebanon

142

142

142

141

141

Young

Old

Afghanistan

143

143

143

143

143

Young

Old

32

World Happiness Report 2024

What is typical for happiness at different ages?33
Figure 2.4 shows average life evaluations in thefour age groups for the world as a whole and foreach of ten regions, separately for males andfemales. For the world as a whole, in recent years,
there is a gradual slight decline in average happinessas age increases.34 As will be shown by Figure 2.5
and 2.6 in the next section, it has not always beenthus, as in the early years of the Gallup World Poll(2006-2010) the young were the happiest group,
followed by those over 60 and then those 30 to
44, with 45-59 as the least happy group.

The ranking gaps are imperfect measures of thehappiness gaps between the old and young,because the distribution of country averages ismuch more tightly spaced in the middle, where asmall change in average happiness can translate
to many ranks. There are fewer countries withlarge rank differences at both ends of the distribution, where the ranks are most consistent. A
country at the top of the overall ranking has tohave pretty high happiness in all age groups,
while in the really unhappy countries there are no
happy age groups. Thus to assess happiness at
different ages it is better to look at the averagereported happiness levels at different ages, as we
now do.

Figure 2.4: Happiness at different ages, 2021-2023

World

Western Europe

Central and Eastern Europe

Commonwealth of
Independent States

Southeast Asia

South Asia

East Asia

Latin America
and Caribbean

North America and ANZ

Middle Eastand North Africa

Sub-Saharan Africa

7
6
5
4

Cantril Ladder

3

7
6
5
4
3
< 30

30–44

45–59

7
6

Males

5

Females

4
3
< 30

30–44

45–59

60+ < 30

30–44

45–59

60+ < 30

Age Group

33

30–44

45–59

60+

60+

World Happiness Report 2024

Norway, Sweden, Germany,
France, the United Kingdom andSpain are countries where the old
are now significantly happier than
the young, while Portugal andGreece show the reverse pattern.

In South Asia, happiness is lowest in the middle
age groups, especially for males, exposing alarge middle age life evaluation gap favouring
females, with a definite U-shape for males.
In East Asia, there is a general slight downward
tilt with age, with females happier than males inall age groups.
In Latin America and the Caribbean, there is a
general downward trend across ages less than60, with an increase thereafter for females.
Male and female happiness is equal under theage of 30, with a growing age gap thereafter
favouring females.

The first panel of Figure 2.4 displays a fairly flat
global pattern of life evaluations across age
groups, with the young on average happier thanthe old, and a slight gender difference favouring
females.35 This global average obscures a rangeof regional experiences. When considering theregional differences, and how they contribute to
the global average, it is important to rememberthat every country has equal weight in the regionaland global averages, so that the regions withmore countries contribute correspondingly moreto the global averages.36 Considering the regionsin the order shown in Table 2.4, Western Europe
has an almost completely flat profile across theage groups, although Table 2.2 and Figures 2.2
and 2.3 show a variety of experiences within theregion. For example, Norway, Sweden, Germany,
France, the United Kingdom and Spain are countries where the old are now significantly happierthan the young, while Portugal and Greece show
the reverse pattern.

In North America, Australia and New Zealand, life
evaluations in 2021-2023 were lowest among theyoung, rising gradually with age to be highest
among the old. The age gap favouring the old isevident in all four countries, while being muchlarger in the United States and Canada. The only
significant gender gap is in older middle age,favouring females.
For the twenty countries of the Middle East andNorth Africa, by contrast, happiness is highest for
the young, especially young females, and thenfalls steadily thereafter before rising again forfemales 60 and over. There is diversity within theregion, with the gap favouring the young foundespecially in Israel, while being reversed in the
UAE and Saudi Arabia, both of which have large
numbers of foreign-born workers in their lower
age groups.

The countries of Central and Eastern Europe showmuch higher life evaluations for the young, witha steady decline across age groups thereafter,
accumulating to a gap between the young and theold of more than a full point on the 0 to 10 scale.
This pattern is slightly more pronounced for females
than for males. The twelve countries in the
Commonwealth of Independent States, with Russia
and Ukraine as the largest, show a more muted
pattern than in Central and Eastern Europe, and witha larger mid-life drop for males than for females.

Averaging across more than 40 countries in
Sub-Saharan Africa, life evaluations are highest
for the young, fairly similar in the two middle agegroups, and then higher for males and lower for
females in the 60+ age group.
What about global differences within age groups?
Within the group of those under the age of 30,
average life evaluations drop significantly withage,37 a finding that has echoes in Chapter 3
dealing with a broader range of evidence onadolescent and youth well-being. Within the
global sample of those over 60, we find life
evaluations rising with age, as is also found in the
Indian evidence in Chapter 5.38 For a global
sample including both of the middle age groups,

The ten countries of Southeast Asia, with Indonesia
the largest and Singapore the smallest, show adeclining structure of happiness across age
groups and a gender difference favouring young
females, with the largest contribution to thiseffect coming from Singapore.
34

Photo Aleksandar Popovski on Unsplash

World Happiness Report 2024

population divided into three birth cohorts: thoseborn before 1964, between 1965 and 1980, andafter 1980.

there is a negative influence from age and apositive one from age-squared, with an implied
low point slightly below age 50.39 Within the
30 to 44 age group, the age effect is generally
down, with no sign of a low point within that age
range. Within the 45 to 59 group, there is an
implied U-shape in age, with an estimated low
point just over 50 years of age. More on this laterin the chapter.

For the population as a whole, Figure 2.5 below
shows for each country the change in happinessfrom 2006-2010 to 2021-2023. Seventeen countries
have increases in average life evaluations of a fullpoint or more, compared to seven countries with
reductions of a point or more on the 0 to 10 scale.
Among the larger gainers, there are several
countries in Eastern Europe where the increases
were more than one-third of their averagehappiness scores in 2006-2010. Some of theworst faring countries, especially Lebanon and
Afghanistan saw their life evaluations halved fromtheir base values.

Is life getting better or worse,and for which age groups?
The most fine-grained national-level indicationof how the quality of life has been changing ineach country is provided in Figures 7 through 34
in the Statistical Appendix. Figures 7-20 plot foreach country the year-by-year trajectories forlife evaluations in each of the four age groups,
and Figures 21-34 repeat the analysis with the35

78.
124. Guatemala
Bangladesh(0.010)(0.887)79.
(0.001)125. Japan
India (0.920)
80.
126. Laos
Egypt(0.022)(0.993)81.
(0.027)127. Singapore
Yemen (0.997)

World Happiness Report 2024

82.
Sweden (0.035)
128. Botswana(1.197)83.
Namibia(1.202)(0.054)129. Zambia
84.
Argentina(0.054)130. Malawi(1.203)85.
(0.062)131. Mexico
Venezuela(1.316)86.
(0.069)132. Tanzania
Jordan(1.522)
Figure 2.5:
Changesin Happiness: from 2006-2010 to 2021-2023
87.
(0.078)133. Comoros
Lebanon (2.324)
88.
134. Madagascar
Afghanistan (0.081)(2.599)89. Algeria (0.100)
90. Hong Kong S.A.R. of China (0.107)
1.
Serbia (1.847)
91. France (0.138)
2. Bulgaria (1.573)
92. United Kingdom (0.187)
3. Latvia (1.473)
93. Netherlands (0.193)
4. Congo (Brazzaville) (1.402)
94. Greece (0.199)
5. Romania (1.313)
95. United Arab Emirates (0.200)
6. China (1.293)
96. Ukraine (0.216)
7. Georgia (1.292)
97. Belgium (0.219)
8. Lithuania (1.229)
98. Norway (0.222)
9. Philippines (1.223)
99. Italy (0.255)
10. Togo (1.207)
100. Turkiye (0.259)
11. Nicaragua (1.169)
101. Denmark (0.273)
12. Kosovo (1.141)
102. Australia (0.273)
13. Mongolia (1.134)
103. Costa Rica (0.296
14. Estonia (1.118)
104. Austria (0.323)
15. Hungary (1.074)
105. Iran (0.338)
16. Bosnia and Herzegovina (1.020)
106. Spain (0.340)
17. North Macedonia (1.000)
107. Sierra Leone (0.341)
18. Armenia (0.981)
108. New Zealand (0.343)
19. Uzbekistan (0.970)
109. Cyprus (0.348)
20. Kyrgyzstan (0.885)
110. Jamaica (0.366)
21. Benin (0.884)
111. Sri Lanka (0.377)
22. Ivory Coast (0.883)
112. Ghana (0.416)
23. Slovenia (0.821)
113. Brazil (0.425)
24. Dominican Republic (0.787)
114. Switzerland (0.439)
25. Uruguay (0.713)
115. Ireland (0.446)
26. Portugal (0.702)
116. Zimbabwe (0.463)
27. Vietnam (0.683)
117. Pakistan (0.479)
28. Tajikistan (0.667)
118. Panama (0.504)
29. Poland (0.638)
119. Colombia (0.507)
30. Iceland (0.637)
120. United States (0.545)
31. Burkina Faso (0.622)
121. Canada (0.599)
32. Slovakia (0.600)
122. Tunisia (0.675)
33. Kazakhstan (0.593)
123. Congo (Kinshasa) (0.688)
34. Peru (0.583)
124. Bangladesh (0.887)
35. Paraguay (0.578)
125. India (0.920)
36. Montenegro (0.560)
126. Egypt (0.993)
37. Nepal (0.554)
127. Yemen (0.997)
38. Chad (0.552)
128. Botswana (1.197)
39. Mozambique (0.524)
129. Zambia (1.202)
40. Taiwan Province of China (0.516)
130. Malawi (1.203)
41. El Salvador (0.513)
131. Venezuela (1.316)
42. Moldova (0.510)
132. Jordan (1.522)
43. Cameroon (0.510)
133. Lebanon (2.324)
44. Russia (0.501)
134. Afghanistan (2.599)
45. Senegal (0.472)

−2

−1.5

−1

�.5

0

.5

1

1.5

2

−2

−1.5

−1

�.5

0

.5

1

1.5

2

46. Czechia (0.462)
47. Niger (0.434)
48.2006–2010
South toKorea
(0.414)
Changes from2021–2023
95% confidence
49. interval
Morocco (0.411)

50. Indonesia (0.410)
51.

Honduras (0.404)

52. Malta (0.386)
53. Kuwait (0.379)
54. State of Palestine (0.366)
55. South Africa (0.321)

36

78.
124. Guatemala
Bangladesh(0.010)(0.887)35. Paraguay (0.578)
79.
(0.001)125. Japan
India (0.920)
36. Montenegro (0.560)
80.
126. Laos
Egypt(0.022)(0.993)
37. Nepal (0.554)
World Happiness Report 2024
81.
(0.027)127. Singapore
Yemen (0.997)
38. Chad (0.552)
82.
Sweden (0.035)
128. Botswana(1.197)39. Mozambique (0.524)
83.
Namibia(1.202)(0.054)129. Zambia
40. Taiwan Province of China (0.516)
84.
Argentina(0.054)130. Malawi(1.203)
41. El Salvador (0.513)
85.
(0.062)131. Mexico
Venezuela(1.316)
42. Moldova (0.510)
86.
Tanzania(0.069)132.
Jordan(1.522)
Figure 2.5:
Changesin Happiness: from 2006-2010 to 2021-2023 (continued)
43. Cameroon(0.510)87.
(0.078)133. Comoros
Lebanon (2.324)
44. Russia (0.501)
88.
134. Madagascar
Afghanistan (0.081)(2.599)45. Senegal (0.472)
89. Algeria (0.100)
46. Czechia (0.462)
90. Hong Kong S.A.R. of China (0.107)
47. Niger (0.434)
91. France (0.138)
48. South Korea (0.414)
92. United Kingdom (0.187)
−2
−1.5
−1
�.5
0
.5
1
1.5
49. Morocco (0.411)
93. Netherlands (0.193)
50. Indonesia (0.410)
94. Greece (0.199)
51. Honduras (0.404)
95. United Arab Emirates (0.200)
52. Malta (0.386)
96. Ukraine (0.216)
53. Kuwait (0.379)
97. Belgium (0.219)
54. State of Palestine (0.366)
98. Norway (0.222)
55. South Africa (0.321)
99. Italy (0.255)
56. Croatia (0.320)
100. Turkiye (0.259)
57. Iraq (0.311)
101. Denmark (0.273)
58. Azerbaijan (0.311)
102. Australia (0.273)
59. Ecuador (0.300)
103. Costa Rica (0.296
60. Mali (0.265)
104. Austria (0.323)
61. Cambodia (0.252)
105. Iran (0.338)
62. Kenya (0.250)
106. Spain (0.340)
63. Chile (0.232)
107. Sierra Leone (0.341)
64. Liberia (0.229)
108. New Zealand (0.343)
65. Thailand (0.172)
109. Cyprus (0.348)
66. Albania (0.171)
110. Jamaica (0.366)
67. Finland (0.162)
111. Sri Lanka (0.377)
68. Malaysia (0.152)
112. Ghana (0.416)
69. Bolivia (0.151)
113. Brazil (0.425)
70. Germany (0.146)
114. Switzerland (0.439)
71. Bahrain (0.143)
115. Ireland (0.446)
72. Israel (0.142)
116. Zimbabwe (0.463)
73. Luxembourg (0.071)
117. Pakistan (0.479)
74. Uganda (0.058)
118. Panama (0.504)
75. Nigeria (0.025)
119. Colombia (0.507)
76. Saudi Arabia (0.014)
120. United States (0.545)
77. Mauritania (0.011)
121. Canada (0.599)
78. Guatemala (0.010)
122. Tunisia (0.675)
79. Japan (0.001)
123. Congo (Kinshasa) (0.688)
80. Laos (0.022)
124. Bangladesh (0.887)
81. Singapore (0.027)
125. India (0.920)
82. Sweden (0.035)
126. Egypt (0.993)
83. Namibia (0.054)
127. Yemen (0.997)
84. Argentina (0.054)
128. Botswana (1.197)
85. Mexico (0.062)
129. Zambia (1.202)
86. Tanzania (0.069)
130. Malawi (1.203)
87. Comoros (0.078)
131. Venezuela (1.316)
88. Madagascar (0.081)
132. Jordan (1.522)
89. Algeria (0.100)
133. Lebanon (2.324)
90. Hong Kong S.A.R. of China (0.107)
134. Afghanistan (2.599)
91. France (0.138)

2

92. United Kingdom (0.187)
93. Netherlands (0.193)
94. Greece (0.199)

Changes from 2006–2010 to 2021–2023
95% confidence
95. interval
United Arab Emirates (0.200)

−2

−1.5

96. Ukraine (0.216)
97. Belgium (0.219)
98. Norway (0.222)
99. Italy (0.255)
100. Turkiye (0.259)
101. Denmark (0.273)

37

−1

�.5

0

.5

1

1.5

2

78.
(0.010)124.
Bangladesh(0.887)
81. Guatemala
Singapore (0.027)
79.
Japan
(0.001)
125.
India(0.920)82. Sweden (0.035)80.
(0.022)126.
Egypt(0.993)83. LaosNamibia(0.054)81.
Singapore(0.027)127.
Yemen(0.997)
84. Argentina(0.054)

World Happiness Report 2024

82.
Sweden(0.062)(0.035)128. Mexico
Botswana(1.197)85.
83.
Namibia(1.202)(0.054)129. Tanzania
Zambia86.
(0.069)84.
Argentina(0.054)130.
(1.203)87. Malawi
Comoros(0.078)85.
Mexico(0.062)131.
Venezuela(1.316)
88. Madagascar(0.081)86.
(0.069)132.
Jordan(1.522)
Figure 2.5:
Changesin Happiness: from 2006-2010 to 2021-2023 (continued)
89. Tanzania
Algeria(0.100)87.
(0.078)133.
Lebanon(2.324)90. Comoros
Hong
Kong
S.A.R. of China (0.107)
88.
Madagascar(0.081)
134.
Afghanistan(2.599)91. France (0.138)
89.
(0.100) (0.187)
92. Algeria
United Kingdom
90.
Hong
Kong
of China (0.107)
93. NetherlandsS.A.R.
(0.193)
91.
France (0.138)
94. Greece(0.199)92.
United(0.187)
95. United Kingdom
Arab Emirates(0.200)

−2

−1.5

−1

�.5

0

.5

1

1.5

2

−2

−1.5

−1

�.5

0

.5

1

1.5

2

−2

−1.5

−1

�.5

0

.5

1

1.5

2

93.
(0.193)
96. Netherlands
Ukraine (0.216)
94.
Greece(0.199)97. Belgium (0.219)
95.
Emirates (0.200)
98. United
NorwayArab(0.222)
96.
Ukraine(0.216)
99. Italy (0.255)
97.
Belgium(0.219)¨
100. Turkiye(0.259)98. Norway
101.
Denmark(0.222)(0.273)
99. Australia
Italy (0.255)
102.
(0.273)100.
(0.259)103. Turkiye
Costa Rica
(0.296101.
Denmark(0.273)104. Austria(0.323)
102.
Australia(0.273)105. Iran (0.338)
103.
Costa (0.340)
Rica (0.296
106. Spain
104.
Austria(0.323)107. Sierra Leone(0.341)105.
108. Iran
New(0.338)
Zealand (0.343)
106.
Spain
109. Cyprus(0.340)(0.348)107.
Leone(0.341)110. Sierra
Jamaica(0.366)108.
New
Zealand(0.343)111. Sri Lanka (0.377)
109.
(0.348)112. Cyprus
Ghana (0.416)
110.
Jamaica(0.366)113. Brazil (0.425)
111.
Sri Lanka (0.377)
114. Switzerland(0.439)112.
Ghana115. Ireland (0.416)(0.446)113.
(0.425)116. Brazil
Zimbabwe(0.463)114. Switzerland(0.439)117.
Pakistan (0.479)
115. Panama
Ireland (0.446)
118.
(0.504)116.
(0.463)119. Zimbabwe
Colombia (0.507)
117.
Pakistan(0.479)120. UnitedStates(0.545)118.
Panama(0.504)121. Canada (0.599)
119.
Colombia(0.507)122. Tunisia(0.675)120.
UnitedStates(0.545)123. Congo (Kinshasa)(0.688)121.
Canada (0.599)
124. Bangladesh(0.887)
122.
Tunisia(0.675)125. India (0.920)
123.
(Kinshasa) (0.688)
126. Congo
Egypt (0.993)
124.
Bangladesh(0.887)127. Yemen (0.997)
125.
(0.920)128. India
Botswana(1.197)126.
Egypt
129. Zambia(0.993)(1.202)
127.
Yemen (1.203)(0.997)130. Malawi
128.
Botswana131. Venezuela (1.197)(1.316)129.
(1.202)132. Zambia
Jordan (1.522)
130. Malawi(1.203)133.
Lebanon(2.324)131. Afghanistan
Venezuela (1.316)
134.
(2.599)132. Jordan (1.522)
133. Lebanon (2.324)
134. Afghanistan (2.599)

Changes from 2006–2010 to 2021–2023
95% confidence interval

38

World Happiness Report 2024

Figure 2.6: Happiness changes from 2006-2010 to 2021-2023

World

Western Europe

Central and Eastern Europe

Commonwealth of
Independent States

Southeast Asia

South Asia

East Asia

Latin America
and Caribbean

North America and ANZ

Middle Eastand North Africa

Sub-Saharan Africa

8
7
6
5

Cantril Ladder

4

8
7
6
5
4
< 30

30–44

45–59

60+

8
7

2006 – 2010

62021 – 2023

5
4
< 30

30–44

45–59

60+ < 30

30–44

45–59

60+ < 30

30–44

45–59

60+

Age Group

In Central and Eastern Europe, life has improvedby a full point or more at all ages, especially in the
middle age groups. Happiness continues to bemuch higher in the younger age groups, althoughby less now than in 2006-2010. The convergenceof happiness levels in Central and Eastern Europe
toward those in Western Europe has continued.
For those under 30, this convergence is complete,
as happiness levels for them are essentially equalin both halves of Europe. For those over 60, thegap between the two halves of Europe is about
half of what it was in 2006-2010, while still being
more than a full point in 2021-2023.

Figure 2.6 returns to a regional focus to show
how average life evaluations have changed
between 2006-2010 and 2021-2023 for each of
the ten regions, as well as for the average of all
countries, for each of four age groups.
Looking first at the global average across countries,
life evaluations have improved very slightly in all
age groups. Once again, this global averagemasks some very different regional trajectories.
Happiness has generally increased for all age
groups in East Asia, Central and Eastern Europe,and the CIS, and fallen in South Asia, the NANZ
group and the Middle East and North Africa.
There are interesting age group differences within
this general pattern.

Life evaluations have also risen for all age groups
in the CIS countries, by on average half as muchas in Central and Eastern Europe, even thoughstarting a half-point lower in 2006-2020. Hencethe increased gap between these two regionalgroups, especially so for the young and lowermiddle age groups.

In Western Europe, life evaluations among theyoung are significantly lower in 2021-2023 thanthey were in 2006-2010, with a lesser drop in lower
middle age and a small increase for those over 60.

39

World Happiness Report 2024

For the United States, Canada, Australia and
New Zealand, happiness has decreased in all age
groups, but especially for the young, so much sothat the young are now, in 2021-2023, the least
happy age group. This is a big change from2006-2010, when the young were happier thanthose in the midlife groups, and about as happy
as those aged 60 and over. For the young, thehappiness drop was about three-quarters of apoint, and greater for females than males.

Finally, average life evaluations in Sub-SaharanAfrica have not changed for those in the middle
age groups, while rising slightly for both theyoung and the old.

Emotions at different ages
How do emotions differ by age? We shall firstconsider negative emotions and then positive
ones, following the definitions in Technical Box 2.

In the Middle East and North Africa, average life
evaluations fell in all groups between 2006-2010
and 2021-2023, by almost twice as much for
those over 60 and for those under 30. Thus
there has been in the past dozen years a
steepening of the age gradient favouring theyoung over the old.

Females have more frequent negative
emotions at all ages
Figure 2.7 shows negative emotions in the years
2021-2023 by age, separately for females andmales. For the world as a whole, the averagefrequency of the selected negative emotions ishigher for females than males, with the gendergap growing slightly from the young to the old.

Figure 2.7: Negative emotions by gender and age, 2021-2023

World

Western Europe

Central and Eastern Europe

Commonwealth of
Independent States

Southeast Asia

South Asia

East Asia

Latin America
and Caribbean

North America and ANZ

Middle Eastand North Africa

Sub-Saharan Africa

.5
.4
.3
.2

Negative Affect

.1

.5
.4
.3
.2
.1
< 30

30–44

45–59

.5
.4

Males

.3

Females

.2
.1
< 30

30–44

45–59

60+ < 30

30–44

45–59

60+ < 30

Age Group
40

30–44

45–59

60+

60+

World Happiness Report 2024

Negative emotions have gone up in someregions, and down in others

Looking across the regions, there is a mixed
pattern. In Western Europe, negative emotions
are relatively less frequent for males than females
at all ages, and decline slightly with age for bothmales and females. Negative emotions in 20212023 were generally more frequent in Central andEastern Europe than in Western Europe, have aslightly larger gender gap, and rise with age forboth females and males, but by more for females
than males. The same pattern repeats whenmoving to the CIS countries, with negative
emotions more frequent at higher ages, and more
for females than males.

We now consider changes in emotions between
2006-2010 and 2021-2023. As shown in Figure 2.8,
negative emotions are more frequent now thanin 2006-2010 everywhere, only slightly so in East
Asia and Western Europe. The big exception isin Central and Eastern Europe, where there
has been a drop in the frequency of negative
emotions in all age groups, in contrast to the restof the world, but consistent with the happinessconvergence taking place within Europe.
Increases in negative emotions have been mostfrequent in South Asia and Sub-Saharan Africa,
especially at higher ages. In Latin America there
has been no increased frequency of negative
emotions among those under 30, but a substantial
increase in the older age groups. The CIS countries
show a similar but somewhat muted pattern.

The three parts of Asia show quite different
patterns. In Southeast Asia, negative emotions
yesterday are more frequent for females thanmales for the two younger age groups, andless frequent for those over 60. In South Asia,
negative emotions are more frequent thanelsewhere in the world, especially at higher ages
and for females. In East Asia, negative emotions
are globally low, and show little difference by
age and gender.
In Latin America and the Caribbean, negative
emotions are more frequent for females thanmales, especially in the middle age groups, andgenerally rise with age.
The group including the United States, Canada,
Australia and New Zealand shows a quite different pattern than elsewhere. Negative emotions
are at all ages more frequent for females thanmales, especially for those under 30. In thisregion, unlike anywhere else except Western
Europe, negative emotions are more frequent
among the young and least frequent for the old.

Photo Bill Wegener on Unsplash

Negative emotions in SubSaharan Africa are
equally frequent for males and females under theage of 30, and rise with age for both genders
thereafter, by more for females than males. In the
Middle East and North Africa, the biggest gender
gap is in the middle age groups, flanked by roughgender equality for the young and old.

41

World Happiness Report 2024

Figure 2.8: Negative affect levels by age 2006-2010 vs 2021-2023

World

Western Europe

Central and Eastern Europe

Commonwealth of
Independent States

Southeast Asia

South Asia

East Asia

Latin America
and Caribbean

North America and ANZ

Middle Eastand North Africa

Sub-Saharan Africa

.5
.4
.3
.2

Negative Affect

.1

.5
.4
.3
.2
.1
< 30

30–44

45–59

60+

.5
.4

2006 – 2010

.3

2021 – 2023

.2
.1
< 30

30–44

45–59

60+ < 30

30–44

45–59

60+ < 30

30–44

45–59

60+

Age Groupof positive emotions, coupled with increases in
the prevalence of physical pain, encourage a
deeper look at why life evaluations as a whole sofrequently rise after a mid-life low. We do this inlater sections.

There is the reverse pattern in the NANZ countries
where negative emotions have increased more forthe young than for the old. No other region shows
negative emotions increasing more for the young
than for the old.

What about changes from 2006-2010 to 20212023? Figure 2.9 shows no change at the global
level, except for those over 60 where positive
emotions are now more frequent than before. The
unchanged global average shows the net effects
of differing regional patterns. The increased
global frequency of positive emotions among
those over 60 is driven by the countries of
Sub-Saharan Africa, Central and Eastern Europe,and the CIS. In all other regions, positive emotions
at all ages are either unchanged or lower in
2021-2013 than they were in 2006-2010.

Positive emotions are more frequent at
lower ages, and have changed less
As shown in Figure 2.9, positive emotions, which
include laughter, enjoyment, and doing interesting
things,40 are based on experience the previous
day, are almost everywhere more frequent in theyoungest age groups, and are gradually lessfrequent at higher ages. The only exception is in
the NANZ group of countries, which show a
U-shape in age, with those 60+ having about thesame frequency of positive emotions as thoseunder 30. Age-related decreases in the frequency
42

World Happiness Report 2024

Figure 2.9: positive affect levels by age 2006-2010 vs 2021-2023

World

Western Europe

Central and Eastern Europe

Commonwealth of
Independent States

Southeast Asia

South Asia

East Asia

Latin America
and Caribbean

North America and ANZ

Middle Eastand North Africa

Sub-Saharan Africa

.8
.7
.6
.5

Posiative Affect

.4

.8
.7
.6
.5
.4
< 30

30–44

45–59

60+

.8
.7

2006 – 2010

.62021 – 2023

.5
.4
< 30

30–44

45–59

60+ < 30

30–44

45–59

60+ < 30

30–44

45–59

60+

Age Group

How unequal is happinessat different ages?

us to measure inequality as the standard deviationof each country’s distribution - the bigger the
average gap between the happier and less happy
people, the higher will be our inequality measure.42

From the outset of our WHR research, we haveemphasised the importance of the distribution ofhappiness. Research has shown that inequality ofwell-being has a bigger effect on overall happinessthan does inequality of income.41 This is, we think,because it is a broader and more encompassing
measure. Inequality in the distribution of happinessreflects inequalities of access to any of the direct
and indirect supports for well-being, including
income, education, health care, social acceptance,
trust, and the presence of supportive social
environments at the family, community andnational levels. People are happier living in countries
where the equality of happiness is greater. The
use of a 0 to 10 scale for life evaluations permits

This is the first report to consider equality ofhappiness by age group, set in a global environment of increasing inequality. At the global level,
averaged across all ages and regions, inequalityof happiness has increased by more than 20%
over the past dozen years. This is shown in theworld panel of Figure 2.10.

People are happier living incountries where the equalityof happiness is greater.
43

World Happiness Report 2024

Figure 2.10: Inequality of Happiness by age group, time and region

World

Western Europe

Central and Eastern Europe

Commonwealth of
Independent States

Southeast Asia

South Asia

East Asia

Latin America
and Caribbean

North America and ANZ

Middle Eastand North Africa

Sub-Saharan Africa

3.0
2.5
2.0

Standard Deviation of Ladder

1.5

3.0
2.5
2.0
1.5

< 30

30–44

45–59

60+

3.0
2.5

2006 – 2010

2.0

2021 – 2023

1.5

< 30

30–44

45–59

60+ < 30

30–44

45–59

60+ < 30

30–44

45–59

60+

Age Group

In light of the diverse regional trends for inequality
at different ages, the overall inequality rankingsby age are not the same as they were a dozen
years ago. Inequality among those over 60 is now
greatest in Latin America, followed closely by
Sub-Saharan Africa, then, significantly lower, bySoutheast and South Asia, followed then by the
Middle East and North Africa, the CIS countries,
and East Asia. Both halves of Europe, and theUnited States, Canada, Australia, and New Zealandgroup currently have the lowest levels of inequality,
without significant age-group differences.

The red line in each panel of Figure 2.10 shows
the most recent values for happiness inequality ineach group, with the grey line showing inequality
by age group in 2006-2010. Inequality of happiness,as measured by the standard deviation of life
evaluations within an age group, has increasedin every region, except in Western Europe, where
it has on average remained constant, with anincrease in inequality among the old being offset
by a drop for the young. In the North America
plus ANZ group, inequality has increased for the
young but not for the old. Every other region has
seen inequality increases for the old that havebeen greater than those for the young, sometimes
by very large amounts, as in Latin America, Southeast Asia, and the Commonwealth of Independent
States. Happiness inequality in Sub-SaharanAfrica has increased by more than 50% for all
age groups, and only slightly less so for those of
middle age than for the old and the young.

For those under 30, inequality of happiness is by
far the greatest in Sub-Saharan Africa, followedby Southeast Asia, South Asia, and MENA.
Although happiness inequality among the young
has grown, it is still lowest in Western Europe,
as it was in our base period of 2006-2010.

44

World Happiness Report 2024

Are there generational differencesin benevolence?

in benevolence have been equally or more present
in recent generations, then that is an encouraging
finding. Either there has not been a significant
generational shift towards less societal connection,or possibly it has been offset by more recent
positive generational shifts or masked by theinability of sheltered-in-place older adults toperform the benevolent acts they would otherwise
have liked to do.

We updated last year the startling finding inWorld Happiness Report 2022 that all three
benevolent actions surveyed in the Gallup World
Poll - donations, volunteering and especially thehelping of strangers - showed remarkably large
increases over their pre-pandemic values. Now
we can expand on those results in two importantways, first by adding a fourth year of COVID
experience and second by seeing the extent
to which benevolence levels and post-COVID
frequencies differ by generation.

To sort out these possibilities, it is useful tocompare the pre-pandemic and COVID-era
frequencies of benevolent acts by birth cohort. To
do this, we divide respondents into three cohorts:
those born before 1965 (Boomers and theirpredecessors), those born between 1965 and1980 inclusive (Gen X), and those born after 1980
(Millennials and Gen Z).

There has been much discussion about possible
shifts of values, including benevolence, from one
generation to the next since the middle of thelast century. In particular, in the US context theMillennials have been alternatively called the ‘megeneration’, the ‘we generation’ or just anothergeneration.43 With almost twenty years of datafrom the Gallup World Poll, it is becoming feasible
to decouple the age of respondents from theiryear of birth, with the latter defining which
generation they represent. These data permit us
to make a more global assessment of generational
shifts in benevolent actions. In addition, the
COVID pandemic provided a natural experimentto capture generational differences in benevolence. It has been argued that greater levels ofsocial trust among older than among younger
Americans was likely to represent mainly agenerational effect rather than a consequenceof the ageing process.44 There have also beenstudies, based on smaller samples of data, of
whether benevolent values have shifted from one
generation to the next, and whether they havechanged over time within a given cohort.45 All
three of our benevolence measures can beinterpreted as proxy measures of the qualityof community-level social capital. How thesebehaviours were altered by COVID for people indifferent generations provides a nice test ofgenerational differences. If there has been agenerational shift, with those born more recently
being less inclined towards benevolent acts,
then we would expect to find that the surge in
benevolence we have found would be larger
among those in earlier generations. If the increases

Figure 2.11 shows the percentage of the population
performing the three benevolent acts by each ofthese birth-year cohorts, with grey bars showing
the 2017-2019 values and the red bars thefrequencies in and after 2020.46
For all cohorts, both before COVID and now, thehelping of strangers is most frequent, followed bydonations and then volunteering. The pre-COVID
generational patterns differ for the three acts. The
helping of strangers was most common among theyounger cohorts, and lowest for those born before
1965, perhaps reflecting in part their lesser ability
to be out and about. Charitable donations wereless frequent in the younger generations than forthe other age groups, perhaps reflecting theirlower disposable incomes. Volunteering was fairly
equal in the three generations. These data do notshow levels that would suggest a generational shift
to less social engagement, although there remains
the problem of separating age and cohort effects.
For that purpose, the COVID experience provides
a very useful natural experiment.
The post-COVID increases are large in both sizeand statistical significance for all three birthcohorts and all three benevolent acts. For all
three acts, the increases in benevolence, whether
measured as shares of the population, or
percentage increases from pre-pandemic levels,
are greatest for Millennials and Gen Z, suggestingthat Millennials are even more likely than their45

World Happiness Report 2024

Figure 2.11: Frequency of benevolent acts by generation, before and since COVID

70%

Helped a Stranger

Donation

Volunteering

60%

50%

40%

30%

20%

10%

0%

Millennials +Gen X

Boomers +Millennials +Gen X

Pre-Pandemic

Boomers +Millennials +Gen X

Boomers +Post-Pandemic

Social support, loneliness andsocial interactions by generationpredecessors47 to increase their benevolent acts
when a new need like COVID arises. In any event,the difference between generations in their
responses is dwarfed by the general size of theincreases in all generations. These benevolence
results, if we compare 2017-2019 to 2020-2023,
apply in every global region.48 This increased
benevolence provides an important part of our
explanation for the relative stability of life evaluations during COVID. The chance to help those inneed, and to see others doing the same, serves togive purpose and increase trust in the benevolenceof others, all of which is associated with higher
ratings of life as a whole.49

There is widespread concern, especially in the
United States, about an emerging epidemic ofloneliness, and about the consequences ofloneliness for mental and physical health.50 In
World Happiness Report 2023 we showcased the
Gallup/Meta social connections and lonelinessdata from seven large countries51 representing six
global regions. We found that in all of the sevencountries, feelings of social support were generally
twice or more prevalent than feelings of loneliness.
In subsequent use of the seven-country data, wehave found that what respondents thought about
the trustworthiness and kindness of others were46

World Happiness Report 2024very strong supports for overall satisfaction withsocial relations.52 This year we are able to provide
full global coverage, since some of the social
connections variables were included in the 2022
Gallup World Poll, and can be analysed using datafor 140 countries.53 We developed separate
measures for each of our three generations, thus
bringing the Gallup/Meta data directly to bear on
how these important relations vary by generation.
Also valuable are data on the reported frequencyof six types of social interactions. These permit us
to compare the extent of social interaction with
reported feelings of loneliness and social support,
and see how they are correlated with our key
overall life evaluation, the Cantril ladder.

Figure 2.12 shows regional averages of individual
responses for each of the three generations.
The first column shows how socially supported
respondents feel using four response possibilities,
with ‘not-at-all’ coded as 0 and ‘very’ as 1.0.54
The second column reports on feelings ofloneliness, using the same scale. Strong social
support is generally two times as prevalent
as loneliness. The third column turns to thereported average frequency of six types of social
interactions, including those with family andfriends, at work, school, community groups,
neighbours and strangers.

Figure 2.12: Social Support, Loneliness, and Social Interactions by Generation

Social Support

LonelinessSocial Interactions

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0

WorldWestern Europe

Central andEastern Europe
Commonwealth of
Independent States
Southeast Asia

South Asia

East Asia

Latin America
and Caribbean
North Americaand ANZ
Middle East andNorth Africa
Sub-Saharan Africa

Millennials +

47

Gen X

Boomers +World Happiness Report 2024

In the four-country group including
North America loneliness isalmost twice as high among theMillennials as among those born
before 1965.

old and young for the NANZ countries shown in
Figures 2.2 and 2.3, and in Table 2.2? To someextent yes, but they can only be part of the story.
If we add the three variables of Figure 2.12 to ourpreferred58 individual-level equation, all threevariables add very significantly to explaining life
evaluations in 2022, the year in which the social
connections data were collected. Feelings ofsocial support are the most important, followed
by loneliness and social interactions.59 If thoseunder 30 in the NANZ region had the same
feelings of loneliness and social support as thoseover 60, their average life evaluations would be
predicted to be higher by slightly more than
one-tenth of a point on the 0 to 10 scale, about
one-seventh of the happiness gap between thoseunder 30 and those 60 and older in that region.

Globally, perceived social support is highest
in East Asia, Western Europe and the NANZ
countries, and lowest in South Asia, especially in
the intermediate age groups. The age gradientfavours the old in Western Europe and the
NANZ countries, and the young in Central andEastern Europe, mirroring what was found earlier
for life evaluations.

Another interesting feature of the Gallup/Meta
results, applicable in all global regions, is that theoldest members of the population, those in theboomer and earlier generations, feel more socially
supported and less lonely than those in theyounger generations despite having less frequentactual interactions with all groups except neighbours. This ability to gain more perceived support
with fewer interactions likely helps to explain whylife satisfaction so often rises after middle age
even as the frequency and seriousness of healthproblems increases. We turn now to consider
these issues in more detail.

Loneliness, when measured on the same scale associal support, is in all regions generally half asprevalent as social support. It displays somewhat
matching patterns, being low where social
support is high, and vice versa. Only in Southeast
Asia, Western Europe and the NANZ countries isloneliness significantly higher for the Millennials
than for the Boomers, a pattern that is reversed
in Central and Eastern Europe.
An earlier study found age to be the most importantfactor in explaining loneliness differences among
survey respondents in the United States.55 They
found a peak in loneliness at age 20, with asteady age-related decline thereafter. This same
phenomenon is evident in the Gallup/Meta data
for the group of four countries including theUnited States. Although overall levels of loneliness are not unduly high in global terms56, there isa significantly different pattern across the generations. Loneliness is almost twice as high among
the Millennials than among those born before
1965.57 Millennials also feel less socially supported
than Boomers in those countries, another place inwhich these countries look different from the restof the world. This is despite the fact that actualsocial connections are much more frequent forMillennials than Boomers, and about as frequent
as for Generation X.

What are the happiest and least happy
stages of life in different countries?
Our international rankings and trends of life
across life stages show big differences. How does
our evidence relate to the many studies of the
U-shape in life evaluations frequently found inWestern Europe and North America? The Gallup
World Poll provides the largest set of countries
ever available to study the generality of the
U-shape in age. As suggested by the name, theU-shape describes a situation where there is a
mid-life low in life satisfaction, with most findings
placing the low point at about 50 years of age.60
The first major age-related study with Gallup
World Poll data61 used evidence from 2006-2010.
The study found a U-shape in the high-income

Do the high prevalence of loneliness and thelesser feelings of social support help to explain
the very large ranking disparities between the48

World Happiness Report 2024

differences in the estimated age of minimum
happiness depending on what other variables
are included, but generally found to be similar
to those based on the age terms alone. For thischapter, we first look at the effects of age alone,
without including other variables, and without
forcing any particular functional form for the
relationship, echoing what was done in World
Happiness Report 2015, where we examined theglobal distribution of life evaluations and emotions
by country, region, age and gender.63 We thenturn later to confirming the generality of this sortof curvature after allowing for our full set ofvariables linked to life evaluation differencesamong individuals and among countries. We find
as well that significant U-shape patterns persist
even after allowing for generational differences.

English speaking countries, accompanied by a flat
profile in Africa and life evaluations falling withage in Eastern Europe, the Commonwealth of
Independent States (CIS) and Latin America. In
this chapter we start with evidence from thesame early years of Gallup World Poll data usedin the earlier analysis, and then repeat the analysis
using the three most recent years, allowing us to
see the extent that age patterns for life evaluations
have changed in the past fifteen years.
Most studies of life evaluations based on large
samples of survey data include both age andage-squared among their variables, with thealmost universal finding being a negative
coefficient on age and a positive one on agesquared. The relative sizes of these twocoefficients can be used to calculate the low
point in the estimated relationship, usually foundto be about 50 years of age.62 These age effects
are sometimes estimated with other variablesin the equation, and sometimes not, with the

Photo Brian Wangenheim on Unsplash

In this chapter we are first interested in knowing
the happiest and least happy age groups in eachcountry. For this purpose it is helpful to have thein-between group split into low-middle (30-44)

49

World Happiness Report 2024

There have been remarkable changes over the
past dozen years, especially in Africa, where forthe continent as a whole the old were the least
happy group in 24 of the 34 counties in the
2006-2010 data, compared to just over one-third
now. Latin America shows a similar pattern, with
unhappy-old countries being much less numerous
now than in 2006-2010. There are also fewer
unhappy-old countries in Western Europe now
than earlier, while in Eastern Europe and the CIS
the change has gone in the reverse direction, withmore unhappy-old countries now than previously.
The overall drop since 2006-2010 in the numberof unhappy-old countries has been offset by anincrease in the number of unhappy-young
countries, and in the 30-44 age group. Thus the
U-shape in age is more prevalent now than it wasa dozen years ago, when it was present in just a
third of the countries. We show below that thischange in patterns by age may be linked in partto generational shifts favouring earlier generations
over the Millennials and their successors.

and high-middle (44-59) groups, because thehigh-middle group includes the most commonly
estimated low points for happiness.64 We consider
a country to exhibit a U-shape in age if average
life evaluations in either of the two middle-age
groups are below those for both the young andthe old. Table 2.3 shows the number of countries
in each global region according to which age
group was the least happy using data from2021-2023. Globally the old age group is the least
happy in about half of the countries, the young
the least happy group in seven countries, with theremaining countries having a mid-life low, mostof them in the 45 to 60 age range. So by thisdefinition the U-shape is currently found in almost
half of our countries. It is currently evident inmore than half the countries of Asia and Africa,
and less than half in Europe and Latin America. In
Western Europe the unhappiest age groups arediverse: one-third each for those below 30, 60+,
and those in between. In all of the countries inCentral and Eastern Europe and in two-thirds of
the CIS countries those 60 and older are the least
happy. In the NANZ group the reverse holds true,
with the young and the early middle age groups
being the least happy.

The most interesting questions for us relate not towhether a U-shape exists but whether and why
these patterns differ from one country or time
period to another.65 The life satisfaction evidence
is matched by other evidence of a mid-life crisis.66
Many factors influencing life evaluations differ inprevalence for people at different ages, and may

How do these results compare with thoserevealed by the Gallup World Poll data for2006-2010, as shown in the Statistical Appendix?

