We
tend to underestimate the average happiness of people around us. The
visualization shown demonstrates this for countries around the world,
using data from Ipsos’ Perils of Perception—a
cross-country survey asking people to guess what others in their
country have answered to the happiness question in the World Value
Survey.

The horizontal axis in this chart shows the actual share
of people who said they are ‘Very Happy’ or ‘Rather Happy’ in the World
Value Survey; the vertical axis shows the average guess of the
same number (i.e. the average guess that respondents made of the share
of people reporting to be ‘Very Happy’ or ‘Rather Happy’ in their
country).

If respondents would have guessed the correct share, all
observations would fall on the red 45-degree line. But as we can see,
all countries are far below the 45-degree line. In other words, people
in every country underestimated the self-reported happiness of others.
The most extreme deviations are in Asia—South Koreans think that 24% of
people report being happy, when in reality 90% do.

The highest
guesses in this sample (Canada and Norway) are 60%—this is lower than
the lowest actual value of self-reported happiness in any country in the
sample (corresponding to Hungary at 69%).

Why do people get their
guesses so wrong? It’s not as simple as brushing aside these numbers by
saying they reflect differences in ‘actual’ vs. reported happiness.

One
possible explanation is that people tend to misreport their own
happiness, therefore the average guesses might be a correct indicator of
true life satisfaction (and an incorrect indicator of reported
life satisfaction). However, for this to be true, people would have to
commonly misreport their own happiness while assuming that others do not misreport theirs.

And
people are not bad at judging the well-being of other people who they
know: There is substantial evidence showing that ratings of one’s
happiness made by friends correlate with one’s happiness, and that
people are generally good at evaluating emotions from simply watching
facial expressions.

In global surveys of happiness and life satisfaction, Germany usually ranks high.
However, these national averages mask large inequalities. In the map
shown we focus on regional inequalities—specifically the gap in life
satisfaction between West and East Germany.

This
map plots self-reported life satisfaction in Germany (using the 0-10
Cantril Ladder question), aggregating averages scores at the level of
Federal States.3
What stands out is a clear divide between the East and the West, along
the political division that existed before the reunification of Germany
in 1990.
For example, the difference in levels between neighboring
Schleswig-Holstein (in West Germany) and Mecklenburg-Vorpommern (in East
Germany) are similar to the difference between Sweden and the US – a considerable contrast in self-reported life satisfaction.

Several
academic studies have looked more closely at this ‘happiness gap’ in
Germany using data from more detailed surveys, such as the German
Socio-Economic Panel (e.g. Petrunyk and Pfeifer 2016).4 These studies provide two main insights:

And
second, the gap has been narrowing in recent years, as the chart shows.
In fact, the finding that the gap is narrowing is true both for the raw
average differences, as well as for the ‘conditional differences’ (i.e.
the differences that are estimated after controlling for socioeconomic
and demographic characteristics).

The
General Social Survey (GSS) in the US is a survey administered to a
nationally representative sample of about 1,500 respondents each year
since 1972, and is an important source of information on long-run trends
of self-reported life satisfaction in the country.5

Using this source, Stevenson and Wolfers (2008)6
show that while the national average has remained broadly constant,
inequality in happiness has fallen substantially in the US in recent
decades.

The authors further note that this is true both when we
think about inequality in terms of the dispersion of answers, and also
when we think about inequality in terms of gaps between demographic
groups. They note that two-thirds of the black-white happiness gap has
been eroded (although today white Americans remain happier on average,
even after controlling for differences in education and income), and the
gender happiness gap has disappeared entirely (women used to be
slightly happier than men, but they are becoming less happy, and today
there is no statistical difference once we control for other
characteristics).7

The
results from Stevenson and Wolfers are consistent with other studies
looking at changes of happiness inequality (or life satisfaction
inequality) over time. In particular, researchers have noted that there
is a correlation between economic growth and reductions in happiness
inequality—even
when income inequality is increasing at the same time. The
visualization here uses data from Clark, Fleche and Senik (2015)8
shows this. It plots the evolution of happiness inequality within a
selection of rich countries that experienced uninterrupted GDP growth.

In
this chart, happiness inequality is measured by the dispersion —
specifically the standard deviation — of answers in the World Value
Survey. As we can see, there is a broad negative trend. In their paper
the authors show that the trend is positive in countries with falling
GDP.