Table 2.3: Numbers of Countries/Territories by Least Happy Age Group,
Period 2021 to 2023
RegionThe Young

Lower Middle

Western Europe

63Central and Eastern Europe00Upper Middle

The Old

Total

4

7

20

0

17

17

Commonwealth of Independent States

0

2

1

7

10

Southeast Asia

0

4

1

4

9

South Asia

0

1

3

2

6

East Asia

0

4

0

2

6

Latin America and Caribbean

0

0

8

11

19

North America and ANZ

1

3

0

0

4

Middle East and North Africa

0

4

6

7

17

Sub-Saharan Africa

0

9

12

14

35

All

7

30

35

71

143

50

World Happiness Report 2024

U-shape in age even when several important life
circumstances are taken into account.68 One
possible explanation is provided by experiments
showing an age-related increase in the relevanceof positive over negative information in both
perception and memory.69 This increase inpositivity occurs against a backdrop of a prevailing
negative bias in the way people view and react to
new information.70 There is a growing strand ofexperimental research suggesting that, as people
age, they generally attach more importance toremembering the positive aspects of their lives,and less to remembering the negative aspects.71
This could help to explain why life evaluations risewith age, especially in countries where thistransfer of attention is more likely. These are likely
to be where a larger fraction of the population
has the basic necessities of life, as suggested
by evidence that the increase in positivity isgreater where there are fewer externally imposedconstraints.72

matter more at one stage of life than another.
Self-assessed health status provides a striking
example. The relevant individual-level Gallup
World Poll question asks respondents whether
they have health problems,with the possible
answers being yes or no. The national level forthis variable is thus the share of respondents inthat age group who have health problems. This
proportion rises strikingly across our three mainage groups, trebling from under 15% for thoseunder 30 to more than 45% for those over 60.
There is also a difference among the age groups
in how much having health problems affects life
evaluations. As shown by our individual levelglobal modelling in the Statistical Appendix, thedamage to life evaluations from having a healthproblem rises from 0.3 for those under 30 toabout 0.45 for those in the middle age groups,
and 0.6 for those 60 and over. Thus not only theprevalence but also the well-being consequencesof health problems are greater for those over 60.
Putting these two differences together suggeststhat the impact of health problems on average life
evaluations rises from 0.045 for those under 30
to 0.3 for those 60 and over, a sixfold increase.67

Does the age-related increase in trust andpositivity, accompanied by possible technologicalobsolescence, and age-related increases indementia, mean that online scammers will more
successfully target the elderly, and make themthe major victims? Early studies of the effects
of scamming concentrated on older victims,
assuming them to be especially vulnerable.73
Ten years ago there was a recognized lack ofevidence comparing the scamming susceptibilityof the young and the old.74 That research gap isbeing filled, with results showing that althoughlesser mental capacities and technological smarts
do increase susceptibility to scams, ageing can
produce a trust that is greater but also wisely
directed,75 so that the older targets are more
likely to be suspicious and less likely to fall forthe scam than are the young.76

Given the general downward influence of healthproblems on the life evaluations of the old, what
helps to explain their greater happiness? One
reason may simply be a lessening of the often
taxing need to balance the competing demandsof work and family pressures. This hypothesis is
supported by the slightly rising prevalence of
freedom to make key life decisions, from 75% of
respondents in the middle groups to 80% for
those over 60. Such freedom is apparently valued
even more highly by the old than by those in
middle age, with a combined effect raising life
satisfaction for those over 60 by about thesame as it is pushed down from middle to old
age by the increasing frequency and severity ofhealth problems.

There is also some evidence that changes in life
evaluations as people age depend on their social
environment. To feel a sense of belonging meetsan essential human need.77 Evidence shows asense of community belonging to have a larger
influence on life satisfaction and to be moreprevalent at higher ages,78 providing yet anotherexplanation for life evaluations that rise at
higher ages.

Is there also perhaps something more fundamental
in the ageing process that might help to explain
the extent to which life evaluations can rise after
middle age even if circumstances do not improve?
That life evaluations can rise after middle age
without any matching improvement in life circumstances is suggested by many studies that find a51

World Happiness Report 2024

Boomers and earlier
generations are happierthan Gen Xers, who are inturn happier than their
21st Century successors.

Millennials and their 21st century successors.82
These differences vary by region, of course, while
across the globe the Millennials as a group, after
taking into account their other life circumstances,have life evaluations that are about one-quarterof a point lower than the Boomers, with Gen X in
between, but closer to the Millennials.83 The U-shapein age continues to operate, both between andwithin generations. Within the boomer group, life
evaluations rise with each extra year of age, while
falling by a bigger annual amount for the Millennials.84

Marriage and the family are important elementsof the social context whose importance to happiness may vary by age. For example, it has beenfound that in some countries that normally exhibita U-shape the protective effects of marriage andliving together are greatest for those in the
middle age group, so that the U-shape is flatter,and mid-life relatively happier for the married, afinding we have been able to confirm with ourglobal data.79

Summary
Overall ranking of happiness
The biggest change this year is within the top 20.
There are two new entrants, Costa Rica andKuwait at 12 and 13. Coupled with the continuing
convergence between the two halves of Europe,with Czechia, Lithuania and Slovenia at positions
18, 19 and 21, have contributed to the fall of theUnited States and Germany from 15 and 16 lastyear to 23 and 24 this year.

An age-related positivity effect also helps to explain
our finding in previous World Happiness Reportsthat life evaluations among the old were maintainedor even improved despite COVID morbidity andmortality being much higher for that age group.80

The top 10 have remained fairly stable, with
Finland still in first position, although now followed
more closely by Denmark. All of the top 10
countries, except for Australia and the Netherlands,
have populations less than 15 million, while in the
top twenty, only Canada and the United Kingdom
have populations over 30 million.

Although age-related positivity research has
mainly focused on positive and negative emotions,
it clearly has implications for overall life evaluations,
as illustrated by results reported above andelsewhere. As people age, the prevailing negativity
bias of younger ages is on average across theworld increasingly offset as age leads people tofocus more on positive news and memories, toaccumulate enriching life experiences,81 to thinkbetter of others, and to rate their lives more highly.

Rankings by age group
Rankings differ a lot for the young and the old. In
some cases these favour the old, as in the UnitedStates and Canada, where the rankings for those
aged 60 and older are 50 or more places higher
than for those under 30. In other cases, especiallyin Central and Eastern Europe, the reverse is true,
with many rankings being more than 40 places
higher for the young than for the old.

We can now exploit the growing number of years
of Gallup World Poll data to attempt to separate
the effects of age from those of being in aparticular generation. For example, the changes inage patterns that we have found when comparing
2006-2010 with 2021-2023 may reflect generational shifts as well as age. To assess thosepossibilities, we have used our individual-level
data to estimate happiness equations (as shownin Table 12 in the Statistical Appendix) showinga U-shape in age appearing in concert withgenerational shifts in average happiness, with the
Boomers and earlier generations being happierthan Gen Xers, who are in turn happier than

Changes in happiness overall and by age group
From 2006-2010 to 2021-2023 changes in overallhappiness varied greatly from country to country,
ranging from increases as large as 1.8 points(Serbia) to decreases as large as 2.6 points(Afghanistan).
52

World Happiness Report 2024

Inequality by age

Central and Eastern Europe had the largest
increases, of the same size for all age groups.
Gains were half as large in the CIS countries. East
Asia also had large increases, especially for theolder population. By contrast, life evaluations fellin South Asia in all age groups, especially in the
middle age groups.

Global happiness inequality has increased bymore than 20% over the past dozen years, in all
regions and age groups, to an extent that differs
a lot by age and by region. It has increased mostfor the old in Latin America, Southeast Asia andthe CIS, and at all ages in SubSaharan Africa,
South Asia, and the Middle East and North Africa.

Happiness also fell significantly in the NANZ
group, by twice as much for the young as for theold. There were also significant declines in the
Middle East and North Africa, with larger declines
for those in the middle age groups than for theold and the young.

Benevolence by generation
The COVID crisis provided a natural experiment tocompare the benevolence of different generations.
The Post-COVID increases in benevolence, whether
measured as shares of the population, or percentage
increases from pre-pandemic levels, are large for all
generations, but especially so for the Millennials
and Generation Z, who are even more likely thantheir predecessors to help others in need.

The convergence of happiness levels in Central
and Eastern Europe toward those in Western
Europe has continued. For those under 30, thisconvergence is essentially complete, as theirhappiness levels are now equal in both halves ofEurope. For those ever 60, the gap between thetwo halves of Europe is about half of what it wasin 2006-2010. But it is still very large, more thana full point in 2021-2023.

Social support, loneliness andsocial interactions by generation
In almost every global region, as confirmed by the
new Gallup/Meta global social connections data,comparably measured feelings of social support
are more than twice as prevalent as loneliness.
Both social support and loneliness affect happiness,with social support usually having the larger
effect. Social interactions add to happiness, with
their effects flowing through increases in social
support and reductions in loneliness.

Emotions at different ages
In 2021-2023 negative emotions were in every
region more prevalent for females than males,with almost everywhere the gender gap being
larger at higher ages. The exception to this global
pattern is provided by the small group of countries
that includes the United States, Canada, Australia
and New Zealand, where females under 30 have
one-third more negative emotions than males, a
gap that is smaller at higher ages. There is nocorresponding gap in life evaluations, as thegender gap is small at all ages, and tends tofavour females.

The U-shape in age
The U-shape in age, with a mid-life low, iswidespread, accompanied by a generational
effect favouring earlier generations. Among thoseborn before 1965, life evaluations rise with age, asalso shown in Chapter 5. Among those born after
1980, happiness falls with each year of age, asalso shown in Chapter 3.

Negative emotions are more frequent now than in
2006-2010 everywhere except East Asia and bothparts of Europe. In Central and Eastern Europe, incontrast to the rest of the world, but consistently
with the happiness convergence taking place
within Europe, negative emotions are now lessfrequent in all age groups than they were in
2006-2010.

As between generations, after taking into accountage and life circumstances other than generation,
those born before 1965 (Boomers and theirpredecessors) have life evaluations about
one-quarter of a point higher than those bornafter 1980 (Millennials and gen Z).85

Positive emotions have not changed much, whilestill remaining more frequent for the young thanfor older age groups.
53

World Happiness Report 2024

Endnotes
1

9	For detailed analysis of the life satisfaction of immigrants
to the United Kingdom and Canada from many source
countries of, see Helliwell et al. (2020).

See Fortin et al. (2015).

2	Our groups follow the approximate demarcation lines
between Boomers and their predecessors, Generation X,
the Millennials (often called Gen Y) and Gen Z (those born
1995 or later. Our global data show that these Westerncentric definitions do not apply to many of the key
generational shifts we find, such as those before and after
the collapse of the USSR, civil wars and genocides, and firstand subsequent generations of migrants from one country
to another. Generational differences have been highlighted
in the workplace (Parry & Unwin 2011, Campbell et al. 2015),
in voting behaviour (Van den Brug & Kritzinger 2012) andvalues more generally (Twenge et al. 2012).

10	Costa Rica is actually a re-entrant, having also been in 12th
position in WHR 2013. Kuwait was out of the rankings lastyear for lack of surveys during the three-year period, so its
ranking in WHR 2024 is based only on the 2023 survey.
11	The statistical appendix contains alternative forms withoutyear effects (Appendix Table 9), and a repeat version of
the Table 2.1 equation showing the estimated year effects(Appendix Table 8). These results continue to confirm that
inclusion of year effects makes no significant difference toany of the coefficients. In these aggregate equations,adding regional or country fixed effects would lower thecoefficients on relatively slow moving variables where mostof the variance is across countries rather than over time,such as healthy life expectancy and the log of GDP. Withequations based on individual observations, where income
and health are measured by individual-level variables,
adding country fixed effects makes little difference to anyof the coefficients.

3	The base period also includes data collected from 27
countries in 2005, as the first round of the Gallup World
Poll was divided between 2005 and 2006. Only one
country, France, had surveys in both 2005 and 2006. Thus
our base period includes all data collected before 2011.
4	A country’s average answer to the Cantril ladder questionis exactly equivalent to a notion of average underlying
satisfaction with life under an assumption of “cardinality:”
the idea that the difference between a 4 and a 3 shouldcount the same as the difference between a 3 and a 2, andbe comparable across individuals. Some social scientists
argue that too little is known about how people choose
their answer to the Cantril ladder question to make thisassumption and that if it is wrong enough, then rankings
based on average survey responses could differ fromrankings based on underlying satisfaction with life (Bond &Lang, 2019). Other researchers have concluded that answers
to the Cantril ladder question are indeed approximately
cardinal (Bloem & Oswald, 2022; Ferrer-i-Carbonell &Frijters, 2004; Kaiser & Oswald, 2022; Krueger & Schkade,2008).

12	The definitions of the variables are shown in Technical Box
2, with additional detail in the online Statistical Appendix.
13	The model’s predictive power is little changed if the year
fixed effects in the model are removed, with adjusted
R-squared falling only from 0.757 to 0.753.
14	The data and rankings for the 2021-2023 averages for thesix variables are to be found in Figures 68-91 of theStatistical Appendix. The underlying annual data used inestimating the equations shown in Table 2.1 can be foundin an online file accompanying the chapter.
15	For example, unemployment responses at the individuallevel are available in most waves of the Gallup World Poll.
While they show an effect size similar to that found in otherresearch, the coefficient has never been significant in the
country-level equation, and their inclusion does not
influence the size of the other coefficients.

5	For any pair of countries, the confidence intervals for themeans (depicted in Figure 2.1 as whiskers) can be used togauge which country’s mean is higher than the other,accounting for statistical uncertainty in the measurementof each. The confidence interval for a country’s rank (given
in Figure 2.1 as text) represents a range of possible values
for the ranking of their mean among all countries, accounting
for uncertainty in the measurement of all of the means(following Mogstad et al., 2024). The ranges are constructed
so that the chance that the range does not contain the
country’s true rank is no more than 5%.

16	Below, we use the term “effect” when describing thecoefficients in these regressions; some caveats to thisinterpretation are discussed later in this section.
17	In the equation for negative affect, healthy life expectancy
takes a significant positive coefficient, despite its positive
simple correlation with life evaluations in this aggregate
dataset. This may be due to the fact that in the global
sample there is a positive correlation between age and thefrequency of reports of negative emotions. Countries withhigher healthy life expectancies have respondents who areon average older, since the sample data are weighted toreplicate the actual age shares of the population.

6	Not every country has a survey every year. The total
sample sizes are reported in Statistical Appendix 1, and arereflected in Figure 2.1 by the size of the 95% confidence
intervals for the mean, indicated by horizontal lines. The
confidence intervals are naturally tighter for countries withlarger samples.

18	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 thesorts of positive connections that eventually provide thebasis for better life evaluations.

7	Countries marked with an * do not have survey informationin 2023. Their averages are based on the 2021 and/or 2022
surveys.
8	The actual average values for each survey year may befound in the online data file supporting the equations inTable 2.1. For Israel, the average ladder for 2021-2022 was
7.61, compared to 6.78 in 2023. The latter average, if
compared to the three-year averages used for othercountries, would put Israel 19th in the rankings.

19	See, for example, the well-known study of the longevity ofnuns, Danner et al. (2001).

54

World Happiness Report 2024

28	See Chen et al. (1995) for differences in response style, andChapter 6 of World Happiness Report 2022 for data on
regional differences in variables thought to be of special
importance in Asian cultures.

20 See Cohen et al. (2003), and Doyle et al. (2006).
21	The meta analysis by Chida & Steptoe (2008) shows
significant linkages from positive affect to health,independent of the effects of negative affect. For a recent
survey of the links running from positive emotions and life
evaluations to subsequent morbidity and mortality, see
Pressman et al. (2019).

29	One slight exception is that the negative effect of corruption
is estimated to be slightly larger (0.87 rather than 0.73),
although not significantly so, if we include a separate
regional variable for Latin America. This is because
perceived corruption is worse than average in Latin
America, and its happiness effects there are offset bystronger close-knit social networks, as described in Rojas(2018). The inclusion of a special Latin American variable
thereby permits the corruption coefficient to take ahigher value.

22	The prevalence of these feedbacks was documented in
Chapter 4 of World Happiness Report 2013, De Neve et al.
(2013).
23	We expected the coefficients on these variables (but noton 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 ofpossible influence is cut when the life evaluations arecoming from an entirely different set of respondents thanare the four social variables. The fact that the coefficientsare reduced only very slightly suggests that the commonsource link is real but very limited in its impact.

30	More precisely, the test vehicle is the equation in column 1
with no year fixed effects, given our wish to compare thefour COVID years to the preceding years. Aknin et al.
(2022), in a study for the Lancet task force, used the highfrequency COVID policy stringency data of Hale el al.
(2021) and longitudinal survey data of well-being in
15 countries to show that COVID deaths and policy
stringency both to have negative partial linkages to mental
health, with the stringency effect being small and offset inmany countries by the corresponding lower death rates.
See also Bu et al. (2020).

24	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 fromother 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 pickup 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 surveybased variables. Because each year’s respondents are froma different random sampling of the national populations,using the previous year’s average data also avoids using
the same respondent’s answers on both sides of theequation. This alternative test produced similarly reassuring
results as shown in Table 13 of Statistical Appendix 1 inWorld 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 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.

31	The corresponding rankings for the two intermediate agegroups are in the Statistical Appendix.
32	Although Rwanda is not in the current rankings, its datafrom earlier years also confirms that past internecine
violence leaves bigger scars on the lives of those who lived
through them. In Rwanda, the average life evaluation of
those 60 and over is lower than that of those under 30 bynearly two-thirds of a point.This is in contrast to natural
disasters, which have been shown, where initial levels ofsocial trust are sufficiently high, to lead to subsequent
increases as people reach out to help others in need. See,for examples, Toya & Skidmore (2014), Yamamura et al.
(2015), Kang & Skidmore (2018), Dussaillant & Guzman(2014) and Aldrich (2011). And for COVID-19, see Bartscher
et al. (2021), Bu et al. (2020), and the COVID death rate
modelling in Helliwell et al. (2021).
33	It has been argued that response styles of respondents (theextent to which they tend to give middling or end-pointanswers, for example) varies by age, and hence might
influence conclusions about relative happiness at different
ages (Stone et al. 2019). However, their evidence suggests
the potential effects on life evaluations are not significant.
See also Benjamin et al. (2023). Barrington-Leigh (2024)
argues that differing use of focal points may be leading tounderestimating the effects of education and income, while
Nilsson et al. (2024) argue that the ladder framing of the
Gallup life evaluation question may induce higher estimatesof the effects of income and power.

25	Actual and predicted national and regional average2021-2023 life evaluations are plotted in Figure 92 of theStatistical Appendix. The 45-degree line in each part of the
Figure shows a situation where the actual and predictedvalues 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.
Southeast and South Asia provide the largest current
example of the former case, and Latin America of the latter.

34	What is plotted here is the average across countries ofeach country’s average happiness in the age group inquestion. If we were instead to use the number of people inour global sample in each age group, we would show
average happiness being greatest for those over 60, sincethose countries with greater longevity (and hence more
people over 60) also have higher average happiness, forthat and other related reasons.

26 See Rojas (2018).
27	If special variables for Latin America and Southeast Asia
are added to the equation in column 1 of Table 2.1, theLatin American coefficient is +0.49 (t=5.2) while that forSoutheast Asia is -0.31 (t=2.3). Special variables for East
Asia and South Asia are not significant.

55

World Happiness Report 2024

45	Leijen et al. (2022) found benevolent values in 2020 to be
similar in all generations in their longitudinal study of Dutch
data. Their Millennials started with a lower benevolencevalue in 2008, but this gradually rises to reach the averageof the other generations by 2020.

35	Montgomery (2022) studies gender differences in the
ranking of life vignettes in the Gallup World Poll, finding a
difference of about the same size as the average global
ladder advantage for females, and hence a sufficient
explanation for the global average gap. This is less likely toaffect the analysis of differences among regions or overtime. There is no matching vignette analysis available for
the Gallup data on emotions.

46	The sample makes use of the data from the 136 countries
with surveys in at least five of the seven years spanning2017-2023. The results are qualitatively similar if theanalysis is done using only the 81 countries (as of midFebruary 2024) with surveys reported for all seven years.
This more restricted sample leaves out countries where
surveys were not possible in 2020, the first year of thepandemic.

36	We have calculated and compared these ‘one-county-onevote’ data with population-weighted averages in several
earlier reports. The latter tend in some regions to reflect
almost entirely the experience of the largest country in theregion, and to depend on circumstances and measurement
issues best studied on a national level rather than as part ofa regional average.

47	For donations, the COVID-induced increases are similar inmagnitude for all generations.

37	The drop is about .04 per year of age in the context of ourfull model specification,including country and year fixed
effects. The drop is only slightly less without any control
variables and is slightly greater for males than females.

48	If we compare the 2021-2023 data to the average of all
previous Gallup years, then there are no increases in
benevolence in the NANZ and Western European countries.
That is because these countries, which have always had
globally high levels of benevolent acts, but have seen
significant drops over the past dozen years. Thus for them
the COVID-induced growth in benevolence represented thereversal of a downward trend rather than an increase overthe levels in 2006-2010.

38	This evidence of life evaluations being higher at higher
ages, even among those over 60 years of age, is not foundin the earlier years of Gallup World Poll data for India, but
is clearly evident in the surveys since 2016, the time period
within which the Indian survey was fielded. In Chapter 5 ofthis volume, the average increase in SWL (on a 0 to 10
scale) is 0.012 per year. In a similar equation for all the
global data from the Gallup World Poll, the increase is0.008 per year. For the South Asian countries as a groupthe average annual increase is .023. As for other regions,the average annual increase is .025 in Latin America and in
the NANZ group, .023 in SE Asia, .013 in East Asia, andapproximately zero in Africa and all parts of Europe andthe CIS.

49	See Dolan et al. (2021) for COVID-related evidence, andmore generally, Aknin et al. (2011), Helliwell et al. (2018),
and Helliwell & Wang (2011).
50	Murthy (2023), Holt-Lunstad et al. (2015), Kannan & Veazie(2023), Leigh-Hunt et al. (2017), Steptoe et al. (2013).
51 Gallup/Meta (2022)
52 Folk et al. (2024).
53 See Gallup/Meta (2023)

39	These equations are run with country fixed effects and thecontrol variables used in the micro equations reported in
the statistical appendix.

54	The intermediate answers ‘a little’ and ‘a lot’ are coded as
0.33 and 0.67 respectively, reflecting a linear conversion of
the original 4-point response scale.This replicates the Likert
scale are adopted in the Mate/Gallup (2022) study,transformed from the 1 to 4 scale to a 0 to 1 scale.

40	The pattern of declining frequency of positive emotions isroughly the same for laughter, enjoyment and doing
something interesting yesterday. It also applies if thesample is split by generation rather than age, reflecting therelatively high correlation between age and generation, due
to the still limited number of years in our synthetic panel.

55 See Shovestul et al. (2020).
56	In 2022 it averaged 27% across all countries, and 21% for
the four-country group including the US, Canada, Australia
and New Zealand.

41 See Goff et al. (2018).
42	We cannot measure inequality for the positive andnegative emotions because they are only available in
the Gallup World Poll as binary yes/no answers about
experiences yesterday.

57	With only a single year of data it is not possible todistinguish age and cohort effects. Those under 30 years ofage (who are only have as numerous as the Millennials+GenZ) have a frequency of loneliness more than twice that for
those 60 and over (who are very similar in number to the
Boomers, and are hence the same people)

43	Twenge et al. (2012) summarise key papers presenting
each of these alternative positions. Their own analysis
modestly favours the ‘me generation’ view, except in thecase of volunteering, where the evidence is more mixed.

58 In column 1 of Table 12 of the Statistical Appendix.
59	The standardized betas for the three variables are .076,.053 and .036, respectively. The estimated coefficients are.623 (t=17.7) for feelings of social support, .456 (t=12.4) for
feelings of loneliness, and .473 (t=8.5) for the reported
frequency of social interactions.

44	See especially Putnam (2000), where it was estimated thegenerational change “might account for perhaps half ofthe overall decline” (p.283) in civic engagement and social
capital in the last third of the 20th century. See also
Putnam (2020).

60	See, for example, Blanchflower & Oswald (2008) and Stoneet al. (2010).
61 Steptoe et al. (2015).

56

World Happiness Report 2024

80	See Carstensen et al. (2020) for survey evidence showing
robustness of the age-related positivity effect during
COVID in a US sample. The authors argue that thisrobustness in the face of a highly salient and powerfulthreat tends to favour its generality. Some argue that thiseffect may be muted or reversed when death is imminent(Charles, 2010).

62	The equation being estimated is y=a +b*age + c*age
squared, The slope is b+2 c*age, equaling zero whereb-2c*age=0, or at age=b/2c. If b is 100 times as large as c,then the age of minimum happiness is 50 years. The
equation is often estimated using age and 100*age-squaredin order to show more significant figures for c. In this casethe low point is equal to 50 years if -c=b., and is less than
50 if -c>b. See Blanchflower (2021) for a survey of studies
using this method, most of which produce minima within
the 45-59 age range.

81	Oishi and Westgate (2022) argue that a rich life, whichprioritises curiosity and seeks challenges, has a value quite
beyond happiness and meaning. They argue that suchrichness ‘grows over time in response to perspectivechanging life experiences’ (Oishi & Westgate, 2022, p. 17).
As such it is likely to provide an additional reason for life
evaluations to rise at older ages.

63	Fortin et al. (2015). That chapter uses data from the
beginning of the Gallup World Poll in 2005-2006 throughmost of 2014.
64 See the review of recent estimates in Blanchflower (2021).

82	It requires a substantial number of years of data to attempt
to identify separate effects for age, time and generation, as
in a single year the three are linked by the identity whereby
for each individual age+ year of birth = year. The ability topartition the effects among age, cohort and time is heavily
dependent on the number of years, the selection ofcohorts, and the functional forms used (Bell & Jones, 2018).
Our identification attempt makes use of an establishedquadratic form for the effects of age on life satisfaction anda fairly well established split of respondents into three
generational groups. It also includes fixed effects for eachyear. The results confirm the usual positive coefficient on
age and a negative coefficient of age squared while
delivering also highly significant generational coefficients,
with t-values of about 10 for the intergenerational differences.
Much of the increase in life satisfaction for those in theolder age group is in this equation transferred from the age
squared term to a generational advantage for the Boomers
and, to a lesser extent, Gen X.

65 In the same vein, see Graham & Ruiz Pozuelo (2017).
66 For a wide-ranging review, see Giuntella et al. (2023).
67	For the young, the effect is 0.15*0.30=0.045, while for theold it is 0.5*0.6=0.3.
68	E.g. Blanchflower (2021). However, see another researchstream (e.g. Gerstorf et al. 2010) that finds in somecountries a sharp drop in subjective well-being as deathbecomes imminent.
69	Charles et al. (2003) and Mather & Carstensen (2003). Zaket al. (2022) finds a corroborating age-related increase inoxytocin release.
70	Baumeister et al. (2001) provide an influential review ofmany sorts of evidence that people perceive and react tothe bad rather than the good, and prefer to avoid losses
rather than to make gains. The authors argue that there is,
or at least may once have been, an evolutionary advantage
in doing so.

83 See column 1 of Table 12.

71	See Reed et al. (2014) for a meta analysis of more than
100 experimental studies showing that events are seen in
more positive terms at higher ages.

84	The annual rise for the Boomers is 0.006 (from column 4 of
Table 12) while the annual fall for the Millennials is 0.029(column 2 of Table 12).

72	According to the socioemotional selectivity theory
advanced by Carstensen (2006) the positivity effect islikely to be absent for those who are constrained byexperimental or life constraints.

85	See column 1 in Table 12 of the Statistical Appendix. That
equation includes country and year fixed effects, gender,
age, age-squared, and individual-level counterparts to thesix variables in the model of Table 2.1. The age effects
within each generation are shown in columns 2 and 4 ofAppendix Table 12.

73 James et al. (2014), Burnes et al. (2017).
74 Reed & Carstensen (2012).
75 Mueller et al. (2020).
76 Walzak (2023).
77 Baumeister & Leary (1995).
78 See Michalski et al. (2020) and Helliwell et al. (2019)79	See Anusic et al. (2014), Clark et al. (2021), Grover &Helliwell (2019) and Helliwell et al. (2019). Using our global
model (from Table 12 in the Statistical Appendix) based on
individual data for those under 50 years of age, weestimated equations for those who are married (orcohabiting) separately from the rest of the population. The
estimated annual drop in life evaluations is one-third lessfor the married/cohabiting group. Thus the global data
confirm the earlier findings based on data mainly from the
UK and other countries in Western Europe.

57

World Happiness Report 2024

References
Campbell, W. K., Campbell, S. M., Siedor, L. E., & Twenge, J. M.
(2015). Generational differences are real and useful. Industrial
and Organizational Psychology, 8(3), 324-331.

Aknin, L. B., Dunn, E. W., & Norton, M. I. (2011). Happinessruns in a circular motion: Evidence for a positive feedback
loop between prosocial spending and happiness. Journal ofHappiness Studies, 13(2), 347–355.

Carstensen, L. L. (2006). The influence of a sense of time on
human development. Science, 312(5782), 1913-1915.

Aknin, L. B., Andretti, B., Goldszmidt, R., Helliwell, J. F.,
Petherick, A., De Neve, J. E., ... & Zaki, J. (2022). Policy
stringency and mental health during the COVID-19 pandemic:
a longitudinal analysis of data from 15 countries. The Lancet
Public Health, 7(5), e417–e426.

Carstensen, L. L., Shavit, Y. Z., & Barnes, J. T. (2020). Ageadvantages in emotional experience persist even under threat
from the COVID-19 pandemic. Psychological science, 31(11),1374-1385.

Aldrich, D. P. (2011). The externalities of strong social capital:
Post-tsunami recovery in Southeast India. Journal of Civil
Society, 7(1), 81–99.

Charles, S. T. (2010). Strength and vulnerability integration: amodel of emotional well-being across adulthood. Psychologicalbulletin, 136(6), 1068.

Anusic, I., Yap, S. C., & Lucas, R. E. (2014). Testing set-point
theory in a Swiss national sample: Reaction and adaptation to
major life events. Social indicators research, 119, 1265-1288.

Charles, S. T., Mather, M., & Carstensen, L. L. (2003). Aging andemotional memory: the forgettable nature of negative images
for older adults. Journal of Experimental Psychology: General,132(2), 310.

Bartscher, A. K., Seitz, S., Siegloch, S., Slotwinski, M., &Wehrhöfer, N. (2021). Social capital and the spread of Covid-19:
Insights from European countries. Journal of Health Economics,
80, 102531.

Chen, C., Lee, S. Y., & Stevenson, H. W. (1995). Response styleand cross-cultural comparisons of rating scales among East
Asian and North American students. Psychological Science,
6(3), 170–175.

Barrington-Leigh, C. P. (2024). The econometrics of happiness:
Are we underestimating the returns to education and income?.
Journal of Public Economics, 230, 105052.

Chida, Y., & Steptoe, A. (2008). Positive psychologicalwell-being and mortality: a quantitative review of prospective
observational studies. Psychosomatic medicine, 70(7), 741-756.

Baumeister, R. F., & Leary, M. R. (1995). The need to belong:
Desire for interpersonal attachments as a fundamental human
motivation. Psychological Bulletin, 117(3), 497.

Cogin, J. (2012). Are generational differences in work values
fact or fiction? Multi-country evidence and implications. The
International Journal of Human Resource Management, 23(11),2268-2294.

Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D.
(2001). Bad is stronger than good. Review of general psychology,
5(4), 323-370.

Cohen, S., Doyle, W. J., Turner, R. B., Alper, C. M., & Skoner, D.
P. (2003). Emotional style and susceptibility to the common
cold. Psychosomatic Medicine, 65(4), 652-657.

Bell, A., & Jones, K. (2018). The hierarchical age–period–cohortmodel: Why does it find the results that it finds?. Quality &quantity, 52, 783-799.

Clark, A. E., d’Albis, H., & Greulich, A. (2021). The age U-shapein Europe: The protective role of partnership. Vienna Yearbookof Population Research, 19, 293-318. https://shs.hal.science/halshs-03467204/document

Benjamin, D. J., Cooper, K., Heffetz, O., Kimball, M. S., & Zhou, J.
(2023). Adjusting for Scale-Use Heterogeneity in Self-Reported
Well-Being (No. w31728). National Bureau of Economic
Research.

Danner, D. D., Snowdon, D. A., & Friesen, W. V. (2001). Positive
emotions in early life and longevity: Findings from the nun
study. Journal of Personality and Social Psychology, 80(5),804–813.

Blanchflower, D. G. (2021). Is happiness U-shaped everywhere?
Age and subjective well-being in 145 countries. Journal of
Population Economics, 34(2), 575-624.

De Neve, J. E., Diener, E., Tay, L., & Xuereb, C. (2013). The
objective benefits of subjective well-being. In J. F. Helliwell, R.
Layard, & J. Sachs (Eds.), World Happiness Report 2013
(pp. 54–79). New York: SDSN.

Blanchflower, D. G., & Oswald, A. J. (2008). Is well-being
U-shaped over the life cycle?. Social science & Medicine, 66(8),1733-1749.
Bloem, J. R., & Oswald, A. J. (2022). The analysis of humanfeelings: a practical suggestion for a robustness test. Review ofIncome and Wealth, 68(3), 689-710.

Dolan, P., Krekel, C., Shreedhar, G., Lee, H., Marshall, C., & Smith,A. (2021). Happy to help: The welfare effects of a nationwide
micro-volunteering programme. IZA Discussion Paper 14431.

Bond, T. N., & Lang, K. (2019). The sad truth about happinessscales. Journal of Political Economy, 127(4), 1629–1640.

Doyle, W. J., Gentile, D. A., & Cohen, S. (2006). Emotional style,nasal cytokines, and illness expression after experimentalrhinovirus exposure. Brain, Behavior, and Immunity, 20(2),175-181.

Bu, F., Steptoe, A., & Fancourt, D. (2020). Who is lonely in
lockdown? Cross-cohort analyses of predictors of lonelinessbefore and during the COVID-19 pandemic. Public health, 186,
31-34.

Dussaillant, F., & Guzmán, E. (2014). Trust via disasters: Thecase of Chile’s 2010 earthquake. Disasters, 38(4), 808–832.

Burnes, D., Henderson Jr, C. R., Sheppard, C., Zhao, R., Pillemer,
K., & Lachs, M. S. (2017). Prevalence of financial fraud andscams among older adults in the United States: A systematic
review and meta-analysis. American journal of public health,107(8), e13-e21.

Ferrer-i-Carbonell, A., & Frijters, P. (2004). How important ismethodology for the estimates of the determinants ofhappiness?. The Economic Journal, 114(497), 641–659.

58

World Happiness Report 2024

Folk, D. P., Helliwell, J. F., Holt-Lunstad, J., Norton, M., & Tov, W.
(2024). Comparing the Effects of Loneliness, Social Support,
and Social Connection on Relationship Satisfaction in Seven
Countries. PsyArXiv. https://doi.org/10.31234/osf.io/vzpk4

Holt-Lunstad, J., Smith, T. B., Baker, M., Harris, T., & Stephenson,
D. (2015). Loneliness and social isolation as risk factors for
mortality: a meta-analytic review. Perspectives on Psychological
Science, 10(2), 227–237.

Fortin, N., Helliwell, J.F., & Wang, S. (2015) How does subjective
well-being vary around the world by gender and age? In J. F.
Helliwell, R. Layard, & J. Sachs (Eds.), World Happiness Report2015 (pp. 42–75). New York: SDSN.

James, B. D., Boyle, P. A., & Bennett, D. A. (2014). Correlates ofsusceptibility to scams in older adults without dementia.
Journal of elder abuse & neglect, 26(2), 107-122.

Fredrickson, B. L. (2001). The role of positive emotions inpositive psychology: The broaden-and-build theory of positive
emotions. American Psychologist, 56(3), 218–226.

Kannan, V. D., & Veazie, P. J. (2023). US trends in social
isolation, social engagement, and companionship—nationallyand by age, sex, race/ethnicity, family income, and work hours,
2003–2020. SSM-Population Health, 21, 101331.

Gallup/Meta (2022). The State of Social Connections study.
https://dataforgood.facebook.com/dfg/docs/2022-state-ofsocial-connections-study

Kaiser, C., & Oswald, A. J. (2022). The scientific value ofnumerical measures of human feelings. Proceedings of the
National Academy of Sciences, 119(42), e2210412119.

Gallup/Meta (2023). The Global State of Social Connections.

Kang, S. H., & Skidmore, M. (2018). The effects of natural
disasters on social trust: Evidence from South Korea.
Sustainability, 10(9), 2973.

Gerstorf, D., Ram, N., Mayraz, G., Hidajat, M., Lindenberger, U.,
Wagner, G. G., & Schupp, J. (2010). Late-life decline in wellbeing across adulthood in Germany, the United Kingdom, andthe United States: Something is seriously wrong at the end oflife. Psychology and aging, 25(2), 477.

Krueger, A. B., & Schkade, D. A. (2008). The reliability ofsubjective well-being measures. Journal of Public Economics,
92(8-9), 1833–1845.
Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V., Turnbull, S.,
Valtorta, N., & Caan, W. (2017). An overview of systematic
reviews on the public health consequences of social isolationand loneliness. Public Health, 152, 157–171.

Giuntella, O., McManus, S., Mujcic, R., Oswald, A. J., Powdthavee,N., & Tohamy, A. (2023). The midlife crisis. Economica, 90(357),
65-110.
Goff, L., Helliwell, J. F., & Mayraz, G. (2018). Inequality ofsubjective well-being as a comprehensive measure of inequality.
Economic Inquiry, 56(4), 2177–2194.

Leijen, I., van Herk, H., & Bardi, A. (2022). Individual andgenerational value change in an adult population, a 12-year
longitudinal panel study. Scientific Reports, 12(1), 17844.

Graham, C., & Ruiz Pozuelo, J. (2017). Happiness, stress, andage: How the U curve varies across people and places. Journalof Population Economics, 30, 225-264.

Mather, M., & Carstensen, L. L. (2003). Aging and attentionalbiases for emotional faces. Psychological science, 14(5),409-415.

Grover, S., & Helliwell, J. F. (2019). How’s life at home? New
evidence on marriage and the set point for happiness. Journalof Happiness Studies, 20(2), 373-390.

Michalski, C. A., Diemert, L. M., Helliwell, J. F., Goel, V., &Rosella, L. C. (2020). Relationship between sense ofcommunity belonging and self-rated health across life stages.
SSM-population health, 12, 100676.

Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A.,
Phillips, T., ... & Tatlow, H. (2021). A global panel database of
pandemic policies (Oxford COVID-19 Government Response
Tracker). Nature human behaviour, 5(4), 529-538.

Mogstad, M. Romano J.P., Shaikh, A. & Wilhelm D.,(2024)
Inference for Ranks with Applications to Mobility across
Neighbourhoods and Academic Achievement across Countries,
The Review of Economic Studies, Volume 91, Issue 1, January2024, Pages 476–518.

Helliwell, J. F., & Wang, S. (2011). Trust and well-being. International Journal of Wellbeing, 1(1), 42–78.
Helliwell, J. F., Aknin, L. B., Shiplett, H., Huang, H., & Wang, S.
(2018). Social capital and prosocial behaviour as sources ofwell-being. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook ofwell-being. (pp. 528–543). Salt Lake City, UT: DEF Publishers.

Montgomery, M. (2022). Reversing the gender gap in happiness.
Journal of Economic Behavior & Organization, 196, 65-78.
Mueller, E. A., Wood, S. A., Hanoch, Y., Huang, Y., & Reed, C. L.
(2020). Older and wiser: age differences in susceptibility toinvestment fraud: the protective role of emotional intelligence.
Journal of Elder Abuse & Neglect, 32(2), 152-172.

Helliwell, J., Huang, H., Norton, M., and Wang, S. (2019).
Happiness at different ages: The social context matters. In
The economics of happiness: How the Easterlin paradox
transformed our understanding of well-being and progress,
ed. M. Rojas, New York: Springer.

Murthy, V.H. (2023). Our Epidemic of Loneliness and Isolation:
The U.S. Surgeon General’s Advisory on the Healing Effects ofSocial Connection and Community. https://www.hhs.gov/sites/
default/files/surgeon-general-social-connection-advisory.pdf

Helliwell, J. F., Shiplett, H., & Bonikowska, A. (2020). Migration
as a test of the happiness set-point hypothesis: Evidence from
immigration to Canada and the United Kingdom. Canadian
Journal of Economics/Revue canadienne d’économique, 53(4),1618–1641.

Nilsson, A. H., Eichstaedt, J. C., Lomas, T., Schwartz, A., & Kjell,O. (2024). The Cantril Ladder elicits thoughts about power andwealth. Scientific Reports, 14(1), 2642.
Oishi, S., & Westgate, E. C. (2022). A psychologically rich
life: Beyond happiness and meaning. Psychological Review,
129(4), 790.

Helliwell, J. F., Huang, H., Wang, S., & Norton, M. (2021). World
happiness, trust and deaths under COVID-19. World Happiness
Report 2021, 13-57.

59

World Happiness Report 2024

Zak, P. J., Curry, B., Owen, T., & Barraza, J. A. (2022).
Oxytocin release increases with age and is associated with life
satisfaction and prosocial behaviors. Frontiers in behavioral
neuroscience, 16, 846234.

Parry, E., & Urwin, P. (2011). Generational differences in workvalues: A review of theory and evidence. International journalof management reviews, 13(1), 79-96.
Pressman, S. D., Jenkins, B. N., & Moskowitz, J. T. (2019).
Positive affect and health: What do we know and where next
should we go?. Annual review of psychology, 70, 627-650.
Putnam, R. D. (2000). Bowling alone: The collapse and revivalof American community. Simon and schuster.
Putnam, R. D. (2020). The upswing: How America came
together a century ago and how we can do it again. Simonand Schuster.
Reed, A. E., & Carstensen, L. L. (2012). The theory behind theage-related positivity effect. Frontiers in psychology, 3, 339.
https://doi.org/10.3389/fpsyg.2012.00339
Reed, A. E., Chan, L., & Mikels, J. A. (2014). Meta-analysis of the
age-related positivity effect: age differences in preferences for
positive over negative information. Psychology and aging,29(1), 1.
Rojas, M. (2018). Happiness in Latin America has social
foundations. In J. F. Helliwell, R. Layard, & J. Sachs (Eds.),
World happiness report 2018 (pp. 114–145). New York: SDSN.
Shovestul, B., Han, J., Germine, L., & Dodell-Feder, D. (2020).
Risk factors for loneliness: The high relative importance of age
versus other factors. PloS one, 15(2), e0229087.
Steptoe, A., Deaton, A., & Stone, A. A. (2015). Subjective
wellbeing, health, and ageing. The Lancet, 385(9968), 640-648.
Steptoe, A., Shankar, A., Demakakos, P., & Wardle, J. (2013).
Social isolation, loneliness, and all-cause mortality in older men
and women. Proceedings of the National Academy of Sciences,110(15), 5797-5801.
Stone, A. A., Schwartz, J. E., Broderick, J. E., & Deaton, A.
(2010). A snapshot of the age distribution of psychologicalwell-being in the United States. Proceedings of the National
Academy of Sciences, 107(22), 9985-9990.
Stone, A. A., Schneider, S., Junghaenel, D. U., & Broderick, J. E.
(2019). Response styles confound the age gradient of four
health and well-being outcomes. Social Science Research, 78,
215-225.
Toya, H., & Skidmore, M. (2014). Do natural disasters enhancesocietal trust?. Kyklos, 67(2), 255–279.
Twenge, J. M., Campbell, W. K., & Freeman, E. C. (2012).
Generational differences in young adults’ life goals, concern forothers, and civic orientation, 1966–2009. Journal of personality
and social psychology, 102(5), 1045.
Van der Brug, W., & Kritzinger, S. (2012). Generational differencesin electoral behaviour. Electoral Studies, 31(2), 245-249.
Walzak, L. C. (2023). Fraud susceptibility across adulthood:Age, context, and the role of individual differences.
https://summit.sfu.ca/_flysystem/fedora/2023-11/etd22730.pdf
Yamamura, E., Tsutsui, Y., Yamane, C., Yamane, S., & Powdthavee,N. (2015). Trust and happiness: Comparative study before andafter the Great East Japan Earthquake. Social Indicators
Research, 123(3), 919–935.

60

Chapter 3Child and AdolescentWell-Being:
Global Trends, Challenges
and Opportunities
Jose Marquez
Manchester Institute of Education, University of Manchester, UK
and Wellbeing Research Centre, University of Oxford, UK
Laura Taylor
Wellbeing Research Centre, University of Oxford, UK
Leoni Boyle
Wellbeing Research Centre, University of Oxford, UK
Wanying Zhou
Wellbeing Research Centre, University of Oxford, UK
Jan-Emmanuel De Neve
Wellbeing Research Centre, University of Oxford, UK

Acknowledgement: The authors are grateful for the support from Ferran Casas;
Gwyther Rees (UNICEF Innocenti); Camila Teixeira (UNICEF); Olivier Thevenon (OECD);
Mental Health and Wellbeing Focus Group (HBSC); Coordinating Team of the AdolescentWell-being Measurement Expert Consultative Group (WHO); as well as the editorsof the WHR, including John F. Helliwell; Lara B. Aknin, and Richard Layard.
doi.org/10.18724/whr-91b0-ek06
This version last updated March 8, 2024. Please see worldhappiness.report for latest data.

There is a global need for
improving data collection andassessment to enhance child andadolescent well-being globally.

Photo Maxim Hopman on Unsplash3

World Happiness Report 2024

Key Insights
Life satisfaction gradually drops from childhood through adolescence into adulthood.
Globally, adolescents aged 15-24 report higher life satisfaction than adults aged 25 or above,
but the gap is narrowing in Western Europe and recently reversed in North America andAustralia and New Zealand (ANZ) due to negative trends for young people. Conversely, thegap is widening in Sub-Saharan Africa due to increasing life satisfaction among the youth.

In middle-to-late adolescence (age 15-24), there was a positive 2006-2019 global trend inlife satisfaction, which ended with the pandemic, in line with adult trends.

Global trends obscure regional variations, some of which differ from adult trends. Negative
trends between 2006 and 2022 at age 15-24 are found in North America and ANZ, Western
Europe, Middle East and North Africa (MENA), and South Asia, and positive trends in
Sub-Saharan Africa, Central and Eastern Europe, the Commonwealth of Independent States,
Latin America and the Caribbean, and Southeast Asia.

In early-to-middle adolescence (age 10-15), global well-being data is lacking, with many
world regions having no available information. Evidence primarily from high-income
countries indicates significant life satisfaction declines post-COVID-19, especially amongfemales, contrasting with East Asian countries, where life satisfaction increased. There ismixed evidence regarding pre-pandemic trends.

Females start reporting lower life satisfaction than males by around age 12. This gap
widens at ages 13 and 15, and the pandemic has amplified these inequalities. These patterns
are primarily observed in high-income countries due to limited data worldwide. In contrast,
global data for middle-to-late adolescence (age 15-24) shows no global gender differencesfrom 2006 until 2013, but from 2014, females began reporting higher life satisfaction thanmales, although the gap has narrowed following the pandemic. This global gender gap
masks regional differences, and is more pronounced in lower-income countries, with nogender differences observed in high-income countries.

Life satisfaction levels, trends and correlates vary across age, gender, world regions andcountries, and economic development levels. This underscores the importance of addressing
current data gaps to enhance our understanding of child and adolescent well-being and how
to promote it globally.

63

World Happiness Report 2024

Defining Well-Being in Childhoodand AdolescenceThis chapter is the first across the 10+ years of
the World Happiness Report that explores childand adolescent well-being in some detail. In
this chapter, we examine data from four wellestablished international datasets with respondents
aged 10-24. We present the global state of childand adolescent well-being, with a focus on levels,
trends, inequalities, and correlates. An important
aspect of this chapter is a discussion of theshortcomings of the available international data
and what action should be taken to improve data
quantity and quality, and thereby improve ourunderstanding of child and adolescent well-being
and how to improve it worldwide.

Similar to adult research featured in previous
World Happiness Reports, this chapter centers onchild and adolescent subjective well-being, which
is how young individuals perceive and assess theirown lives. Every time we use the term ‘well-being’in this chapter, we refer to subjective well-being.
The prevailing theoretical framework for subjective
well-being in childhood and adolescence (andadulthood) includes affective evaluations (positive
and negative emotions), cognitive evaluations(life satisfaction), and sometimes also comprises
eudaimonic evaluations (such as meaning andpurpose).6 However, there are nuanced differences
between approaches in adult and child/adolescentsubjective well-being research. Cognitive evaluations, covering overall life satisfaction, also tend
to consider domain-specific assessments, suchas satisfaction with school, school peers, physicalappearance, and time use, to cite a few.7
Additionally, in certain fields like health sciences,mental health is integral to child and adolescentwell-being, and the terms are often used interchangeably.8 It is important to acknowledge thatthese components are primarily derived from aWestern perspective due to the origin of muchof the research. In this chapter, we focus on
cognitive evaluations, specifically overall life
satisfaction measured on a 0-10 response scale,driven by data availability and comparability.
There are slight differences in the life satisfaction/
evaluation scales used across the data sets
examined, which are described below. However,for ease, we refer to them as life satisfaction as
this is the established term in the child andadolescent literature.9

Defining Childhood and AdolescenceIn this chapter, we define childhood and adolescence within the age range of 10 to 24, reflecting
critical brain development stages.1 The extended
upper age boundary might surprise some readers,
but contemporary understanding considers
adolescence to persist until around 24 years ofage,2 aligning with the ongoing brain development
linked to adolescence that extends into the early
20s,3 and other aspects of individual development
and cultural norms.4 Similarly, childhood spans
birth (or conception) to around 10 years, however,we acknowledge that childhood can extend
beyond age 10, prompting us to include bothterms childhood and adolescence throughout
this chapter.
In the analysis and discussion, we differentiate
between early-to-middle adolescence (ages 10-15)
and middle-to-late adolescence (ages 15-24).
This distinction is needed due to differences inavailable international datasets, requiring distinct
approaches to analyze and interpret the data.
We also acknowledge that significant hormonal,
physical, neurobiological, psychological, social,and environmental changes occur not only fromage 10-24, but also within the two age ranges
examined.5 We consider these in the interpretationof the results (e.g. life satisfaction declines fromage 10 to 15).

Child and Adolescent Well-Being:
What We Know
While research into subjective well-being in
adulthood has been an established field for many
decades,10 subjective well-being as a specific fieldwith children and adolescents is a more recent
field of inquiry. Particularly in the last 15 years,
increased interest in this field has been driven byadvances in child development theory, increased
children’s rights legislation, and developments in

64

World Happiness Report 2024

The best predictor for adultlife satisfaction is subjective
well-being and emotional healthduring childhood.

positive psychology and social science.11 Interest
has also increased following some specific researchfindings. A detailed literature review is beyond
the scope of this chapter, but there are a few key
findings worth noting. Most of them refer toschool-age children and adolescents.
Research has highlighted the importance ofconsulting children directly, as their subjective
well-being is weakly correlated with that of adults
or families, and parents’ reports of their children’swell-being are not always aligned with children’s
own reports.12 There is evidence supporting thevalidity and reliability of measuring child subjective
well-being and related factors from age 8.13
Evidence from the health literature further
supports children as reliable and accurate reportersof their health and well-being, emphasizing theimportance of their self-reported perceptions inunderstanding their experiences.14 There is also
specific evidence on the validity of the Cantril
Ladder as a measure for adolescent samples of11, 13, and 15-year-olds.15

is evidence that indicates that the best predictor
for adult life satisfaction is subjective well-being
and emotional health during childhood, and thatthe next major influence on emotional health,after family, is school both in childhood andadolescence.24 In addition, further researchsuggests that subjective well-being in adolescencepredicts levels of income in adulthood, evenwhen employing family-fixed-effects (with siblingclusters) and controlling for factors such as
education, intelligence quotient, physical health,height, self-esteem, and later happiness. Thesefindings were mediated by a higher probabilityof obtaining a college degree, getting hired andpromoted, having higher degrees of optimismand extraversion, and less neuroticism.25 Thus,
childhood and adolescence represent periods ofconsiderable importance and a unique window
opportunity for intervention, allowing for strong
and positive impacts on global society.

Children and adolescents generally report higher
subjective well-being than adults, with variations
across societies and vulnerable groups, including
females, immigrants, children in care, and certain
minorities.16 Subjective well-being trajectories
show a decline from age 10 to late adolescenceand adulthood,17 varying among groups andcountries, with evidence suggesting a more
profound decline in lower-income countries.18
Furthermore, studies indicate that adolescentsubjective well-being is declining in many
countries, including evidence from both before19
and after the onset of the COVID-19 pandemic.20
There is also evidence that these declines are
more pronounced among females than males,21
and that the drivers of this decline may differ
between countries,22 which emphasizes the needfor cross-cultural insights.

A range of factors have been found to explain
variations in child and adolescent subjective
well-being. There is a nuanced association withsocio-economic status, with stronger links tomaterial deprivation – especially when measured
via child-derived indices – than family income.26
Relationships, both with parents and peers, playa substantial role, and schools are considered as
key domains where policy interventions can makea significant impact. Factors like bullying andschool-related anxiety influence subjective
well-being, but this relationship is nuanced andvaries across population groups, countries,
and measures.27 Other influential drivers include
aspects related to various life domains, including
health, physical activity, time use, neighborhood,safety, and children’s rights.28 Most of the drivers
identified in the literature are factors in theclose environment, such as family, school, andcommunity.29 Associations with subjective
well-being have been found for some child-Childhood and adolescence, besides being crucial
life stages in their own right,23 are subjects ofinterest for their impact on individuals as theytransition into adulthood. Research on developmental trajectories from these periods to adulthood
reveals a significant influence on later life outcomes,
encompassing adult well-being, labor market
success, physical health, and relationships. There65

World Happiness Report 2024

representation for the subset used in this chapter,focusing on adolescents aged 15-24 (middle-to-lateadolescence). However, the GWP collects global
data from 120-140 countries in most world regions,
including many low-income countries. By contrast,
PISA, HBSC, and Children’s Worlds collect nationally
representative samples of adolescents aged 10 to
15 (early-to-middle adolescence) in some 20-70
countries and territories, mostly in high-income,
Western societies.

focused macro-level factors (e.g.spending onfamilies and education as a percentage of GDP).30
However, for many others, and particularly
macro-economic factors, associations are foundfor adults but not for children and adolescents
aged 10-15.31 Indeed, researchers have reported
counterintuitive results, such as findings on thenegative association between the country level of
economic development and adolescent subjective
well-being at age 15.32 However, some of thesecounterintuitive findings may be the results ofadolescents from non-high-income countries not
being considered in these analyses as data inthese countries is not available for younger
adolescents.33

The World Happiness Report 2023 underscoredthe natural approach of measuring a nation’s
happiness by asking a nationally-representative
sample about their life satisfaction.36 In the annual
World Happiness Report rankings, the Cantril
Ladder from the Gallup World Poll gauges
well-being or “happiness”.37 In this chapter onchildhood and adolescence well-being, data isdrawn from these four surveys focusing on theiroverall life satisfaction/evaluation measures on an
11-point response scale. This is the only comparable
measure in the four data sets, although eachsurvey uses a slightly different version, as described in Box 1. This 11-point scale enhances
sensitivity for adolescent respondents in mostcountries compared to shorter scales,38 andenables us to develop measures of subjective
well-being inequalities (e.g. gender and age-based
differences) that are consistent across surveys.
As explained earlier, for ease, we use the term life
satisfaction throughout this chapter.

It is important to note that most of what we know
about child and adolescent subjective well-being
is mainly from adolescents in high-income
countries. Thus, improved worldwide data collection is crucial for understanding and promoting
adolescent subjective well-being globally.
International Data on Child andAdolescent Well-Being
Despite the surge of interest in these criticaldevelopmental periods, the available international
data on child and adolescent subjective well-being
remains notably limited. As a result, while subjective
well-being research within specific culturalcontexts is abundant,34 international researchremains comparatively scarce. Four majorcross-sectional datasets provide information on
child and/or adolescent subjective well-being(see Box 1 for details): the Programme for International Student Assessment (PISA) survey; theHealth Behaviour in School-aged Children (HBSC)
survey; the International Survey of Children’s
Well-being (ISCWeB or “Children’s Worlds”); andthe Gallup World Poll (GWP). Children’s Worlds
explicitly centers on child subjective well-being,
and not only measures life satisfaction (thecognitive component) but also affective andeudaimonic components, which are crucial forholistic analyses.35 In contrast, the other studies
measure subjective well-being but not as theirprimary focus. The GWP, though collecting
nationally representative data for their entire
sample aged 15+ to late adulthood, lacks such

The four surveys examined represent significant
endeavors in collecting extensive internationalchild and adolescent well-being data. However,before delving into the results of our analyses,
it is essential to acknowledge a few key data
limitations affecting the analysis and the subsequent discussion. A primary challenge is the lackof a standardized subjective well-being measureacross surveys. Two surveys (HBSC and GWP)
utilize a version of the Cantril Ladder, akin to theone used for the adult global happiness ranking
in the World Happiness Report, while PISA andChildren’s Worlds employ a question about
overall life satisfaction. Another limitation stems
from the age distribution in the datasets; none
cover the entire span from childhood to lateadolescence or adulthood, constraining the ability

66

World Happiness Report 2024

Box 3.1: Which large international datasets include measures of child andadolescent well-being?

PISA is the OECD’s Programme for International
Student Assessment which surveys nationally
representative samples of young people aged
15 across 70 countries/territories.39 The main
focus of the questionnaire is young people’sability to apply their mathematical, reading,and science skills to real-life challenges. Whilesubjective well-being questions have inconsistently been included in the PISA, data on life
satisfaction has been systematically collectedin most participating countries in the last threewaves (2015, 2018, 2022). In 2022, 74 countries
and territories collected life satisfaction data
(43 high-income, 24 upper-middle-income,7 lower-middle-income, zero low-income in
the 2022 edition). PISA employs a one-item
measure of life satisfaction: “The following
question asks how satisfied you feel about
your life, on a scale from “0” to “10”. Zeromeans you feel ‘not at all satisfied’ and “10”means ‘completely satisfied’. Overall, howsatisfied are you with your life as a wholethese days?”.

the bottom ‘0’ is the worst possible life foryou. In general, where on the ladder do you
feel you stand at the moment? Tick the box
next to the number that best describes where
you stand.”The Children’s Worlds survey explores the
subjective well-being of children aged 8, 10,
and 12, using nationally representative samplesof 1000 children in up to 35 countries per
wave. There have been three waves of datacollection (2011-12, 2013-14, 2017-19), plus a
post-COVID-19 wave in 2020-22, which was
not nationally representative. The 2017-19
wave expanded to include 30-35 countries(depending on age group; 21-25 high-income,five upper-middle-income, five lower-middleincome, and zero low-income). This study
includes a “0” to “10” life satisfaction item:“How satisfied are you with each of thefollowing things in your life? […] 0 = Not at
all satisfied; 10 = totally satisfied […]. Your life
as a whole”. Data from children aged 8 was
excluded in this chapter as the response scale
used was different making comparability
more challenging.

The HBSC survey is conducted in collaboration
with the WHO Regional Office for Europe.
It assesses the health and well-being ofadolescents across Europe, North America,
and – more recently – Central Asia, using
nationally representative samples at ages
11, 13, and 15. There are six waves of data that
include subjective well-being measurements(2002, 2006, 2010, 2014, 2018, 2022). The
most recent waves included 39 European andNorth American countries and 5 Commonwealth of Independent States (CIS) in Central
Asia in 2022 (35 high-income, seven uppermiddle-income, one lower-middle-income,and one low-income). Subjective well-being isassessed using an adapted version of the
Cantril Ladder measuring life satisfaction:“Here is a picture of a ladder. The top of theladder ‘10’ is the best possible life for you, and

The Gallup World Poll has tracked the mostimportant issues annually worldwide since2005. Responses from 15-64-year-olds are
representative across 140-160 countries, andthe sample includes many lower-middle andlow-income countries. For the 15-24 age group
employed in this chapter, the sample is not
representative. The Cantril Ladder is used toassess life satisfaction: “Please imagine aladder with steps numbered from zero at thebottom to 10 at the top. The top of the ladderrepresents the best possible life for you, andthe bottom of the ladder represents the worst
possible life for you. On which step of theladder would you say you personally feel you
stand at this time?”.

67

World Happiness Report 2024

to examine inequalities across different sociodemographic groups, and assess how factors
within distinct life domains contribute to variationsin adolescent life satisfaction. Subsequently, we
discuss our findings, as well as shortcomings ininternational child and adolescent subjective
well-being data and how these limitations impact
our understanding. Finally, we conclude thechapter by highlighting initiatives that are making
major inroads in improving data availability andassessing and promoting child and adolescentwell-being, which serve as inspiration for exploring
further necessary steps to collectively enhance
the well-being of children and adolescents globally.

to consistently analyze well-being responses
across various age groups. Additionally, a
predominant focus on high- and upper-middleincome countries (mostly Western nations) in thedata covering early-to-middle adolescence (age10-15) raises concerns about the generalizabilityof many findings to lower-income countries.
A more detailed discussion of these and otherlimitations follows at the end of this chapter.
This Chapter
Despite these limitations, this chapter provides acomprehensive examination of global child andadolescent subjective well-being. It begins by
exploring life satisfaction levels and trends amongchildren and adolescents, with consideration ofregional, gender, and age group variations. The
following section offers an overview of current
country-level life satisfaction in the post-pandemic
world and how these vary across different age
groups. We then present correlational analyses

Trends in Child andAdolescent Well-Being

Photo Tucker Tangeman on Unsplash

The four data sets examined differ significantly inparticipant age, data collection commencement,
number and frequency of waves, representative-

68

World Happiness Report 2024

gender gap has narrowed after the COVID-19
pandemic. Despite 95% confidence interval
overlap in 2022 in Figure 3.1B, small genderdifferences are still observed in 2022 in thecorrelational analysis presented below inTable 3.3, which uses a slightly different (moreglobal) sample of countries.

ness of the samples, and participating countries
and territories. These differences, describedabove, imply that different analytical approaches
are needed to study trends across these data
sets. Specifically, in middle-to-late adolescence(age 15-24), we use the GWP to study population
changes over time at the global- and regional-level,similar to past World Happiness Report editions.40
The small n per country and wave (see Table A1.1
in Appendix 1) prevent us from conducting
rigorous assessments at the country level. In
contrast, in early-to-middle adolescence (age10-15), conducting robust analyses at the global
level and in most regions is not possible. Thus,
we analyze PISA, HBSC, and Children’s Worlds
data to examine trends in country means anddiscuss them in the context of regional trends
when feasible.

The use of slightly different samples of countries
in different parts of the analysis is needed due todata limitations. In 2020, the number of countries
where data were collected dropped significantly,
especially in low-income countries (see Table A1.1
in Appendix 1). In the global trends in Figures 3.1A
and 3.1B, we decided to use data only from thecountries where data were collected in 2020
(i.e. consistent sample) to ensure that thesetrends represent a consistent sample of countries.
The main caveat is that these global trends are
somewhat less global as they exclude a numberof low-income countries that were not sampled in2020. For clarity, Figure A1.1 in Appendix 1 shows
a comparison of global trends using an inconsistentsample (i.e. considering all the countries withavailable data each year) and a consistent sample(i.e. considering only the countries where data
were collected in 2020). The former shows a peakin 2020/21 due to the reduced number of lowincome countries, where life satisfaction tends tobe lower on average. In contrast to this approach,in the analysis of regional trends presented in thenext section, we considered more adequate touse data from all the countries with available
data each year as otherwise some regions (e.g.
Sub-saharan Africa) would represent a small, farless representative sample of countries. The main
caveat is that 2020-21 levels in certain lowincome regions are to be interpreted with caution.

Furthermore, in view of evidence of pre-COVID-19
trends in adolescent life satisfaction in multiple
countries,41 the further negative impact of the
COVID-19 pandemic on children and young
people’s subjective well-being,42 and genderdifferences in adolescent subjective well-being
trends,43 our analyses emphasize the distinction
between pre- and post-COVID-19 trends andexamine gender differences whenever possible.
Global Levels and Trends in Middle-to-Late
Adolescence (Age 15-24): Gallup World PollIn our analysis of global trends using the GWP
data, we assign countries equal weight in theanalysis regardless of their population to replicate
the main analysis presented in previous editionsof the World Happiness Report for the adult
population (e.g. Figure 2.2 in the 2022 edition).44
Four main findings emerge (Figure 3.1A). First,
global life satisfaction is higher at age 15-24 thanat age 25 or above. Second, trends in middle-tolate adolescence (age 15-24) are similar to thoseobserved in the adult population45 and those
aged 25 or above before the COVID-19 pandemic,
with evidence of a moderate increase in global
life satisfaction between 2006 and 2019. Third,
the pandemic ended positive global trends. And
fourth, there are no gender differences until 2013,
but females aged 15-24 begin to report higher life
satisfaction than males from 2014, although thisRegional Levels and Trends in Middle-to-Late
Adolescence (Age 15-24): Gallup World Poll
Positive 2006 to 2022 global trends contrast withthe large body of research reporting on international declines in youth subjective well-being in
the last 10-15 years. Since declines have largely
been documented in high-income, Western
nations, it seems plausible that positive global
trends mask regional and country trends moving
in opposite directions, with increases in less69

World Happiness Report 2024

Fig. 3.1A: Global levels and trends in life satisfaction from 2006 through 2022.
Gallup World Poll (age 15-24 vs. age 25+)

Life Satisfaction
6.4
6.2
Age 15-24
6
5.8
5.6
Age 25+5.4
5.2
5
2005-06

2010

2014

2018

2022

Fig. 3.1B: Global levels and trends in life satisfaction from 2006 through 2022.
Gallup World Poll (age 15-24), by gender

Life Satisfaction
6.4
Female
6.2

Male

6

5.8

5.6
2005-06

2010

2014

70

2018

2022

World Happiness Report 2024

the periods 2006-12 and 2013-18. Regarding
post-COVID-19 trends in these regions, life
satisfaction levels in 2022 are similar to 2019 in allthese regions, except for Western Europe, wherea clear decline is apparent. However, the small
sample size, leading to larger 95% confidence
intervals, may obscure further declines in North
America and ANZ. The 2020 increase in South
Asia is explained by Afghanistan, which drags
down South Asian levels throughout the entire
series, especially in recent years (see Figure A1.2A
in Appendix 1), and did not collect data in 2020.
There are no gender differences in any of theseregions, except for the MENA, which is the only
world region where females consistently exhibit
higher life satisfaction than males throughout theentire series.

surveyed regions compensating for potential
declines in the most commonly surveyed regions.
We now turn our attention to this issue by examining trends at regional and country levels. Using
GWP data, we explore regional trends across the
10 world regions commonly examined in theWorld Happiness Report (see Figure 3.2A and
Figure A1.2C, Figure 3.2B, Figure 3.2C, andFigure A1.3), revealing four key findings.
Positive Regional Trends
Positive trends emerged in various regions during
the period 2006-2019, including the CIS, Central
and Eastern Europe, Sub-Saharan Africa, Latin
America and the Caribbean, and Southeast Asia.
The former two regions exhibit more sustained
trends, while the latter three display greater
volatility. Comparatively, life satisfaction levels in
2022, when contrasted with 2019, remain similarin the CIS and Central and Eastern Europe,decrease in Sub-Saharan Africa, and slightly
increase in Latin America and the Caribbean.
There is also some evidence of a positive trend in
East Asia, where life satisfaction is substantiallyhigher in 2006 than in 2007 partly due to the
non-inclusion of Mongolia in 2006, which drags
down the East Asian series, and the non-inclusionof Taiwan in 2007, the happiest country in theregion in 2006. When considering 2007 or 2008as a reference instead, a positive pre-COVID-19
trend is observed in East Asia, followed by afurther increase from 2019 to 2022. There isminimal evidence of gender differences throughout
the series in the CIS, Central and Eastern Europe,
Latin America and the Caribbean, and Southeast
Asia. In contrast, females exhibit higher life
satisfaction than males in East Asia during theperiod 2006-11 and in Sub-Saharan Africa during
the period 2018-21.

Sub-Regional and Country Trends
Positive and negative trends at both sub-regionaland country levels can be observed within specificregions. For instance, when separating North
America from ANZ in Figure A1.3 in Appendix 1,
despite limitations in sample size, we identify
stable pre-COVID-19 trends from 2006 to 2019
and a post-COVID-19 decline in ANZ. In contrast,
declining trends in North America appear tohave started several years before the COVID-19
pandemic. Rigorous assessment of genderdifferences is hindered by a small sample size. It isconceivable that other intra-regional trends may
exist in some of these large and diverse regions.
Unfortunately, small sample size limitations
involving this age group restrict our ability tothoroughly explore this question. However, inFigure A1.2A-B in Appendix 1, we present someinstances of country-level trends in countries
where previous evidence on adolescent subjective
well-being is almost non-existent as these data
are rarely collected in these nations. This includes
some positive trends (Mongolia, Togo, Ivory
Coast, and Gabon) and negative trends (Lebanon
and Afghanistan).

Negative Regional Trends
Negative trends preceding the COVID-19 era(2006/07-2019) are evident in the Middle Eastand North Africa (MENA), South Asia, North
America (Canada and the United States), Australia
and New Zealand (ANZ), and Western Europe. In
Western Europe, despite minimal 95% confidence
interval overlap between 2006 and 2019 estimates,
the declining trend is noticeable when comparing

Contrasts with Adult Trends
When comparing regional trends for those ages
15-24 and aged 25 or above, contrasting patterns
are evident. These are illustrated in Figure A1.3 inAppendix 1, as well as in Figure 3.2C for some71World Happiness Report 2024

Fig. 3.2A: Regional levels and trends in life satisfaction from 2006 through 2022.
Gallup World Poll (age 15-24)

Life Satisfaction (age 15-24)
8
7.5
North America and ANZ

7

Western Europe

6.5

Latin America & Caribbean
Central and Eastern Europe

6

East Asia
Middle East & North Africa

5.5Southeast Asia
Commonwealth of Independent States

5South Asia

4.5

Sub-Saharan Africa

4
3.5
2005-06

2010

2014

2018

selected regions. The gap is notably large in the
CIS, Latin America and the Caribbean, and especially in Central and Eastern Europe. Additionally,
the gap varies over the years in some regions asshown in Figure 3.2C. In Sub-Saharan Africa, thegap widened from 2013 due to stable life satisfaction levels among those aged 25+ and positive
trends among those aged 15-24. In contrast, thegap has narrowed in Western Europe for over a
decade due to a moderate negative trend among
adolescents (age 15-24) and a moderate positive
trend among those aged 25 and above. In North
America and ANZ, despite 95% confidence
interval overlaps in some years, there is evidenceof a potential reversal in this gap in recent years,
suggesting that life satisfaction could now behigher among those aged 25+ than among those
aged 15-24, which is not observed anywhere else.
Separate analyses for North America and ANZ are
shown in Figure A1.3 in Appendix 1, which suggest
the same pattern in both regions – and especiallyin North America – despite 95% confidence
interval overlaps likely due to small sample size.

2022

Further evidence of age-based differences inregional trends can be observed in Chapter 2.46
Regional and Country Levels and Trends in
Early-to-Middle Adolescence (Age 10-15).
Data from PISA (Age 15), HBSC (Age 15, 13, 11),and Children’s Worlds (Age 12, 10)
In early-to-middle adolescence (age 10-15), global
subjective well-being analyses are not possible
due to limited data, mainly available in highincome Western countries. Consequently, we
focus on regional and country trends, startingwith those aged 15 and then shifting to age 10-13.
Assessing regional trends is complex in PISA and
HBSC, and unfeasible in Children’s Worlds, due toa limited number of participating countries anddata gaps across waves. This limitation hampers
the ability to make robust, evidence-based claims
about regional trends. As a result, we turn tonational trends, presented in Tables A1.2-A1.3 inAppendix 1, with discussions considering regional
contexts where possible.

72

World Happiness Report 2024

Fig. 3.2B: Regional levels and trends in life satisfaction from 2006 through 2022.
Gallup World Poll (age 15-24), by gender

Western Europe

Central and Eastern Europe
8.5

8.5

8

8

7.5

Female7.5

Female

7

7

Male

6.5

6.5

Male

6

6

5.5

5.5

5

5

4.5

4.5

4

4

3.5

3.5

2005-06

2010

2014

2018

2022

2005-06

Southeast Asia

2010

2014

2018

2022

Commonwealth of Independent States

8.5

8.5

8

8

7.5

7.5

7

7

6.5

6.5

Female6Female

6

5.5

5.5

5

5
Male

4.5
4

4

3.5

3.5

2005-06

2010

2014

2018

Male

4.5

2022

2005-06

2010

2014

2018

2022

East Asia

South Asia
8.5

8.5
8

8

7.5

7.5

7

7

6.5

6.5

Female66

5.5

Female

5.5

Male

5

5

4.5

4.5
Male

4

4
3.5

3.5

2005-06

2010

2014

2018

2022

2005-06

73

2010

2014

2018

2022

World Happiness Report 2024

Fig. 3.2B: Regional levels and trends in life satisfaction from 2006 through 2022. (continued)
Gallup World Poll (age 15-24), by gender

North America and ANZ

Latin America & Caribbean
8.5

8.5

8

8

7.5
7

Female7.5

Female

7

6.5

6.5

6
5.5

5.5

5

5

4.5

4.5

4

4

3.5

3.5

2005-06

2010

2014

Male

6

Male

2018

2022

2005-062010

Middle East & North Africa

2014

2018

2022

Sub-Saharan Africa
a

8.5

8.5
8

8

7.5

7.5

7

7
6.5

6.5

Female

6

6

5.5

5.5

5

5
4.5

Male

4.5
4

4

3.5

3.5

2005-06

2010

2014

2018

Female2022

Male

2005-06

Age 15

2010

2014

2018

2022

The second key finding at age 15 is the existenceof mixed results concerning pre-COVID-19 trends,
with notable disparities in country means andtrends between PISA and HBSC. This is detailedin Table 3.1, and further explored in Appendix 2(Table A2.1 and Figure A2.1). PISA indicates a
pre-COVID-19 decline in most countries with datafrom 2015 and 2018, encompassing North America,
Western Europe, Central and Eastern Europe, theCIS (Russia), Latin America and the Caribbean,
MENA, and East Asia. This decline is more prominent among females, particularly in Central andEast Europe, the CIS (Russia), Latin America andthe Caribbean, and East Asia (see Table A1.2 inAppendix 1). Notably, the only pre-COVID-19
increase is observed in South Korea, though thisFrom PISA and HBSC data, two key findings
emerge. Firstly, both datasets reveal a significant
post-COVID-19 decline in most countries withavailable data (mainly high-income, Western
nations), with a more pronounced decrease
among females. Notably, some countries in East
Asia (Japan, Macau, Taiwan, and Hong Kong)show an increase, as indicated by PISA data, theonly study collecting information in these regions.
In HBSC, post-pandemic declines are noted incountries in North America (Canada), Western
Europe, Central and Eastern Europe, and the CIS. In
PISA, similar declines are observed across countries
in these regions, as well as in the MENA, Latin
America and the Caribbean, and Southeast Asia.

74

World Happiness Report 2024

Fig. 3.2C: Regional levels and trends in life satisfaction from 2006 through 2022.
Gallup World Poll (age 15-24 vs. age 25+)

Sub-Saharan Africa
8
7.5
7
6.5
6
5.5

Age 15-24

5
4.5
4
Age 25+
3.5
2005-06

2010

2014

2018

2022

North America and ANZ
8

Age 25+7.5
76.5

Age 15-24

6
5.5
5
4.5
4
3.5
2005-06

2010

2014

2018

2022

and 2018, this decline is absent in HBSC datafrom 2013/14 to 2017/18. In Western Europe,
HBSC data depicts a mixed picture, predominantly
showing increases, while PISA consistently
indicates declines. In Central and Eastern Europe,
HBSC shows an overall increase and PISA an
overall decline. In Russia, the only CIS country
with comparable data, PISA indicates a decline,
contrasting with HBSC’s absence of statisticallymasks a decline among females and an increase
among males (see Table A1.2 in Appendix 1).
This underscores the importance of assessing
inequalities across distinct socio-demographic
groups within countries, achievable only throughnationally representative samples of the studied
population group.
In contrast, in many countries where PISA
indicates a pre-COVID-19 decline between 201575

World Happiness Report 2024

the series, exhibits a continuous negative trend
predating the pandemic, persisting into 2022,
primarily driven by a decrease among females(see Figure A1.4 in Appendix 1). Males in Canada
experience a negative trend affecting only
15-year-olds post-COVID-19, while females endure
a prolonged negative trend impacting those aged
13 and 15 for over a decade before the pandemic,
as well as those aged 11 after the pandemic.
Negative pre-pandemic trends (2005/06-17/18)
are also observed in the MENA countries Turkey
and Israel. Conversely, sustained pre-pandemic
positive trends are noted across several HBSC
waves in countries in Central and Eastern Europe,
including Croatia, Latvia, and Estonia. The picture
is more mixed in the CIS and Western Europe.
Lastly, the post-pandemic decline observed inthose aged 15 is mirrored in those aged 13 and 11,
affecting all regions with available data, including
North America (Canada), Western Europe, andCentral and Eastern Europe. This decline isprevalent across most countries surveyed in2017/18 and 2021/22, with a more substantial
impact on females and older age groups.

significant changes (p<0.05). Discrepancies incountry means, as seen in Table A2.1 and Figure
A2.1 in Appendix 2, may explain disparities intrends. Although most differences in country
means are minor, a few instances reveal surprisingly large gaps, with PISA estimates generally
lower than HBSC estimates. In the U.K., forinstance, HBSC 2017/18 shows life satisfactionlevels almost 1 point higher than PISA 2018.
Considering research evidence of subjective
well-being declines throughout adolescence,47
this discrepancy may partly be explained by thefact that most students surveyed in HBSC areenrolled in Year 10, while in PISA, unlike mostparticipating countries, most students surveyed
in England, Northern Ireland, Scotland and Wales
are enrolled in Year 11. Appendix 2 delves into a
detailed discussion of factors potentiallyexplaining PISA-HBSC discrepancies, including
variations in the life satisfaction measure, the year
and month of data collection, survey context(e.g., right after/before taking an academic test in
PISA), the target population (e.g. differences in
the average age and school year), and samplingissues (notably exclusions in PISA).

Moving to younger children and adolescents,
trend analyses in Children’s Worlds (age 12, 10)
are not feasible due to the data limitations
explained earlier. However, country-level
estimates by survey wave and gender, presented
in Table A1.5 in Appendix 1, suggest a decline
in most participating countries following the
COVID-19 pandemic.

In view of the above, it is evident that while there
is robust evidence of post-COVID-19 declines innearly all examined countries (with increases inmost East Asian nations), caution is warranted
when interpreting evidence on pre-COVID-19
trends at age 15, where contrasting results
emerge in many countries. National studies, suchas those in the U.K. outlined in Appendix 2, canprovide further support to trends observed ininternational studies. However, in countries
lacking alternative data, making definitive claims
about trends during these years for this age
group is challenging. Further research is essential
to elucidate the factors contributing to these
discrepancies.

Current Global State of Child andAdolescent Well-Being
Providing an overview of the current global stateof child and adolescent subjective well-being in
the post-pandemic world is imperative given
the widespread post-COVID-19 decline in life
satisfaction, along with age-related andgeographic patterns and the earlier-discussed
data limitations. Country means in life satisfaction
across age groups, studies, and countries/territories
are outlined in Tables 3.2A-J (alphabeticallyordered within each of the 10 world regions) andTables A1.6A-D in Appendix 1 (countries ranked
by GDP).

Age 10-13
Unlike PISA, HBSC collects data from younger
adolescents aged 13 and 11, enabling an examinationof longer-term trends starting from 2001/02.
Table A1.3 and Figure A1.4 in Appendix 1 show
stability in most countries and sustained prepandemic trends in some regions. Canada, theonly North American country with data throughout76World Happiness Report 2024

Photo Yingchou Han on Unsplash

image TK

Middle-to-Late Adolescence (Age 15-24):
Gallup World Poll

Early-to-Middle Adolescence (Age 10-15):
PISA, HBSC, and Children’s Worlds

There are notable regional differences in life
satisfaction in middle-to-late adolescence, with
Israel, parts of Central and Eastern Europe(Croatia, Serbia, Lithuania, Romania), andNorthern Europe reporting the highest levels
and Sub-Saharan Africa and South Asia recording
the lowest in the GWP (age 15-24). The poor
performance in South Asia is mostly driven by
the extremely low life satisfaction reported byadolescents in Afghanistan. All this is consistent
with adult data, with the exception of the cited
Central and Eastern European countries, which
are found much lower in the adult ranking, andNorth America, ANZ, and Western Europe, where
adult life satisfaction is much higher comparedto other regions.48

The main observation is the limited numberof countries collecting subjective well-being
data in early-to-middle adolescence, primarily
high-income Western nations (although
Table A2.1 in Appendix 2 indicates increased
participation from lower-income countries inrecent years, notably in PISA 2022). In theavailable data, regional variations are evident.
PISA 2022 data reveal the highest life satisfactionin Central and Eastern Europe (particularly in the
Balkans), and the CIS, with the lowest in East
Asia, North America and ANZ, and MENA. HBSC
data, primarily focused on Europe, North America
and the CIS, indicates highest life satisfactionlevels in the Balkans and CIS countries, andlowest in North America (Canada) and specific
European nations like Ireland, the U.K., Italy,77

World Happiness Report 2024

Table 3.1: Regional and country in PISA (2015-18-22) and HBSC (2013/14-17/18-21/22)
PISA (Age 15)

HBSC (Age 15)

Country means
Country

TrendsCountry means

Trends

2015

2018

2022

2015–18

2018–222013/142017/18

2021/222013/14–17/18

2017/18–21/22

Austria

7.52

7.14

6.69

-0.39

-0.44

7.53

7.29

7.20

-0.24

non.sign.

Belgium

7.49

Western Europe

Belgium (Flemish)

6.89

7.48

7.46

0.60

non.sign.

Belgium (French)

7.19

7.22

6.81

non.sign.

-0.41

Cyprus

6.99

non.sign.

-0.39

-0.37

Denmark*

7.19

Finland

7.89

FranceGermany
Greece

7.37

7.45
7.48

7.41

-0.28

-0.21

7.63

7.19

6.77

-0.44

-0.42

6.97

7.28

6.91

0.32

7.35

7.02

6.51

-0.33

-0.51

7.05

7.33

7.12

0.28

-0.21

6.91

6.99

6.62

non.sign.

-0.37

7.16

7.04

6.70

non.sign.

-0.34

7.48

7.49

6.66

non.sign.

-0.83

Iceland

7.80

7.34

6.90

-0.46

-0.44

7.47

7.26

7.12

-0.21

-0.14

Ireland*

7.30

6.74

6.59

-0.57

-0.15

7.01

6.89

6.22

non.sign.

-0.67

Italy

6.89

6.91

6.53

non.sign.

-0.38

6.95

7.11

6.55

0.16

-0.55

Luxembourg

7.38

7.04

7.01

7.35

7.10

0.33

-0.24

7.11

6.71

6.48

-0.40

-0.23

7.83

7.50

7.29

-0.33

-0.21

7.36

7.33

6.90

non.sign.

-0.43

7.54

7.48

7.05

non.sign.

-0.43

Portugal

7.36

7.13

7.06

-0.24

non.sign.

6.99

7.35

7.05

0.36

-0.29

Spain

7.42

7.35

6.88

non.sign.

-0.46

7.30

7.64

6.77

0.34

-0.87

6.83

7.10

6.80

0.27

-0.30

7.54

7.34

6.99

-0.19

-0.35

6.81

7.11

6.51

0.30

-0.60

Greenland

Malta
Netherlands*

-0.34
6.24

NorwaySweden

6.91

Switzerland

7.72

7.38

7.06

-0.34

-0.31

U.K.*

6.98

6.16

6.07

-0.81

non.sign.

U.K. (England)

6.94

6.12

6.01

-0.82

non.sign.

U.K. (Northern Ireland)

7.24

6.58

6.50

-0.67

non.sign.

7.47

7.10

7.61

7.46

U.K. (Scotland)

7.17

6.25

6.48

-0.92

0.23

7.14

7.03

6.66

non.sign.

-0.37

U.K. (Wales)

7.13

6.45

6.16

-0.68

-0.29

6.93

7.09

6.61

0.16

-0.48

Average

7.37

6.94

6.69

-0.51

-0.31

7.17

7.25

6.90

0.15

-0.43

7.71

7.56

8.14

non.sign.

0.58

Bulgaria

7.42

7.15

7.04

-0.26

non.sign.

7.43

7.59

7.10

0.16

-0.49

Croatia

7.90

7.69

7.37

-0.22

-0.32

7.49

7.72

7.57

0.23

-0.15

Czech Republic

7.05

6.91

6.56

-0.14

-0.36

6.99

7.43

7.26

0.44

-0.17

Estonia

7.50

7.19

6.91

-0.31

-0.28

7.33

7.35

6.95

non.sign.

-0.40

Hungary

7.17

7.12

7.21

non.sign.

non.sign.

7.09

7.14

6.99

non.sign.

non.sign.

7.06

7.00

6.73

non.sign.

7.47

6.95

Central and Eastern Europe
Albania

8.01

Kosovo7.87

Latvia*

7.37

7.16

6.76

-0.21

-0.40

Lithuania

7.86

7.61

7.14

-0.26

-0.47

Montenegro

7.75

7.69

7.52

non.sign.

-0.16

-0.44

-0.49

North Macedonia
Poland

7.65
7.18

6.74

6.26

Romania

7.53

Serbia

7.48

-0.27
-0.52

7.11

7.82

7.42

0.70

-0.39

6.80

7.03

6.20

0.23

-0.84

7.61

7.94

7.76

0.32

7.85

7.89

-0.18
non.sign.

Slovakia

7.47

7.22

7.02

-0.25

-0.20

7.06

7.36

6.00

0.29

-1.35

Slovenia

7.17

6.86

6.61

-0.32

-0.25

7.41

7.45

7.08

non.sign.

-0.36

Average

7.44

7.21

7.18

-0.27

-0.33

7.26

7.48

7.15

0.34

-0.38

Note: Countries marked with an asterisk (*) should exercise caution when interpreting estimates, as they may not fully meet one or more PISA sampling standards

78World Happiness Report 2024

Table 3.1: Regional and country trends at age 15 in PISA (2015-18-22) andHBSC (2013/14-17/18-21/22) continued
PISA (Age 15)

HBSC (Age 15)

Country means
Country

2015

2018

Trends2022

2015–18

Country means

2018–22

Trends2013/142017/18

2021/222013/14–17/18

2017/18–21/22

8.19

8.06

8.15

non.sign.

non.sign.

CIS
Armenia
Azerbaijan

7.83

Baku (Azerbaijan)

6.80

Georgia

7.62

7.57

Kazakhstan

8.41

8.18

Kyrgyzstan

7.97

-0.21

7.95

Republic of Moldova

7.01

Russia

7.76

7.32

-0.44

7.87

7.85

6.94

6.92

7.31

7.34

7.58

7.68

7.95

7.18

6.98

6.63

-0.19

-0.36

7.18

6.98

6.63

-0.19

-0.36

7.56

7.47

Tajikistan

7.70

non.sign.

-0.15

non.sign.
8.00

UkraineUzbekistan

non.sign.

8.20

Average

7.76

7.32

7.61

-0.44

-0.18

North America and ANZ
CanadaNew Zealand*

6.27

U.S.*

7.36

6.75

Average

7.36

6.75

-0.60
6.27

-0.60

Middle East and North Africa
IsraelJordanMorocco
Qatar

6.76
7.41

6.84

Saudi Arabia
Tunisia

non.sign.

6.77

6.77

-0.56

non.sign.

7.36
6.90

Turkey

6.12

5.62

4.90

-0.50

-0.72

United Arab Emirates

7.30

6.88

6.85

-0.42

non.sign.