Why could it be that happiness inequality falls with rising income inequality?

Clark,
Fleche, and Senik argue that part of the reason is that the growth of
national income allows for the greater provision of public goods, which
in turn tighten the distribution of subjective well-being. This can
still be consistent with growing income inequality, since public goods
such as better health affect incomes and well-being differently.

Another
possibility is that economic growth in rich countries has translated
into a more diverse society in terms of cultural expressions (e.g.
through the emergence of alternative lifestyles), which has allowed
people to converge in happiness even if they diverge in incomes, tastes
and consumption. Steven Quartz and Annette Asp explain this hypothesis
in a New York Times article, discussing evidence from experimental psychology.

Evolution of happiness inequality within countries during periods of
uninterrupted economic growth
19811985199019952000200520071.41.61.822.22.4BelgiumPortugalFranceAustriaNorwayFinlandItalyCanadaGreat
BritainSwedenSpainNetherlands

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The link between happiness and income
The link across countries
Higher national incomes go together with higher average life satisfaction

If
we compare life satisfaction reports from around the world at any given
point in time, we immediately see that countries with higher average
national incomes tend to have higher average life satisfaction scores.
In other words: People in richer countries tend to report higher life
satisfaction than people in poorer countries. The scatter plot here
shows this.

Each dot in the visualization represents one country.
The vertical position of the dots shows national average self-reported
life satisfaction in the Cantril Ladder (a scale ranging from 0-10 where
10 is the highest possible life satisfaction); while the horizontal
position shows GDP per capita based on purchasing power parity (i.e. GDP
per head after adjusting for inflation and cross-country price
differences).

This correlation holds even if we control for other
factors: Richer countries tend to have higher average self-reported life
satisfaction than poorer countries that are comparable in terms of
demographics and other measurable characteristics. You can read more
about this in the World Happiness Report 2017, specifically the discussion in Chapter 2.

As we show below, income and happiness also tend to go together within countries and across time.

Self-reported Life Satisfaction vs GDP per capita, 2017

The vertical axis shows the national average of the self-reported life satisfaction on a scale ranging from 0-10, where 10
is the highest possible life satisfaction.
The horizontal axis shows GDP per capita adjusted for inflation and cross-country price differences.
Life satisfaction (country average; 0-10)234567

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The link within countries
Higher personal incomes go together with higher self-reported life satisfaction

Above
we point out that richer countries tend to be happier than poorer
countries. Here we show that the same tends to be true within countries:
richer people within a country tend to be happier than poorer people in
the same country. The visualisations here show us this this by looking
at happiness by income quintiles.

Firstly we show each country in
individual panels: within each panel is a connected scatter plot for a
specific country. This means that for each country, we observe a line
joining five points: each point marks the average income within an
income quintile (horizontal axis) against the average self-reported life
satisfaction of people at that income quintile (vertical axis).

What
does this visualization tell us? We see that in all cases lines are
upward sloping: people in higher income quintiles tend to have higher
average life satisfaction. Yet in some countries the lines are steep and
linear (e.g. in Costa Rica richer people are happier than poorer people
across the whole income distribution); while in some countries the
lines are less steep and non-linear (e.g. the richest group of people in
the Dominican Republic is as happy as the second-richest group).

In
a second visualization we present the same data, but instead of
plotting each country separately, showing all countries in one grid.

Do
income and happiness tend to go together? The visualization here shows
that the answer to this question is yes, both within and across
countries.

It may take a minute to wrap your head around this
visualization, but once you do, you can see that it handily condenses
the key information from the previous three charts into one.

To
show the income-happiness correlation across countries, the chart plots
the relationship between self-reported life satisfaction on the vertical
axis and GDP per capita on the horizontal axis. Each country is an
arrow on the grid, and the location of the arrow tells us the
corresponding combination of average income and average happiness.

To show the income-happiness correlation within
countries, each arrow has a slope corresponding to the correlation
between household incomes and self-reported life satisfaction within
that country. In other words: the slope of the arrow shows how strong
the relationship between income and life satisfaction is within that
country. (This chart gives you a visual example of how the arrows were constructed for each country). 9

If
an arrow points northeast, that means richer people tend to report
higher life satisfaction than poorer people in the same country. If an
arrow is flat (i.e. points east), that means rich people are on average
just as happy as poorer people in the same country.

As we can see,
there is a very clear pattern: richer countries tend to be happier than
poorer countries (observations are lined up around an upward-sloping
trend), and richer people within countries tend to be happier than
poorer people in the same countries (arrows are consistently pointing
northeast).

It’s important to note that the horizontal axis is
measured in a logarithmic scale. The cross-country relationship we would
observe in a linear scale would be different, since at high national
income levels, slightly higher national incomes are associated with a
smaller increase in average happiness than at low levels of national
incomes. In other words, the cross-country relationship between income
and happiness is not linear on income (it is ‘log-linear’). We use the
logarithmic scale to highlight two key facts: (i) at no point in the
global income distribution is the relationship flat; and (ii) a doubling
of the average income is associated with roughly the same increase in
the reported life-satisfaction, irrespective of the position in the
global distribution.

In
the charts above we show that there is robust evidence of a strong
correlation between income and happiness across and within countries at
fixed points in time. Here we want to show that, while less strong,
there is also a correlation between income and happiness across time.
Or, put differently, as countries get richer, the population tends to
report higher average life satisfaction.

The chart shown here uses
data from the World Value Survey to plot the evolution of national
average incomes and national average happiness over time. To be
specific, this chart shows the share of people who say they are ‘very
happy’ or ‘rather happy’ in the World Value Survey (vertical axis),
against GDP per head (horizontal axis). Each country is drawn as a line
joining first and last available observations across all survey waves.11

As
we can see, countries that experience economic growth also tend to
experience happiness growth across waves in the World Value Survey. And
this is a correlation that holds after controlling for other factors
that also change over time (in this chart
from Stevenson and Wolfers (2008) you can see how changes in GDP per
capita compare to changes in life satisfaction after accounting for
changes in demographic composition and other variables).

The
observation that economic growth does not always go together with
increasing life satisfaction was first made by Richard Easterlin in the
1970s. Since then, there has been much discussion over what came to be
known as the ‘Easterlin Paradox’.

At the heart of the paradox was
the fact that richer countries tend to have higher self-reported
happiness, yet in some countries for which repeated surveys were
available over the course of the 1970s, happiness was not increasing
with rising national incomes. This combination of empirical findings was
paradoxical because the cross-country evidence (countries with higher
incomes tended to have higher self-reported happiness) did not, in some
cases, fit the evidence over time (countries seemed not to get happier
as national incomes increased).

Notably, Easterlin and other
researchers relied on data from the US and Japan to support this
seemingly perplexing observation. If we look closely at the data
underpinning the trends in these two countries, however, these cases are
not in fact paradoxical.

Let us begin with the case of Japan.
There, the earliest available data on self-reported life satisfaction
came from the so-called ‘Life in Nation surveys’, which date back to
1958. At first glance, this source suggests that mean life satisfaction
remained flat over a period of spectacular economic growth (see for
example this chart from Easterlin and Angelescu 2011).12 Digging a bit deeper, however, we find that things are more complex.

Stevenson and Wolfers (2008)13
show that the life satisfaction questions in the ‘Life in Nation
surveys’ changed over time, making it difficult—if not impossible—to
track changes in happiness over the full period. The visualization here
splits the life satisfaction data from the surveys into sub-periods
where the questions remained constant. As we can see, the data is not
supportive of a paradox: the correlation between GDP and happiness
growth in Japan is positive within comparable survey periods. The reason
for the alleged paradox is in fact mismeasurement of how happiness
changed over time.

Health
is an important predictor of life satisfaction, both within and among
countries. In this visualization, we provide evidence of the
cross-country relationship.

Each dot in the scatterplot represents
one country. The vertical position of the dots shows national life
expectancy at birth, and the horizontal position shows national average
self-reported life satisfaction in the Cantril Ladder (a scale ranging
from 0-10 where 10 is the highest possible life satisfaction).

As
we can see, there is a strong positive correlation: countries where
people tend to live longer are also countries where people tend to say
more often that they are satisfied with their lives. A similar
relationship
holds for other health outcomes (e.g. life satisfaction tends to be
higher in countries with lower child mortality).

The
relationship plotted in the chart clearly reflects more than just the
link between health and happiness, since countries with high life
expectancy also tend to be countries with many other distinct
characteristics. However, the positive correlation between life
expectancy and life satisfaction remains after controlling for
observable country characteristics, such as incomes and social
protection. You can read more about this in the World Happiness Report 2017, specifically the discussion in Chapter 2.