Average

6.93

6.45

6.57

-0.49

-0.72

6.09

7.56

6.78

Latin America and the Caribbean
Argentina

6.69

Brazil

7.59

7.05

6.85

-0.53

-0.21

Chile

7.37

7.03

6.41

-0.34

-0.62

Colombia

7.88

7.62

6.96

-0.27

-0.66

Costa Rica

8.21

7.96

7.32

-0.25

-0.64

Dominican Republic

8.50

8.09

7.44

-0.41

-0.65

-0.16

-0.85

El Salvador

7.40

Guatemala7.72

Jamaica*

5.83

Mexico8.27

8.11

Panama*7.26
7.04

Paraguay

7.32

Peru

7.50

7.31

6.37

-0.19

-0.94

Uruguay

7.70

7.54

7.03

-0.16

-0.50

Average

7.88

7.59

6.97

-0.29

-0.63

Note: Countries marked with an asterisk (*) should exercise caution when interpreting estimates, as they may not fully meet one or more PISA sampling standards

79World Happiness Report 2024

Table 3.1: Regional and country trends at age 15 in PISA (2015-18-22) andHBSC (2013/14-17/18-21/22) continued
PISA (Age 15)

HBSC (Age 15)

Country means
Country

2015

Trends2018

2022

2015–18

Country means

2018–222013/142017/18

Trends2021/222013/14–17/18

2017/18–21/22

East Asia
China (B-S-J-G)

6.83

Hong Kong*

6.48

6.27

6.49

-0.20

0.22

Japan

6.80

6.18

6.76

-0.62

0.58

Macau

6.59

6.07

6.41

-0.52

0.34

-0.16

Mongolia

7.20

South Korea

6.36

6.52

6.36

0.15

Taiwan

6.59

6.52

6.85

non.sign.

0.33

Average

6.61

6.31

6.68

-0.30

0.26

Southeast Asia
Brunei Darussalam

5.86

Cambodia

7.65

Indonesia

7.22

Malaysia

7.04

Philiphines
Thailand

-0.40

6.97
7.71

7.64

VietnamAverage

6.63

7.12

non.sign.

-0.51

7.35
7.71

7.34

6.97

-0.46

Note: Countries marked with an asterisk (*) should exercise caution when interpreting estimates, as they may not fully meet one or more PISA sampling standards

Photo Artur Rekstad on Unsplash

image TK

80

World Happiness Report 2024

Table 3.2A: Adolescent life satisfaction in Western Europe, by country and age
GWP

PISA

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

Austria

7.34

6.69

7.20

7.70

8.36

Belgium

6.95

8.05

8.27

7.46

7.78

8.05

Belgium (Flemish)
Belgium (French)
Cyprus6.88

Denmark

7.45

HBSC

CW

6.81

7.20

7.80

7.45

7.99

8.46

7.19

7.10

7.32

7.76

Finland

7.41

7.41

7.46

7.66

8.13

France

6.83

6.77

6.91

7.16

7.68

Germany

6.58

6.51

7.12

7.50

8.17

Greece

6.66

6.62

6.70

7.09

8.43

6.66

6.78

6.98

Iceland

7.76

6.90

7.12

7.28

7.79

Ireland

7.08

6.59

6.22

6.87

7.73

Italy

6.71

6.53

6.55

7.16

7.55

Luxembourg

7.12

7.10

7.46

8.12

Malta

6.69

6.24

6.48

7.00

7.81

Netherlands

7.30

7.29

6.90

7.22

7.95
7.67

Greenland

Norway

7.28

7.05

7.24

Portugal

6.83

7.06

7.05

7.54

8.21

Spain

6.46

6.88

6.77

7.13

8.25

7.24

6.91

6.80

6.91

7.83

Switzerland

7.06

6.99

7.21

7.95

U.K. (England)

6.01

6.51

6.89

7.37

U.K. (North Ireland)

6.50

U.K. (Scotland)

6.48

6.66

6.97

7.64

U.K. (Wales)

6.16

6.61

7.09

7.70

Spain (Catalonia only)
Sweden

U.K.

6.92

9.28

8.69

8.78

7.41

8.11

8.71

9.13

8.18

8.88

7.76

8.52

6.07

Table 3.2B: Adolescent life satisfaction in Central and Eastern Europe, by country and age
GWP

PISA

HBSC

CW

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

8.01

8.14

8.67

9.18

8.51

9.16

7.64
8.50

9.03

8.00

8.53

9.00

9.09

Albania

6.51

Bosnia and Herzegovina

6.88

Bulgaria

6.29

7.04

7.10

7.34

Croatia

7.51

7.37

7.57

7.91

8.47

Czech Republic

7.17

6.56

7.26

7.51

8.04

Estonia

6.79

6.91

6.95

7.26

7.91

Hungary

7.03

7.21

6.99

7.33

7.92

Kosovo6.94

7.87

Latvia

6.86

6.76

6.73

7.06

7.64

Lithuania

7.39

7.14

6.95

7.21

7.71

Montenegro

6.56

7.52

North Macedonia

6.58

7.65

7.42

7.79

8.42

Poland

6.55

6.26

6.20

6.28

7.06

Romania

7.62

7.53

7.76

8.06

8.67

Serbia

7.53

7.48

7.89

8.30

8.84

Slovak Republic

6.70

7.02

6.00

6.28

6.91

Slovenia

7.17

6.61

7.08

7.34

8.05

81

World Happiness Report 2024

Table 3.2C: Adolescent life satisfaction in Commonwealth of Independent States,by country and age
GWP

PISA

HBSC

CW

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

8.15

8.52

8.80

Armenia

6.16

Azerbaijan

5.34

Azerbaijan (Baku)

6.80

Georgia

6.08

7.62

Kazakhstan

6.52

8.41

Kyrgyz Republic

6.15

Moldova, Republic of

6.94

Russian Federation

6.34

Tajikistan

5.61

Ukraine6.23

Uzbekistan

5.98

7.01

7.97

8.30

8.49

7.95

8.30

8.60

7.70

8.02

8.47

8.00

8.07

8.10

7.76

8.20

Table 3.2D: Adolescent life satisfaction in East Asia, by country and age
GWP

PISA

HBSC

CW

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

7.74

7.55

China, People's Republic of

6.05

Hong Kong S.A.R. of China

5.33

6.49

Japan

6.51

6.76

Mongolia

5.94

7.20

South Korea

6.59

6.36

Taiwan Province of China

7.12

6.85

Macao S.A.R. of China

6.41

7.36
7.80

7.91

Table 3.2E: Adolescent life satisfaction in Latin America and Caribbean region,by country and age
GWP

PISA

HBSC

CW

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

Argentina

6.55

6.69

Bolivia

6.23

Brazil

6.46

Chile

6.65

6.41

7.76

8.44

Colombia

5.95

6.96

Costa Rica

6.93

7.32

Dominican Republic

6.38

7.44

Ecuador

6.40

6.85

El Salvador

6.72

7.40

Guatemala

6.65

7.72

Honduras

6.47

Jamaica

5.81

5.83

Mexico6.77

7.26

Nicaragua

6.84

Panama

6.94

Paraguay

6.18

7.32

Peru

6.23

6.37

Uruguay

6.77

7.03

Venezuela

5.59

8.82

7.04

82

World Happiness Report 2024

Table 3.2F: Adolescent life satisfaction in Middle East and North Africa,
by country and age
GWP

PISA

HBSC

CW

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

8.15

7.94

8.58

8.78

7.28

8.11

Algeria

5.54

Bahrain

6.52

Egypt

4.38

Iran

5.46

Iraq

5.61

Israel

7.98

Jordan4.86

Kuwait7.39

Lebanon

2.93

Libya

5.93

Morocco

5.34

Palestine, State of

5.25

Qatar

6.77

6.76

6.77

Saudi Arabia

6.45

Tunisia

4.87

Turkish Republicof Northern Cyprus

5.32

7.36

Türkiye, Republic of

5.07

4.90

United Arab Emirates

6.54

6.85

Yemen

3.93

Table 3.2G: Adolescent life satisfaction in North America and ANZ, by country and age
GWP

PISA

HBSC

CW

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

6.63

7.00

7.54

Australia

6.94

Canada6.70

New Zealand

6.85

U.S.

6.61

6.27

Table 3.2H: Adolescent life satisfaction in South Asia, by country and age
GWP

PISA

HBSC

CW

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

7.55

7.66

8.22

7.96

Afghanistan

1.96

Bangladesh

4.72

India

4.33

Nepal

5.67

Pakistan

5.17

Sri Lanka

4.80

83

World Happiness Report 2024

Table 3.2I: Adolescent life satisfaction in Southeast Asia, by country and age
GWP

PISA

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

8.12

8.46

Brunei Darussalam

HBSC

CW

5.86

Cambodia

4.62

7.65

Indonesia

5.81

7.22

Lao P.D.R.

4.90

Malaysia

6.41

Myanmar

4.40

Philippines

6.12

Singapore

6.45

6.63

6.97

Thailand

6.75

7.12

Vietnam

6.06

7.35

Table 3.2J: Adolescent life satisfaction in Sub-Saharan Africa, by country and age
GWP

PISA

2020/222022

2021/222021/222021/222020/222020/22

Age 15-24

Age 15

Age 15

Age 13

Age 11

Age 12-13

Age 10-11

8.60

8.86

Benin

4.52

Botswana

4.09

Burkina Faso

4.98

Cameroon

5.36

Chad

4.56

Comoros

4.01

Congo

5.78

Congo, Democratic Republic of the

3.37

Côte d'Ivoire

5.32

Eswatini

3.84

Ethiopia

4.48

Gabon

5.52

Gambia

4.52

Ghana

4.84

Guinea

5.05

Kenya

4.78

Lesotho

3.80

Liberia

4.73

Madagascar

4.17

Malawi

3.83

Mali

4.47

Mauritania

4.61

Mauritius

6.03

Mozambique

5.32

Namibia

5.05

Niger

4.63

Nigeria

5.28

Senegal5.06

Sierra Leone

3.19

South Africa

5.75

Tanzania

4.15

Togo

4.34

Uganda4.69

Zambia4.09

Zimbabwe

3.77

HBSC

84

CW

World Happiness Report 2024

Fig. 3.3: Life satisfaction declines throughout early-to-middle adolescence(HBSC 2021/22), by gender

Western Europe

Commonwealth of Independent States

9.5

9.5

8.5

8.5

Male
Male

Female7.5

7.5

Female

6.5

6.5

5.5

5.5
Age 11

Age 13

Age 11

Age 15

Age 13

Age 15Central and Eastern Europe

North America (Canada only)
9.5

9.5

8.5

8.5

Male

Male
7.5

7.5

6.5

6.5

FemaleFemale5.5
Age 11

Age 13

5.5
Age 15

Age 1185

Age 13

Age 15

World Happiness Report 2024

PISA 2022 data reveals thehighest life satisfaction for
individuals aged 15 is found inCentral and Eastern Europe.

have been explored in some of the previous
analyses, and further insights are provided in thecorrelational analysis presented in this section.
Middle-to-Late Adolescence (Age 15-24):
Gallup World Poll
The correlational analysis of GWP data (age15-24) considers all the countries where data
were collected in 2022, and delves into sociodemographic factors (gender, rural/urban
residence, household income, and country GDP)and 10 items on satisfaction with different aspectsof life.49 This analysis is summarized in Table 3.3,
which shows the results for Model 1 (socio-demographic factors only) and Model 2 (10 satisfaction
items, controlling for socio-demographic factors).
Separate analyses by GDP levels are available in
Tables A1.7.A-D in Appendix 1.

Malta, Poland, and Slovakia. HBSC and Children’sWorlds data also show that regional and country
differences decrease in younger children andadolescents.
Finally, it is also evident that younger children
and adolescents consistently report higher life
satisfaction than their older counterparts, illustrating
an early start to the decline from childhood to
middle age. Figure 3.3 shows the decline from age
11 to 15 in HBSC data, indicating a larger decline
among females, particularly between age 11 and13, with some variation across regions. Including
data from the other three studies, Tables 3.2A-J
shows that this declining pattern is evident fromage 10 to 15 in all countries, and continues into
late adolescence (age 15-24) in most of them. The
decline from age 10-12 to age 15-24 is remarkably
larger in lower-income countries. This is observedin the lowest-income countries with available
data, including Algeria, Turkey, Bangladesh, Sri
Lanka, Tajikistan, Indonesia and South Africa. In
contrast, in many Western countries (mostly inEurope), there is no further decline at age 15-24,
and in some of them (notably, Iceland, Ireland,
Sweden, and the U.K.), there seems to be higher
life satisfaction at age 15-24 than at age 15.

Inequalities Across Socio-Demographic Groups
On average, females report a life satisfaction
0.09 points higher than males (Table 3.3), andthis gap is larger in lower-income countries, withno gender differences noted in high-income
countries (Tables A1.7.A-D in Appendix 1). Similarly,
life satisfaction is -0.10 points lower in ruralcommunities compared to urban ones, and thisgap is larger in lower-income countries, with nodifferences observed in high-income countries.
Additionally, compared to those in the first(lowest) quintile of the country’s householdincome distribution, those in the third, fourth,and fifth (highest) quintiles report 0.16, 0.30, and0.43 points higher life satisfaction respectively,
with smaller effects in high-income countries thanlower-income ones. Moreover, compared toresidents of high-income countries, those inupper-middle-, lower-middle-, and low-income
countries report -0.63, -1.74, and -2.91 points
lower life satisfaction respectively. This association can also be visualized in Table A1.7.A-D in
Appendix 1, which ranks all countries by GDP.

Adolescent Well-Being Inequalities
and Correlates
To offer a more comprehensive view of thecurrent global state of child and adolescentwell-being, we enhance the preceding analyses
on subjective well-being levels and trends bypresenting a series of correlational analyses.
Using data from the GWP and PISA, we examine
subjective well-being inequalities across sociodemographic groups, and life domain factors
explaining variation in adolescent subjective
well-being. Inequalities across gender and age

Chapter 2 of this World Happiness Reportpresents a similar analysis for those aged 15-29,
including a broader range of correlates. This
analysis suggests that the relative importance ofhousehold income diminishes when controllingfor other important factors.50

86

World Happiness Report 2024

Table 3.3: Correlates of life satisfaction. Gallup World Poll 2022 (age 15-24)  Model 1Model 2
S.E.

S.E.

Socio-demographic factors
Gender (ref.: male)
Female

0.09

**

0.03

0.12

***

0.03

-0.10

**

0.03

-0.08

**

0.03

Urban/rural residence (ref.: urban residence)
Rural residenceHousehold income (ref.: lowest 20%)
Second 20%

0.04

0.04

0.03

Middle 20%

0.16

***

0.04

0.11

*

0.05
0.05

Fourth 20%

0.30

***

0.04

0.24

***

0.05

Highest 20%

0.43

***

0.05

0.33

***

0.05

Upper middle-income countries

-0.63

***

0.04

-0.40

***

0.04

Lower middle-income countries

-1.74

***

0.04

-1.40

***

0.04

Low-income countries

-2.91

***

0.05

-2.07

***

0.06

*

0.03

Country's economic development (ref.: high-income countries)

10 satisfaction items
Satisfied with the public transportation system in your city/area

0.08

Satisfied with the roads and highways in your city/area

-0.04

0.03

Satisfied with the quality of air in your city/area

0.03

0.04

Satisfied with the quality of water in your city/area

0.02

Satisfied with the availability of affordable housing in your city/area

0.20

Satisfied with the education system/schools in your city/area

0.07

Satisfied with the quality healthcare in your city/area

0.24

***

0.04

Satisfied with opportunities to meet people and make friends in your city/area

0.28

***

0.04

Satisfied with the city/area where you live

0.37

***

0.04

Satisfied with your standard of living (things you can buy and do)

1.42

***

0.04

0.04
***

0.03
0.04

Note. Significance Levels: * 0.05 ** 0.01 *** 0.001. Model 1: R2= 0.15; N=25,877, p<.001. Model 2: R2= 0.25; N=22699, p<.001

Satisfaction with Different Aspects of Lifebetween life satisfaction and satisfaction withthe public transportation system and access toquality healthcare.

Life satisfaction tends to be higher amongst
those who report being satisfied with: standardsof living, the city or region they reside in, opportunities for social interactions and friendships intheir city or area, accessibility of quality healthcare
services in their city or area, availability of good
and affordable housing in their city or area, andthe public transportation system. Satisfaction
with standards of living has – by far – the mostsignificant impact on life satisfaction, emphasizing
the role of material well-being. In contrast, statistically significant associations (p<0.05) are notfound for the quality of roads, air quality, water
quality, and the quality of the schools and education
system within their city or area.

•	In lower-middle-income countries, there are no
statistically significant associations (p<0.05)
between life satisfaction and satisfaction withthe public transportation system, access toquality healthcare and opportunities to meet
people and make friends. In contrast, there is anassociation with satisfaction with the education
system/schools.
•	In low-income countries, there are no statisticallysignificant associations (p<0.05) between life
satisfaction and satisfaction with the public
transportation system, opportunities to meet
people and make friends, and the city/area ofresidence. In contrast, there is an association
with satisfaction with water quality and theeducation system/schools.

Separate analyses by GDP levels reveal additional
insights:•	In upper-middle-income countries, there are no
statistically significant associations (p<0.05)

87World Happiness Report 2024

among females than males since 2014 globally(see Figure 3.1B and Table 3.4). This could largely
be explained by the focus on mostly high-income,
Western countries in the available data. Indeed,
GWP results at age 15-24 show no genderdifferences (p<0.05) in high-income countries(Table A1.7A in Appendix 1). Evidence from HBSC
(Table A1.4 and Figure A1.4 in Appendix 1), andChildren’s Worlds (Table A1.5 in Appendix 1) frommainly high-income Western countries shows thatthe gender gap is rarely observed at age 10-11,
tends to become noticeable from age 12, andwidens at age 13-15.
Photo Muhammad Taha Khan on Unsplash

Similarly, life satisfaction is lower in more
populated areas compared to more rural areas.
The GWP analysis at age 15-24 reveals the
opposite pattern globally, but no statisticallysignificant differences in high-income countries(Table A1.7A in Appendix 1). This suggests againthat these differences could partly be explainedby the different nature of countries collecting
PISA and GWP data.
Moreover, compared to those in the lowest
quintile of the household possessions distribution
within each country, those in higher quintiles
report increasingly higher life satisfaction. By
contrast, there is a negative association between
the level of economic development in the countryof residence (log GDP)51 and life satisfaction. This
association is also illustrated in Table A1.7 inAppendix 1, which ranks all countries by GDP. This
table shows a distinct pattern at age 15-24, whena clear positive association is evident, comparedto age 10-15, when no clear association isobserved, arguably due in part to the nature ofparticipating countries in each study.

Middle Adolescence (Age 15): PISA
Similar to the GWP correlational analysis, thecorrelational analysis of PISA 2022 data (age 15)
examines inequalities across socio-demographic
groups (gender, rural/urban residence, household
possessions, and economic development of thecountry of residence) and 10 items on satisfaction
with different aspects of life. The results arepresented in Table 3.4. Model 1 examines sociodemographic factors in the 74 countries andterritories with available life satisfaction data,
and Model 2 examines the 10 satisfaction items –controlling for socio-demographic factors – in the
13 countries where these data were collected (Brazil,
Hong Kong, Hungary, Ireland, Macau, Mexico, theNetherlands, New Zealand, Panama, Saudi Arabia,
Slovenia, Spain, and the United Arab Emirates).

Satisfaction with Different Aspects of Life
The 10 satisfaction items analysis in Model 2
shows that, compared to those who report not
being satisfied, life satisfaction is higher among
those who report being satisfied with their body
image (1.02 points), their relationship with theirparents (1.01 points), their life at school (0.88
points), their health (0.57 points), their use of
their time (0.56 points), their neighborhood(0.24 points), and what they learn at school(0.13 points). Interestingly, differences are not

Inequalities Across Socio-Demographic Groups
On average, life satisfaction is -0.78 points lower
among females than males. This contrasts withfindings at age 15-24 showing higher life satisfaction88World Happiness Report 2024

Table 3.4: Correlates of life satisfaction. PISA (age 15)  Model 1Model 2
S.E.

S.E.

Socio-demographic factors
Gender (ref.: male)
Female

-0.78

***

0.01

-0.51

***

0.01

*

0.04

Urban/rural residence (ref.: population of +10 million people)
1 million to 10 million

0.02

0.03

-0.09

100,000 to 1 million

0.26

***

0.03

-0.02

0.04

15,000 to 100,000

0.34

***

0.03

0.02

0.04

3,000 to 15,000

0.55

***

0.03

0.07

0.04

Less than 3,000

0.62

***

0.03

0.05

0.05

Second 20%

0.17

***

0.02

0.20

***

0.02

Middle 20%

0.27

***

0.02

0.28

***

0.03

Fourth 20%

0.39

***

0.02

0.40

***

0.04

Highest 20%

0.50

***

0.02

0.49

***

0.05

Log GDP

-0.04

***

0.01

Household possessions (ref.: lowest 20%)

10 satisfaction items
Satisfied with your health

0.57

***

0.02

Satisfied with the way you look

1.02

***

0.02

Satisfied with what you learn at school

0.13

***

0.02

Satisfied with the friends you have

-0.05

*

0.03

Satisfied with the neighbourhood you live in

0.24

***

Satisfied with all the things you have

0.02

0.02
0.03

Satisfied with how you use your time

0.56

***

0.02

Satisfied with the relationship with your parents/guardians

1.01

***

0.02

Satisfied with your relationships with your teachers

-0.11

***

0.02

Satisfied with your life at school

0.88

***

0.02

Note. Significance Levels: * 0.05 ** 0.01 *** 0.001. Model 1 (43 countries): R2 = 0.03; N=295,345, p<.001. Model 2 (13 countries):
R2 = 0.25; N=92,666, p<.001. GDP= Gross Domestic Product per capita, constant prices. We follow World Bank Analytical
Classifications (GNI per capita in US$; Atlas methodology (World Bank, n.d.)), to categorize countries as high-income, upper
middle-income, lower middle-income and low-income.

below. We also present a discussion on thenecessary steps to collectively enhance thewell-being of children and adolescents globally.

statistically significant (p<0.05) for satisfaction
with the things you have (material well-being), anda small negative association is found for satisfaction
with the friends you have (-0.05 points) andrelationships with teachers (-0.11 points). These
results (especially those involving a small effectsize) are to be interpreted with caution given thesmall number of countries considered in Model 2
due to data availability limitations.

Main FindingsLife Satisfaction Levels
In the post-pandemic world, the life satisfaction of
those aged 10-15 tends to be the highest in Central
and Eastern Europe (notably in the Balkans), andthe CIS, and the lowest in East Asia, North Americaand ANZ, and MENA. However, it is important tonote that, for this age group, data is only available
in some world regions, including mostly highincome countries. Cross-country inequalities
among younger adolescents (age 10-15) are smaller
compared to older adolescents (age 15-24).

Discussion
The analyses presented in this chapter haveprovided insights into the state of child andadolescent subjective well-being and the key data
limitations affecting the field. These are discussed

89

Photo Aditya Enggar Perdana on Unsplash

World Happiness Report 2024

shaped by distinct trends for these age groups
observed in the last 15 years.

For those aged 15-24, global data is available andthe highest life satisfaction is observed in Israel,
Northern Europe and some Central and Eastern
European countries, and the lowest in Sub-Saharan Africa and South Asia. This contrasts with theadult ranking in Chapter 2, where most Central
and Eastern European countries rank much lower,
and life satisfaction in North America and ANZ
and Western Europe is much higher compared toother regions. These differences have been

Trends in Middle-to-Late Adolescence
In middle-to-late adolescence (age 15-24), there
was a positive 2006-2019 global trend in life
satisfaction, in line with adult trends, which ended
with the pandemic, in line with adult trends.
Global trends mask regional trends – which, at thesame time, mask sub-regional and national trends
90World Happiness Report 2024

discussed in the next section. Despite discrepancies
affecting some countries and regions, there isconsistent evidence in others, including declines
in North America (Canada, and the U.S.), someWestern European countries (e.g. Austria, Iceland,
Ireland, and the Netherlands), and two MENA
countries (Turkey and Israel), which are largely
driven by female declines. Canada emerges as
the country with the longest female decline in
the available data, which started in the early2010s, and is still ongoing. In contrast, HBSC
data (age 11, 13, 15) shows evidence of positive
trends in some countries in Central and Eastern
Europe (Estonia, Latvia, Croatia) in the 2000s
and early 2010s.

– that do not always match adult trends. Our
results suggest that the widely reported
pre-COVID-19 declines in young people’s subjective well-being observed across countries52 may
concentrate in those regions that tend to collectdata systematically (e.g. North America andWestern Europe), as well as in other regions suchas the MENA and South Asia. However, when
young people from (almost) the whole world areconsidered, these negative trends start to lookless global as positive trends are observed in
Sub-Saharan Africa, Central and Eastern Europe,
the CIS, Latin America and the Caribbean, andSoutheast Asia. As a result of these trends, while
life satisfaction for those aged 15-24 and adults
was the highest in Western Europe, and North
America and ANZ in the late 2000s, in 2022 thisheld true only for adults, while for those aged
15-24 life satisfaction was on par and not higher
compared to Central and Eastern Europe, andLatin America and the Caribbean.

Age Decline
Consistent with existing literature,53 we find life
satisfaction declines from childhood throughadolescence into adulthood. This decline is more
pronounced among females and in lower-income
countries. From age 15 to 24, declines are notobserved in multiple European countries, andincreases are observed in some of them. Moreover,although adults tend to report lower life satisfaction
than adolescents, the gap between those aged
15-24 and those 25 and older is contracting inWestern Europe and reversing in North America,
juxtaposed with a widening gap in Sub-SaharanAfrica.

Trends in Early-to-Middle AdolescenceIn early-to-middle adolescence (age 10-15),
assessments of global trends are not possible asdata is only available mostly in high-income,
Western countries. PISA, HBSC and Children’sWorlds data show substantial post-COVID-19
declines in almost all of the countries analyzed,
which tend to be more severe among females
and older adolescents. However, as opposed
to declines in (mostly) Western countries, increases
are observed in most East Asian countries atage 15.

Gender Differences
Also consistent with prior research,54 we observe
no gender differences at age 10-11, but females
start to report lower life satisfaction than males ataround age 12 and the gap further expands fromage 13 to 15. This gender gap has widened after
the pandemic. In contrast, when moving fromanalyses in early-to-middle adolescence (age10-15) in mostly high-income, Western countries,
to global analyses in middle-to-late adolescence(age 15-24), a distinct picture emerges. Our global
analyses show no gender differences between
2006 and 2013 at age 15-24, but that females
started reporting higher life satisfaction thanmales from 2014. The global gender gap has
narrowed after the pandemic. Regional analyses
at age 15-24 show that gender differences are
small or non-existent in most world regions

While there seems to be robust evidence regarding
post-pandemic trends, evidence is more mixed
with regards to pre-pandemic trends, including
some discrepancies between HBSC and PISA atage 15. Contrasting results emerge in many
Western European nations and almost all theCentral and Eastern European countries examinedat age 15 in the 5-6 years preceding the COVID-19
pandemic. These discrepancies highlight theneed for caution when interpreting trends for thisage group only on the basis of evidence frominternational studies in the absence of further
evidence from national studies (see Appendix 2).
This speaks of the need to address shortcomings
in the available international data, which is91

World Happiness Report 2024

Females start to report lower
life satisfaction than males ataround age 12 and the gap further
expands between age 13 to 15.

levels (satisfaction with living standards). Amongthe life domain factors examined, satisfaction
with living standards emerges as the strongest
correlate of life satisfaction by far. Moreover,
we find differences across levels of economic
development again. For instance, satisfaction withschools and the education system is positively
associated with life satisfaction in lower middleand low-income countries, but not in upper
middle- and high-income countries. The
correlational analysis in middle adolescence(age 15) highlights the importance of body image,relationships with parents, school life, health, andtime-use to the life satisfaction of 15-year-olds,
although this is only reflective of the 13 countries
that collected these data in PISA 2022, which
limits the generalizability of these results.

during most years in the 2006-2022 series,
with the notable exception of the MENA, wherefemales consistently report higher life satisfaction
than males. In 2022, this gender gap favoring
females over males was more pronounced inlower-income countries, with no gender differences
observed in high-income countries.
Other Inequalities
Beyond the gender, age and geographic
inequalities discussed above, the correlational
analysis in middle-to-late adolescence (age 15-24)
shows higher life satisfaction in urban areas thanin rural areas. However, this is not observed inhigh-income countries or in early-to-middle
adolescence (age 15), where data was collected
mainly in high-income nations.

Several of the above findings underscore disparities
when considering adolescents from lower-income
countries and regions with limited subjective
well-being data, challenging prevailing literaturelargely derived from high-income, Westerncountries. Given that the majority of our understanding of child and adolescent subjective
well-being stems from data in these more affluent
Western contexts, the implications are substantial
for global initiatives aimed at enhancing thewell-being of children and young people worldwide.
These and other data limitations are discussed next.

We also find that the higher the country’s GDP
the higher the average life satisfaction in middleto-late adolescence (age 15-24), which contrasts
with findings in early-to-middle adolescence (age10-15), where no association is observed at age
10-12, and a paradoxical negative association isfound at age 15.55 Recent evidence suggests
this contradiction may stem from limited datacollection in non-high-income countries inearly-to-middle adolescence because whenadolescents from these countries are considered,a positive association is observed in middle
adolescence (age 15-17). Notably, this associationis stronger in lower-income countries foradolescents but this pattern reverses with age
in adulthood.56

Addressing Limitations in International Data
on Child and Adolescent Well-Being
The existing data limitations represent a substantial
challenge in generating evidence-based insights
to advance the well-being of children and adolescents on a global scale. Despite substantial efforts
in the past 15 years to enhance data availability
across countries, a considerable gap persists inglobal data for children and adolescents comparedto adults. This chapter has shone some light on
the key gaps and limitations in international data,
together with some other data issues (see Appendix
2) that warrant attention. The main limitations are:

Correlates of Life Satisfaction
The correlational analyses presented in thischapter using GWP data in middle-to-late adolescence (age 15-24) underscore the significance ofsocio-economic indicators not only at the national
level (GDP) but also at the household (householdincome quintile within each country) and individual•	The absence of a common subjective wellbeing measure. Establishing at least one identicalsubjective well-being item in each survey would
facilitate data comparability across children’s,adolescents’, and adults’ subjective well-being.
92

World Happiness Report 2024

rigorously examine what explains variations insubjective well-being levels and trends – andamong whom.
•	The ages of the samples. No one dataset spans
across the range of middle-childhood (whenself-report measures become reliable, at aroundage 8) to late-adolescence (at the upper bound,age 24), nor into adulthood. Moreover, datafrom representative samples in late adolescence(age 16-24) is lacking. This means that comparing
across ages and exploring how subjective
well-being changes globally over time is limited
to using multiple datasets, which are notconsistent and comparable.

Photo Andrej Lisakov on Unsplash•	Data in early-to-middle adolescence (age10-15) is only available in high- and upper
middle-income countries, mostly in theWestern World. This is largely due to the factthat gaining access to children in lower-income
countries is challenging, expensive, and time
consuming for researchers.57 School is acommon point of access for researchers tosurvey children across the world, and children
in lower-income countries have less access toschooling and are less likely to attend for a
myriad of reasons.58 The findings in this chapter
referring to early-to-middle adolescents (age10-15), and those from the existing literature,largely represent what we find in high-income,and a few middle-income countries. As noted,
there is recent evidence suggesting that, whenadolescents from lower-income countries are
considered, findings may contradict the existing
literature from higher-income countries.59 Thisis troubling, as it means that children andadolescents across the largest parts of theworld, who arguably need the most support,
are not represented in global samples, which
prevent us from reaching a better understanding
on how we can promote their well-being.

This becomes crucial in the absence of large
international panel surveys exploring well-beingfrom childhood to adulthood globally.
•	The limited number of subjective well-being
measures. Since the existing surveys, on thewhole, are not primarily subjective well-being
focused – with the exception of Children’sWorlds – they tend to only include one aspectof subjective well-being, predominantly overall
life satisfaction, usually measured using a single
item. It would be advantageous to have thecapacity to explore affect and eudaimonia inthe child and adolescent surveys to give us a
more nuanced understanding of how the threecomponents interact in global samples, comparable with what exists in the literature on adults.
It is also important to improve data availability
on drivers of subjective well-being to be able toPromoting the Well-Being of Children andAdolescents Globally
It is evident that for younger populations
international data collection and availability are
lagging due to a variety of difficulties in collecting
data from younger people. However, there is a
global appetite for improving data collection and93World Happiness Report 2024

The widely reported negative
trends in adolescent well-beingin Western Europe and North
America, supported by our
analysis, contrast with positive
pre-pandemic trends in regions
like Sub-Saharan Africa andpositive post-pandemic trends
in East Asia.

goal in the field: obtaining more comparable
longitudinal data across countries to enable morerobust evidence on how to promote child andadolescent well-being.
Improving Current Approaches to Assess Childand Adolescent Well-Being Across Nations
Various international actors are leading the way
on this issue. For example, the OECD has established a Child Well-Being Data Portal64 with datafrom the major international surveys and a focus
on inclusivity, with consideration of inequalities
across relevant socio-demographic groups.
Moreover, OECD’s WISE Centre is updating the‘Guidelines on Measuring Subjective Well-being’,
also among children and adolescents, with acall for considering broader and more globally
inclusive measures.65 Similarly, UNICEF Innocenti
is expanding its work on child well-being in
high-income nations to include the views andexperiences of children living in lower-income
countries, where this type of work is far more
scarce.66 Moreover, the World Health Organisation,in collaboration with the Partnership for Maternal,
Newborn and Child Health and United Nations
partners and together with the support of anExpert Consultative Group, is developing anadolescent well-being measurement approach for
use at global, regional, and country levels, with anemphasis on existing data use and adolescentand youth engagement.67 Apart from thesewell-known international actors, other organizations
such as the Wellbeing for Planet Earth Foundation68
are working to establish a more inclusive andglobal understanding of well-being by incorporating
cross-cultural perspectives.

assessment to enhance child and adolescentwell-being globally. This is being materialized inefforts in three main areas.
Addressing Shortcomings in International Data
The four data providers whose data has beenused in this chapter have made huge efforts overthe last decade in expanding the number ofavailable measures (e.g. the OECD has included awell-being questionnaire in the last two editionsof PISA), and the number of participating countries,
including more non-Western non-high-income
countries (e.g. HBSC, which began to collect data
in Central Asian countries in its latest wave, and has
a global Linked Projects initiative which enablescountries outside of Europe and North America
to collect comparable data using a common
protocol).60 Beyond these four studies, there aresome excellent initiatives making inroads at theinternational level with the collection of data fromlower-income populations such as PISA for
Development by the OECD;61 and Multiple Indicator
Cluster Surveys (MICS) by UNICEF.62 In Europe,
there is ongoing work to conduct the firstcross-national birth cohort survey of childwell-being through GUIDE (Growing Up In Digital
Europe), supported by the Coordinate project.63
This project is mobilizing researchers and organizations, fostering coordinated efforts to enhance
the harmonization and accessibility of international
survey data, specifically focusing on panel survey
data for examining the well-being of children andyoung individuals as they grow up. This initiative
will contribute significantly to a key longer-term

From Data to Action
At the international level, the OECD has recently
published a report to facilitate the developmentof policies aimed at enhancing child well-being,69
and UNICEF is working towards policy initiatives
to promote child well-being.70 At the national
level, South Korea is a good from-data-to-action
example. In its efforts to make children’s happiness a national priority, South Korea aims tointegrate a child-centered perspective into all
government policy by using insights into childand adolescent subjective well-being.71 An

94

World Happiness Report 2024

studies, a broader age coverage from age 8 to lateadolescence and into adulthood, and collecting
data from more world regions, with particularattention to improving data collection in middleand low-income countries.

important consideration is that initiatives such as
this require effective inter-sectoral collaborations
encompassing data collection, analysis, andevidence-based responses, which may be difficult
to achieve at the national and international levels.
In certain contexts, regional or local cooperation
can be more feasible, particularly where regions
and local authorities wield influence over crucial
aspects of children’s lives, such as educationand social services. A pioneering project thatexemplifies the potential of such initiatives is#BeeWell, a youth-centered programme led by
the University of Manchester, The Gregson Family
Foundation and Anna Freud, initially launched in
Greater Manchester, U.K., and now expanding toother locations in England. This programmebrings together academic expertise, policymakers, and hundreds of local organizations
to make adolescents’ well-being everybody’s
business. Utilizing data from the #BeeWell survey(co-produced with adolescents) and linked todata from other sources, it offers schools andlocal authorities personalized well-being
dashboards and advice to facilitate evidencebased responses. #BeeWell provides valuable
insights into the efficacy of bottom-up approaches
for assessing and promoting child and adolescentwell-being. While focused on a Western sociocultural context, these insights can be applicable
to regions and countries facing challenges incountry-level initiatives, fostering progress acrossdiverse parts of the world.

The chapter aims to provide the most accurate
picture of the global state of child and adolescentsubjective well-being that is possible despite
existing data limitations. The analysis reveals anuanced picture: life satisfaction levels, trends,
and correlates vary across age, gender, world
regions, countries, and levels of economic
development. Notably, the analysis suggests thatshifting the focus from constantly surveyed
high-income countries in the Western World
reveals different patterns. For instance, the widelyreported negative trends in adolescent subjective
well-being (and related constructs) in Western
Europe and North America, supported by our
analysis, contrast with positive pre-pandemic
trends in regions like Sub-Saharan Africa andpositive post-pandemic trends in East Asia.
These and other findings presented in thischapter underscore the importance of addressing
data limitations to understand what drives
positive and negative change – and among whom
– in different parts of the world.

Conclusions
There is a growing interest in improving child andadolescent well-being globally. Despite notable
progress in research and data availability over the
past 15 years, along with recent ground-breaking
initiatives at regional, national, and international
levels, a significant data gap persists for children
and adolescents compared to adults. Theselimitations prevent researchers from substantially
improving our understanding of how to promotechild and adolescent well-being worldwide.
This chapter outlines the crucial necessary next
steps to address existing data limitations: the useof – at least one – standardized subjective wellbeing measure(s) across the available international

95

World Happiness Report 2024

Endnotes
36 Helliwell et al. (2023)

1	Batalle et al. (2019); Innocenti and Price (2005); Watson
et al. (2006)

37 Lucas and Diener (2009); Veenhoven (2008)2	Nelson et al. (2016); Sawyer et al. (2018)

38 Casas (2016); Cummins (2016)3	Blakemore (2018); Foulkes et al. (2018); Giedd et al. (1999);
Tamnes et al. (2017); Vijayakumar et al. (2016)

39	We follow World Bank Analytical Classifications (GNI per
capita in US$; Atlas methodology (World Bank, n.d.)), to
categorize countries as high-income, upper middle-income,lower middle-income and low-income. World Bank (n.d.).
World Bank Country and Lending Groups. Country
Classification. The World Bank. Accessible at:https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups4	Arian et al. (2013)
5

Sawyer et al. (2018)6

Diener et al. (2002)

7

Huebner (2004)

8	Campbell et al. (2021); Fuhrmann et al. (2022); Orben andPrzybylski (2019); Stewart-Brown et al. (2009)40	Helliwell et al. (2019); Helliwell et al. (2020); Helliwell et al.
(2022)

9	Aymerich et al. (2021); González-Carrasco et al. (2017);
Savahl et al. (2023)

41 Marquez and Long (2021)
42 Engel De Abreu et al. (2021); Pigaiani et al. (2020)

10 Diener et al. (1999); Pavot and Diener (2004)
11

43 Engel De Abreu et al. (2021); Marquez and Long (2021)

Savahl et al. (2021)

44 Helliwell et al. (2022)

12	Bedin and Sarriera (2014); Casas (2011); Casas et al. (2012);
Clair (2012)45 Helliwell et al. (2022)
46 See Chapter 2 of the current World Happiness Report

13	Bevans et al. (2010); Gilman and Huebner (2003); Lewis
et al. (2011)
14 Bevans et al. (2010); Riley (2004)

47	Casas and González-Carrasco (2019); Goldbeck et al.
(2007); Orben et al. (2022)

15 Levin and Currie (2014)

48 See Chapter 2 of the current World Happiness Report

16	Cerna et al. (2023); Marquez and Main (2021); Weinstein(2022)

49 Cummins (1996)
50 See Chapter 2 of the current World Happiness Report

17	Casas and Gonzále-Carrasco (2019); Goldbeck et al.
(2007); Orben et al. (2022)

51	PISA includes mostly high-income countries, so logGDP is
the only option.

18 Casas et al. (2014)

GWP includes many high-, upper middle-, lower middle-,
and low-income countries, which allows for a morenuanced comparison across GDP tiers. We believe thisis preferable to visualize the magnitude of well-being
inequalities across countries from different GDP tiers
compared to the other inequalities examined. Thisapproach also facilitates the discussion on the links
between GDP and life satisfaction (negative or non-existentin high-income countries; positive everywhere else) and thelimitations on available data that explain counterintuitive
results found in the literature (Marquez et al., 2024).

19 Marquez and Long (2021); The Children’s Society (2020)
20 Cosma et al. (2023); Savahl et al. (2022)
21 Cosma et al. (2023); Marquez and Long (2021)
22 Marquez and Long (2021)
23 Ben-Arieh (2008)
24 Clark et al. (2018)25 De Neve and Oswald (2012)
26 Knies (2017); Main (2014)52 Cosma et al. (2023); Due et al. (2019)27 Casas and González (2017); Marquez and Main (2020)

53 Daly (2022); Orben et al. (2022)

28	Azzopardi et al. (2019); Casas et al., 2018; Department for
Education (2022); OECD (2021)

54 Chen et al. (2020); Orben et al. (2022);
55 Campbell et al. (2021); Rudolf and Bethmann (2023)

29 Lee and Yoo (2015)

56 Marquez et al. (2024)

30 Bradshaw (2015)57 Byrne et al. (2021); Colom and Rohloff (2018)31	Casas et al. (2022); Helliwell et al. (2023); Lee and Yoo(2015)

58 Huisman and Smits (2009)59 Marquez et al. (2024)

32 Rudolf and Bethmann (2023)
33 Marquez et al. (2024)
34 Diener and Suh (2003); Helliwell et al. (2023)
35 Diener et al. (1999); Huebner (2004)96

World Happiness Report 2024

60	Cosma et al. (2023); see Gallup World Poll retrieved from:https://www.gallup.com/analytics/318875/global-research.
aspx ; see PISA Participants retrieved from:https://www.oecd.org/pisa/aboutpisa/pisa-participants.
htm; see HBSC Participants retrieved from:https://hbsc.org/network/countries/; see Children’sWorlds retrieved from: https://isciweb.org/wp-content/
uploads/2019/12/Session1-ChildrensWorlds.pdf
61 OECD (2018)
62	See MICS by UNICEF retrieved from: https://mics.unicef.org/
63	See Coordinate Project retrieved from:https://www.coordinate-network.eu/
64	See OECD retrieved from: https://www.oecd.org/els/family/child-well-being/data/
65 Mahoney (2023); OECD (2017)
66	See UNICEF retrieved from: https://www.unicef.org/
health/child-and-adolescent-health-and-well-being;
Gramoda et al. (2020)
67	Guthold et al. (2023)
68	See Wellbeing for Planet Earth retrieved from:https://www.globalwellbeinginitiative.org/about-us
69 OECD (2021)
70	See https://www.unicef.org/globalinsight/media/2116/file/
UNICEF-Global-Insight-Understanding-Child-SubjectiveWellbeing-2021.pdf
71 OECD (2019)97

World Happiness Report 2024

References
Anaya, L. M., & Zamarro, G. (2023). The role of student
effort on performance in PISA: Revisiting the gender gap
in achievement. Oxford Economic Papers, gpad018.
https://doi.org/10.1093/oep/gpad018

Bevans, K. B., Riley, A. W., & Forrest, C. B. (2010).
Development of the Healthy Pathways Child-Report Scales.
Quality of Life Research, 19(8), 1195–1214. https://doi.
org/10.1007/s11136-010-9687-4

Anders, J., Has, S., Jerrim, J. et al. Is Canada really an
education superpower? The impact of non-participation on
results from PISA 2015. Educ Asse Eval Acc 33, 229–249 (2021).
https://doi.org/10.1007/s11092-020-09329-5

Blakemore, S.-J. (2018). Avoiding Social Risk in Adolescence.
Current Directions in Psychological Science, 27(2), 116–122.
https://doi.org/10.1177/0963721417738144
Bradshaw, J. (2015). Subjective Well-Being and Social Policy:
Can Nations Make Their Children Happier? Child Indicators
Research, 8(1), 227–241. https://doi.org/10.1007/s12187-0149283-1

Andersson, C., & Sandgren Massih, S. (2023). PISA 2018: Did
Sweden exclude students according to the rules? Assessmentin Education: Principles, Policy & Practice, 30(1), 33–52.
https://doi.org/10.1080/0969594X.2023.2189566

Byrne, J., Day, E., & Raftree, L. (2021) The Case for Better
Governance of Children’s Data: A Manifesto. UNICEF. New York.

Arain, M., Haque, M., Johal, L., Mathur, P., Nel, W., Rais, A.,
Sandhu, R., & Sharma, S. (2013). Maturation of the adolescent
brain. Neuropsychiatric Disease and Treatment, 9, 449–461.
https://doi.org/10.2147/NDT.S39776

Campbell, A. (1976). Subjective measures of well-being.
American Psychologist, 31(2), 117–124.
https://doi.org/10.1037/0003-066X.31.2.117

Atasever, U., Jerrim, J., & Tieck, S. (2023). Exclusion rates
from international large-scale assessments: An analysis of
20 years of IEA data. Educational Assessment, Evaluation andAccountability. https://doi.org/10.1007/s11092-023-09416-3

Campbell, O. L. K., Bann, D., & Patalay, P. (2021). The gender
gap in adolescent mental health: A cross-national investigationof 566,829 adolescents across 73 countries. SSM - Population
Health, 13, 100742. https://doi.org/10.1016/j.ssmph.2021.100742

Aymerich, M., Cladellas, R., Castelló, A., Casas, F., & Cunill, M.
(2021). The Evolution of Life Satisfaction Throughout
Childhood and Adolescence: Differences in Young People’s
Evaluations According to Age and Gender. Child Indicators
Research, 14(6), 2347–2369. https://doi.org/10.1007/
s12187-021-09846-9

Cantril, H. (1965). The pattern of human concerns.
New Brunswick, NJ: Rutgers University Press
Casas, F. (2011). Subjective Social Indicators and Child andAdolescent Well-being. Child Indicators Research, 4(4),555–575. https://doi.org/10.1007/s12187-010-9093-z
Casas, F. (2016). Children, Adolescents and Quality of Life: The
Social Sciences Perspective Over Two Decades. In: Maggino, F.
(eds) A Life Devoted to Quality of Life. Social Indicators
Research Series, 60. https://doi.org/10.1007/978-3-319-20568-7_1

Azzopardi, P. S., Hearps, S. J. C., Francis, K. L., Kennedy, E. C.,
Mokdad, A. H., Kassebaum, N. J., Lim, S., Irvine, C. M. S., Vos, T.,
Brown, A. D., Dogra, S., Kinner, S. A., Kaoma, N. S., Naguib, M.,
Reavley, N. J., Requejo, J., Santelli, J. S., Sawyer, S. M., Skirbekk,
V., … Patton, G. C. (2019). Progress in adolescent health andwellbeing: Tracking 12 headline indicators for 195 countries
and territories, 1990–2016. The Lancet, 393(10176), 1101–1118.
https://doi.org/10.1016/S0140-6736(18)32427-9

Casas, F., Coenders, G., González, M., Malo, S., Bertran, I., &Figuer, C. (2012). Testing the Relationship Between Parents’and Their Children’s Subjective Well-Being. Journal ofHappiness Studies, 13(6), 1031–1051. https://doi.org/10.1007/
s10902-011-9305-3

Ball, J., Grucza, R., Livingston, M., Ter Bogt, T., Currie, C., &De Looze, M. (2023). The great decline in adolescent risk
behaviours: Unitary trend, separate trends, or cascade? Social
Science & Medicine, 317, 115616. https://doi.org/10.1016/
j.socscimed.2022.115616

Casas, F., & González, M. (2017). School: One world or two
worlds? Children’s perspectives. Children and Youth Services
Review, 80, 157–170. https://doi.org/10.1016/j.
childyouth.2017.06.054

Banks, J., & Smyth, E. (2015). ‘Your whole life depends on it’:Academic stress and high-stakes testing in Ireland. Journalof Youth Studies, 18(5), 598–616. https://doi.org/10.1080/
13676261.2014.992317

Casas, F., & González-Carrasco, M. (2019). Subjective Well-Being
Decreasing With Age: New Research on Children Over 8. Child
Development, 90(2), 375–394. https://doi.org/10.1111/
cdev.13133

Batalle, D., O’Muircheartaigh, J., Makropoulos, A., Kelly, C. J.,
Dimitrova, R., Hughes, E. J., Hajnal, J. V., Zhang, H., Alexander,
D. C., Edwards, A. D., & Counsell, S. J. (2019). Different patternsof cortical maturation before and after 38 weeks gestationalage demonstrated by diffusion MRI in vivo. NeuroImage, 185,
764–775. https://doi.org/10.1016/j.neuroimage.2018.05.046

Casas, F., González-Carrasco, M., & Luna, X. (2018). Children’s
rights and their subjective well-being from a multinational
perspective. European Journal of Education, 53(3), 336–350.
https://doi.org/10.1111/ejed.12294
Casas, F., González-Carrasco, M., Oriol, X., & Malo, S. (2022).
Economic and Children’s Subjective Well-Being Indicators at
the National Level in 35 Countries. Child Indicators Research,
15(5), 1539–1563. https://doi.org/10.1007/s12187-022-09918-4

Bedin, L. M., & Sarriera, J. C. (2014). Dyadic Analysis of
Parent-Children Subjective Well-Being. Child Indicators
Research, 7(3), 613–631. https://doi.org/10.1007/s12187014-9235-9

Casas, F., Tiliouine, H., & Figuer, C. (2014). The Subjective
Well-being of Adolescents from Two Different Cultures:
Applying Three Versions of the PWI in Algeria and Spain. Social
Indicators Research, 115(2), 637–651. https://doi.org/10.1007/
s11205-012-0229-z

Ben-Arieh, A. (2008). The Child Indicators Movement:
Past, Present, and Future. Child Ind Res 1, 3–16 (2008).
https://doi.org/10.1007/s12187-007-9003-1

98

World Happiness Report 2024

Department for Education. (2022). State of the nation 2021:
Children and young people’s wellbeing. Department for
Education.

Cerna, L., Brussino, O., & Mezzanotte, C. (2023). Social andemotional skills (SES): Latest evidence on teachability andimpact on life outcomes (OECD Education Working Papers
304; OECD Education Working Papers, Vol. 304).
https://doi.org/10.1787/ba34f086-en

Diener, E., Kahneman, D., & Helliwell, J. (2010). International
Differences in Well-Being (1st ed.). Oxford University Press
New York. https://doi.org/10.1093/acprof:oso/9780199732739.001.0001

Chen, X., Cai, Z., He, J., & Fan, X. (2020). Gender Differencesin Life Satisfaction Among Children and Adolescents: A
Meta-analysis. Journal of Happiness Studies, 21(6), 2279–2307.
https://doi.org/10.1007/s10902-019-00169-9

Diener, E., Lucas, R. E., & Oishi, S. (2002). Subjective well-being:
The science of happiness and life satisfaction. In C. R. Snyder &S. J. Lopez (Eds.), Handbook of positive psychology(pp. 463–73). Oxford University Press.

Clair, A. (2012). The Relationship Between Parent’s
Subjective Well-Being and the Life Satisfaction of Their
Children in Britain. Child Indicators Research, 5(4), 631–650.
https://doi.org/10.1007/s12187-012-9139-5

Diener, E., & Suh, E. M. (Eds.). (2003). Culture and subjective
well-being. MIT press.

Clark, A. E., Flèche, S., Layard, R., Powdthavee, N., & Ward, G.
(2018). The Origins of Happiness: The Science of Well-Being
over the Life Course. Princeton University Press.
https://doi.org/10.2307/j.ctvc77dgq

Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999).
Subjective well-being: Three decades of progress. Psychological
Bulletin, 125(2), 276–302. https://doi.org/10.1037/00332909.125.2.276

Colom, M., & Rohloff, P. (2018). Cultural considerations for
informed consent in paediatric research in low/middle-income
countries: A scoping review. BMJ Paediatrics Open, 2(1),e000298. https://doi.org/10.1136/bmjpo-2018-000298

Due, P., Eriksson, C., Torsheim, T., et al. (2019). Trends in highlife satisfaction among adolescents in five Nordic countries
2002–2014. Nordisk välfärdsforskning, 4, 54–66.
https://doi.org/10.18261/issn.2464-4161-2019-02-03

Cosma A., Abdrakhmanova, S., Taut, D., Schrijvers, K.,Catunda,
C., & Schnohr, C. (2023). A focus on adolescent mental healthand well- being in Europe, central Asia and Canada. HealthBehaviour in School-aged Children international report fromthe 2021/2022 survey. Volume 1. Copenhagen: WHO Regional
Office for Europe; 2023. Licence: CC BY-NC-SA 3.0 IGO.

Elgar, F. J., Sen, A., Gariépy, G., Pickett, W., Davison, C.,
Georgiades, K., Hammami, N., Azevedo Da Silva, M., Gordon, D.,& Melgar-Quiñonez, H. R. (2021). Food insecurity, state fragility
and youth mental health: A global perspective. SSM Population Health, 14, 100764. https://doi.org/10.1016/j.
ssmph.2021.100764

Cosma, A., Stevens, G., Martin, G., Duinhof, E. L., Walsh, S. D.,
Garcia-Moya, I., Költo, A., Gobina, I., Canale, N., Catunda, C.,
Inchley, J., & De Looze, M. (2020). Cross-National Time Trends
in Adolescent Mental Well-Being From 2002 to 2018 and the
Explanatory Role of Schoolwork Pressure. Journal of Adolescent
Health, 66(6), S50–S58. https://doi.org/10.1016/j.
jadohealth.2020.02.010

Engel De Abreu, P. M. J., Neumann, S., Wealer, C., Abreu, N.,
Coutinho Macedo, E., & Kirsch, C. (2021). Subjective Well-Beingof Adolescents in Luxembourg, Germany, and Brazil During the
COVID-19 Pandemic. Journal of Adolescent Health, 69(2),211–218. https://doi.org/10.1016/j.jadohealth.2021.04.028
Foulkes, L., Leung, J. T., Fuhrmann, D., Knoll, L. J., & Blakemore,
S. (2018). Age differences in the prosocial influence effect.
Developmental Science, 21(6), e12666. https://doi.org/10.1111/
desc.12666

Cummins, R. A. (1996). The domains of life satisfaction: An
attempt to order chaos. Social Indicators Research, 38(3),303–328. https://doi.org/10.1007/BF00292050

Fuhrmann, D., Madsen, K. S., Johansen, L. B., Baaré, W. F. C., &Kievit, R. A. (2022). The midpoint of cortical thinning between
late childhood and early adulthood differs between individuals
and brain regions: Evidence from longitudinal modelling in a
12-wave neuroimaging sample. NeuroImage, 261, 119507.
https://doi.org/10.1016/j.neuroimage.2022.119507

Cummins, R. A. (2016). The Theory of Subjective Wellbeing
Homeostasis: A Contribution to Understanding Life Quality.
In F. Maggino (Ed.), A Life Devoted to Quality of Life (Vol. 60,
pp. 61–79). Springer International Publishing. https://doi.
org/10.1007/978-3-319-20568-7_4
Cummins, R., & Gullone, E. (2000). Why we should not use
5-point Likert scales: The case for subjective quality of life
measurement. Proceedings, Second International Conferenceon Quality of Life in Cities, 74–93.

Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu,
H., Zijdenbos, A., Paus, T., Evans, A. C., & Rapoport, J. L. (1999).
Brain development during childhood and adolescence: A
longitudinal MRI study. Nature Neuroscience, 2(10), 861–863.
https://doi.org/10.1038/13158

Daly, M. (2022). Cross-national and longitudinal evidence fora rapid decline in life satisfaction in adolescence. Journal of
Adolescence, 94(3), 422–434. https://doi.org/10.1002/
jad.12037

Gilman, R., & Huebner, S. (2003). A review of life satisfaction
research with children and adolescents. School Psychology
Quarterly, 18(2), 192–205. https://doi.org/10.1521/
scpq.18.2.192.21858

Deaton, A. (2008). Income, Health, and Well-Being around
the World: Evidence from the Gallup World Poll. Journal of
Economic Perspectives, 22(2), 53–72. https://doi.org/10.1257/
jep.22.2.53

Goldbeck, L., Schmitz, T. G., Besier, T., Herschbach, P., &Henrich, G. (2007). Life Satisfaction Decreases during
Adolescence. Quality of Life Research, 16(6), 969–979.
http://www.jstor.org/stable/27641333

De Neve, J.-E., & Oswald, A. J. (2012). Estimating the influenceof life satisfaction and positive affect on later income using
sibling fixed effects. PNAS Proceedings of the National
Academy of Sciences of the United States of America, 109(49),19953–19958. https://doi.org/10.1073/pnas.1211437109

99

World Happiness Report 2024

González-Carrasco, M., Casas, F., Viñas, F., Malo, S., Gras, M. E.,& Bedin, L. (2017). What Leads Subjective Well-Being toChange Throughout Adolescence? An Exploration of Potential
Factors. Child Indicators Research, 10(1), 33–56.
https://doi.org/10.1007/s12187-015-9359-6

Inchley, J. (Ed.). (2016). Growing up unequal: Gender andsocioeconomic differences in young people’s health andwell-being: health behaviour in school-aged children (HBSC)
study: International Report from the 2013/2014 survey. World
Health Organization, Regional Office for Europe.

Gromada, A., Rees, G., & Chzhen, Y(2020). Worlds of Influence:
Understanding What Shapes Child Well-being in RichCountries, Innocenti Report Card, no. 16, UNICEF Office of
Research - Innocenti, Florence

Inchley, J., Currie, D., Budisavljevic, S., Torsheim, T., Jåstad, A.,& Cosma, A. (2020). Spotlight on adolescent health andwell-being. Findings from the 2017/2018 Health Behaviour in
School-aged Children (HBSC) survey in Europe and Canada.
International report. Volume 1. Key findings. Copenhagen:
WHO Regional Office for Europe; 2020

Guthold, R., Newby, H., Keogh, S., Afifi, R. A., Austrian, K., Baird,
S., Blum, R. W., Bundy, D. A. P., Deardorff, J., Engel, D., Klein, J.
D., Kostelecky, S. M., Mackworth-Young, C., Marquez, J.,
NicGabhainn, S., Requejo, J., Ross, D. A., Saewyc, E., & Mohan,
A. (2023). Developing a Global Approach for Measurementof Adolescent Well-Being. The Journal of adolescent health:official publication of the Society for Adolescent Medicine,73(6), 972–974. https://doi.org/10.1016/j.
jadohealth.2023.08.029
Handa, S., Pereira, A., & Holmqvist, G. (2023). The Rapid
Decline of Happiness: Exploring Life Satisfaction among Young
People across the World. Applied Research in Quality of Life,
18(3), 1549–1579. https://doi.org/10.1007/s11482-023-10153-4

Innocenti, G. M., & Price, D. J. (2005). Exuberance in thedevelopment of cortical networks. Nature Reviews Neuroscience,
6(12), 955–965. https://doi.org/10.1038/nrn1790
Jerrim, J. (2021). PISA 2018 in England, Northern Ireland,
Scotland and Wales: Is the data really representative of all
four corners of the UK? Review of Education, 9(3), e3270.
https://doi.org/10.1002/rev3.3270
Knies, G. (2017). Income effects on children’s life satisfaction:
Longitudinal evidence for England. Institute for Socialand Economic Research. University of Exeter.
https://repository.essex.ac.uk/21104/1/2017-02.pdf

Helliwell, J., Huang, H., & Wang, S. Changing World Happiness.
In Helliwell, J., Layard, R., & Sachs, J. (2019). World Happiness
Report 2019, New York: Sustainable Development Solutions
Network.

Lee, B. J., & Yoo, M. S. (2015). Family, School, and Community
Correlates of Children’s Subjective Well-being: An International
Comparative Study. Child Indicators Research, 8(1), 151–175.
https://doi.org/10.1007/s12187-014-9285-z

Helliwell, J., Huang, H., Wang, S., & Norton, M. Social Environments
for World Happiness. In Helliwell, J., Layard, R., Sachs, J., &De Neve, J E. (2020). World Happiness Report 2020, New
York: Sustainable Development Solutions Network.
Helliwell, J., Huang, H., Wang, S., & Norton, M. Happiness,
Benevolence, and Trust During COVID-19 and Beyond. In
Helliwell, J., Huang, H., Wang, S., & Norton, M.Helliwell, J. F.,
Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B., & Wang, S.
(Eds.). (2022). World Happiness Report 2022. New York:
Sustainable Development Solutions Network.

Levin, K. A., & Currie, C. (2014). Reliability and validity of an
adapted version of the Cantril Ladder for use with adolescentsamples. Social Indicators Research, 119(2), 1047–1063.
https://doi.org/10.1007/s11205-013-0507-4
Lewis, A. D., Huebner, E. S., Malone, P. S., & Valois, R. F. (2011).
Life Satisfaction and Student Engagement in Adolescents.
Journal of Youth and Adolescence, 40(3), 249–262.
https://doi.org/10.1007/s10964-010-9517-6
Lucas, R. E., & Diener, E. (2009). Personality and subjective
well-being. In E. Diener (Ed.), The science of well-being: Thecollected works of Ed Diener (pp. 75–102). Springer Science +Business Media.

Helliwell, J., Huang, H., Wang, S., & Norton, M.Helliwell, J. F.,
Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B., & Wang, S.
(Eds.). (2022). World Happiness Report 2022. New York:
Sustainable Development Solutions Network.

Mahoney, J. (2023), “Subjective well-being measurement:
Current practice and new frontiers”, OECD Papers on
Well-being and Inequalities, No. 17, OECD Publishing, Paris,
https://doi.org/10.1787/4e180f51-en.

Helliwell, J. F., Layard, R., Sachs, J. D., Aknin, L. B., De Neve,
J.-E., & Wang, S. (Eds.). (2023). World Happiness Report 2023
(11th ed.). Sustainable Development Solutions Network.
Holte, A., Barry, M. M., Bekkhus, M., Borge, A. I. H., Bowes, L.,
Casas, F., Friborg, O., Grinde, B., Headey, B., Jozefiak, T., Lekhal,R., Marks, N., Muffels, R., Nes, R. B., Røysamb, E., Thimm, J. C.,
Torgersen, S., Trommsdorff, G., Veenhoven, R., … Zachrisson, H.
D. (2014). Psychology of Child Well-Being. In A. Ben-Arieh, F.
Casas, I. Frønes, & J. E. Korbin (Eds.), Handbook of Child
Well-Being (pp. 555–631). Springer Netherlands.
https://doi.org/10.1007/978-90-481-9063-8_13

Main, G. (2014). Child Poverty and Children’s Subjective
Well-Being. Child Indicators Research, 7(3), 451–472.
https://doi.org/10.1007/s12187-014-9237-7
Main, G., Montserrat, C., Andresen, S., Bradshaw, J., & Lee,B. J. (2019). Inequality, material well-being, and subjective
well-being: Exploring associations for children across 15 diverse
countries. Children and Youth Services Review, 97, 3–13.
https://doi.org/10.1016/j.childyouth.2017.06.033

Huebner, E. S. (2004). Research on Assessment ofLife Satisfaction of Children and Adolescents. Social
Indicators Research, 66(1/2), 3–33.
https://doi.org/10.1023/B:SOCI.0000007497.57754.e3

Marquez, J. (2023). Gender Differences in School Effects
on Adolescent Life Satisfaction: Exploring Cross-National
Variation. Child & Youth Care Forum. https://doi.org/10.1007/
s10566-023-09756-7

Huisman, J., & Smits, J. (2009). Effects of Household- andDistrict-Level Factors on Primary School Enrollment in 30
Developing Countries. World Development, 37(1), 179–193.
https://doi.org/10.1016/j.worlddev.2008.01.007

Marquez, J., Casas, F., Taylor, L., & De Neve, JE. (2024, underreview). Economic development and adolescent wellbeing in
139 countries. [under review in Child Indicators Research].
Preprint: https://osf.io/preprints/socarxiv/7cqen/

100World Happiness Report 2024

Savahl, S., Casas, F., & Adams, S. (2023). Considering a Bifactor
Model of Children’s Subjective Well-Being Using a Multinational
Sample. Child Indicators Research, 16(6), 2253–2278.
https://doi.org/10.1007/s12187-023-10058-6

Marquez, J., & Long, E. (2021). A Global Decline in Adolescents’Subjective Well-Being: A Comparative Study Exploring
Patterns of Change in the Life Satisfaction of 15-Year-Old
Students in 46 Countries. Child Indicators Research, 14(3),1251–1292. https://doi.org/10.1007/s12187-020-09788-8

Savahl, S., Lee, B. J., & Casas, F. (eds.), (2022). Children’s
views on their lives and well-being during Covid- 19: A reporton the Children’s Worlds project, 2020-2022. Jerusalem, Israel:Children’s Worlds Project (ISCWeB).

Marquez, J., & Main, G. (2021). Can Schools and Education
Policy Make Children Happier? A Comparative Study in
33 Countries. Child Indicators Research, 14(1), 283–339.
https://doi.org/10.1007/s12187-020-09758-0

Save the Children. (2021). Adolescent Wellbeing Framework –A Critical Opportunity to Fulfil Human Rights and Drive
Change. Save the Children International. https://resourcecentre.
savethechildren.net/pdf/adolescent_wellbeing_framework.pdf/

Nelson, E. E., Jarcho, J. M., & Guyer, A. E. (2016). Social
re-orientation and brain development: An expanded andupdated view. Developmental Cognitive Neuroscience, 17,
118–127. https://doi.org/10.1016/j.dcn.2015.12.008

Sawyer, S. M., Azzopardi, P. S., Wickremarathne, D., & Patton,
G. C. (2018). The age of adolescence. The Lancet Child &Adolescent Health, 2(3), 223–228. https://doi.org/10.1016/
S2352-4642(18)30022-1

OECD (2017). PISA for Development Assessment and Analytical
Framework: Reading, Mathematics and Science. Preliminary
Version. OECD Publishing, Paris.

Stevenson, B., & Wolfers, J. (2013). Subjective Well-Being
and Income: Is There Any Evidence of Satiation? American
Economic Review, 103(3), 598–604. https://doi.org/10.1257/
aer.103.3.598

OECD. (2018). PISA for Development Assessment and Analytical
Framework: Reading, Mathematics and Science. OECD.
https://doi.org/10.1787/9789264305274-en
OECD. (2019). Rejuvenating Korea: Policies for a Changing
Society. OECD. https://doi.org/10.1787/c5eed747-en

Stewart-Brown, S., Tennant, A., & Tennant, R. (2009). Internal
construct validity of the Warwick-Edinburgh Mental Well-being
Scale (WEMWBS): a Rasch analysis using data from the
Scottish Health Education Population Survey. Health Qual Life
Outcomes 7, 15. https://doi.org/10.1186/1477-7525-7-15

OECD. (2021). Measuring What Matters for Child Well-Being
and Policies. OECD. https://doi.org/10.1787/e82fded1-en
Orben, A., Lucas, R. E., Fuhrmann, D., & Kievit, R. A. (2022).
Trajectories of adolescent life satisfaction. Royal Society OpenScience, 9(8), 211808. https://doi.org/10.1098/rsos.211808

Sutcliffe, K., Ball, J., Clark, T. C., Archer, D., Peiris-John, R.,
Crengle, S., & Fleming, T. (Theresa). (2023). Rapid and unequal
decline in adolescent mental health and well-being 2012–2019:
Findings from New Zealand cross-sectional surveys. Australian& New Zealand Journal of Psychiatry, 57(2), 264–282.
https://doi.org/10.1177/00048674221138503

Orben, A., & Przybylski, A. K. (2019). The association between
adolescent well-being and digital technology use. Nature
Human Behaviour, 3(2), 173–182. https://doi.org/10.1038/
s41562-018-0506-1

Tamnes, C. K., Herting, M. M., Goddings, A.-L., Meuwese, R.,
Blakemore, S.-J., Dahl, R. E., Güroglu, B., Raznahan, A., Sowell,
E. R., Crone, E. A., & Mills, K. L. (2017). Development of thecerebral cortex across adolescence: A multisample study ofinter-related longitudinal changes in cortical Vol., surface area,
and thickness. The Journal of Neuroscience, 37(12), 3402–3412.
https://doi.org/10.1523/JNEUROSCI.3302-16.2017

Pavot, W., & Diener, E. (2004). The subjective evaluation ofwell-being in adulthood: Findings and implications. Ageing
International, 29(2), 113–135. https://doi.org/10.1007/
s12126-004-1013-4
Pigaiani, Y., Zoccante, L., Zocca, A., Arzenton, A., Menegolli, M.,
Fadel, S., Ruggeri, M., & Colizzi, M. (2020). Adolescent Lifestyle
Behaviors, Coping Strategies and Subjective Wellbeing during
the COVID-19 Pandemic: An Online Student Survey. Healthcare,
8(4), 472. https://doi.org/10.3390/healthcare8040472

The Children’s Society (2020). The Good Childhood Report2020. https://www.childrenssociety.org.uk/sites/default/
files/2020-11/Good-Childhood-Report-2020.pdf

Riley, A. W. (2004). Evidence That School-Age Children Can
Self-Report on Their Health. Ambulatory Pediatrics, 4(4),371–376. https://doi.org/10.1367/A03-178R.1
Rudolf, R., & Bethmann, D. (2023). The Paradox of Wealthy
Nations’ Low Adolescent Life Satisfaction. Journal of HappinessStudies, 24(1), 79–105. https://doi.org/10.1007/s10902-02200595-2
Ryff, C. D., Boylan, J. M., & Kirsch, J. A. (2021). Eudaimonic andHedonic Well-Being: An Integrative Perspective with Linkages
to Sociodemographic Factors and Health. In M. T. Lee, L. D.
Kubzansky, & T. J. VanderWeele (Eds.), Measuring Well-Being(1st ed., pp. 92–135). Oxford University Press New York.
https://doi.org/10.1093/oso/9780197512531.003.0005
Savahl, S., Casas, F., & Adams, S. (2021). The Structure ofChildren’s Subjective Well-being. Frontiers in Psychology, 12,650691. https://doi.org/10.3389/fpsyg.2021.650691

The Children’s Society (2020). The Good Childhood Report
2023. https://www.childrenssociety.org.uk/sites/default/
files/2023-09/The%20Good%20Childhood%20Report%20
2023.pdf
Twenge, J. M., Martin, G. N., & Campbell, W. K. (2018). Decreasesin psychological well-being among American adolescents after
2012 and links to screen time during the rise of smartphone
technology. Emotion, 18(6), 765–780. https://doi.org/10.1037/
emo0000403
UNICEF. (2013). UNICEF Multiple Indicator Cluster Surveys.
https://mics.unicef.org
Veenhoven, R. (2008). Healthy happiness: Effects of happinesson physical health and the consequences for preventive
health care. Journal of Happiness Studies: An Interdisciplinary
Forum on Subjective Well-Being, 9(3), 449–469.
https://doi.org/10.1007/s10902-006-9042-1

101

World Happiness Report 2024

Vijayakumar, N., Allen, N. B., Youssef, G., Dennison, M., Yücel,M., Simmons, J. G., & Whittle, S. (2016). Brain development
during adolescence: A mixed-longitudinal investigation ofcortical thickness, surface area, and volume. Human Brain
Mapping, 37(6), 2027–2038. https://doi.org/10.1002/
hbm.23154
Von Der Embse, N., Jester, D., Roy, D., & Post, J. (2018). Test
anxiety effects, predictors, and correlates: A 30-year metaanalytic review. Journal of Affective Disorders, 227, 483–493.
https://doi.org/10.1016/j.jad.2017.11.048
Watson, R. E., DeSesso, J. M., Hurtt, M. E., & Cappon, G. D.
(2006). Postnatal growth and morphological development of
the brain: a species comparison. Birth Defects Research Part B:
Developmental and Reproductive Toxicology, 77(5), 471-484.
Weinstein, N. (2022). ‘Life is getting better’ for most lookedafter children. Children & Young People Now.
https://www.cypnow.co.uk/news/article/life-is-getting-betterfor-most-looked-after-children
Wierzbicka, A. (2004). “Happiness” in Cross-Linguistic &Cross-Cultural Perspective. Daedalus, 133(2), 34–43.
http://www.jstor.org/stable/20027911

102

Chapter 4

Supporting the
Well-being of an Aging
Global Population:
Associations between
Well-being and Dementia
Karysa Britton
PhD Student, Washington University in St. Louis
Kyrsten C. Hill
Postdoctoral Researcher, Washington University in St. Louis
Emily C. Willroth
Assistant Professor, Washington University in St. LouisAll three authors contributed equally. This research was supported by the National
Institutes of Health (R00AG071838). The content is solely the responsibility of theauthors and does not represent the official views of the National Institutes of Health.
doi.org/10.18724/whr-21fb-sb82
This version last updated March 8, 2024. Please see worldhappiness.report for latest data.

4

Photo Center for Ageing Better

More than a decade of researchdemonstrates that people withhigher well-being are less likely
to develop dementia.

World Happiness Report 2024

Key Insights

As the global population of older adults increases, the number ofworldwide dementia cases is also expected to increase.
Dementia is associated with reduced quality of life and lower
well-being, and thus dementia prevention is critical to maintain thewell-being of an aging global population.
Higher levels of well-being have been robustly associated with lower
risk for future dementia, suggesting that increasing well-being maybe
a promising non-pharmacological approach to dementia prevention.
Among individuals living with dementia, environmental changes
and activities that enhance autonomy, competence, and relatedness
have been shown to improve well-being.

105World Happiness Report 2024

By the year 2050, the World Health Organization
estimates that the global population of people 65
and older will double1 (see Figure 4.1 for historic
population growth of older adults by world
region). As the global population of older adults
continues to rise, the number of people living withdementia is also expected to grow, reachingapproximately 139 million dementia cases by theyear 2050.2 Dementia is an age-related clinicalsyndrome that results in progressive or persistentloss of memory and thinking abilities,3 which inturn can negatively impact well-being.4 Given that
there is currently no cure for dementia andbiomedical treatments remain limited, it is vital toevaluate and implement non-pharmacologicaldementia prevention strategies. A growing bodyof evidence suggests that well-being may be a
promising target for dementia prevention efforts,
given its associations with better cognitive healthand lower dementia risk.5 However, dementia
prevention science is still a long way away frompreventing all dementia cases.6 Thus, it is also
crucial to evaluate and implement strategies tosupport the well-being of people living withdementia and their care partners.
In this chapter, we begin by reviewing evidence
for well-being as a potential prevention target
that may reduce the risk for dementia, in turnpromoting continued well-being in later life. Then,
we review evidence for strategies to increase
the well-being of people living with dementia.
Throughout the chapter, we consider evidence
from international data sources and describe
innovative dementia care models from aroundthe world. We conclude by discussing how theseresearch findings can inform policy to support
the well-being of an aging global population.

Fig. 4.1: Population of Adults Age 65 and Older

400

Millions

300

200

100

0
1960

1970

1980

1990

2000

2010

East Asia & PacificSouth Asia

Latin America & Caribbean

Middle East & North Africa

North America

Sub-Saharan Africa

2020

Europe & Central Asia

Figure 4.1. Data were retrieved from https://data.worldbank.org/indicator/SP.POP.65UP.TO. The World Bank (2022).
Population ages 65 and above, total.

106

World Happiness Report 2024

Fig. 4.2: Higher well-being may
support memory and thinking abilities
and lower risk for later dementia.

Healthy
Cognition

Higher well-being
supports maintenanceof healthy cognitionand lowers risk forlater dementia

Presymptomatic

Higher well-being
protects cognition
from the accumulationof dementia-related
neuropathology

MCI

Well-being
interventions may
slow cognitive decline

Dementia

Well-being-enhancing
activities and environments can support
well-being of people
living with dementia

Figure 4.2. In individuals with healthy cognition, researchsuggests that higher well-being may support memory andthinking abilities and lower risk for later dementia. Afterdementia-related neuropathology accumulates but while
individuals remain pre-symptomatic, evidence suggests thatwell-being protects memory and thinking abilities from theaccumulating neuropathology. In the early stages of cognitive
impairment (e.g., mild cognitive impairment; MCI), well-being
interventions are a promising but largely untested strategy
to slow declines in memory and thinking abilities. Finally,
well-being-enhancing activities and environments are crucial
for supporting the well-being of people living with dementia
and their care partners.

Well-being as a Protective Factor
against Dementia
More than a decade of research demonstrates
that people with higher well-being are less likely
to develop dementia.7 These studies have definedwell-being in many different ways, including
positive emotional experiences, cognitive
evaluations of one’s satisfaction with their life,
and one’s sense that their life has purpose or
meaning. However, a recent meta-analysis
suggests that the association between well-being
and dementia may be more consistent for some
types of well-being, such as sense of purpose,than for others, such as positive affect.8 Prior
research suggests that well-being may protect
health through social, behavioral, and biologicalpathways,9 and similar mechanisms may link
well-being to lower dementia risk. For example,
research suggests that well-being promotessocial engagement, which is critical for supporting
cognitive functioning and preventing dementia.10
Higher well-being also supports positive healthbehaviors that are beneficial for cognitive andbrain health, such as greater physical activity andabstinence from smoking.11 Finally, researchsuggests that higher well-being is associated with
better cardiovascular functioning,12 which in turn
reduces dementia risk.13
To test the possibility that well-being may protect
against dementia, dozens of research studies
have followed people across middle and older
adulthood in numerous countries and world
regions, including Australia, China, Europe, Israel,
Korea, Singapore, and the United States.14 Thesestudies have found that people with higher
well-being have better memory and thinking
abilities,15 experience less declines in memory
and thinking abilities,16 and are less likely todevelop dementia.17 For example, a researchstudy conducted at the Rush Alzheimer’s Disease
Center in Chicago, United States, found thatpeople with higher well-being appear resilientto the brain diseases that cause dementia.18
Well-being was assessed at the beginning of thestudy period, and memory and thinking abilities
were assessed yearly for the rest of the participants’
lives. After participants died, the researchers
conducted autopsies to quantify the amount of107

World Happiness Report 2024

dementia-related neuropathology that waspresent in participants’ brains. People with higher
levels of well-being experienced better-thanexpected memory and thinking abilities andless-than-expected declines in memory andthinking abilities in their final years of life relative
to the amount of dementia-related neuropathologythat researchers discovered in their brains during
autopsy (see Figure 4.3.). The association waspresent above and beyond other known resilience
factors (i.e., socioeconomic status, education,
cognitive activity, personality, low depression)and known dementia risk factors (i.e., genetic
risk for dementia, medical comorbidities). Thissuggests that well-being may protect memory
and thinking abilities from the brain diseases thatcause dementia.

Resilience to Dementia-Related Neuropathology

Fig. 4.3: Resilience to Dementia-Related Neuropathy

2

0

-2

-2

0

2

Well-being
Figure 4.3. People with higher well-being at the study baseline demonstrated better-than-expected memory and thinking
abilities relative to the amount of dementia-related neuropathology present in their brains (i.e., cognitive resilience). Both well-being
and cognitive resilience are shown in units of standard deviations. Willroth, E. C., James, B. D., Graham, E. K., Kapasi, A., Bennett,
D. A., & Mroczek, D. K. (2023). Well-being and cognitive resilience to dementia-related neuropathology. Psychological Science, 34(3),283-297. Copyright © 2022 (the authors).

108

Photo Center for Ageing Better

World Happiness Report 2024

Overview of Causal Evidence
Taken together, high-quality international data
sources provide strong evidence that higher
well-being is associated with lower dementia risk(see Table 4.1). However, this does not necessarilymean that well-being causes lower dementia risk.
Establishing a causal effect of well-being on
dementia is challenging because well-being anddementia share many common causes, including
lifestyle, medical, and socioeconomic factors.
For example, social isolation, low educational
attainment, or poor physical and mental health
may simultaneously reduce well-being andincrease dementia risk. Reverse causality is also
possible. For example, lower levels of well-being
may be an early indicator of underlying brainchanges that occur prior to the development ofdementia. In this case, intervening to improvewell-being may not necessarily change the courseof underlying brain changes or future dementia.
Therefore, it is crucial to establish whether theeffects of well-being on dementia are causal
because this determines whether interventions
and policies that increase well-being would also
reduce dementia incidence. A strong causal pathfrom well-being to dementia would increase thelikelihood of positive feedback to well-being
attributable to the lower incidence of dementia.
Randomized controlled trials are one of themost common methods researchers use toinvestigate causality. In randomized controlled
trials, researchers randomly assign participants
to either an experimental condition in which thetheorized causal variable is manipulated or to acontrol condition. This random assignment
reduces the risk of confounding or reverse
causality. Randomized controlled trials ofwell-being interventions have been shown toeffectively increase well-being.19 However,further research is needed to test the effects of
those interventions on cognitive health anddementia incidence.

109

World Happiness Report 2024

Table 4.1: Selection of research findings regarding the association
between well-being and dementia risk.
Authors

Country ofData Collection

Well-being
Measurement

Cognitive
Measurement

Key Findings

Sutin et al. 2020

Austria, Belgium,
Czech Republic,
Denmark, France,
Germany, Greece,
Israel, Italy, theNetherlands, Poland,
Spain, Sweden,
Switzerland“How often do
you feel that yourlife has meaning?”

Incident cognitive
impairment basedon scores on
memory and verbal
fluency tasks

People who experienced moremeaning in life were less likely
to develop cognitive impairment
across a nine-year period.
Findings were consistent in four
regions of Europe and Israel.

Willroth et al., 2022

United States

Satisfaction
with Life Scale;
Ryff PsychologicalWell-being Scale

Functioning on
19 cognitive tests
relative to the amountof neuropathology
present in participants’brains at autopsy

People with higher well-being
had better-than-expected
memory and thinking abilities
and less-than-expected declines
in memory and thinking abilities
relative to the dementia-related
neuropathology present in theirbrains and discovered at
autopsy.

Ma et al. 2021

Europe, United States

Genetic variants
associated withoverall life
satisfaction andpositive affectbased on the largest
meta-analysis
of genome-wide
association studies

Alzheimer’s diseasediagnosis according
to the National
Institute on Aging
Alzheimer's
Association (NIA/AA),
the NINCDS-ADRDA
criteria, DSM-IV
criteria, or the ICD-10
criteria, or autopsy-confirmed

Higher genetically-predicted
well-being was associatedwith a lower risk for dementia.
The effect was considered“causally suggestive.”

Bell et al., 2022

Austria, Belgium,
China, Czech Republic,
Denmark, England,
France, Germany,
Greece, Ireland, Israel,
Italy, the Netherlands,
Poland, Singapore,
Sweden, Switzerland,
Spain, United States,

Purpose andmeaning in life,
positive affect,
life satisfaction,
optimism

Incident cognitive
impairment ordementia based onclinical diagnoses,
cognitive statusassessments,
task-based cognitive
functioning, and/or
neuropsychiatricinterviewMeta-analytic findings suggestthat purpose and meaning inlife are associated with lower
incident cognitive impairment
and dementia. Results weremixed for life satisfaction andoptimism, and positive affectwas not significantly associatedwith incident cognitive
impairment or dementia.

Table 4.1. Selection of research findings regarding the association between well-being and dementia risk.

An alternative to experimental randomization
is Mendelian randomization, a technique thatleverages the random allocation of genetic
variants to test for causal effects. In a recent
Mendelian randomization study, researchers
examined associations between genetic variants
associated with well-being (i.e., life satisfaction
and positive affect) and incidence of dementia.20
The researchers found that genetically predicted
well-being was associated with a lower risk fordementia. The strength of the evidence was“suggestive” of a causal effect, which means that
more research is needed to investigate thispossibility further.

Future Directions for Research on Well-being
and Dementia Risk
As the evidence for a potentially causal effectof well-being on lower dementia risk grows, acritical next step is to test well-being-enhancing
interventions to prevent or delay dementia. The
field may benefit from testing the effects ofexisting well-being interventions on changes inmemory and thinking abilities and, ultimately,
dementia diagnosis. This will require longer-term
follow-ups than most previous randomized
controlled trials of well-being interventions.
Researchers may also consider pairing well-being110World Happiness Report 2024

More research is neededon the groups at greatest
risk for developing dementia,
including people living in lowand middle-income countries,
racial and ethnic minority
groups, and people of lower
socioeconomic status.

interventions with existing lifestyle interventions
designed to lower dementia risk, such as thosetargeting health behaviors and other lifestylefactors. Given the pressing need to lower dementia
risk worldwide, it is important to test well-beingenhancing interventions that are highly scalable
and are accessible and effective in racially,
ethnically, and geographically diverse samples.21
To increase the likelihood of successful intervention,
researchers should investigate several other basic
science questions about the association between
well-being and dementia. First, it is not clear
when in the lifespan well-being may reduce
dementia risk. On the one hand, increasing
well-being as early in the lifespan as possible
may enable individuals to experience life-long
benefits of higher well-being, and these benefits
may accumulate to lower dementia risk in latelife. On the other hand, increasing well-being in
midlife and older adulthood when individuals are
at greatest risk for developing dementia may be
an effective strategy. Relatedly, we don’t yet
know on what timescale well-being may impact
cognitive functioning or dementia risk. Studies
have observed associations between well-being
and later cognitive functioning or dementia risk
across one- to 20-year intervals, but the impactof these different timescales on the strength ofassociations has not been tested. In addition toquestions about timing, more research is needed
to test the associations between well-being anddementia in socioculturally diverse samples.
One of the strengths of existing research on
well-being and dementia is the use of samples
from many different countries and world regions.
However, more research is needed on the groups
at greatest risk for developing dementia, including
people living in low- and middle-income countries,
racial and ethnic minority groups, and people oflower socioeconomic status.

accelerating scientific progress. Multi-site andmulti-study collaborations are also useful, as they
allow researchers to test their questions in large
samples and to evaluate the generalizability offindings across diverse populations. Moving
forward, the field would also benefit from morewidespread adoption of preregistration. Preregistration involves specifying research questions,hypotheses, methods, and/or analytic approaches
prior to collecting or analyzing data. This enables
researchers to distinguish predicted findings from
unexpected or exploratory findings, which inturn can help readers calibrate confidence inresearchers’ findings. Finally, research findings
should be made widely accessible to the researchcommunity, healthcare providers, policymakers,
and the general public.

Well-being in People Livingwith Dementia
In the previous section, we considered well-being
across the lifespan as a potential resource tolower dementia risk, in turn further supporting
well-being in older adulthood. However, dementia
prevention science is still a long way away frompreventing all dementia cases, with 10 million new
diagnoses each year.22 As the global population ofpeople living with dementia grows, it is crucially
important to evaluate strategies to increase thewell-being of people living with dementia.