Life satisfaction vs Life expectancy, 2015

The vertical axis shows life expectancy at birth. The horizontal axis shows self-reported life satisfaction in the Cantril
Ladder (0-10 point scale with higher values representing higher life satisfaction).

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Mental health and happiness

When
trying to discern a relationship between mental health and happiness,
it is important to distinguish between macro and micro-level
correlations. When we look at macro-level, cross-country trends
in the prevalence of mental health disorders such as depression versus
self-reported life satisfaction there is no clear relationship: it does
not appear that countries with higher depression rates have lower
self-reported happiness.

However, when we look at micro-level,
within-country correlations the data tells a different story. In this
visualization, we provide evidence of the relationship between health
and subjective well-being within countries—specifically; we focus here
on mental health and self-reported life satisfaction.

Each bar in
the visualization measures the extent to which mental illness
(depression and anxiety) is associated with self-reported life
satisfaction, once we control for physical illness and other factors
such as income and education. In other words, the bars show a
‘conditional correlation’—the strength of the link between mental
illness and happiness after accounting for other factors.

The
negative values show that people who have been diagnosed with depression
or anxiety tend to be more likely to have lower self-reported life
satisfaction.

The size of the coefficients, particularly in the
US, and Australia, tell us that the relationship we observe is very
strong. For context, in the UK, the US and Australia the magnitude of
the
correlation between mental illness and life satisfaction is higher than
the magnitude for the correlation between income and life satisfaction.

Clearly,
this correlation is likely the result of a two-way relationship:
depressed and anxious people are less likely to be happy, and unhappy
people are more likely to be depressed or anxious. Nevertheless, it is
still important to bear in mind that anxiety, depression and unhappiness
often go together.

Other studies provide further evidence of not
only the negative correlation between depression and life satisfaction
(i.e individuals with more severe depression report lower life
satisfaction), but also evidence that treatment and improvement in
depression are linked to increases in self-reported life satisfaction.15

Evidence
suggests that this correlation between happiness and mental health may
not only be realised through self-reported life satisfaction, but also
through the strength of behaviours related to happiness,
such as smiling and facial expression. When asked to visualize happy
and sad situations, researchers found individuals with depression to
show notably less facial muscle activity relative to those without – a
suggestion sign of both lower emotivity and disengagement.16

Conditional correlation between mental illness and life satisfaction

The correlation reported is conditional on the factors listed in the footnote. Mental illness refers to depression and
anxiety.
-0.2-0.15-0.1-0.050Indonesia-0.07United Kingdom-0.11Australia-0.18United States

Note:
Specifically, the correlation corresponds to the relationship between
life satisfaction and mental illness after accounting for differences in
income,
education, marital status, physical illness, employment status and gender.
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Life satisfaction through life events
How do common life events affect happiness?

Do people tend to adapt to common life events by converging back to a baseline level of happiness?

Clark et al. (2008)17
use data from the German Socio-Economic Panel to identify groups of
people experiencing significant life and labour market events, and trace
how these events affect the evolution of their life satisfaction. The
visualization here shows an overview of their main findings. In each
individual chart, the red lines mark the estimated effect of a different
event at a given point in time (with ‘whiskers’ marking the range of
confidence of each estimate).

In all cases the results are split
by gender, and time is labeled so that 0 marks the point when the
corresponding event took place (with negative and positive values
denoting years before and after the event). All estimates control for
individual characteristics, so the figures show the effect of the event
after controlling for other factors (e.g. income, etc.).

The first
point to note is that most events denote the evolution of a latent
situation: People grow unhappy in the period building up to a divorce,
while they grow happy in the period building up to a marriage.

The
second point is that single life events do tend to affect happiness in
the short run, but people often adapt to changes. Of course, there are
clear differences in the extent to which people adapt. In the case of
divorce, life satisfaction first drops, then goes up and stays high. For
unemployment, there is a negative shock both in the short and long-run,
notably among men. And for marriage, life satisfaction builds up
before, and fades out after the wedding.

A
number of papers have noted that long-term paraplegics do not report
themselves as particularly unhappy, when compared to non-paraplegics
(see for example the much-cited paper by Brickman, Coates, and
Janoff-Bulman, 1978).18

This
assertion has received attention because it tells us something about
the very meaning of well-being and has important consequences for
policy. It is, for example, considered in courts of law with respect to
the compensation for disability.