Given the complexity of research on well-being
and dementia risk, future research on this topic
will benefit from the continuation and adoptionof open science practices. For example, many
existing studies of well-being and dementia havemade their data publicly available. This allows
the research community to reproduce scientificfindings and test new research questions,

People living with dementia or exhibiting cognitive
decline often experience decreased well-being.23
Yet, it is still possible to live well with dementia.
Well-being and quality of life are widely studied111

World Happiness Report 2024

and often used interchangeably in research
examining the lived experiences of people withdementia.24 Quality of life is a multidimensional
concept that can include individuals’ physicalcondition, mood, relationships, financial situation,and engagement in activities.25 Some researchers
suggest that well-being is a component of qualityof life, whereas others define well-being as an
outcome of quality of life. Similar to well-being,studies have found declines in quality of life forpeople living with cognitive impairment anddementia.26Awareness of one’s diagnosis andprognosis also play a role, such that individuals
with cognitive impairment and dementia reportlower quality of life when they are aware of theirdiagnosis and when they expect their condition
to worsen over time.27

hand, self-reports by people living with mild andmoderate-to-severe dementia have been found to
be reliable across several studies.33 Proxy ratings
by relatives and staff are often lower thanself-ratings of well-being and are therefore notinterchangeable.34 Researchers generally agree
that self-ratings by people living with dementia
should be used whenever possible, as they better
capture individuals’ subjective perceptions ofwell-being.35 Methods also exist that enable
researchers to observe people living with dementia
and rate the extent to which they engage inbehaviors typically associated with well-being.
A systematic review examining quality of life andwell-being in people living with dementia foundthat the Quality of Life in Alzheimer’s Diseasescale36 was the most commonly used measure ofquality of life and the Psychological Well-Being in
Cognitively Impaired Persons scale37 was themost commonly used measure of well-being.38
A conceptual scoping review identified 35 selfreport instruments that have been used to assess
well-being in people living with dementia, but
only six of those measures were specificallydesigned for people living with dementia.39 Thereis a lack of consensus on optimal measurement
instruments and a need for more rigorously
tested measures of well-being and quality of life
in people living with dementia.40 Further, researchis needed that assesses specific aspects ofwell-being in people living with dementia rather
than quality of life more generally. As well-being
and quality of life are not clearly distinguished in
the literature, the remainder of this section will
use well-being as an umbrella term that includes
quality of life.

Despite these findings, research also shows thatpeople living with dementia retain personal
strengths and positive lived experiences.28 A
recent study using a nationally representative
sample of community-dwelling older adults foundthat life satisfaction did not differ for people
living with and without dementia.29 However,this study also found that dementia status wasmodestly associated with lower life satisfaction
via greater limitations in activities of daily living.
Qualitative research from the perspective ofpeople living with dementia highlights the importance of living with and adapting to change while
also striving for continuity.30Additionally, people
living with dementia report that the socioculturaland physical environment can be both helpful andharmful for the quality of life and well-being.31
Well-being Measurement in People Living with
Dementia

Individual Interventions and Strategies

Researchers have developed several tools to test
how different factors impact the quality of life
and well-being of people living with dementia.
These tools include self-report measures thatpeople living with dementia complete directly, as
well as proxy ratings from nurses, clinicians, andfamily members. Some researchers have debatedthe self-report abilities of people living withdementia. On the one hand, researchers haveargued that declines in cognitive functioning candistort self-reports of well-being.32 On the other

Using the tools described above, researchers
have identified several factors that contributeto the well-being of people living with dementia(see Table 4.2, Table 4.3 and Figure 4.4). Social
Determination Theory posits that the basic
psychological needs of autonomy, competence,and relatedness are essential for psychologicalwell-being.41 According to Social Determination
Theory, autonomy refers to a feeling of choice
and ownership over one’s actions, competence112World Happiness Report 2024with and without cognitive impairment.45 This
review examined a wide range of activities andinterventions, such as animal-facilitated activities,
cultural arts interventions, exercise programs,
massage, life review/reminiscence, and outdoor
activities such as gardening. Results suggested
that activities are most effective when tailoredto the individual. Research emphasizes theimportance of activities that are not only pleasant
but also personally meaningful to the individual.46
Consistent with Social Determination Theory,
engagement in meaningful activity is thought
to promote well-being in people living withdementia by addressing fundamental
psychological needs.47

refers to a sense of mastery, and relatedness
refers to a sense of mutual belonging andsupport.42 Qualitative research suggests thatthese basic psychological needs are often
negatively impacted by dementia.43As such,interventions targeting autonomy, competence,and relatedness may help to enhance well-being
among people living with dementia.
Qualitative studies highlight the importance ofactivity engagement to promote well-being inpeople living with dementia.44 An integrative
review of 45 studies found that engagementin activities is related to positive affect andwell-being among nursing home residents bothFig. 4.4: Several activities and interventions have been shown to support
the well-being of people living with dementia.

SocialEngagement/ Support
GroupsReminiscenceand Life
ReviewMontessoribased
Activities

Physical
Activity

Strategies
to increasewell-being ofpeople withdementia

OutdoorActivities

Story
Telling

AnimalFacilitated
Therapy

Cultural Arts
Interventions

Figure 4.4. Several activities and interventions have been shown to support the well-being of people living with dementia.

113

World Happiness Report 2024

Table 4.2: Strategies and activities for increasing well-being for people living with dementia.
Individual Interventions and Strategies
Strategy or Activity

Benefits

Evidentiary Support

Animal facilitated therapy

Improved mood; Improved verbalizations

Consistent evidence
supporting benefits

Behavioral activation

Improved health-related quality of life; Improved
everyday function; Increased participation inmeaningful activities

Consistent evidence supporting
benefits, but evidence is limited
to few studies

Cultural arts interventions(e.g., music, dance, andvisual arts interventions)Improved mood; Increased quality of life,
meaning, and engagement; Decreased agitation
and aggressive behaviors; Enhanced
communication; Positive impacts on cognitive
processes; Decreased anxiety

Consistent evidence supporting
benefits; Methodology has been
criticized

Literature / storytelling

Increased positive affect and life satisfaction;
Increased meaning, engagement, and pleasure;
Improved communication

Mixed evidenceMontessori-based activities

Increased engagement and positive affect;
Improved eating behaviors; Benefits for memory
and attention

Mixed evidence

Outdoor activities(e.g., gardening)

Increased life satisfaction, engagement, andenjoyment; Decreased agitation

Mixed evidence

Physical exercise

Improved mood, sleep, and cognition;
Decreased agitation; Increased mobility
and functional ability

Consistent evidence supporting
benefits for people living withdementia in nursing homes; Mixed
evidence for individuals not living
in nursing homes

Reminiscence and life review

Improved mood and well-being;
Improved autobiographical memory

Consistent evidence
supporting benefits

Robotic animal companions

Increased social engagement

Consistent evidence supporting
benefits of reduced agitationand depression; Mixed evidence
for benefits on QoLSocial engagement /support groups

Increased well-being; Foster a sense of
belonging; Provide coping strategies;
Improved self-esteem

Consistent evidence
supporting benefits

Table 4.2 Strategies and activities for increasing well-being for people living with dementia.

Engagement in social and leisure activities canenhance well-being in people living with dementia
by increasing feelings of agency, autonomy, andpurpose while also providing opportunities forsocial connection.48 Engaging in hobbies andmaintaining an active social life have been foundto be more strongly related to well-being inpeople living with dementia compared to othereveryday activities.49 Systematic reviews of theliterature consistently find that social interaction
is important to the well-being of people livingwith dementia,50 and for people in general (seeChapter 2). Although additional research isneeded in this area, studies have shown thatsocial support groups for people living withdementia may have benefits for self-esteem andwell-being.51 Support groups can offer a sense of

belonging, increase social interaction, and provide
strategies for coping, each of which is related toreductions in depressive symptoms and improvements in well-being among people living withdementia.52 Social and leisure activities therefore
have the potential to increase autonomy, competence, and relatedness among people living withdementia, leading to improvements in well-being.
Cultural arts interventions have gained attention
as one potential way to increase engagement
in meaningful activity and improve well-being
among people living with dementia. Researchon cultural arts interventions has found music
therapy, visual arts interventions, and dance/
movement therapy to increase well-being inpeople living with dementia.53 Despite these
positive outcomes, researchers have cited a114

World Happiness Report 2024

Photo Center for Ageing Better

Some reminiscence interventions take a narrative
approach based on sharing stories and memories.
Others take an integrative approach to help
individuals make sense of their life stories.58 One
particular integrative intervention called life
review involves the creation of a life storybook
containing photographs and written accounts.59
Studies suggest that structured life review
interventions, including the use of life story
books, may have greater benefits for mood andwell-being in people living with dementia relative
to other reminiscence approaches.60 Reminiscenceand life review interventions are typically led bytrained professionals (e.g., psychologists, social
workers, nurses) and can be conducted in individual
or group formats. However, there is a growinginterest in intergenerational reminiscence interventions using trained young adult volunteers,such as college students.61
Intergenerational programs are a promising way
to enhance well-being among people living withdementia while also promoting social connection
and relatedness. Intergenerational programs bring
together different generations by involving themin combined activities. Examples include intergenerational classrooms, where students complete
their curriculum alongside older adult volunteers
or senior care residents,62 or arts programs suchas intergenerational choirs.63 Intergenerational
programs have also been developed specificallyfor people living with dementia, with the mostcommon being music, art, and narrative/reminiscence programs.64 These programs are mutually
beneficial for younger and older generations
and have been associated with increased
activity engagement, reduced social isolation, andenhanced well-being among people living withdementia.65

need for greater methodological rigor andtheoretical underpinnings in research on culturalarts interventions. Across cultural arts interventions, research provides the strongest support
for music therapy, and systematic reviews haveshown significant effects of music therapy onlowering anxiety in people living with dementia.54
Although the mechanisms underlying the effects
of music therapy are not well understood, it islikely that cultural arts interventions play a rolein supporting the basic psychological needs ofpeople living with dementia. Using a Social
Determination Theory framework, a cultural arts
intervention was recently developed with afocus on promoting autonomy, competence,and relatedness among older adults with mild
cognitive impairment.55
There is also a growing body of researchsupporting the benefits of reminiscenceinterventions for people living with dementia.
Reminiscence interventions are widely used indementia care but have also shown psychologicalbenefits for cognitively unimpaired older adults.56
Reminiscence interventions for people living withdementia have used several different structures
and approaches. Broadly, reminiscence involves
the discussion of past experiences with anotherperson or group, often using prompts such as
photographs, music, or personal possessions.57

Intergenerational programs
are a promising way to enhancewell-being among people
living with dementia while also
promoting social connection
and relatedness.
115

World Happiness Report 2024

Advances in technology have also been used
to enhance individual and group interventions
promoting well-being in people living withdementia. For example, one study found thatparticipants showed greater improvements inwell-being when virtual reality was used toproject realistic memories during reminiscencetherapy.66 Digital storytelling, which uses

technology to create audio-visual story clips,
has also been incorporated into reminiscenceinterventions. Given increased interest in digitalstorytelling, more rigorous research is needed todetermine the effectiveness and mechanism ofthese methods.67 Robotic animal companions,which can replace more traditional animal-assisted
therapies, represent another unique use of

Table 4.3: Environmental factors supporting well-being for people living with dementia.
Environmental factors
Strategy or Activity

Benefits

Evidentiary Support

Aging in place

Maintenance of autonomy and independence;
Comfort and security of a familiar environment;
Reduced financial burden; Increased social
engagement; Engagement with naturalenvironments and access to public space;
Improved well-being

Consistent evidence
upporting benefits

Dementia Villages

Improved well-being; Increased social
engagement; Maintenance of physicalhealth; Engagement with everyday activities

Consistent evidence supporting
benefits, but evidence is limited
to few studies

Long-term care facilities

Increased social engagement;
Improved mood

Mixed evidence; Benefits mayvary based on well-being initiatives
in place

Assistive Technology(e.g., sensors, location
monitoring, cognitive
stimulation, medication
dispensers)

Improved mood, coping, stress, autonomy,
activities of daily living, overall health andwell-being; Reduced wandering; Fall prevention;
Independence

Mixed evidenceSnoezelen Rooms

Improved well-being via sensory stimulation;
Reduced agitation; Improved mood

Consistent evidence supporting
benefits, but evidence is limited
to few studies

Table 4.3 Environmental factors supporting well-being for people living with dementia.

116World Happiness Report 2024

People living with dementia who
continue to live in their homes
benefit from the comfort andsecurity of a familiar space, theopportunity to maintain healthysocial relationships with friends
and family, continued participationin activities with others in theircommunity, engagement withnatural environments, andreduced financial burden.

technology in the care of people living withdementia.68 Technology has also been incorporated
into cultural arts interventions, with one study
utilizing a touchscreen-based art intervention
where people living with dementia viewed art
on a tablet computer.69 In fact, one systematic
review found that a diverse range of touchscreen-based interventions have been used withpeople living with dementia.70 The authors noted
that, while more research is needed in this area,
there is some evidence that these interventions
may be beneficial for well-being.
Environmental Factors
In addition to individual activities and interventions, several environmental factors can support
the well-being of people living with dementia (seeTable 4.3). Community-dwelling older adults
prefer remaining in their own homes within thecommunity instead of moving to a long-term carefacility.71 This is known as “aging in place,” andresearch suggests that people living with dementia can experience well-being benefits from aging
in place.72 This is especially true for people livingwith dementia since their risk of death increases
after being placed in long-term care facilities.73
People living with dementia who continue to livein their homes benefit from the comfort andsecurity of a familiar space, the opportunity tomaintain healthy social relationships with friends
and family, continued participation in activities
with others in their community, engagement withnatural environments, and reduced financial
burden.74 In alignment with Social Determination
Theory, the ability to maintain one’s autonomy
and independence by continuing to live at home
is one way to improve the well-being of people
living with dementia.

independence of people aging in place and todivert the need for transfer to long-term care. The
use of a screener to identify the specific abilities
and limitations of an individual living with dementia
may be an important first step in understanding
the adjustments to the home that should be
made. For individuals in the earlier stages ofdementia, small changes in the home can have apositive impact on well-being. The National
Institute on Aging in the United States suggests
making changes to reduce fall risk, including
removing area rugs, installing grab bars aroundthe home, and placing light switches at thebottom and top of stairs for easy access.76 In
addition to these recommendations, the National
Health System in the United Kingdom suggests
incorporating contrasting colors to help individuals
with dementia differentiate between objects,
removing mirrors to avoid confusion, adding
visual cues such as clear labels around the home,replacing analog clocks with digital clocks, andadding easy-to-read calendars to assist withorientation to time.77

While many older adults and people living withdementia prefer aging in place, researchersemphasize the importance of safety while doing
so. As people living with dementia progress tolater stages, they typically lose the ability tocomplete activities of daily living.75 To address
safety concerns, researchers have sought to makeadaptations to homes through simple, low-costchanges and through the assistance of technology.
These methods have been used to enhance thePeople living with dementia can also use technological aids in their homes to support aging inplace and maintenance of autonomy. Literaturesuggests that assistive technology is both feasible
and acceptable for people living with dementia
and their caregivers, although people in the laterstages of dementia may experience challenges
using technologies.78 Importantly, studies have117

Photo Center for Ageing Better

World Happiness Report 2024

promoting their well-being. However, challenges
using GPS should be considered and can include
forgetting to take the device when leaving thehome or low battery of the device. Wearable
sensors have also been successfully used for falldetection, and installation of light pathways on
the ground and brightly lit handrails are utilized
for fall prevention.84 These mechanisms promote
safety and independence, putting less strain on
both the person living with dementia and theircaregiver. Item locators and reminder systems
can be used to further enhance independence.
Item locators can be placed on objects such as
phones or television remote controls and can
reduce search time, whereas reminder systems
can improve medication compliance and reduce
hospitalization.85

reported improved mood, coping, stress, autonomy,
activities of daily living, and overall health inresponse to the adoption of assistive technology,
all culminating in better overall well-being.79
Assistive technology in the home can include
sensors, location monitoring, cognitive stimulation
mechanisms, and medication dispensing devices.80
Both sensors and GPS can be useful for people
living with dementia who engage in wandering, or
getting lost or confused about their location.81
Sensors can be installed on doors to detect
wandering and forced entry and can alert caregivers in case of emergency.82 GPS is also usefulfor detecting night wandering and wandering
during the winter. GPS can be used to promoteautonomy and encourage outdoor activities,
which are especially important for overall healthin this population.83 Importantly, people livingwith dementia have reported that using GPS has
provided them with a sense of freedom, further

A recent systematic review highlights the use ofmobile applications to support activities of daily118

World Happiness Report 2024

a “normal” life for the residents).89 Well-being is
supported through social relationships andopportunities to engage in activities of interest,
including eating at restaurants, attending concerts,
and maintaining physical health through walking
in the outdoor spaces within the village. Like theuse of assistive technology in one’s own home
within the community, dementia villages utilize
sensors to aid in the maintenance of the autonomyof their residents. An alternative to dementia
villages in the United States is the Green House
Project, which is comprised of individual residencesthat focus on viewing individuals with dementia
as people outside of their medical label.

living, including maintaining hygiene, cooking,remembering appointments, and even setting adinner table.86 This type of support may bolster
a fundamental psychological need of Social
Determination Theory, a sense of mastery andcompetence, that can be reduced in people livingwith dementia. This is accomplished with applications that support various types of cognition,
including memory, by providing prompts andreminders. For example, calendar applications areused to remind people living with dementia of
their daily schedule, while visual or vocal prompts
help remind people to complete tasks. In sum,
aging in place can enhance the well-being ofpeople living with dementia, and aging in place
can be supported with inexpensive home
modifications and technological aids. However,additional research and innovation on technologicalsystems are still needed to maximize their efficacy
and address ethical considerations.87
While aging in place is an option for people livingwith mild to moderate dementia, it may not be an
option for everyone due to advanced diseasestage or lack of access to at-home caregivers. An
alternative option for people living with advanced
dementia is an innovative residential care model
known as dementia villages, which are communities
that encourage a supportive, homelike environment that is conducive to well-being.88 The
overarching goal of dementia villages is todeinstitutionalize dementia through a patient-centered approach. Several countries havebuilt or are building dementia villages to promote
the well-being of people living with advanced
dementia, including Amsterdam, Australia,
Denmark, France, Germany, Ireland, Italy, Japan,
New Zealand, Norway, Scotland, Switzerland.
The first dementia village, De Hogeweyk, wasconceived in the Netherlands and aimed toprovide individuals with an engaging life using
meaningful activities. The Hogeweyk Care
Concept includes six pillars of a dementia village:
1) favorable surroundings (e.g., a familiar homespace, outdoor space), 2) life’s pleasure andmeaning, 3) health, 4) lifestyle (e.g., acknowledging
that the person living with dementia is the same
person they were before their diagnosis), 5) staff
and volunteers trained in dementia care, and6) the organization (e.g., policies and staff facilitate

Consistent with Social Determination Theory,dementia villages promote both autonomy andrelatedness, supporting the well-being of people
living with dementia. Of note, while dementia
villages were designed to promote well-being,
little research has been conducted to assess if
there are meaningful differences in the well-beingof dementia village residents compared toindividuals living in more traditional dementia
care environments.90 More research is needed tobetter understand the impacts of dementia
villages in comparison to traditional long-termcare facilities. Further, countries should continue
to develop and assess care models that aredesigned with the goal of enhancing thewell-being of people living with dementia.
If aging in place or residing in a dementia village
is not an option, long-term care facilities likenursing homes may be an alternative. One waythat well-being is encouraged in this setting isthrough the use of multisensory environments.
Research exploring two types of multisensory
environments including Snoezelen rooms andlandscaped gardens suggests that they both aid
in the well-being of people living with dementia.91
Snoezelen rooms were developed in the
Netherlands and are used to stimulate the senses
via light, smell, sound, and taste.92 Like the dementia
villages, Snoezelen rooms utilize a patient-centeredapproach. This mode of multisensory stimulation
is effective for people living with dementia atvarious stages of the disease and increases
well-being by reducing agitation and improving
mood symptoms including depression and anxiety.93

119World Happiness Report 2024

Together, long-term care facilities can enhance
the well-being of people living with dementia byimplementing various interventions and activities
including multisensory environments, animal-facilitated activities, cultural arts interventions, exercise
programs, massage, life review/reminiscence, andoutdoor activities such as gardening.
Despite the challenges that can accompany adiagnosis of dementia, people living with dementia
can live well. This is evidenced through bothindividual and group activities that promote
well-being via engagement in interventions andhobbies that facilitate a sense of purpose andimprove mood. Importantly, each of these methods
can be applied to people living with dementia
both in the community and in care facilities likedementia villages or nursing homes, aiming topromote well-being. The global population ofpeople living with dementia continues to grow,invoking a pressing need for researchers andpolicymakers to place well-being at the forefrontof approaches to care.
Moreover, research is needed to extend well-being
interventions developed for broader older adult
populations to people living with dementia. For
example, positive psychological interventions
using practices such as gratitude and savoring
have been shown to increase well-being amongolder adults. In a sample of healthy, communityliving adults aged 60+, a ‘three good things in life’
gratitude intervention was found to increasewell-being from baseline to day 45.94 In a pilot
study examining a savoring intervention, older
adults who completed the intervention with highfidelity reported increased happiness overtime.95Another study found that older adults who
engaged in a positive psychological intervention
showed increases in life satisfaction and subjective
happiness compared to the control group.96
However, prior studies testing positive psychological interventions typically exclude people
experiencing cognitive impairment or living withdementia. Future research is needed to determine
whether these well-being interventions are
effective for people living with dementia and to
determine best practices for adapting theseinterventions for this population.

Policy Implications
The research reviewed in this chapter suggeststhat policies designed to increase well-being may
lower dementia risk, which in turn would result ina happier and healthier older adult population.
Policies that enable equitable access to well-beingand health-enhancing activities may be especiallybeneficial, such as those that increase equitable
access to education, safe public spaces forphysical and social activity, health screenings,
and affordable and effective health care. Suchpolicies would provide individuals with theresources needed to maintain their well-being
and health, resulting in widespread benefits fordementia prevention.
In addition, resources should be invested to test
the long-term effects of well-being-enhancing
interventions on cognitive health and dementia.
Because targeting well-being as a dementia
prevention strategy requires large-scaledissemination in the general population, resources
should be invested into potentially scalable
interventions such as those that can be delivered
digitally, single-session interventions, andmicro-interventions.97

Policies that enable equitable
access to well-being- andhealth-enhancing activities may
be especially beneficial, suchas those that increase equitable
access to education, safe public
spaces for physical and social
activity, health screenings,
and affordable and effective
health care. Such policies would
provide individuals with theresources needed to maintaintheir well-being and health,resulting in widespread benefits
for dementia prevention.

120

World Happiness Report 2024

Conclusion

Photo Center for Ageing Better

All over the world, people are living longer thanever. In most countries, the average person can
expect to live to age 65 or older. As the global
population ages, it is crucial to develop andimplement dementia prevention strategies and tohelp individuals live well with dementia. This
chapter reviewed evidence from multiple scientificdisciplines and world regions showing thatinvesting in well-being across the lifespan isessential to achieve these goals. In early andmidlife, higher well-being may serve as a protective
factor that prevents or delays dementia onset,
which in turn further supports late-life well-being.
In older adulthood, interventions and policies thatfacilitate continued engagement in social, physical,
and intellectual activities are critical for the
maintenance of both well-being and cognitive
health. Among older adults living with dementia,
interventions, environments, and policies that
support the basic needs of autonomy, competence,and relatedness may help promote well-being.

In addition to policies designed to decrease
dementia prevalence, policies are needed toenhance the well-being of people living withdementia. A critical first step is to invest resources
into collecting more high-quality data on thewell-being of individuals living with dementia,
ideally using self-report instruments that enable
individuals living with dementia to report theirown well-being. Such data are invaluable tobetter understand the lived experiences ofindividuals living with dementia, and to enableevaluations of the impact of different environmentson the well-being of people living with dementia.
Given existing research suggesting individuals
living with dementia benefit from continuing tolive at home or in the community, policies should
aim to increase access to and the affordability ofassistive technology and paid care partners toenable more individuals living with dementia toremain at home. For individuals who can nolonger safely live at home, assisted living facilities
should aim to create more home-like environments
and to implement activities and interventions
shown to enhance well-being. Critically, thedevelopment and evaluation of the policies
described here will require a shift away from a
deficit-focused medical model and toward astrengths-based model that recognizes andpreserves the personhood of people living withdementia.

121

World Happiness Report 2024

Endnotes
1

World Health Organization (2020)

20 Ma et al. (2021)

2

Alzheimer’s Association (2020)

3

Alzheimer’s Association (2023)

4

Meléndez et al. (2018); Wilson et al. (2013)

5

See Willroth et al. (2023) for review

6

Alzheimer’s Disease International (2023)

21	Kubzansky et al. (2023). Time-intensive, multi-component,
face-to-face interventions often show the largest effectsizes. However, light-touch interventions delivered indigital formats are likely to be more scalable for delivery to
the general population. Thus, more work is needed toenhance and test the effectiveness of these more scalable
alternatives.

7

Willroth (2023)

22 Alzheimer’s Disease International (2023)

8	Bell et al. (2022); Sutin et al. (2018); Willroth et al., (2023).
Beck et al. (2023). Results of one meta-analysis found that
purpose in life was significantly associated with a reduced
risk of dementia; however, results for positive affect were
non-significant (Bell et al., 2022). There were not enoughstudies on life satisfaction to conduct a meta-analysis, however, individual study results were mixed. Consistent withthese meta-analytic findings, another paper by Sutin andcolleagues (2018) examined associations between several
well-being constructs (life satisfaction, optimism, mastery,
purpose in life, and positive affect) and incident dementia,
and found that only purpose in life was significantly
associated with dementia after adjusting for covariates(psychological distress, other clinical and behavioral risk
factors, income/wealth, and genetic risk). Similarly, Willrothand colleagues (2023) found that both life satisfaction andeudaimonic well-being were associated with greater
cognitive resilience to dementia-related neuropathology,
but only the association of eudaimonic well-being remained
when adjusting for covariates (socioeconomic status,
education, cognitive activity, low neuroticism, low depression, ApoE genotype, medical comorbidities). Finally, a
recent individual participant data meta-analysis found that
positive affect, but not life satisfaction, was significantly
associated with lower incident dementia (Beck et al., 2023).
These findings highlight the complex associations between
particular facets of well-being and dementia risk.
9

Cross & Grimm (2018)26 Missotten et al. (2008); van de Beek et al. (2019)27 Sites et al. (2017)
28 Wolverson et al. (2016)
29 Gotanda et al. (2023)
30 Górska et al. (2018)
31 Górska et al. (2018)32 Katschnig (1997)33	Brod et al. (1999); Hoe et al. (2005); Thorgrimsen et al.
(2003)
34	Griffiths et al. (2020); Ready et al (2004); Römhild et al.
(2018)
35 Brod et al. (1999); Thorgrimsen et al. (2003)
36 Logsdon et al. (1999)

39 Clarke et al. (2020)
40 Bowling et al. (2015)12 Sin (2016)41 Deci & Ryan (2011)13 Livingston et al. (2020)

42 Ryan & Deci (2017)14 Wilroth et al. (2023)

43 Górska et al. (2018)15 Dewitte et al. (2021); Lee (2016); Wagner et al. (2022)

44 Górska et al. (2018)16	Hittner et al. (2020); Gerstorf et al. (2007); Zainal &Newman (2022); Boyle et al. (2010); Kim et al. (2019)45 Shyrock & Meeks (2022)

17	Bell et al. (2022); Sutin et al. (2018); Boyle et al. (2010);
Peitsch et al. (2016); Rawtaer et al. (2017); Zhu et al. (2022);
Sutin et al. (2020)

19	Carr et al. (2023). A meg-analysis of 198 meta-analyses of
4,065 primary studies found that interventions designed toenhance well-being through pathways consistent withpositive psychological theory had small to medium positiveeffects on well-being, and these well-being increases werepartially maintained at 7.5 months post-intervention.

25 Logsdon et al. (1999)

38 Martyr et al. (2018)

Grant et al. (2009)18 Boyle et al. (2012); Willroth et al. (2022)

24 Clarke et al. (2020); Kaufmann et al. (2016)37 Burgener et al. (2005)

10	Middleton & Yaffe (2009); Livingston et al. (2020); Marioni
et al. (2015)1123	Meléndez et al. (2018); Wilson et al. (2013). Memory andthinking changes can impact well-being directly by causing
frustration or embarrassment, or indirectly by impacting
one’s ability to participate in necessary or valued activities.
In addition, the brain changes that cause dementia candirectly impact mood.

46 Dewitte et al. (2022); Nyman & Szymczynska (2016)
47 Nyman & Szymczynska (2016)
48 Orgeta et al. (2019)49 Giebel & Sutcliffe (2018)
50 Shropshire (2020)
51 Leung (2015)
52 Leung et al. (2015); Logsdon et al. (2007)122

World Happiness Report 2024

53 de Medeiros & Basting (2014)
54 Shryock & Meeks (2022); Ueda et al. (2013)
55 Huang et al. (2023)
56 Tam et al. (2021)
57 Woods et al. (2018)58 Subramaniam & Woods (2012)93	Cox et al. (2004); Van Weert et al. (2005); Solé et al.
(2022); Berkheimer et al. (2017)
94 Killen & Macaskill (2015)
95 Smith & Hanni (2019)96 Ramírez et al. (2014)
97 Kubzansky et al. (2023)

59 Haight et al. (2006); Haight et al. (1992)
60 Shryock & Meeks (2022); Subramaniam & Woods (2012)
61 Xu et al. (2023)
62 Proulx et al. (2023)
63 Harris & Caporella (2019)64 Galbraith et al. (2015)
65 Gerritzen et al. (2020)
66 Tominari et al. (2021)
67 Rios Rincon et al. (2022)
68 Tummers et al. (2020)
69 Tyack et al. (2017)
70 Tyack & Camic (2017)71 Ratnayake et al. (2022); Wiles et al. (2012)72 Ratnayake et al. (2022)
73 McClendon et al. (2006)
74 Ratnayake et al. (2022); Wiles et al. (2012)
75 Circo et al. (2015)
76 National Institute on Aging (2023)
77 Soilemezi et al. (2019)78	Farina et al. (2019); Malmgren et al. (2020); Astell et al.
(2019); Kruse et al. (2020)
79 Kruse et al. (2020)80	Soilemezi et al. (2019); Kruse et al. (2020); Daly et al.
(2019); Behera et al. (2021); Gettel et al. (2021); Pappadà etal. (2021)
81

Behera et al. (2021)

82 Pappadà et al. (2021)
83 Behera et al. (2021); Liu et al. (2017)84 Pappadà et al. (2021)
85 Behera et al. (2021)
86 Pappadà et al. (2021)
87 Astell et al. (2019); Kruse et al. (2020)88 Harris et al. (2017)89 Harris et al. (2017)
90 Hestevik et al. (2022); Krier et al. (2023)
91

Cox et al. (2004)

92 Van Weert et al. (2005)123

World Happiness Report 2024

References
Aging in Place: Tips on Making Home Safe and Accessible.
(n.d.). National Institute on Aging. Retrieved September 21,
2023, from https://www.nia.nih.gov/health/infographics/
aging-place-tips-making-home-safe-and-accessible

Burgener, S. C., Twigg, P., & Popovich, A. (2005). Measuring
psychological well-being in cognitively impaired persons.
Dementia, 4(4), 463–485. https://doi.
org/10.1177/1471301205058303

Alzheimer’s Association. (2023a). Alzheimer’s Disease Facts
and Figures. https://www.alz.org/alzheimers-dementia/
facts-figures

Carr, A., Finneran, L., Boyd, C., Shirey, C., Canning, C., Stafford,
O., ... & Burke, T. (2023). The evidence-base for positive
psychology interventions: a mega-analysis of meta-analyses.
The Journal of Positive Psychology, 1-15.

Alzheimer’s Association. (2023b). What is Dementia? https://www.alz.org/alzheimers-dementia/what-is-dementia

Ciro, C. A., Anderson, M. P., Hershey, L. A., Prodan, C. I., & Holm,
M. B. (2015). Instrumental Activities of Daily Living Performance and Role Satisfaction in People With and Without Mild
Cognitive Impairment: A Pilot Project. The American Journal of
Occupational Therapy, 69(3), 6903270020p1-6903270020p10.
https://doi.org/10.5014/ajot.2014.015198

Alzheimer’s Disease International. (2023). Dementia statistics.
https://www.alzint.org/about/dementia-facts-figures/dementia-statistics
Astell, A. J., Bouranis, N., Hoey, J., Lindauer, A., Mihailidis, A.,
Nugent, C., Robillard, J. M., & Technology and Dementia
Professional Interest Area ... (2019). Technology and Dementia:
The Future is Now. Dementia and Geriatric Cognitive Disorders,
47(3), 131–139. https://doi.org/10.1159/000497800

Clarke, C., Woods, B., Moniz-Cook, E., Mountain, G., Øksnebjerg,
L., Chattat, R., Diaz, A., Gove, D., Vernooij-Dassen, M., &Wolverson, E. (2020). Measuring the well-being of people withdementia: A conceptual scoping review. Health and Quality ofLife Outcomes, 18(1), 249. https://doi.org/10.1186/s12955-02001440-x

Beck, E. D., Yoneda, T., James, B. D., Bennett, D. A., Hassenstab,J., Katz, M. J., ... & Graham, E. K. (2023). Personality predictorsof dementia diagnosis and neuropathological burden: An
individual participant data meta-analysis. Alzheimer’s &Dementia.

Cox, H., Burns, I., & Savage, S. (2004). Multisensory Environments for Leisure: Promoting Well-being in Nursing Home
Residents With Dementia. Journal of Gerontological Nursing,30(2), 37–45. https://doi.org/10.3928/0098-9134-2004020108

Behera, C. K., Condell, J., Dora, S., Gibson, D. S., & Leavey, G.
(2021). State-of-the-Art Sensors for Remote Care of People
with Dementia during a Pandemic: A Systematic Review.
Sensors, 21(14), Article 14. https://doi.org/10.3390/s21144688

Cross, M. P., & Grimm, M. (2018). Subjective well-being andphysical health. Handbook of Well-Being.

Bell, G., Singham, T., Saunders, R., John, A., & Stott, J. (2022).
Positive psychological constructs and association with reduced
risk of mild cognitive impairment and dementia in older adults:
A systematic review and meta-analysis. Ageing Research
Reviews, 77, 101594. https://doi.org/10.1016/j.arr.2022.101594

Daly Lynn, J., Rondón-Sulbarán, J., Quinn, E., Ryan, A., McCormack, B., & Martin, S. (2019). A systematic review of electronic
assistive technology within supporting living environments forpeople with dementia. Dementia, 18(7–8), 2371–2435. https://doi.org/10.1177/1471301217733649

Berkheimer, S. D., Qian, C., & Malmstrom, T. K. (2017). Snoezelen Therapy as an Intervention to Reduce Agitation in Nursing
Home Patients With Dementia: A Pilot Study. Journal of theAmerican Medical Directors Association, 18(12), 1089–1091.
https://doi.org/10.1016/j.jamda.2017.09.009

de Medeiros, K., & Basting, A. (2014). “Shall I compare thee to adose of donepezil?”: Cultural arts interventions in dementia
care research. The Gerontologist, 54(3), 344–353. https://doi.
org/10.1093/geront/gnt055

Bowling, A., Rowe, G., Adams, S., Sands, P., Samsi, K., Crane, M.,
Joly, L., & Manthorpe, J. (2015). Quality of life in dementia: A
systematically conducted narrative review of dementia-specificmeasurement scales. Aging & Mental Health, 19(1), 13–31.
https://doi.org/10.1080/13607863.2014.915923
Boyle, P. A., Buchman, A. S., Barnes, L. L., & Bennett, D. A.
(2010). Effect of a purpose in life on risk of incident Alzheimer
disease and mild cognitive impairment in community-dwellingolder persons. Archives of General Psychiatry, 67(3), 304–310.
https://doi.org/10.1001/archgenpsychiatry.2009.208

Deci, E. L., & Ryan, R. M. (2011). Self-determination theory. In P.
A. M. Van Lange, E. T. Higgins, & A. W. Kruglanski (Eds.),
Handbook of theories of social psychology. Sage. http://digital.
casalini.it/9781446269008
Dewitte, L., Haugan, G., Vandenbulcke, mathieu, & Dezutter, J.
(2022). Living meaningfully with dementia. In Dementia &society (pp. 63–80). Cambridge University Press. https://doi.
org/10.1017/9781108918954.005
Dewitte, L., Lewis, N. A., Payne, B. R., Turiano, N. A., & Hill, P. L.
(2021). Cross-lagged relationships between sense of purpose inlife, memory performance, and subjective memory beliefs inadulthood over a 9-year interval. Aging & Mental Health, 25(11),
2018–2027. https://doi.org/10.1080/13607863.2020.1822284

Boyle, P. A., Buchman, A. S., Wilson, R. S., Yu, L., Schneider, J.
A., & Bennett, D. A. (2012). Effect of purpose in life on therelation between alzheimer disease pathologic changes on
cognitive function in advanced age. Archives of General
Psychiatry, 69(5), 499–504. https://doi.org/10.1001/archgenpsychiatry.2011.1487
Brod, M., Stewart, A. L., Sands, L., & Walton, P. (1999). Conceptualization and measurement of quality of life in dementia: The
Dementia Quality of Life Instrument (DQoL). The Gerontologist,
39(1), 25–36. https://doi.org/10.1093/geront/39.1.25

Farina, N., Sherlock, G., Thomas, S., Lowry, R. G., & Banerjee, S.
(2019). Acceptability and feasibility of wearing activity
monitors in community-dwelling older adults with dementia.
International Journal of Geriatric Psychiatry, 34(4), 617–624.
https://doi.org/10.1002/gps.5064

124

World Happiness Report 2024

Hestevik, Christine Hillestad, Rosness, Tor, Holte, Hilde H., &Bjerk, Maria. (2022, January 14). Dementia villages and othermodern forms of residential care for persons with dementia.
Norwegian Institute of Public Health. https://www.fhi.no/en/
publ/2022/Dementia-villages-and-other-modern-forms-of-residential-care-for-persons-with-dementia/

Galbraith, B., Larkin, H., Moorhouse, A., & Oomen, T. (2015).
Intergenerational programs for persons with dementia: A
scoping review. Journal of Gerontological Social Work, 58(4),357–378. https://doi.org/10.1080/01634372.2015.1008166
Gerritzen, E. V., Hull, M. J., Verbeek, H., Smith, A. E., & de Boer,B. (2020). Successful elements of intergenerational dementia
programs: A scoping review. Journal of Intergenerational
Relationships, 18(2), 214–245. https://doi.org/10.1080/1535077
0.2019.1670770

Hittner, E. F., Stephens, J. E., Turiano, N. A., Gerstorf, D.,
Lachman, M. E., & Haase, C. M. (2020). Positive affect isassociated with less memory decline: Evidence from a 9-year
longitudinal study. Psychological Science, 31(11), 1386–1395.
https://doi.org/10.1177/0956797620953883

Gerstorf, D., Lövdén, M., Röcke, C., Smith, J., & Lindenberger, U.
(2007). Well-being affects changes in perceptual speed inadvanced old age: Longitudinal evidence for a dynamic link.
Developmental Psychology, 43(3), 705–718. https://doi.
org/10.1037/0012-1649.43.3.705

Hoe, J., Katona, C., Roch, B., & Livingston, G. (2005). Use of the
QOL-AD for measuring quality of life in people with severe
dementia—The LASER-AD study. Age and Ageing, 34(2),130–135. https://doi.org/10.1093/ageing/afi030

Gettel, C. J., Chen, K., & Goldberg, E. M. (2021). Dementia Care,
Fall Detection, and Ambient-Assisted Living Technologies Help
Older Adults Age in Place: A Scoping Review. Journal ofApplied Gerontology. https://doi.
org/10.1177/07334648211005868

Huang, C., Yan, Y., Lin, R., Sun, W., Ye, Y., Wang, N., & Li, H.
(2023). Effect of self-determination theory-based integrated
creative art (SDTICA) program on older adults with mild
cognitive impairment in nursing homes: Study protocol for acluster randomised controlled trial. BMC Geriatrics, 23(1), 238.
https://doi.org/10.1186/s12877-023-03896-0

Giebel, C. M., & Sutcliffe, C. (2018). Initiating activities of dailyliving contributes to well-being in people with dementia andtheir carers. International Journal of Geriatric Psychiatry, 33(1),e94–e102. https://doi.org/10.1002/gps.4728

Katschnig, H. (1997). How useful is the concept of quality of life
in psychiatry? Current Opinion in Psychiatry, 10(5). https://journals.lww.com/co-psychiatry/fulltext/1997/09000/how_
useful_is_the_concept_of_quality_of_life_in.1.aspx

Górska, S., Forsyth, K., & Maciver, D. (2018). Living withdementia: A meta-synthesis of qualitative research on the lived
experience. The Gerontologist, 58(3), e180–e196. https://doi.
org/10.1093/geront/gnw195

Kaufmann, E. G., & Engel, S. A. (2016). Dementia and well-being: A conceptual framework based on Tom Kitwood’s modelof needs. Dementia, 15(4), 774–788. https://doi.
org/10.1177/1471301214539690

Gotanda, H., Tsugawa, Y., Xu, H., & Reuben, D. B. (2023). Lifesatisfaction among persons living with dementia and those
without dementia. Journal of the American Geriatrics Society,71(4), 1105–1116. https://doi.org/10.1111/jgs.18174
Grant, N., Wardle, J., & Steptoe, A. (2009). The Relationship
Between Life Satisfaction and Health Behavior: A Cross-cultural
Analysis of Young Adults. International Journal of Behavioral
Medicine, 16(3), 259–268. https://doi.org/10.1007/s12529-0099032-x
Griffiths, A. W., Smith, S. J., Martin, A., Meads, D., Kelley, R., &Surr, C. A. (2020). Exploring self-report and proxy-reportquality-of-life measures for people living with dementia in carehomes. Quality of Life Research, 29(2), 463–472. https://doi.
org/10.1007/s11136-019-02333-3

Kim, G., Shin, S. H., Scicolone, M. A., & Parmelee, P. (2019).
Purpose in life protects against cognitive decline among older
adults. The American Journal of Geriatric Psychiatry, 27(6),593–601. https://doi.org/10.1016/j.jagp.2019.01.010
Krier, D., Boer, B. de, Hiligsmann, M., Wittwer, J., & Amieva, H.
(2023). Evaluation of Dementia-Friendly Initiatives, Small-Scale
Homelike Residential Care, and Dementia Village Models: A
Scoping Review. Journal of the American Medical Directors
Association, 24(7), 1020-1027.e1. https://doi.org/10.1016/j.
jamda.2023.03.024

Haight, B. K., Gibson, F., & Michel, Y. (2006). The Northern
Ireland life review/life storybook project for people withdementia. Alzheimer’s & Dementia, 2(1), 56–58. https://doi.
org/10.1016/j.jalz.2005.12.003

Kruse, C. S., Fohn, J., Umunnakwe, G., Patel, K., & Patel, S.
(2020). Evaluating the Facilitators, Barriers, and Medical
Outcomes Commensurate with the Use of Assistive Technology to Support People with Dementia: A Systematic Review
Literature. Healthcare, 8(3), Article 3. https://doi.org/10.3390/
healthcare8030278

Haight, B. K., Jones, G. M. M., & Miesen, B. M. L. (1992). The
structured life-review process: A community approach to theageing client. In Caregiving in dementia: Research andapplications (Vol. 1, pp. 272–292).
Harris, J. Topfer, L., & Ford, C. (2017) Dementia Villages: Innovative Residential Care for People With Dementia. https://www.
cadth.ca/dementia-villages-innovative-residential-care-people-dementia
Harris, P. B., & Caporella, C. A. (2019). Making a university
community more dementia friendly through participation in anintergenerational choir. Dementia, 18(7–8), 2556–2575. https://doi.org/10.1177/1471301217752209

Killen, A., & Macaskill, A. (2015). Using a gratitude intervention
to enhance well-being in older adults. Journal of HappinessStudies, 16(4), 947–964. https://doi.org/10.1007/s10902-0149542-3

Kubzansky, L. D., Kim, E. S., Boehm, J. K., Davidson, R. J.,
Huffman, J. C., Loucks, E. B., Lyubomirsky, S., Picard, R. W.,
Schueller, S. M., Trudel-Fitzgerald, C., VanderWeele, T. J.,
Warran, K., Yeager, D. S., Yeh, C. S., & Moskowitz, J. T. (2023).
Interventions to modify psychological well-being: Progress,
promises, and an agenda for future research. Affective Science,
4(1), 174–184. https://doi.org/10.1007/s42761-022-00167-w

125

World Happiness Report 2024

Middleton, L. E., & Yaffe, K. (2009). Promising Strategies for thePrevention of Dementia. Archives of Neurology, 66(10),1210–1215. https://doi.org/10.1001/archneurol.2009.201

Lee, P.-L. (2016). A Joyful Heart is Good Medicine: Positive
Affect Predicts Memory Complaints. The American Journal of
Geriatric Psychiatry, 24(8), 662–670. https://doi.org/10.1016/j.
jagp.2016.04.001
Leung, P., Orrell, M., & Orgeta, V. (2015). Social support groupinterventions in people with dementia and mild cognitive
impairment: A systematic review of the literature. International
Journal of Geriatric Psychiatry, 30(1), 1–9. https://doi.
org/10.1002/gps.4166

Missotten, P., Squelard, G., Ylieff, M., Di Notte, D., Paquay, L., De
Lepeleire, J., & Fontaine, O. (2008). Quality of life in older
Belgian people: Comparison between people with dementia,
mild cognitive impairment, and controls. International Journalof Geriatric Psychiatry, 23(11), 1103–1109. https://doi.
org/10.1002/gps.1981

Liu, L., Miguel Cruz, A., Ruptash, T., Barnard, S., & Juzwishin, D.
(2017). Acceptance of Global Positioning System (GPS)Technology Among Dementia Clients and Family Caregivers.
Journal of Technology in Human Services, 35(2), 99–119.
https://doi.org/10.1080/15228835.2016.1266724

Nyman, S. R., & Szymczynska, P. (2016). Meaningful activities
for improving the wellbeing of people with dementia: Beyond
mere pleasure to meeting fundamental psychological needs.
Perspectives in Public Health, 136(2), 99–107. https://doi.
org/10.1177/1757913915626193

Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C.,
Banerjee, S., Brayne, C., Burns, A., Cohen-Mansfield, J., Cooper,C., Costafreda, S. G., Dias, A., Fox, N., Gitlin, L. N., Howard, R.,
Kales, H. C., Kivimäki, M., Larson, E. B., Ogunniyi, A., … Mukadam, N. (2020). Dementia prevention, intervention, and care:2020 report of the Lancet Commission. The Lancet,
396(10248), 413–446. https://doi.org/10.1016/S01406736(20)30367-6

Orgeta, V., Tuijt, R., Leung, P., Verdaguer, E. S., Gould, R. L.,
Jones, R., & Livingston, G. (2019). Behavioral activation for
promoting well-being in mild dementia: Feasibility andoutcomes of a pilot randomized controlled trial. Journal of
Alzheimer’s Disease, 72(2), 563–574. https://doi.org/10.3233/
JAD-190696
Pappadà, A., Chattat, R., Chirico, I., Valente, M., & Ottoboni, G.
(2021). Assistive Technologies in Dementia Care: An Updated
Analysis of the Literature. Frontiers in Psychology, 12, 644587.
https://doi.org/10.3389/fpsyg.2021.644587

Logsdon, R. G., Gibbons, L. E., McCurry, S. M., & Teri, L. (1999).
Quality of life in Alzheimer’s disease: Patient and caregiverreports. Journal of Mental Health and Aging, 5(1), 21–32.

Peitsch, L., Tyas, S. L., Menec, V. H., & John, P. D. S. (2016).
General life satisfaction predicts dementia in community living
older adults: A prospective cohort study. International
Psychogeriatrics, 28(7), 1101–1109. https://doi.org/10.1017/
S1041610215002422

Logsdon, R. G., McCurry, S. M., & Teri, L. (2007). Evidence-based interventions to improve quality of life forindividuals with dementia. Alzheimers Care Today, 8, 309.
Ma, Y.-H., Yang, Y.-X., Shen, X.-N., Chen, S.-D., Tan, L., Dong, Q.,& Yu, J.-T. (2021). Evaluation relationships between subjective
wellbeing, personality traits, and Alzheimer’s disease: A
two-sample Mendelian randomization study. Journal of
Psychiatric Research, 137, 498–505. https://doi.org/10.1016/j.
jpsychires.2021.03.033

Proulx, J. D. E., Van de Vondervoort, J. W., Hamlin, J. K.,
Helliwell, J. F., & Aknin, L. B. (2023). Are real-world prosociality
programs associated with greater psychological well-being inprimary school-aged children? International Journal ofEnvironmental Research and Public Health, 20(5). https://doi.
org/10.3390/ijerph20054403

Malmgren Fänge, A., Carlsson, G., Chiatti, C., & Lethin, C.
(2020). Using sensor-based technology for safety andindependence – the experiences of people with dementia andtheir families. Scandinavian Journal of Caring Sciences, 34(3),648–657. https://doi.org/10.1111/scs.12766

Ramírez, E., Ortega, A. R., Chamorro, A., & Colmenero, J. M.
(2014). A program of positive intervention in the elderly:
Memories, gratitude and forgiveness. Aging & Mental Health,18(4), 463–470. https://doi.org/10.1080/13607863.2013.856858

Martyr, A., Nelis, S. M., Quinn, C., Wu, Y.-T., Lamont, R. A.,
Henderson, C., Clarke, R., Hindle, J. V., Thom, J. M., Jones, I. R.,
Morris, R. G., Rusted, J. M., Victor, C. R., & Clare, L. (2018).
Living well with dementia: A systematic review and correlational meta-analysis of factors associated with quality of life,
well-being and life satisfaction in people with dementia.
Psychological Medicine, 48(13), 2130–2139. Cambridge Core.
https://doi.org/10.1017/S0033291718000405
McClendon, M. J., Smyth, K. A., & Neundorfer, M. M. (2006).
Long-Term-Care Placement and Survival of Persons With
Alzheimer’s Disease. The Journals of Gerontology: Series B,
61(4), P220–P227. https://doi.org/10.1093/geronb/61.4.P220
Meléndez, J. C., Satorres, E., Redondo, R., Escudero, J., &Pitarque, A. (2018). Wellbeing, resilience, and coping: Are there
differences between healthy older adults, adults with mild
cognitive impairment, and adults with Alzheimer-type dementia? Archives of Gerontology and Geriatrics, 77, 38–43. https://doi.org/10.1016/j.archger.2018.04.004

Ratnayake, M., Lukas, S., Brathwaite, S., Neave, J., & Henry, H.
(2022). Aging in Place: Delaware Journal of Public Health, 8(3),28–31. https://doi.org/10.32481/djph.2022.08.007
Rawtaer, I., Gao, Q., Nyunt, M. S. Z., Feng, L., Chong, M. S., Lim,W. S., Lee, T.-S., Yap, P., Yap, K. B., & Ng, T. P. (2017). Psychosocial risk and protective factors and incident mild cognitive
impairment and dementia in community dwelling elderly:
Findings from the singapore longitudinal ageing study. Journalof Alzheimer’s Disease, 57(2), 603–611. https://doi.org/10.3233/
JAD-160862
Ready, R. E., Ott, B. R., & Grace, J. (2004). Patient versus
informant perspectives of quality of life in mild cognitive
impairment and Alzheimer’s Disease. International Journal of
Geriatric Psychiatry, 19(3), 256–265. https://doi.org/10.1002/
gps.1075

126

World Happiness Report 2024

Sutin, A. R., Luchetti, M., Stephan, Y., & Terracciano, A. (2020).
Meaning in life and risk of cognitive impairment: A 9-Yearprospective study in 14 countries. Archives of Gerontology andGeriatrics, 88, 104033. https://doi.org/10.1016/j.archger.2020.104033

Rios Rincon, A. M., Miguel Cruz, A., Daum, C., Neubauer, N.,
Comeau, A., & Liu, L. (2022). Digital Storytelling in Older Adults
With Typical Aging, and With Mild Cognitive Impairment or
Dementia: A Systematic Literature Review. Journal of Applied
Gerontology, 41(3), 867–880. https://doi.
org/10.1177/07334648211015456

Sutin, A. R., Stephan, Y., & Terracciano, A. (2018). Psychologicalwell-being and risk of dementia. International Journal of
Geriatric Psychiatry, 33(5), 743–747. https://doi.org/10.1002/
gps.4849

Römhild, J., Fleischer, S., Meyer, G., Stephan, A., Zwakhalen, S.,
Leino-Kilpi, H., Zabalegui, A., Saks, K., Soto-Martin, M., Sutcliffe,C., Rahm Hallberg, I., Berg, A., & on behalf of the RightTimePlaceCare Consortium. (2018). Inter-rater agreement of the
Quality of Life-Alzheimer’s Disease (QoL-AD) self-rating andproxy rating scale: Secondary analysis of RightTimePlaceCaredata. Health and Quality of Life Outcomes, 16(1), 131. https://doi.org/10.1186/s12955-018-0959-y

Tam, W., Poon, S. N., Mahendran, R., Kua, E. H., & Wu, X. V.
(2021). The effectiveness of reminiscence-based intervention
on improving psychological well-being in cognitively intact
older adults: A systematic review and meta-analysis. International Journal of Nursing Studies, 114, 103847. https://doi.
org/10.1016/j.ijnurstu.2020.103847

Ryan, R. M., & Deci, E. L. (2017). Self-Determination Theory:
Basic Psychological Needs in Motivation, Development, andWellness. Guilford Publications. https://books.google.com/
books?id=Bc_DDAAAQBAJ
Shropshire, M. (2020). Reminiscence intervention for community-dwelling older adults without dementia: A literature review.
British Journal of Community Nursing, 25(1), 40–44. https://doi.
org/10.12968/bjcn.2020.25.1.40

Thorgrimsen, L., Selwood, A., Spector, A., Royan, L., de
Madariaga Lopez, M., Woods, R. T., & Orrell, M. (2003). Whose
quality of life is it anyway?: The validity and reliability of the
Quality of Life-Alzheimer’s Disease (QoL-AD) Scale. Alzheimer
Disease & Associated Disorders, 17(4). https://journals.lww.
com/alzheimerjournal/fulltext/2003/10000/whose_quality_of_life_is_it_anyway___the_validity.2.aspx

Shryock, S. K., & Meeks, S. (2022). Activity, activity personalization, and well-being in nursing home residents with and without
cognitive impairment: An integrative review. Clinical Gerontologist, 45(5), 1058–1072. https://doi.org/10.1080/07317115.2020.1
844356

Tominari, M., Uozumi, R., Becker, C., & Kinoshita, A. (2021).
Reminiscence therapy using virtual reality technology affects
cognitive function and subjective well-being in older adults
with dementia. Cogent Psychology, 8(1), 1968991. https://doi.or
g/10.1080/23311908.2021.1968991

Sin, N. L. (2016). The Protective Role of Positive Well-Being in
Cardiovascular Disease: Review of Current Evidence, Mechanisms, and Clinical Implications. Current Cardiology Reports,
18(11), 106. https://doi.org/10.1007/s11886-016-0792-z

Tummers, A. I. M., Hillen, M. F., De Kort, Y. A. W., & Ijsselsteijn,
W. A. (2020). Use of robotic animal companions in dementia
care: A qualitative study of the immediate and long-term
effects to enhance quality of life and feelings of wellbeing forindividuals living with dementia. Annual Review of CyberTherapy and Telemedicine, 18, 45–49. APA PsycInfo.

Smith, J. L., & Hanni, A. A. (2019). Effects of a savoring
intervention on resilience and well-being of older adults.
Journal of Applied Gerontology, 38(1), 137–152. https://doi.
org/10.1177/0733464817693375

Tyack, C., & Camic, P. M. (2017). Touchscreen interventions andthe well-being of people with dementia and caregivers: A
systematic review. International Psychogeriatrics, 29(8),1261–1280. Cambridge Core. https://doi.org/10.1017/
S1041610217000667

Social activity, cognitive decline and dementia risk: A 20-year
prospective cohort study | BMC Public Health | Full Text. (n.d.).
Retrieved October 10, 2023, from https://bmcpublichealth.
biomedcentral.com/articles/10.1186/s12889-015-2426-6

Tyack, C., Camic, P. M., Heron, M. J., & Hulbert, S. (2017).
Viewing art on a tablet computer: A well-being intervention forpeople with dementia and their caregivers. Journal of Applied
Gerontology, 36(7), 864–894. https://doi.
org/10.1177/0733464815617287

Soilemezi, D., Drahota, A., Crossland, J., & Stores, R. (2019). The
Role of the Home Environment in Dementia Care and Support:
Systematic review of Qualitative Research. Dementia, 18(4),1237–1272. https://doi.org/10.1177/1471301217692130
Solé, C., Celdrán, M., & Cifre, I. (2022). Psychological and
Behavioral Effects of Snoezelen Rooms on Dementia. Activities,
Adaptation & Aging, 0(0), 1–16. https://doi.org/10.1080/019247
88.2022.2151805
Stites, S. D., Karlawish, J., Harkins, K., Rubright, J. D., & Wolk, D.
(2017). Awareness of mild cognitive impairment and mild
Alzheimer’s Disease dementia diagnoses associated with lower
self-ratings of quality of life in older adults. The Journals ofGerontology: Series B, 72(6), 974–985. https://doi.org/10.1093/
geronb/gbx100
Subramaniam, P., & Woods, B. (2012). The impact of individual
reminiscence therapy for people with dementia: Systematic
review. Expert Review of Neurotherapeutics, 12(5), 545–555.
https://doi.org/10.1586/ern.12.35

Ueda, T., Suzukamo, Y., Sato, M., & Izumi, S.-I. (2013). Effects ofmusic therapy on behavioral and psychological symptoms ofdementia: A systematic review and meta-analysis. Ageing
Research Reviews, 12(2), 628–641. https://doi.org/10.1016/j.
arr.2013.02.003
van de Beek, M., van Steenoven, I., Ramakers, I. H. G. B., Aalten,
P., Koek, H. L., Olde Rikkert, M. G. M., Manniën, J., Papma, J. M.,
de Jong, F. J., Lemstra, A. W., & van der Flier, W. M. (2019).
Trajectories and determinants of quality of life in dementia withlewy bodies and Alzheimer’s Disease. Journal of Alzheimer’s
Disease, 70(2), 389–397. https://doi.org/10.3233/JAD-190041
Van Weert, J. C. M., Van Dulmen, A. M., Spreeuwenberg, P. M.
M., Ribbe, M. W., & Bensing, J. M. (2005). Behavioral and Mood
Effects of Snoezelen Integrated into 24-Hour Dementia Care.
Journal of the American Geriatrics Society, 53(1), 24–33.
https://doi.org/10.1111/j.1532-5415.2005.53006.x

127World Happiness Report 2024

Wagner, M., Guimond, A.-J., Kubzansky, L. D., Zhang, Y.,
Bennett, D. A., Barnes, L. L., & Grodstein, F. (2022). Negativeand Positive Psychosocial Factors in Relation to Cognitive
Health in Older African Americans. Innovation in Aging, 6(3),igac019. https://doi.org/10.1093/geroni/igac019
Wiles, J. L., Leibing, A., Guberman, N., Reeve, J., & Allen, R. E. S.
(2012). The Meaning of “Aging in Place” to Older People. The
Gerontologist, 52(3), 357–366. https://doi.org/10.1093/geront/
gnr098Willroth, E. C., James, B. D., Graham, E. K., Kapasi, A., Bennett,
D. A., & Mroczek, D. K. (2022). Well-being and cognitive
resilience to dementia-related neuropathology. Psychological
Science, 09567976221119828. https://doi.
org/10.1177/09567976221119828
Willroth, E. C., Pfund, G. N., McGhee, C., & Rule, P. (2023).
Well-being as a protective factor against cognitive decline anddementia: A review of the literature and directions for future
research. The Journals of Gerontology: Series B, 78(5),765–776. https://doi.org/10.1093/geronb/gbad020
Wilson, R. S., Boyle, P. A., Segawa, E., Yu, L., Begeny, C. T.,
Anagnos, S. E., & Bennett, D. A. (2013). The influence ofcognitive decline on well-being in old age. Psychology andAging, 28(2), 304–313. https://doi.org/10.1037/a0031196
Wolverson, E. L., Clarke, C., & Moniz-Cook, E. D. (2016). Living
positively with dementia: A systematic review and synthesis of
the qualitative literature. Aging & Mental Health, 20(7),676–699. https://doi.org/10.1080/13607863.2015.1052777
Woods, B., O’Philbin, L., Farrell, E., Spector, A., & Orrell, M.
(2018). Reminiscence therapy for dementia. Cochrane Databaseof Systematic Reviews, 3. https://doi.org/10.1002/14651858.
CD001120.pub3
World Health Organization. (2020). Decade of Healthy Aging
2021-2030.
Xu, L., Fields, N. L., Daniel, K. M., Cipher, D. J., & Troutman, B. A.
(2023). Reminiscence and digital storytelling to improve thesocial and emotional well-being of older adults with Alzheimer’s Disease and related dementias: Protocol for a mixed
methods study design and a randomized controlled trial. JMIRRes Protoc, 12, e49752. https://doi.org/10.2196/49752
Zainal, N. H., & Newman, M. G. (2022). Life satisfaction preventsdecline in working memory, spatial cognition, and processing
speed: Latent change score analyses across 23 years. European
Psychiatry, 65(1), e27. https://doi.org/10.1192/j.eurpsy.2022.19
Zhu, X., Luchetti, M., Aschwanden, D., Sesker, A. A., Stephan, Y.,
Sutin, A. R., & Terracciano, A. (2022). Satisfaction With Life andRisk of Dementia: Findings From the Korean Longitudinal
Study of Aging. The Journals of Gerontology: Series B, 77(10),1831–1840. https://doi.org/10.1093/geronb/gbac064

128Chapter 5

Differences in Life
Satisfaction amongOlder Adults in India
Ronak Paul
Senior Research Fellow
Department of Public Health and Mortality Studies
International Institute for Population Sciences
Manacy Pai, Ph.D.
Associate Professor
Department of Sociology and Criminology
Kent State University
Muhammad Thalil, Ph.D.
Postdoctoral Scholar, Center for Healthy Aging
The Pennsylvania State University
Shobhit Srivastava, Ph.D.
Independent Researcher

Acknowledgement: we would like to express our sincere gratitude to the editors
for their invaluable insights and suggestions on our chapter drafts.
doi.org/10.18724/whr-2f21-he52
This version last updated March 8, 2024. Please see worldhappiness.report for latest data.

Though population aging reflectssocial and economic progress,
scientists across the globe
continue to debate the factorsthat contribute to quality of life
in older age.

Photo Sapeksh Singh Siwach on Unsplash5World Happiness Report 2024

Key Insights

Older age is associated with higher life satisfaction in India,
refuting some claims that the positive association between age
and life satisfaction only exists in high-income nations.
On average, older men in India are more satisfied with life thanolder women (Table 5.1), but when taking all other measures into
account, older women report higher life satisfaction than theirmale counterparts (Table 5.2).
Older adults with secondary or higher education and those ofhigher social castes report higher life satisfaction than counterparts
without formal education and those from scheduled castes andscheduled tribes.
Satisfaction with living arrangements, perceived discrimination,and self-rated health emerge as the top three predictors of life
satisfaction in this study.

131

World Happiness Report 2024

Life satisfaction, which is the best indicator of
quality of life3 reflects the subjective assessmentof one’s life as a whole. Although participants,
when inquired, rate their present quality of life,
life satisfaction in older ages may serve as ameasure to evaluate whether one’s life has beensuccessful overall.4 Older adults with higher life
satisfaction report healthy behaviors5, whichtranslates into better overall health.6 However,it can become difficult to maintain higher levelsof life satisfaction with advancing age, often
accompanied by social, physical, and mental
health challenges.7 As such, zeroing in on thefactors that contribute to life satisfaction iscentral to creating policies and programs that
can improve the quality of life in older ages.8
A systematic review of 24 studies amongolder adults (60+ years) in Asian countries,
including India, has identified age, gender,social relationships, social engagement, living
arrangements, education, income, caste, religion,
health behaviors, health conditions, and healthcare to affect life satisfaction in later life.9 Few
studies have examined life satisfaction amongolder adults in India, yet those that have foundthat factors such as poor childhood, financial
status, lack of social support in late life, physicalfrailty, and feelings of loneliness are associated

Photo Amol Sonar on Unsplash

India’s older population is the second largest
worldwide, with 140 million Indians aged 60 andover, second only to its 250 million Chinesecounterparts.1 Additionally, the average growthrate for Indians aged 60 and above is three times
higher than the overall population growth rate of
the country.2 Though population aging reflectssocial and economic progress, scientists acrossthe globe continue to debate the factors that
contribute to quality of life in older age.

with lower levels of life satisfaction.10 However,past studies have focused on particular factors
determining satisfaction with life in old age,meaning that comprehensive assessment ofdiverse predictors of life satisfaction among older
men and women in a gendered socioculturalcontext of India remains limited. In this chapter,
we strive to fill this gap by estimating the extent
to which various sociodemographic, household,and health-related factors contribute to thedifferences in life satisfaction among older
adults in India.

Methods
Data

Though population aging
reflects social and economic
progress, scientists acrossthe globe continue to debate
the factors that contribute toquality of life in older age.

This chapter used the baseline wave of theLongitudinal Aging Study in India (LASI, 2017-19),
which is the Health and Retirement Studies’ (HRS)
Indian adaptation11. The LASI provides vital
information on demography, health symptoms,
conditions, disabilities, health service utilization,
household socioeconomic status, family andsocial networks, and life expectations of 73,396
adults aged 45 years and above.12 LASI used a132

World Happiness Report 2024

multistage stratified area probability cluster
sampling design to arrive at the final observation
units: older individuals aged 45 and above, as
well as their spouses of any age.13
This study used a sample of 31,902 individuals
aged 60 and above, whom we refer to as “older
adults’’ from here on forward. The sample who
responded to the questions on life satisfaction
totals 30,795 older adults.

Explanatory Variables
Given their relevance to older adults’ life
satisfaction, we included sociodemographic,
health, and household characteristics in thisstudy. The sociodemographic characteristicsof the older adults are:• Age group was coded as young-old (60-69
years), old-old (70-79 years), and oldest-old(80+ years).

Outcome Variable• Gender was coded as male and female.

All respondents reported their life satisfaction,
which served as this study’s primary outcome
variable. Life satisfaction is defined as a person’s
cognitive appraisal of their life as a whole.14
During LASI round-I, the interviewers collected
information on the following five life satisfaction
indicators:• Level of education was coded as no formal
education, up to primary, secondary andabove.
• Work status was categorized as neverworked, currently not working, currently
working, and retired.
• Marital status was coded as currently notmarried and currently married.

• In most ways, the respondents’ life is close
to ideal.

• The importance of religion was coded as
not important and important.

• The conditions of the respondents’ life
are excellent.

• Living arrangement satisfaction was coded
as satisfied, neutral, and not satisfied.

• The respondents are satisfied with their lives.
• The respondents have achieved the essential
things they want in their lives so far.

• Victim of ill-treatment (within one year of
the interview) was coded as no and yes.

• If the respondents could relieve their life, theywould change almost nothing.

• Perceived discrimination was coded as noand yes.

Ratings were provided on a scale ranging from
1-7, where one meant strongly disagree and sevenmeant strongly agree. The life satisfaction score15
of older persons, which ranges from 5-35, wasthen calculated by adding the five indicators withgreater scores implying higher levels of life
satisfaction. To maintain comparability andcontinuity with life satisfaction measures used inother chapters of the World Happiness Report,
we transformed this life satisfaction variable into
a 0-10 scale by subtracting 5 and then dividing by
3. Notably, the average Indian older adult scored6.32 points in the life satisfaction scale. It is worth
pointing out that life satisfaction in our study isself-reported, thus there always lingers thepossibility of misreporting due to the fear ofsocial stigma.

• Social participation was coded as: socially
active and socially inactive. The family andsocial networks module of the LASI survey
questionnaire includes detailed questions
about older adults’ participation and engagement in social activities, organizations orsociety. The survey asked participants whether
they were a member of any of the organizations,religious groups, clubs, or societies from agiven list and how many meetings/regulargatherings, if any, they attend in a year. Olderadults who engage in the above social activities
were classified as “socially active” and otherwise as “socially inactive.”133

World Happiness Report 2024

activity limitations (range: 0–6). Older individuals who struggled with activities for morethan three months were labeled “faces difficulties.” The “no difficulty” group comprised
those with no trouble with any of the ADLs.
We included ADLs given that difficulty ineveryday functioning and independence canbe crucial for one’s life satisfaction.

The next set of variables included the following
health-related characteristics of older adults:• Depression symptoms were coded as: 0 “Notdepressed” and 1 “Depressed.” Major depression among older adults with symptoms ofdysphoria was calculated using the CIDI-SF(Short Form Composite International Diagnostic Interview), with a cut-point of 3 on ascale of 0-10.16 This scale estimates a probable
psychiatric diagnosis of major depression andhas been validated in field settings and widelyused in population-based health surveys.

• Difficulty in Instrumental Activities of Daily
Living (IADL) included items on whether older
adults experienced any difficulty when performing the following seven activities: grocery
shopping, preparing meals, making phone
calls, taking medication, doing householdchores, managing finances, and getting
oneself to an otherwise unfamiliar location.
Each item response was coded as 0 = nodifficulty or 1 = any difficulty. Those whoreported trouble with any of these activities
for more than three months were labeled“faces difficulties.” Otherwise, they werecategorized as having “no difficulty.” Even
though IADLs may not require hands-on-personal assistance, difficulty in executing IADLs
may compromise independent living, which
could adversely affect life satisfaction.

• Self-rated health was coded as good, average,
and poor.
• Chronic morbidity was coded as: no condition,
single condition, or multiple conditions. LASI
collected information on whether an older
adult was ever diagnosed with hypertension
or high blood pressure, diabetes or high bloodpressure, cancer or malignant tumor, chroniclung diseases, chronic heart diseases, stroke,bone, or joint diseases, any neurological or
psychological problems, or high cholesterol.
Individuals having no diseases, any one ofthe diseases and two or more diseases werecategorized into “no condition”, “single
condition”, and “multiple conditions,”
respectively.
• Physical activity was coded as: physicallyinactive or physically active. Physical activity
was assessed based on WHO guidelines for
persons aged 18 and above.17 Older adults
who performed at least 75 minutes ofvigorous-intensity physical activity or atleast 150 minutes of moderate-intensity
physical activity in a day or a combinationof both were classified as “physically active.”
Otherwise, they were categorized as“physically inactive”.

• Covered by any health insurance was codedas yes, no.
Further, we considered the following household-related characteristics –• Based on recommendations for “better”
indicators of SES in LMICs18older adults’ SES
was assessed using the monthly per-capita
consumption expenditure (MPCE) quintile.
Sets of 11 and 29 questions on the expenditures on food and non-food items, respectively,
were used to canvass the sample households.
Food expenditure was collected based on areference period of seven days, while thenon-food expenditure was collected usingreference periods of 30 days and 365 days.19
Food and non-food expenditures have beenstandardized to the 30-day reference period.
The income quintile variable was divided into
five quintiles i.e., from poorest to richest.

• Difficulty in Activities of Daily Living (ADL)
included items on whether older adults
experienced any difficulty with the following
six activities: (a) walking across a room, (b)dressing, (c) bathing, (d) eating, (e) getting inand out of bed, and (f) toileting. Responses
for the six items (1 = yes; 0 = no) weresummed, with higher scores indicating more

• Religious affiliation was coded as Hinduism,
Islam, and Others.
134

Photo Jeet Dhanoa on Unsplash

World Happiness Report 2024

135

World Happiness Report 2024

coefficient of determination statistic (R2). The
DA gives the dominance statistic that denotes
the prediction power of each predictor, thepercentage variation in older adults’ life
satisfaction explained by each predictor andranks showing the relative importance of eachpredictor variable.26• Given the documented link between poorer
health and lower SES among certain castes,20
we also included respondent’s self-reported
social group, and categories were Scheduled
Tribe (ST), Scheduled Caste (SC), Other
Backward Class (OBC) and Others.
• The place of residence was coded as ruralor urban.

Our background checks showed that the regression
models did not violate the multicollinearity andheteroscedasticity assumptions. Standard errors
were corrected for weighting and clustering in all
estimations, given that LASI utilized a multistage
sampling strategy.

• The region of residence (south, north, central,western, eastern, and north-eastern) was also
included as a covariate in the analyses toassess regional disparities. This variable wasconstructed by including the 29 states andsix union territories of India during 2017-1821
into six categories based on administrative
similarity.

Results

Statistical Methods
We began by showing older adults’ absoluteand percentage distributions based on theirbackground characteristics. Bivariate analysis
examined variations in older adults’ average
life satisfaction score by their backgroundcharacteristics. Additionally, one-way ANOVA
tests were used to discern the difference inaverage life satisfaction scores across theindependent variables. Further, multivariablelinear regression analysis was used to examine
the associations between the life satisfaction
score and the independent variables. The partial
correlation coefficients in regression models
showed the difference in the life satisfaction
score of older adults belonging to a specificcategory compared to older adults from thereference category, given that the effect of all theother independent variables remains constant.22
Next, dominance analysis (DA)23 was used todetermine the relative importance of independent
variables in explaining the variation in the outcome variable.24 Here, we used the DA method
developed by Budescu with the older adults’ life
satisfaction score as the outcome variable.25
The DA computed sub-regression models withthe same outcome variable and different setsof independent variables to determine thecontribution of each predictor to the overallmodel prediction power denoted by the

Table 5.1 presents an overview of the socioeconomic and demographic characteristics ofthe study participants. The distribution reveals
that 11% of the older adults fell into the oldest-old
age group, with 59% categorized as young-old
and 30% as old-old. Approximately 53% of theparticipants were women. Notably, 56% had not
pursued formal education, while 21% had attained
secondary or higher education levels. In terms of
employment status, 7% were retired, while nearly
31% were currently working. Furthermore, 38%
were not currently married, and for 21% of theparticipants, religion held no importance.
Concerningly, 6% expressed dissatisfaction with
their current living arrangements. Instances of illtreatment were reported by 5% of respondents,
while 18% reported experiencing discrimination
at some point. Social inactivity affected almost
9% of participants, with a similar proportion
experiencing depression. Moreover, 23% rated
their health as poor, and 24% reported having
multiple health conditions. Physical inactivity wasprevalent among 72% of older adults, and 23%
faced difficulty with Activities of Daily Living(ADL), while 48% encountered challenges with
Instrumental Activities of Daily Living (IADL).
Alarmingly, only 18% had health insurance coverage.

136

World Happiness Report 2024

Table 5.1: Distribution of older adults by socio-demographic, health-related,and household characteristics in India 2017–19
Characteristics

All older adults
N

Characteristics%

All older adults
N

Age group%

Chronic morbidity status

Young-old

18,75560.9

No condition

14,079Old-old

8,898

28.9

Single condition

9,134

29.7

Oldest-old

3,142

10.2

Multiple conditions

7,582

24.6

Male

14,78548.0

Physically active

7,984

25.9

Female16,01052.0

Physically inactive

22,811

74.1

Gender

45.7

Physical activity status

Level of education

Difficulty in ADL

No formal education

16,514

53.6

Faces difficulty

6,295

20.4

Up to primary

7,417

24.1

No difficulty

24,50079.6

Secondary and above

6,864

22.3

Difficulty in IADL
Faces difficulty

13,35943.4

Currently working

8,861

28.8

No difficulty

17,436

56.6

Currently not working

10,558

34.3

Covered by health insurance

Never worked

8,751

28.4

No

24,355

79.1

Retired

2,625

8.5

Yes

6,440

20.9

Currently not married

11,14636.2

Poor

12,656

41.1

Currently married

19,64963.8

Not poor

18,13958.9

Not important

5,979

19.4

Hinduism

22,528

73.2

Very important

24,816

80.6

Islam

3,582

11.6

Others

4,685

15.2

Working status

Current marital status

Household income quintile

Importance of religion

Religion of household

Living arrangement satisfaction
Not satisfied

1,421

4.6

Caste of household

Neutral

5,248

17.0

SC-ST group

10,111

32.8

Satisfied

24,126

78.3

Non-SC-ST group

20,684

67.2

Received ill-treatment

Place of residence

Yes

1,267

4.1

Rural

20,38366.2

No29,528

95.9

Urban

10,41233.8

Faces discrimination in life

Country Region

Yes

4,750

15.4

Southern

7,291

23.7

No

26,045

84.6

Northern

7,726

25.1

Central

2,017

6.5

4,133

13.4
18.2

Social participation
Socially inactive

2,474

8.0

Western

Socially active

28,321

92.0

Eastern

5,601

North-eastern

4,027

13.1

Aggregate number

30,795100.0

Depression symptoms
Depressed

2,104

6.8

Not depressed

28,691

93.2

Self-rated health
Poor

6,686

21.7

Average

19,624

63.7

Good

4,485

14.6

Note: (a) N: Unweighted sample size, %: Unweighted percentage;(b) ADL: Activities of daily living, IADL: Instrumental activities of daily living.

137

World Happiness Report 2024

Fig. 5.1: Represents the bar plot for life satisfaction score by gender and marital statusamong older adults. In all age groups, unmarried women reported the lowest levels oflife satisfaction.
Mean LS score with 95% CI
Youngest-old

6.4

6.1

Old-old

6.5

6.4
6

Oldest-old

6.4

6.2

6.4
6

6.4

6.4

6.2

Gender Marital Status

Male and marriedFemale and married

Male and not marriedFemale and not married

Figure 5.1 represents the bar plot for life satisfaction score by gender and marital status amongolder adults. In all age groups, unmarried womenreported the lowest levels of life satisfaction.

In Table 5.2, we delve into the life satisfaction ofolder adults based on their backgrounds. Notably,
women consistently reported lower life satisfaction
than men, while those without formal education
tended to show significantly lower satisfaction
compared to their educated peers. Retirement
seemed to correlate with lower satisfaction levels,
contrasting with those still in the workforce.
Interestingly, unmarried individuals, those lessconcerned with religion, and those discontent
with their living situations displayed notably
lower satisfaction scores. Moreover, experiencesof ill-treatment or discrimination, social inactivity,
depression, poor self-rated health, and difficulties
with daily activities were all linked to diminishedlife satisfaction among older adults. Financial
status, caste, and rural residence also emerged as
influential factors, with those from the westernregion of India reporting the highest satisfactionlevels, followed by counterparts in the central,north-eastern, and northern regions.

138

World Happiness Report 2024

Table 5.2: Life satisfaction score summary of older adults by socio-demographic,
health-related, and household characteristics in India 2017-18
Characteristics

Life satisfaction score of older adults
Mean

SD

ANOVA statistic(p-value)

Young-old

6.3

2.4

1.17 (0.3111)

Old-old

6.3

2.4

Oldest-old

6.3

2.4

Characteristics

Age group

Life satisfaction score of older adults
Mean

SD

ANOVA statistic(p-value)

No condition

6.3

2.4

4.08 (0.0169)

Single condition

6.3

2.4

Multiple conditions

6.4

2.5

Chronic morbidity status

GenderPhysical activity status

Male

6.4

2.4

Female6.2

2.4

43.60 (<0.001)

No formal education

6.0

2.4

Faces difficulty

6.0

2.5

Up to primary

6.4

2.3

No difficulty

6.4

2.4

Secondary and above

7.0

2.3

Difficulty in IADL
Faces difficulty

6.0

2.4

6.2

2.4

No difficulty

6.6

2.4

Level of education

6.3

2.3

Physically inactive

6.3

2.4

139.50 (<0.001)

Currently not working

6.1

2.5

Covered by health insurance

Never worked

6.3

2.4

No

6.4

2.4

Retired

7.2

2.2

Yes

6.1

2.5

Currently not married

6.1

2.5

Poor

6.1

2.4

Currently married

6.4

2.4

Not poor

6.5

2.4

Not important

5.7

2.5

Hinduism

6.3

2.5

Very important

6.5

2.4

Islam

6.2

2.3

Others

6.5

2.2

3.8

2.6

Neutral

5.2

2.4

SC-ST group

6.1

2.3

Satisfied

6.7

2.2

Non-SC-ST group

6.4

2.4

Yes

5.0

2.7

Rural

6.1

2.4

No

6.4

2.4

Urban

6.7

2.4

Yes

5.5

2.5

Southern

6.0

2.6

No

6.5

2.4

Northern

6.3

2.3

Central

6.3

2.6

Socially inactive

5.9

2.8

Western

7.3

2.2

Socially active

6.4

2.4

Eastern

5.9

2.3

Depressed

5.2

2.7

Not depressed

6.4

2.4

Current marital status

98.13 (<0.001)

411.81 (<0.001)

43.08 (<0.001)

Household income quintile
162.34 (<0.001)

Importance of religion

163.82 (<0.001)

Religion of household
511.95 (<0.001)

Living arrangement satisfaction
Not satisfied

4.67 (0.0306)

Difficulty in ADL
438.18 (<0.001)

Working status
Currently working

Physically active

25.38 (<0.001)

Caste of household

1836.82 (<0.001)

Received ill-treatment

109.18 (<0.001)

Place of residence
373.07 (<0.001)

Faces discrimination in life

307.40 (<0.001)

Country Region
740.61 (<0.001)

Social participation
94.96 (<0.001)

Depression symptoms
492.64 (<0.001)

Self-rated health
Poor

5.6

2.6

Average

6.4

2.3

Good

6.9

2.3

465.96 (<0.001)

Note: (a) Mean: Average life satisfaction score, SD: Standard deviation;(b) ADL: Activities of daily living, IADL: Instrumental activities of daily living.

139

North-eastern

6.5

2.0

Overall life satisfaction score

6.3

2.4

214.00 (<0.001)

World Happiness Report 2024

Table 5.3: Multivariable association between life satisfaction and socio-demographic,
health-related and household characteristics of older adults in India 2017-18 from linear
regression models
Characteristics

Life satisfaction of older adults
Coef

95% CI

Characteristics

p-value

Life satisfaction of older adults
Coef

Age group

No condition(ref)

95% CI

p-value

Young-old(ref)Single condition-0.034(-0.095, 0.027)

0.274

Old-old

0.090(0.031, 0.149)

0.003

Multiple conditions

0.107(0.037, 0.177)

0.003

Oldest-old

0.241(0.150, 0.332)<0.001

Physical activity status(-0.046, 0.079)

0.614(0.209, 0.346)<0.001(-0.096, 0.021)

0.209(-0.284, -0.149)<0.001(0.137, 0.243)<0.001

GenderMale(ref)Female0.085(0.017, 0.153)

0.014

Physically active(ref)Physically inactive

0.016

Difficulty in ADL

Level of education

Faces difficulty(ref)No difficulty

0.278

No formal education(ref)

Up to primary

0.214(0.146, 0.283)<0.001

Difficulty in IADL

Secondary and above

0.697(0.616, 0.778)<0.001

Faces difficulty(ref)No difficulty-0.038Working status
Currently working(ref)Currently not working-0.023(-0.093, 0.047)

0.525

No

(ref)

Never worked

0.138(0.054, 0.222)

0.001

Yes

-0.217Retired

0.255(0.140, 0.370)<0.001

Household income quintile

Covered by health insurance

Current marital status
Currently not married(ref)Currently married

0.126(0.067, 0.185)<0.001(ref)Very important

0.218(0.153, 0.283)(ref)Not poor

0.190

Religion of household

Importance of religion
Not important

Poor<0.001

Living arrangement satisfaction

Hinduism

(ref)

Islam

0.023

(-0.062, 0.107)

0.601

Others0.012(-0.093, 0.117)

0.821(0.134, 0.258)<0.001(-0.017, 0.111)

0.150

Caste of household

Not satisfied(ref)SC-ST group(ref)Neutral

1.107(0.984, 1.229)<0.001

Non-SC-ST group

0.196

Satisfied

2.270(2.154, 2.385)<0.001

Place of residence

Received ill-treatment
Yes

(ref)

No

0.368

(0.248, 0.488)<0.001

(ref)

No

0.511

(0.440, 0.582)<0.001Social participation
Socially inactive(ref)Socially active

0.086(-0.007, 0.178)

0.071

Depression symptoms
Depressed(ref)Not depressed

0.647(0.553, 0.741)<0.001

(ref)

Urban

0.047

Country Region

Faces discrimination in life
Yes

Rural

Southern(ref)Northern

0.266(0.187, 0.345)<0.001

Central

0.647(0.540, 0.754)<0.001

Western

1.327(1.241, 1.413)<0.001

Eastern

0.277(0.197, 0.357)<0.001

North-eastern

0.485(0.327, 0.644)<0.001

Adjusted R-squared

0.209

Analytical sample size

30,795

Self-rated health
Poor

(ref)

Average

0.388(0.322, 0.454)<0.001

Good

0.521

(0.427, 0.616)<0.001Chronic morbidity status

Note: (a) Coef: Partial correlation coefficients, CI: Confidence Interval, (ref): Reference category;(b) ADL: Activities of daily living, IADL: Instrumental activities of daily living.

140

World Happiness Report 2024

Moreover, individuals who reported satisfaction
with their current living arrangements(Coefficient: 2.218), those who did not experience
ill treatment (Coefficient: 0.368) or discrimination(Coefficient: 0.511), those who were not depressed(Coefficient: 0.647), and those who rated theirhealth as good (Coefficient: 0.521) all exhibited
significantly higher likelihoods of having higher
life satisfaction scores. Furthermore, older adults
from non-poor households (Coefficient: 0.190),
Non-Scheduled Caste/Tribe backgrounds(Coefficient: 0.196), and those residing in westernregions of India (Coefficient: 1.327) also
demonstrated significantly higher likelihoods ofreporting higher life satisfaction scores comparedto their respective counterparts from otherdemographic and socioeconomic backgrounds.

Photo Ashwini Chaudhary Monty on Unsplash

In Table 5.3, we present the multivariable regression estimates adjusting for various socioeconomic
and demographic characteristics to determine
their associations with life satisfaction amongolder adults. Our findings indicate that certain
demographic and socioeconomic factors are
significantly associated with higher life satisfaction
scores. Specifically, older adults categorized as‘oldest-old’ exhibited a significantly greater
likelihood of higher life satisfaction compared tothose classified as ‘young-old’ (Coefficient: 0.241).
Additionally, older women, individuals withsecondary education or higher, currently married
respondents, and those who considered religion
as very important demonstrated a significantly
higher likelihood of reporting higher life satisfaction
scores compared to their respective counterparts.

141

World Happiness Report 2024

Fig. 5.2: Contribution (%) of independent variable to overall variation in Life Satisfaction
among older adults in India during 2017–18

Contribution to Life satisfaction score (%)
Living arrangement satisfaction

48.2

Self-rated health

9.1

Faces discrimination in life

7.6

Levels of education

7.5

Importance of religion

5.4

Depression symptoms

5.0

Received ill-treatment

2.9

Difficulty in IADL

2.6

Urban

2.3

Working status

1.7

Household income quintile

1.3

Country region

1.2

Current marital status

1.2

Caste of household

1.1

Covered by health insurance

1.0

Social participation

0.5

Difficulty in ADL

0.4

Gender

0.3

Age group

0.2

Chronic morbidity status

0.2

Physical activity status

0.1

Religion of household

0.1

Figure 5.2 shows the relative importance ofindependent variables in predicting the life
satisfaction score of older adults. The independent
variables collectively explained away 15.8% of
the variation in life satisfaction. We observedthat satisfaction with living arrangements (rank 1),
self-rated health (rank 2) and perceived discrimination (rank 3) were the top 3 predictors of life
satisfaction among older adults. These threevariables accounted for 65% of the variation inolder adults’ life satisfaction scores. On thecontrary, the dominance analysis output showedthat gender, age, morbidity status, physicalactivity and religion ranked 18th, 19th, 20th, 21st,and 22nd (of the 22 independent variables) in“their influence on life satisfaction amongolder Indians. These relatively non-significant
life satisfaction score predictors accounted
for 0.9% of the predicted variance.