However, comparing differences in
self-reported life satisfaction among people with different disability
statuses is not an ideal source of evidence regarding the effect of
tragedy on happiness. Non-paraplegics are potentially different to
paraplegics in ways that are hard to measure. A better source of
evidence are longitudinal surveys where people are tracked over time.

Oswald and Powdthavee (2008)19
use data from a longitudinal survey in the UK to explore whether
accidents leading to disability imply long-term shocks to life
satisfaction.

The chart here, from Oswald and Powdthavee, shows
the average reported life satisfaction of a group of people who became
seriously disabled (at time T) and remained seriously disabled in the
two following years (T+1 and T+2). Here, ‘seriously disabled’ means that
disability prevented them from being able to do day-to-day activities.

Comparisons
of happiness among countries suggest that culture and history shared by
people in a given society matter for self-reported life satisfaction.
For example, as the chart here shows, culturally and historically
similar Latin American countries have a higher subjective well-being
than other countries with comparable levels of economic development.
(This chart plots self-reported life satisfaction as measured in the
10-point Cantril ladder in the vertical axis, against GDP per capita in
the horizontal axis).

Latin America is not a special case in this
respect. Ex-communist countries, for example, tend to have lower
subjective well-being than other countries with comparable
characteristics and levels of economic development.

Academic
studies in positive psychology discuss other patterns. Diener and Suh
(2002) write: “In recent years cultural differences in subjective
well-being have been explored, with a realization that there are
profound differences in what makes people happy. Self-esteem, for
example, is less strongly associated with life satisfaction, and
extraversion is less strongly associated with pleasant affect in
collectivist cultures than in individualist cultures”.20

A
particular channel through which social environment may affect
happiness is freedom: the society we live in may crucially affect the
availability of options that we have to shape our own life.

This
visualization shows the relationship between self-reported sense of
freedom and self-reported life satisfaction using data from the Gallup World Poll.
The variable measuring life satisfaction corresponds to country-level
averages of survey responses to the Cantril Ladder question (a 0-10
scale, where 10 is the highest level of life satisfaction); while the
variable measuring freedom corresponds to the share of people who agree
with the statement “In this country, I am satisfied with my freedom to
choose what I do with my life”.21

As
we can see, there is a clear positive relationship: countries where
people feel free to choose and control their lives tend to be countries
where
people are happier. As Inglehart et al. (2008)22 show, this positive
relationship holds even after we control for other factors, such as
incomes and strength of religiosity.

Interestingly,
this chart also shows that while there are some countries where the
perceived sense of freedom is high but average life satisfaction is low
(e.g. Rwanda); there are no countries where the perceived sense of
freedom is low but average life satisfaction is high (i.e. there are no
countries in the top left area of the chart).

A number of studies have found that there is a link between emotional exposure to negative content in news and changes in mood.

Johnston and Davey (1997),23
for example, conducted an experiment in which they edited short TV news
to display positive, neutral or negative material, and then showed them
to three different groups of people. The authors found that people who
watched the ‘negative’ clip were more likely to report a sad mood.

This
link between emotional content in news and changes in mood is all the
more important if we consider that media gatekeepers tend to prefer
negative to positive coverage of newsworthy facts (see, for example,
Combs and Slovic 197924).

Of course, mood is not the same as life satisfaction. Yet, as we discuss below in the section on measurement and data quality,
surveys measuring happiness often do capture emotional aspects of
well-being. And in any case, people’s perceptions of what it means to
lead a meaningful life are heavily influenced by their expectations of
what is possible and likely to occur with their lives; and this has also
been shown to depend on media exposure.25

Data Quality and Measurement
Can ‘happiness’ really be measured?

The
most natural way to attempt to measure subjective well-being is to ask
people what they think and feel. Indeed, this is the most common
approach.

In practice, social scientists tend to rely on questions
inquiring directly about happiness, or on questions inquiring about
life satisfaction. The former tend to measure the experiential or
emotional aspects of well-being (e.g. “I feel very happy”), while the
latter tend to measure the evaluative or cognitive aspects of well-being
(e.g. “I think I lead a very positive life”).

Self-reports about
happiness and life satisfaction are known to correlate with things that
people typically associate with contentment, such as cheerfulness and
smiling. (In this scatter plot
you can see that countries where people have higher self-reported life
satisfaction are also countries where people tend to smile more).

Experimental
psychologists have also shown that self reports of well-being from
surveys turn out to correlate with activity in the parts of the brain
associated with pleasure and satisfaction. And various surveys have
confirmed that people who say they are happy also tend to sleep better
and express positive emotions verbally more frequently.

The
following table, adapted from Kahneman and Krueger (2006)26, provides a
list of the variables that researchers have found to be related to
self-reported happiness and life satisfaction.

The
main conclusion from the evidence is that survey-based measures of
happiness and life satisfaction do provide a reasonably consistent and
reliable picture of subjective well-being.
Correlates of high life satisfaction and happiness
Smiling frequency
Smiling with the eyes (”unfakeable smile”)
Ratings of one’s happiness made by friends
Frequent verbal expressions of positive emotions
Sociability and extraversion
Sleep quality
Happiness of close relatives
Self-reported health
High income, and high income rank in a reference group
Active involvement in religion
Recent positive changes of circumstances (increased income, marriage)
Is ‘life satisfaction’ the same as ‘happiness’?

In
this entry we discuss data and empirical research on happiness and life
satisfaction. However, it is important to bear in mind that “life
satisfaction” and “happiness” are not really synonyms. And this is of
course reflected in the data, since self-reported measures of these two
variables come from asking different kinds of questions.

The World Value Survey asks directly about happiness: “Taking
all things together, would you say you are (i) Very happy, (ii) Rather
happy, (iii) Not very happy, (iv) Not at all happy, (v) Don’t know.”

The Gallup World Poll, on the other hand, uses the Cantril Ladder question and asks respondents to evaluate their life: “Please
imagine a ladder, with steps numbered from 0 at the bottom to 10 at the
top. The top of the ladder represents the best possible life for you
and the bottom of the ladder represents the worst possible life for you.
On which step of the ladder would you say you personally feel you stand
at this time?”

As the following scatter plot shows, these
two measures are clearly closely related (countries that score high in
one measure also tend to score high in the other), yet they are not
identical (there is substantial dispersion, with many countries sharing
the same score in one variable but diverging in the other).

The
differences in responses to questions inquiring about life satisfaction
and happiness are consistent with the idea that subjective well-being
has two sides: an experiential or emotional side, and an evaluative or
cognitive side. Of course, the limits between emotional and cognitive
aspects of well-being are blurred in our minds; so in practice both
kinds of questions measure both aspects to some degree. Indeed, social
scientists often construct ‘subjective well-being indexes’ where they
simply average out results from various types of questions.

Happiness vs Life satisfaction, 2014

Vertical axis shows average self-reported life satisfaction scores in the Cantril Ladder.
Horizontal axis shows share of people who say they are ‘very happy’ or ‘rather happy’ – as opposed to ‘not very happy’
or ‘not at all happy’.

Note:
Cantril ladder question: “Please imagine a ladder, with steps numbered
from 0 at the bottom to 10 at the top. The top of theladder represents
the
best possible life for you and the bottom of the ladder
represents the worst possible life for you. On which step ofthe ladder
would you say you
personally feel you stand at this time?” World
Value Survey question: “Taking all things together, would you say you
are (i) Very happy, (ii) Rather
happy, (iii) Not very happy or (iv) Not at all happy”.
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Are happiness averages really meaningful?

The
most common way to analyze data on happiness consists in taking
averages across groups of people. Indeed, cross-country comparisons of
self-reported life satisfaction, such as those presented in ‘happiness
rankings’, rely on national averages of reports on a scale from 0 to 10
(the Cantril Ladder).

Is it reasonable to take averages of life
satisfaction scores? Or, in more technical terms: are self reports of
Cantril scores really a cardinal measure of well-being?

The
evidence tells us that survey-based reports on the Cantril Ladder do
allow cardinal measurement reasonably well—respondents have been found
to translate verbal labels, such as ‘very good’ and ‘very bad’, into
roughly the same numerical values.27 28

But
as with any other aggregate indicator of social progress, averages need
to be interpreted carefully, even if they make sense arithmetically.
For example, if we look at happiness by age in a given country, we may
see that older people do not appear to be happier than younger people.
Yet this may be because the average-by-age figure from the snapshot
confounds two factors: the age effect (people from the same cohort do
get happier as they grow older, across all cohorts) and the cohort
effect (across all ages, earlier generations are less happy than more
recent generations). If the cohort effect is very strong, the snapshot
can
even give a picture that suggests people become less happy as they grow
older, even though the exact opposite is actually true within all
generations.