Discussion
Subjective well-being (SWB) can be characterized
as a favorable assessment of one’s life coupledwith positive affect. In the field of gerontology,142

World Happiness Report 2024

overall SWB is commonly assessed using
evaluations of happiness, self-esteem, and life
satisfaction. While happiness usually reflects theemotional facet of SWB, self-esteem and life
satisfaction capture the cognitive appraisal of
one’s sense of self and life overall.27
Gender and Life Satisfaction
Empirical research on gender and life satisfaction
in later life has yielded mixed findings.28 Somestudies have found older women to report higher
life satisfaction than older men,29 others havefound older women to report lower life
satisfaction than their male peers,30 and yetothers observed no significant gender differencein life satisfaction.31 In the present study, we findthat older women in India have higher levels oflife satisfaction than older men.
This is somewhat surprising given that womenare exposed to more everyday life stressors(e.g., workplace discrimination; secondary social
status within families and society at large) andare considered to be more susceptible to them.32
Some studies33 have found women’s disadvantages
in health contribute towards their lower life
satisfaction compared to older men.34 For
instance, although women outlive men, they havelower levels of mental and physical health and agreater burden of later life sickness and disability,
possibly lowering their satisfaction with life.35
Previous research has identified different factorsthat impact life satisfaction disparately for womenand men. One study36 found monthly income toaffect life satisfaction only among older womenwhile others found that having adequate income
affected the life satisfaction among both womenand men.37 Likewise, research has reported on therole’s education, marital status, religion, depression,and physical activity and exercise play in genderdifferentials in life satisfaction.38

In the present study, we findthat older women in India havehigher levels of life satisfaction
than older men.

One factor that may contribute to older women’s
greater life satisfaction relative to older men issocial resources, namely social support. Women,
in general, possess wider and more diverse social
networks, including a greater number of friends
and confidants,39 which likely translates into not
only more social support but diverse forms of it.
Men, alternatively, report fewer intimate social
relationships and mostly rely on their spouses forsupport with far fewer people they deem to be
their confidants.40 Older women in India, especiallyof older cohorts, also may have invested more inbuilding and maintaining family ties (e.g., organizing
gatherings, writing holiday and birthday cards,
doing physical and emotional care work) whileolder Indian men may have shouldered the
responsibility of building family finances.41
Gendered division of labor of this nature may
explain differences in support later in life, which
may reflect gender differences in life satisfaction
with older women reporting to be more satisfied
with life than their older male peers.
Age and Life Satisfaction
Given the social, functional, and cognitive losses
tied to aging, the general public, including older
adults themselves believe that life satisfaction
decreases with advancing age.42 Yet, a number of
empirical studies — both cross-sectional andlongitudinal — have shown that life satisfaction
either rises or stays constant as one ages.43 For
instance, a meta-analysis of data from 145 countries
reports a U-shaped linkage between age and life
satisfaction in most countries, including 109
developing nations.44
In the present study, we find some interesting
patterns. In the bivariate analysis, we find thatlife satisfaction was not associated with age ofolder adults. Other studies45 have found similar
patterns, where over 6 years, the overall levelof life satisfaction declined among those aged
80 and above. Likewise, other authors havefound that life satisfaction decreased among
participants in their late 70s and 80s.46 Thismay be because with increasing age, individuals
experience increasing rates of disease andfunctional deficits,47 declining social and physicalactivity,48 increasing sensory limitations49 and143

World Happiness Report 2024

problems with memory, attention, and othercognitive functions.50 Moreover, aging also isaccompanied by loss of social roles, friends, andfamily. Such experiences can erode an individual’s
sense of self-efficacy and self-worth. Reduced
psychological resources of such nature, in turn,can lead to disengagement, depression, andapathy, all likely manifested in lowered satisfaction
with one’s life.51
That said, at the multivariate level, upon controlling
several conceptually relevant covariates, wefound that increased age was accompanied
by higher levels of life satisfaction. Several
explanations have been offered to explain thisfinding, where despite the functional, physical,
social, and cognitive losses, increasing age isaccompanied by increasing life satisfaction.
Mirowsky’s “age as maturity” hypothesis52 suggeststhat with age, people become experienced,
accomplished, and mature, which translates into
lower frustration, fewer negative emotions, less
emptiness, and more life satisfaction. Based on
Baltes and Baltes’ (1990) selective optimization
with compensation theory,53 some have argued
that life satisfaction increases with age because
older adults adopt accommodative strategies tomaximize the gains and minimize the deficits,
which help sustain or even improve satisfaction
with life. Similarly, Carstensen’s (1999; 2006)
socioemotional selectivity theory postulates thatas people become more aware that time islimited, they learn to regulate their emotions,savor the most valuable moments in everyday life,and surround themselves with close friends andfamily, all of which may help sustain high levels of
subjective well-being.54 Relatedly, it is possible
that, as found in one previous study,55 individuals
in this age group have acclimated to major lifetransitions, such as retirement, and are investingin personally and socially fulfilling activities, which
may improve life satisfaction.
We also find that a greater proportion of older
adults in our sample report being married andsocially active, which may mean greater socialand emotional support and reduced risk forloneliness, which remains a major risk factor for
diminishing health and well-being.56 Similarly,
good mental and physical health also mean

Taken together, the patternfound in this study surrounding
age and life satisfaction refutes
some claims that the positive
association between age andlife satisfaction only exists inhigh-income nations while
life satisfaction declines withadvancing age in countries that aresocioeconomically constrained.
higher life satisfaction57 and a greater proportionof older Indians in our study report not being
depressed, having no difficulty in carrying out
daily living activities, and either have no chronic
illnesses or a single condition. Most also reported
facing no discrimination or ill-treatment andbeing satisfied with their living situations. Recent
studies have found older Indians to express astrong desire to “age in place,” as this may reflect
the human urge to preserve autonomy, independence, and social bonds.58 Unsurprisingly then,satisfaction with living arrangement emerges tobe the highest contributory factor to life
satisfaction in this study and this matches recent
Indian studies, which find that living conditions
and being satisfied with those conditions areconsequential for later-life health.59
Taken together, the pattern found in this study
surrounding age and life satisfaction refutes someclaims that the positive association between age
and life satisfaction only exists in high-income
nations while life satisfaction declines withadvancing age in countries that are socioeconomically constrained.60 Our finding on age
and life satisfaction also corroborates the findings
in Chapter 2 of this report, which reveals an
overall improvement in the levels of life satisfaction
at higher ages in the global sample of older adults
and among those living in South Asian countries.
One way of extending the present research is toconsider additional dimensions of subjective
well-being. Although unlike several prior studies
that are limited to single-item measurement of life144

World Happiness Report 2024

better cognitive health.68 Diverse sources of social
support and strong cognitive function may beconsequential for older adults’ life satisfaction.
Though grounded in prior research, these
suppositions remain to be empirically tested
within the context of aging in India.

satisfaction, we were able to measure this important
marker of SWB using a multiple-item scale, future
research replicating this work should consider
employing multiple measures of subjective
well-being, including happiness.61 62
Educational Differences in Life Satisfaction

Caste and Life Satisfaction

We found a significant association between
educational status and life satisfaction amongolder adults in India. Older Indians with higher
levels of education were significantly moresatisfied with life compared to their peers without
any formal education. Several social, health,and demographic factors could explain the
educational differences in life satisfaction amongolder Indians.
For instance, while education may have anappreciable impact on quality of life, at least
partly, through its link to material assets, such as
employment and income, it also “develops habits,
skills, resources, and abilities that enable people
to achieve a better life,”63 ultimately impacting life
satisfaction. That education is positively associatedwith life satisfaction, in fact, is found in previous
studies as well.64

Like in the case of social class, caste candetermine access to multiple flexible resources,
including knowledge, power, prestige, andmainstream social connections.69 These flexible
resources, often available to higher social caste
individuals, are consequential throughout the life
course and particularly in later life because they
can be mobilized to avoid risks, deploy protective
strategies, and preserve and promote health andwell-being.70
In the present study, we find a significant difference
between the SC/ST and non-SC/ST groups.
Compared to the SC/ST group, older Indians who
belong to the non-SC/ST group were moresatisfied with their life. At the multivariate level
as well, those in the non-SC/ST group reported
higher life satisfaction than their SC-ST peers,

Recent research in India also highlights that lackof education can decrease health care utilizationand increase the likelihood of mental and physicalillnesses,65 which can negatively affect satisfaction
with life in older ages. The educational differencesin life satisfaction among older Indians in ourstudy also could be interpreted in the light ofrecent research findings in India and otherlow- and middle-income countries that older
adults with lower education endure a higher
risk of depression than their peers with higher
education.66
Future studies should consider mechanisms
connecting educational attainment and life
satisfaction among older Indians. One potential
mechanism could be that those with moreeducation have a more diverse social network,67
which may translate into more diverse forms of
support. Diversity in social networks also maymean interactions with different types of individuals
and more diverse social activities, both of which
could promote higher order processing leading to

Photo Ashwini Chaudhary Monty on Unsplash

145

Photo Kalai Venthan Gopal on Unsplash

World Happiness Report 2024

146

World Happiness Report 2024

Conclusion

though the overall size of difference diminishedafter controlling for several social structural
factors, including education, perceived
discrimination, and experiences of ill-treatment.
Corroborating previous studies, we find that thevariations in life satisfaction between castes werestrongly related to education. Satisfaction with
one’s life often is the result of the cumulativeadvantages, which are influenced by education,
both directly and indirectly through its linkage
with social and economic resources of income,power, wealth, and mainstream social
connections. In addition to education, we alsofind that older Indians who never experience
discrimination or ill-treatment are more satisfied
with their lives and that experiences of discrimination and ill-treatment contribute significantly tothe caste-based discrepancies in life satisfaction.
These findings are not surprising given theenduring hold caste has on the lives of people inIndia.71 There is ample research on caste-based
discrepancies in financial distress, lack of accessto quality health care and social services.72 Given
the stigma attached to lower social caste, studies
also have found that SC/ST groups are significantly less likely to seek treatment for healthconditions than their non-SC/ST counterparts.73
One recent study found that various factors suchas education, perceived social standing, andsatisfaction with one’s living arrangements andplace of residence contributed to the caste
disparities in life satisfaction among older
Indians.74 Aside from this, research points out thepsychological toll that stigma takes on those oflower social caste groups. Those socially deprived
may experience reduced sense of self-worth andself-efficacy, increased feelings of anger, anxiety,depression, and envy, and withdrawal from social
interactions and activities.75 This ultimately cannegatively affect satisfaction with life.76

The present study substantially contributes tothe literature on later-life subjective well-being
in India. And we do so by employing a sizable,
heterogenous, nationally representative sampleof older Indians. We found that older men, thosein the higher age groups, currently married,
and those who were educated report higher life
satisfaction compared to their respective peers.
Lower satisfaction with living arrangements,
perceived discrimination, and poor self-rated
health were important factors associated withlow life satisfaction among older Indians. Findingsof this study indicate that strengthening family
networks to ensure a comfortable living
arrangement for older adults, men, widowed,
and those without formal education in particular,
and bolstering social networks to reduce
discrimination may enhance well-being in older age.

Corroborating previous studies,
we find that the variations in life
satisfaction between castes werestrongly related to education.
147

World Happiness Report 2024

Endnotes1

See United Nations (2020)2	The average growth rate for Indians aged 60 and above is
300% higher than the overall population growth rate inIndia. See Agarwal et al. (2016) and Arokiasamy (2016)
3

See Papi & Cheraghi (2021)

4	See Diener et al. (1999), Mroczek & Spiro III (2005) andPark & Kang (2022)
5

See Vijayakumar et al. (2016)

6	See Chia et al. (2023), Selivanova & Cramm (2014) and Wu
et al. (2016)7

See Papi et al. (2019)8	See Mroczek & Spiro III (2005), Park & Kang (2022) andSrivastava et al. (2022)
9

See Khodabakhsh (2022)

10	See Muhammad & Joy (2022), Nagargoje et al. (2022),
Muhammad et al. (2023) and Pai et al. (2023).
11	The University of Southern California (USC), the Harvard
School of Public Health, and the International Institute forPopulation Sciences (IIPS) collaborated to conduct thisnationally representative survey.
12	See The Longitudinal Ageing Study in India 2017-18- National Report (2020)13	In rural areas, the survey used a three-stage sample
strategy, whereas in urban areas, a four-stage sampling
design was used. The first stage in each state/UT requiredchoosing Primary Sampling Units (PSUs), which are
sub-districts (Tehsils/Talukas), and the second stage
involved choosing villages in rural regions and wards inurban areas within the chosen PSUs. In the third step,households were chosen from various settlements in rural
areas. Sampling in metropolitan areas required an additional measure. Specifically, one Census Enumeration Block(CEB) was chosen at random in each urban area in thethird stage. Households from this CEB were chosen in the
fourth stage. In the survey report, the detailed methodology was released, together with complete information on
the survey design and data collection (30). Additional
information about the survey instruments, fieldwork, datacollecting and processing, response rates, and sample
design is accessible to the public elsewhere.15
14 See Diener et al. (1985, 1999, 2009)15	This scale showed a high degree of internal consistency.
Cronbach’s alpha value: 0.90
16 See Kessler et al. (1998) and Trainor et al. (2013)22 See Cameron & Trivedi (2005)23	The partial correlation coefficient (PCC) from the regression model and the General Dominance analysis estimates
are two different measures. In simple terms, the PCC
displays the strength of association between the life
satisfaction score and independent variable(s). However,the DA reveals which independent variable is more
important than others in explaining the life satisfaction
score of older adults.
24 See Budescu (1993) and Grömping (2007)
25 See Budescu (1993) and Luchman (2021)
26 See Luchman (2021)
27 See Plouffe (2010) and Rosenberg (1979).
28 See Kim et al. (2021)
29	See Camacho et al. (2019), Kim et al. (2021), Macia et al.
(2015) and Muhammad & Joy (2022)
30	See Chen et al. (2022a, 2022b), Cheng & Chan (2006) andOkabayashi et al. (2019)31 See Meggiolaro & Ongaro (2015)32	See Bird & Rieker (2008), McDonough & Walters (2001)
and McLeod & Kessler (1990)
33	See Chen et al. (2022), Cheng & Chan (2006) and Tobiasz-Adamczyk et al. (2017)34	See Bramhankar et al. (2023), Camacho et al. (2019), Lee &Williams (2023), Mandi & Bansod (2023), Mekonnen et al.
(2022) and Pai et al. (2023)
35	See Freedman et al. (2016), J. Lee et al. (2021) and Whitson
et al. (2010)
36 See Kim et al. (2021)
37 See S.-H. Lee et al. (2020)
38 See Kim et al. (2021) and S.-H. Lee et al. (2020)
39 See Paskulin & Vianna (2007) and Shye et al. (1995)
40 See Antonucci & Akiyama (1987)
41	See Sharma et al. (2016), Silverstein et al. (2006) andUgargol & Bailey (2018)
42 See Lacey et al. (2006)43	See Berg et al. (2009), Carstensen et al. (2011), Diener &Ryan (2009), Hansen & Blekesaune (2022), Schilling(2006), Siedlecki et al. (2008) and Wettstein et al. (2016,2022)

17 See WHO (2020)44	See Blanchflower (2021), Blanchflower & Oswald (2008),
Hansen & Blekesaune (2022)

18 See Hu et al. (2017)45 See Berg et al. (2009)19	See The Longitudinal Ageing Study in India 2017-18- National Report (2020)

46 See Enkvist et al. (2012) and Mroczek & Spiro III (2005)

20 See Borooah (2018) and Nayar (2007)
21	Since then, the Indian state of Jammu and Kashmir has
been divided to form two union territories of J&K and
Ladakh.

47 See Freedman & Martin (1998) and Maresova et al. (2019)
48 See Milanović et al. (2013)49 See Cavazzana et al. (2018)
50 See Murman (2015)148

World Happiness Report 2024

51 See Borg et al. (2008)
52 See Mirowsky & Ross (1992)
53 See Baltes & Baltes (1990)
54 See Carstensen et al. (2003)
55 See de Grip et al. (2012)
56 See National Institute on Aging (2019)57 See Puvill et al. (2016)
58 See Muhammad et al. (2021) and Puvill et al. (2016)
59	See Kandapan et al. (2023) and Srivastava & Shaw, et al.
(2021)
60	See Deaton, (2008), Hansen & Blekesaune (2022), Morgan
et al. (2015) and Swift et al. (2014)61	Though used interchangeably, happiness and life satisfaction are distinct with the former gauging transient experiences and the latter used to appraise whether one’s life has
been successful overall
62	Some also have argued in favor of assessing multiple
domains of life satisfaction, including satisfaction witheconomic status, housing, health, neighborhood, social
networks, and family relationships
63 See Ross & Mirowsky (2010)64	See Berg et al. (2009), Cho et al. (2015), Kim et al. (2021)
and Ngoo et al. (2015)
65	See Roy et al. (2020), Srivastava & Purkayastha, et al.
(2021)
66 See Brinda et al. (2016) and Saravanakumar et al. (2022)
67 See Fischer & Beresford (2015)
68	See Bishop (2013), Henderson et al. (2022), Prior et al.
(2022) and Rhee et al. (2021)
69 See Hatzenbuehler et al. (2013) and Link & Phelan (1995)
70 See Hatzenbuehler et al. (2013) and Link & Phelan (1995)
71 See Borooah (2018), Chalam (2007) and Nayar (2007)
72 See Li et al. (2009) and Zhang (2015)
73 See Dey et al. (2012)
74 See Muhammad et al. (2022)
75 See Johri & Anand (2022)
76	Given that LASI only collected data from community-dwelling adults, factors affecting life satisfaction among thoseresiding in formal care institutions, including assisted living
facilities and nursing homes remain to be determined. In
addition to social group status, such as social caste, future
work should consider the additional stressors faced bythose older adults not being able to “age in place.”

149

World Happiness Report 2024

References
Agarwal, A., Lubet, A., Mitgang, E., Mohanty, S., & Bloom, D. E.
(2016). Population Aging in India: Facts, Issues, and Options(SSRN Scholarly Paper 2834212). https://doi.org/10.2139/
ssrn.2834212

Budescu, D. V. (1993). Dominance analysis: A new approachto the problem of relative importance of predictors in
multiple regression. Psychological Bulletin, 114(3), 542–551.
https://doi.org/10.1037/0033-2909.114.3.542

Antonucci, T. C., & Akiyama, H. (1987). An examination ofsex differences in social support among older men andwomen. Sex Roles: A Journal of Research, 17(11–12), 737–749.
https://doi.org/10.1007/BF00287685

Camacho, D., Lee, Y., Bhattacharya, A., Vargas, L. X., Kimberly,
L., & Lukens, E. (2019). High Life Satisfaction: Exploring theRole of Health, Social Integration and Perceived Safety among
Mexican Midlife and Older Adults. Journal of Gerontological
Social Work, 62(5), 521–542. https://doi.org/10.1080/01634372.
2019.1621975

Arokiasamy, P. (2016). G. Giridhar, K. M. Sathyanarayana,
Sanjay Kumar, K. S. James and Moneer Alam (eds), Population
Ageing in India, Cambridge University Press, Cambridge, 2014,
Hardback, 250 pp., ISBN 13: 9781107073326. Ageing & Society,36(2), 445–447. https://doi.org/10.1017/S0144686X15001300
Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives
on successful aging: The model of selective optimization withcompensation. In Successful aging: Perspectives from thebehavioral sciences (pp. 1–34). Cambridge University Press.
https://doi.org/10.1017/CBO9780511665684.003
Berg, A. I., Hoffman, L., Hassing, L. B., McClearn, G. E., &Johansson, B. (2009). What matters, and what matters most,for change in life satisfaction in the oldest-old? A study over
6 years among individuals 80+. Aging & Mental Health, 13(2),
191–201. https://doi.org/10.1080/13607860802342227
Bird, C. E., & Rieker, P. P. (2008). Gender and health: The
effects of constrained choices and social policies (pp. xv, 256).
Cambridge University Press.
Bishop, J. (2013). Networked: The New Social Operating
System. International Journal of E-Politics, 4, 64–66.
https://doi.org/10.4018/jep.2013040106
Blanchflower, D. G. (2021). Is happiness U-shaped
everywhere? Age and subjective well-being in 145 countries.
Journal of Population Economics, 34(2), 575–624.
https://doi.org/10.1007/s00148-020-00797-z
Blanchflower, D. G., & Oswald, A. J. (2008). Is well-being
U-shaped over the life cycle? Social Science & Medicine (1982),
66(8), 1733–1749. https://doi.org/10.1016/j.socscimed.
2008.01.030
Borg, C., Fagerström, C., Balducci, C., Burholt, V., Ferring, D.,
Weber, G., Wenger, C., Holst, G., & Hallberg, I. R. (2008). Lifesatisfaction in 6 European countries: The relationship tohealth, self-esteem, and social and financial resources amongpeople (Aged 65-89) with reduced functional capacity.
Geriatric Nursing (New York, N.Y.), 29(1), 48–57.
https://doi.org/10.1016/j.gerinurse.2007.05.002
Borooah, V. (2018). Caste, religion, and health outcomes inIndia, 2004-14. Economic and Political Weekly, 53, 65–73.
Brinda, E. M., Rajkumar, A. P., Attermann, J., Gerdtham, U. G.,
Enemark, U., & Jacob, K. S. (2016). Health, Social, and Economic
Variables Associated with Depression Among Older People in
Low and Middle Income Countries: World Health Organization
Study on Global AGEing and Adult Health. The American
Journal of Geriatric Psychiatry, 24(12), 1196–1208.
https://doi.org/10.1016/j.jagp.2016.07.016

Cameron, A., & Trivedi, P. (2005). Microeconometrics: Methods
and Applications. https://doi.org/10.1017/CBO9780511811241
Carstensen, L. L., Fung, H. H., & Charles, S. T. (2003). Socioemotional Selectivity Theory and the Regulation of Emotion in
the Second Half of Life. Motivation and Emotion, 27(2), 103–123.
https://doi.org/10.1023/A:1024569803230
Carstensen, L. L., Turan, B., Scheibe, S., Ram, N., Ersner-Hershfield,
H., Samanez-Larkin, G. R., Brooks, K. P., & Nesselroade, J. R.
(2011). Emotional Experience Improves With Age: Evidence
Based on Over 10 Years of Experience Sampling. Psychology
and Aging, 26(1), 21–33. https://doi.org/10.1037/a0021285
Cavazzana, A., Röhrborn, A., Garthus-Niegel, S., Larsson, M.,
Hummel, T., & Croy, I. (2018). Sensory-specific impairment
among older people. An investigation using both sensory
thresholds and subjective measures across the five senses.
PloS One, 13(8), e0202969. https://doi.org/10.1371/journal.
pone.0202969
Chalam, K. S. (2007). Caste—Based Reservations and Human
Development in India (First Edition). Sage India.
Chen, M., Fu, Y., & Chang, Q. (2022). Life satisfaction amongolder adults in urban China: Does gender interact withpensions, social support and self-care ability? Ageing & Society,42(9), 2026–2045. https://doi.org/10.1017/
S0144686X20001877
Cheng, S.-T., & Chan, A. C. M. (2006). Relationship with others
and life satisfaction in later life: Do gender and widowhood
make a difference? The Journals of Gerontology. Series B,
Psychological Sciences and Social Sciences, 61(1), P46-53.
https://doi.org/10.1093/geronb/61.1.p46
Chia, F., Huang, W.-Y., Huang, H., & Wu, C.-E. (2023). Promoting
Healthy Behaviors in Older Adults to Optimize Health-Promoting
Lifestyle: An Intervention Study. International Journal ofEnvironmental Research and Public Health, 20(2), 1628.
https://doi.org/10.3390/ijerph20021628
Cho, J., Martin, P., Poon, L. W., & Georgia Centenarian Study.
(2015). Successful aging and subjective well-being among
oldest-old adults. The Gerontologist, 55(1), 132–143.
https://doi.org/10.1093/geront/gnu074
de Grip, A., Dupuy, A., Jolles, J., & van Boxtel, M. (2012).
Retirement and Cognitive Development: Are the Retired
Really Inactive? (SSRN Scholarly Paper 2149479).
https://doi.org/10.2139/ssrn.2149479

150

World Happiness Report 2024

Henderson, A. K., Walsemann, K. M., & Ailshire, J. A. (2022).
Religious Involvement and Cognitive Functioning at theIntersection of Race-Ethnicity and Gender Among Midlife
and Older Adults. The Journals of Gerontology. Series B,
Psychological Sciences and Social Sciences, 77(1), 237–248.
https://doi.org/10.1093/geronb/gbab034

Deaton, A. (2008). Income, Health, and Well-Being around
the World: Evidence from the Gallup World Poll. Journal of
Economic Perspectives, 22(2), 53–72. https://doi.org/10.1257/
jep.22.2.53
Dey, S., Nambiar, D., Lakshmi, J. K., Sheikh, K., & Reddy, K. S.
(2012). Health of the Elderly in India: Challenges of Access andAffordability. In Aging in Asia: Findings From New andEmerging Data Initiatives. National Academies Press (US).
https://www.ncbi.nlm.nih.gov/books/NBK109208/

Hu, P., Wang, S., & Lee, J. (2017). Socioeconomic Gradients of
Cardiovascular Risk Factors in China and India: Results fromthe China Health and Retirement Longitudinal Study andLongitudinal Aging Study in India. International Journal of
Public Health, 62(7), 763–773. https://doi.org/10.1007/
s00038-017-0968-3

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The
Satisfaction With Life Scale. Journal of Personality Assessment,
49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13

Johri, A., & Anand, P. V. (2022). Life Satisfaction andWell-Being at the Intersections of Caste and Gender in India.
Psychological Studies, 67(3), 317–331. https://doi.org/10.1007/
s12646-022-00667-6

Diener, E., Oishi, S., & Lucas, R. E. (2009). Subjective
Well-Being: The Science of Happiness and Life Satisfaction.
In S. J. Lopez & C. R. Snyder (Eds.), The Oxford Handbook of
Positive Psychology (pp. 186–194). Oxford University Press.
https://doi.org/10.1093/oxfordhb/9780195187243.013.0017

Kandapan, B., Pradhan, J., & Pradhan, I. (2023). Living
arrangement of Indian elderly: A predominant predictorof their level of life satisfaction. BMC Geriatrics, 23(1), 88.
https://doi.org/10.1186/s12877-023-03791-8

Diener, E., & Ryan, K. (2009). Subjective well-being: A general
overview. South African Journal of Psychology, 39(4), 391–406.
https://doi.org/10.1177/008124630903900402

Kessler, R. C., Andrews, G., Mroczek, D., Ustun, B., & Wittchen,
H.-U. (1998). The World Health Organization Composite
International Diagnostic Interview short-form (CIDI-SF).
International Journal of Methods in Psychiatric Research, 7(4),171–185. https://doi.org/10.1002/mpr.47

Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999).
Subjective well-being: Three decades of progress. Psychological
Bulletin, 125(2), 276–302. https://doi.org/10.1037/00332909.125.2.276
Enkvist, A., Ekström, H., & Elmståhl, S. (2012). What factors
affect life satisfaction (LS) among the oldest-old? Archivesof Gerontology and Geriatrics, 54(1), 140–145.
https://doi.org/10.1016/j.archger.2011.03.013

Khodabakhsh, S. (2022). Factors Affecting Life Satisfactionof Older Adults in Asia: A Systematic Review. Journal ofHappiness Studies, 23(3), 1289–1304. https://doi.org/10.1007/
s10902-021-00433-x

Fischer, C. S., & Beresford, L. (2015). Changes in Support
Networks in Late Middle Age: The Extension of Gender and
Educational Differences. The Journals of Gerontology Series B:
Psychological Sciences and Social Sciences, 70(1), 123–131.
https://doi.org/10.1093/geronb/gbu057

Kim, J., Lee, M., & Dan, H. (2021). Gender Differences in Factors
Affecting Life Satisfaction of the Elderly with Multimorbidity in
Korea. Nursing Reports, 11(1), 54–63. https://doi.org/10.3390/
nursrep11010006

Freedman, V. A., & Martin, L. G. (1998). Understanding trends
in functional limitations among older Americans. American
Journal of Public Health, 88(10), 1457–1462.

Lacey, H. P., Smith, D. M., & Ubel, P. A. (2006). Hope I Die
before I Get Old: Mispredicting Happiness Across the Adult
Lifespan. Journal of Happiness Studies, 7(2), 167–182.
https://doi.org/10.1007/s10902-005-2748-7

Freedman, V. A., Wolf, D. A., & Spillman, B. C. (2016).
Disability-Free Life Expectancy Over 30 Years: A Growing
Female Disadvantage in the US Population. American Journalof Public Health, 106(6), 1079–1085. https://doi.org/10.2105/
AJPH.2016.303089

Lee, J., Meijer, E., Phillips, D., & Hu, P. (2021). Disability Incidence
Rates for Men and Women in 23 Countries: Evidence on Health
Effects of Gender Inequality. The Journals of Gerontology.
Series A, Biological Sciences and Medical Sciences, 76(2),328–338. https://doi.org/10.1093/gerona/glaa288

Grömping, U. (2007). Estimators of Relative Importancein Linear Regression Based on Variance Decomposition.
The American Statistician, 61(2), 139–147.
https://doi.org/10.1198/000313007X188252

Lee, S.-H., Lee, J., & Choi, I. (2020). Life Satisfaction in
Later Life: The Interplay of Marital Condition and Income
among Elderly Koreans. Sustainability, 12(8), Article 8.
https://doi.org/10.3390/su12083483

Hansen, T., & Blekesaune, M. (2022). The age and well-being“paradox”: A longitudinal and multidimensional reconsideration.
European Journal of Ageing, 19(4), 1277–1286.
https://doi.org/10.1007/s10433-022-00709-y

Li, L. W., Zhang, J., & Liang, J. (2009). Health among theOldest-Old in China: Which Living Arrangements Make a
Difference? Social Science & Medicine (1982), 68(2), 220–227.
https://doi.org/10.1016/j.socscimed.2008.10.013

Hatzenbuehler, M. L., Phelan, J. C., & Link, B. G. (2013). Stigma
as a Fundamental Cause of Population Health Inequalities.
American Journal of Public Health, 103(5), 813–821.
https://doi.org/10.2105/AJPH.2012.301069

Link, B. G., & Phelan, J. (1995). Social conditions as fundamental
causes of disease. Journal of Health and Social Behavior,Spec No, 80–94.
Luchman, J. N. (2021). Determining relative importancein Stata using dominance analysis: Domin and domme.
The Stata Journal, 21(2), 510–538. https://doi.
org/10.1177/1536867X211025837

151

World Happiness Report 2024

Macia, E., Duboz, P., Montepare, J. M., & Gueye, L. (2015).
Exploring Life Satisfaction Among Older Adults in Dakar.
Journal of Cross-Cultural Gerontology, 30(4), 377–391.
https://doi.org/10.1007/s10823-015-9275-8

Nagargoje, V. P., James, K. S., & Muhammad, T. (2022).
Moderation of marital status and living arrangements in therelationship between social participation and life satisfaction
among older Indian adults. Scientific Reports, 12(1), 20604

Maresova, P., Javanmardi, E., Barakovic, S., Barakovic Husic, J.,
Tomsone, S., Krejcar, O., & Kuca, K. (2019). Consequences ofchronic diseases and other limitations associated with old age– a scoping review. BMC Public Health, 19(1), 1431.
https://doi.org/10.1186/s12889-019-7762-5

Nayar, K. R. (2007). Social exclusion, caste & health: A review
based on the social determinants framework. The Indian
Journal of Medical Research, 126(4), 355–363.
Ngoo, Y. T., Tey, N. P., & Tan, E. C. (2015). Determinants of Life
Satisfaction in Asia. Social Indicators Research, 124(1), 141–156.

McDonough, P., & Walters, V. (2001). Gender and health:Reassessing patterns and explanations. Social Science &Medicine (1982), 52(4), 547–559. https://doi.org/10.1016/
s0277-9536(00)00159-3

Okabayashi, S., Kawamura, T., Wakai, K., Ando, M., Tsushita, K.,
Ohira, H., Ukawa, S., & Tamakoshi, A. (2019). Lifestyle andpsychosocial factors and a decline in competence in dailyliving among Japanese early elderly people: From anage-specified community-based cohort study (NISSIN project).
Environmental Health and Preventive Medicine, 24, 28.
https://doi.org/10.1186/s12199-019-0787-7

McLeod, J. D., & Kessler, R. C. (1990). Socioeconomic status
differences in vulnerability to undesirable life events. Journalof Health and Social Behavior, 31(2), 162–172.

Pai, M., Muhammad, T., Das, S., Chaudhary, M., & Srivastava, S.
(2023). Gender differences in the association between physicalfrailty and life satisfaction among older adults in India.
Psychogeriatrics, 23(6), 930-943.

Meggiolaro, S., & Ongaro, F. (2015). Life satisfaction amongolder people in Italy in a gender approach. Ageing & Society,35(7), 1481–1504. https://doi.org/10.1017/S0144686X14000646
Milanović, Z., Pantelić, S., Trajković, N., Sporiš, G., Kostić, R., &James, N. (2013). Age-related decrease in physical activity andfunctional fitness among elderly men and women. ClinicalInterventions in Aging, 8, 549–556. https://doi.org/10.2147/
CIA.S44112

Papi, S., & Cheraghi, M. (2021). Multiple factors associated withlife satisfaction in older adults. Przeglad Menopauzalny =Menopause Review, 20(2), 65–71. https://doi.org/10.5114/
pm.2021.107025

Mirowsky, J., & Ross, C. E. (1992). Age and depression.
Journal of Health and Social Behavior, 33(3), 187–205.
https://doi.org/10.2307/2137349
Morgan, J., Robinson, O., & Thompson, T. (2015). Happiness andage in European adults: The moderating role of gross domestic
product per capita. Psychology and Aging, 30(3), 544–551.
https://doi.org/10.1037/pag0000034
Mroczek, D. K., & Spiro III, A. (2005). Change in Life Satisfaction
During Adulthood: Findings From the Veterans Affairs
Normative Aging Study. Journal of Personality and Social
Psychology, 88(1), 189–202. https://doi.org/10.1037/00223514.88.1.189

Papi, S., Karimi, Z., Zilaee, M., & Shahry, P. (2019). Malnutrition
and Its Relation to General Health and Multimorbidity in theOlder People. Journal of Holistic Nursing And Midwifery, 29(4),228–235. https://doi.org/10.32598/JHNM.29.4.228
Park, J.-H., & Kang, S.-W. (2022). Factors Related toLife Satisfaction of Older Adults at Home: A Focus on
Residential Conditions. Healthcare, 10(7), Article 7.
https://doi.org/10.3390/healthcare10071279
Paskulin, L. M. G., & Vianna, L. A. C. (2007). [Sociodemographic
profile and self-referred health conditions of the elderly in acity of Southern Brazil]. Revista De Saude Publica, 41(5),757–768. https://doi.org/10.1590/s0034-89102007000500010

Muhammad, T., Balachandran, A., & Srivastava, S. (2021).
Socio-economic and health determinants of preference forseparate living among older adults: A cross-sectional study in
India. PLoS ONE, 16(4), e0249828. https://doi.org/10.1371/
journal.pone.0249828

Plouffe, L. (2010). M.J. Stones, Albert Kozma, and J.K.
McNeil. Psychological Weil-Being in Later Life. Toronto:
Butterworths, 1991, pp. 160. Canadian Journal on Aging /La Revue Canadienne Du Vieillissement, 11, 202–204.
https://doi.org/10.1017/S0714980800011740

Muhammad, T., & Joy, A. M. (2022). Does childhood financial
status relate to satisfaction in late life: Findings from thelongitudinal aging study in India. Aging and Health Research,
2(1), 100054. https://doi.org/10.1016/j.ahr.2022.100054

Prior, N. H., Bentz, E. J., & Ophir, A. G. (2022). Reciprocal
processes of sensory perception and social bonding: An
integrated social-sensory framework of social behavior. Genes,
Brain and Behavior, 21(3), e12781. https://doi.org/10.1111/
gbb.12781

Muhammad, T., Pai, M., Afsal, K., Saravanakumar, P., & Irshad, C. V.
(2023). The association between loneliness and life satisfaction:
examining spirituality, religiosity, and religious participation as
moderators. BMC geriatrics, 23(1), 1-17.
Muhammad, T., Paul, R., Meher, T., Rashmi, R., & Srivastava, S.
(2022). Decomposition of caste differential in life satisfaction
among older adults in India. BMC Geriatrics, 22(1), 832.
https://doi.org/10.1186/s12877-022-03526-1
Murman, D. L. (2015). The Impact of Age on Cognition.
Seminars in Hearing, 36(3), 111–121.
https://doi.org/10.1055/s-0035-1555115

Puvill, T., Lindenberg, J., de Craen, A. J. M., Slaets, J. P. J., &Westendorp, R. G. J. (2016). Impact of physical and mental
health on life satisfaction in old age: A population based
observational study. BMC Geriatrics, 16(1), 194.
https://doi.org/10.1186/s12877-016-0365-4
Rhee, T. G., Marottoli, R. A., & Monin, J. K. (2021). Diversity ofsocial networks versus quality of social support: Which is more
protective for health-related quality of life among older adults?
Preventive Medicine, 145, 106440. https://doi.org/10.1016/j.
ypmed.2021.106440

152World Happiness Report 2024

Rosenberg, C. E. (1979). Conceiving The Self. Basic Books.
Ross, C. E., & Mirowsky, J. (2010). Why education is the key
to socioeconomic differentials in health. Handbook of Medical
Sociology, Sixth Edition, 33–51.
Roy, M., Bhatta, T., & Burnette, J. D. (2020). Intersectional
Effects of Gender, Education, and Geographic Region on
Functional Health Disparities Among Older Adults in India.
Journal of Aging and Health, 32(9), 1188–1197.
https://doi.org/10.1177/0898264319899246
Saravanakumar, P., Muhammad, T., & Srivastava, S. (2022).
Cross-sectional associations of personal efforts and beliefs
and depressive symptoms among older adults in India.
Scientific Reports, 12(1), Article 1. https://doi.org/10.1038/
s41598-022-17578-1
Schilling, O. (2006). Development of Life Satisfaction in
Old Age: Another View on the “Paradox’’. Social Indicators
Research, 75(2), 241–271. https://doi.org/10.1007/s11205-0045297-2
Selivanova, A., & Cramm, J. M. (2014). The relationshipbetween healthy behaviors and health outcomes amongolder adults in Russia. BMC Public Health, 14(1), 1183.
https://doi.org/10.1186/1471-2458-14-1183
Sharma, N., Chakrabarti, S., & Grover, S. (2016). Genderdifferences in caregiving among family—Caregivers of people
with mental illnesses. World Journal of Psychiatry, 6(1), 7–17.
https://doi.org/10.5498/wjp.v6.i1.7
Shye, D., Mullooly, J. P., Freeborn, D. K., & Pope, C. R. (1995).
Gender differences in the relationship between social network
support and mortality: A longitudinal study of an elderly
cohort. Social Science & Medicine (1982), 41(7), 935–947.
https://doi.org/10.1016/0277-9536(94)00404-h
Siedlecki, K. L., Tucker-Drob, E. M., Oishi, S., & Salthouse, T. A.
(2008). Life satisfaction across adulthood: Different determinants
at different ages? The Journal of Positive Psychology, 3(3),153–164. https://doi.org/10.1080/17439760701834602
Silverstein, M., Cong, Z., & Li, S. (2006). Intergenerationaltransfers and living arrangements of older people in rural
China: Consequences for psychological well-being. The
Journals of Gerontology. Series B, Psychological Sciences andSocial Sciences, 61(5), S256-266. https://doi.org/10.1093/
geronb/61.5.s256
Social isolation, loneliness in older people pose health risks.
(2019, April 23). National Institute on Aging. https://www.nia.
nih.gov/news/social-isolation-loneliness-older-people-posehealth-risks
Srivastava, S., Muhammad, T., Sulaiman, K. M., Kumar, M., &Singh, S. K. (2022). Types of household headship andassociated life satisfaction among older adults in India:
Findings from LASI survey, 2017-18. BMC Geriatrics, 22(1), 78.
https://doi.org/10.1186/s12877-022-02772-7
Srivastava, S., Purkayastha, N., Chaurasia, H., & Muhammad, T.
(2021). Socioeconomic inequality in psychological distressamong older adults in India: A decomposition analysis. BMC
Psychiatry, 21(1), 179. https://doi.org/10.1186/s12888-021-03192-4

Srivastava, S., Shaw, S., Chaurasia, H., Purkayastha, N., &Muhammad, T. (2021). Feeling about living arrangements
and associated health outcomes among older adults in India:
A cross-sectional study. BMC Public Health, 21(1), 1322.
https://doi.org/10.1186/s12889-021-11342-2
Swift, H. J., Vauclair, C.-M., Abrams, D., Bratt, C., Marques, S., &Lima, M.-L. (2014). Revisiting the Paradox of Well-being: The
Importance of National Context. The Journals of Gerontology:
Series B, 69(6), 920–929. https://doi.org/10.1093/geronb/
gbu011
Trainor, K., Mallett, J., & Rushe, T. (2013). Age related differencesin mental health scale scores and depression diagnosis: Adult
responses to the CIDI-SF and MHI-5. Journal of Affective
Disorders, 151(2), 639–645. https://doi.org/10.1016/j.
jad.2013.07.011
The Longitudinal Ageing Study in India 2017-18—National
report. (2020). International Institute for Population Sciences,
National Programme for Health Care of Elderly, Ministry of
Health and Family Welfare, Harvard T. H. Chan School of Public
Health, and the University of Southern California.
Tobiasz-Adamczyk, B., Galas, A., Zawisza, K., Chatterji, S., Haro,J. M., Ayuso-Mateos, J. L., Koskinen, S., & Leonardi, M. (2017).
Gender-related differences in the multi-pathway effect of social
determinants on quality of life in older age—The COURAGE in
Europe project. Quality of Life Research, 26(7), 1865–1878.
https://doi.org/10.1007/s11136-017-1530-8
Ugargol, A. P., & Bailey, A. (2018). Family caregiving for older
adults: Gendered roles and caregiver burden in emigranthouseholds of Kerala, India. Asian Population Studies, 14(2),194–210. https://doi.org/10.1080/17441730.2017.1412593
United Nations. (2020). Department of Economic and Social
Affairs, Population Division. World Population Ageing 2019((ST/ESA/SER.A/444).).
Vijayakumar, G., Devi, E. S., & Jawahar, P. (2016). Life Satisfactionof Elderly in Families and Old Age Homes: A Comparative
Study. International Journal of Nursing Education, 8(1), 94.
https://doi.org/10.5958/0974-9357.2016.00017.9
Wettstein, M., Schilling, O. K., & Wahl, H.-W. (2016). “Still feeling
healthy after all these years”: The paradox of subjective
stability versus objective decline in very old adults’ health andfunctioning across five years. Psychology and Aging, 31(8),815–830. https://doi.org/10.1037/pag0000137
Wettstein, M., Wahl, H.-W., & Schlomann, A. (2022). The Impactof the COVID-19 Pandemic on Trajectories of Well-Being of
Middle-Aged and older Adults: A Multidimensional andMultidirectional Perspective. Journal of Happiness Studies,23(7), 3577–3604. https://doi.org/10.1007/s10902-02200552-z
Whitson, H. E., Landerman, L. R., Newman, A. B., Fried, L. P.,
Pieper, C. F., & Cohen, H. J. (2010). Chronic Medical Conditions
and the Sex-based Disparity in Disability: The Cardiovascular
Health Study. The Journals of Gerontology: Series A, 65A(12),1325–1331. https://doi.org/10.1093/gerona/glq139
WHO. (2020). WHO guidelines on physical activity andsedentary behaviour: At a glance. https://www.who.int/
publications-detail-redirect/9789240014886

153

World Happiness Report 2024

Wu, T.-T., Chen, I.-J., Cho, S.-L., & Chiou, A.-F. (2016). The
Relationship Between Health-Promoting Behaviors andMetabolic Syndrome in Community-Dwelling Older Adults.
Biological Research for Nursing, 18(5), 549–557.
https://doi.org/10.1177/1099800416655882
Zhang, L. (2015). Living Arrangements and Subjective
Well-Being among the Chinese Elderly. Open Journal ofSocial Sciences, 3(3), Article 3. https://doi.org/10.4236/
jss.2015.33024

154