This example is in fact taken from the real world: using data from the US, Sutin et al. (2013)29
showed that self-reported feelings of well-being tend to increase with
age across generations, but overall levels of well-being depend on when
people were born.
How much does language matter for cross-country comparisons of happiness?

Linguistic
differences are often seen as a major obstacle for making cross-country
comparisons of happiness. However, there is evidence suggesting that
comparability issues, at least in respect to language, are less
problematic than many people think.

Studies have shown, for
example, that in interviews in which respondents are shown pictures or
videos of other individuals, respondents can broadly identify whether
the individual shown to them was happy or sad; and this is also true
when respondents were asked to predict the evaluations of individuals
from other cultural communities. (For evidence of this see Sandvik et
al., 1993; Diener and Lucas, 1999).30

Studies
have also shown that ‘indigenous emotions’ across cultures (i.e.
feelings that are unique in that they do not have equivalents in the
English language) are not experienced any more frequently or differently
than common translated emotions. (See Scollon et al. 2005).31

The
conclusion, therefore, seems to be that there is some basic
understanding among humans about what it means to be ‘happy’.
Survey-based measures of self-reported life satisfaction are informative
about cross-country differences, even if these comparisons are
obviously noisy.

Data Sources
Cross-country estimates
World Happiness Report

    Data: Country averages of self-reported life satisfaction (Question: Cantril Ladder).
    Underlying source: Gallup World Poll
   
Time span: The data appendix in Chapter 2 of the World Happiness Report
2017 includes series from 2005 to 2016 (shorter for some countries).
    Available at: World Happiness Report 2017

European Commission – Eurobarometer Interactive

   
Data: Life satisfaction (Question: “On the whole are you very
satisfied, fairly satisfied, not very satisfied or not at all satisfied
with the life you lead ?”)
    Geographical coverage: EU countries
    Time span: 1973 – 2015
    Available at: http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm/Chart/index

The World Value Survey (WVS)

    Data:
    Happiness and life satisfaction, as well as many other social and
    cultural characteristics from cross-national time-series surveys. The
    micro data (i.e. a dataset with one observation per respondent) is also
    open to the public.
    Geographical coverage: The
    WVS covers almost 100 societies (nearly 90% of the world’s population).
    But not all countries have observations in each survey wave.
    Time span: Several waves of surveys from 1981 to 2014
    Available at: http://www.worldvaluessurvey.org/WVSDocumentationWVL.jsp

Pew Global Attitudes Survey

    Data: Life satisfaction (Question: Cantril Ladder). Country tables report answers by score (i.e. country-level histograms)
    Geographical coverage: 38,000 respondents in forty-four countries
    Time span: Several waves of surveys from 2002
    Available at: www.pewglobal.org

Commonly-cited country-specific estimates with open access
US General Social Survey

    Data: Time-series data on happiness and many other social and cultural characteristics (Question: “Taken
    all together, how would you say things are these days–would you say
    that you are very happy, pretty happy, or not too happy?”)
    Geographical coverage: US
    Time span: Yearly surveys since 1972
    Available at: https://gssdataexplorer.norc.org

British Office for National Statistics

    Data:
    Personal well-being estimates (life satisfaction, whether you feel the
    things you do in life are worthwhile, happiness and anxiety); broken
    down by age, disability, relationship status, ethnicity, religion, sex
    and sexual identity, using the Annual Population Survey three year
    combined dataset.
    Geographical coverage: UK and constituent countries
    Time span: Pooled data (2017 release pools 2013-2015) /li>
    Available at: https://gssdataexplorer.norc.org

Germany – Deutsche Post Glücksatlas

    Data: Subnational averages of self-reported life satisfaction
    Geographical coverage: Germany
    Time span: Pooled data (2012-2016 or latest release)
    Available at: http://www.gluecksatlas.de/

Commonly-cited country-specific surveys with restricted access

    German socio-economic panel (SOEP)
    British Household Panel Survey (BHPS)
    Household, Income and Labour Dynamics in Australia survey (HILDA)

Data archives and repositories

The World Database of Happiness, hosted at the ‘Erasmus University Rotterdam’ here,
provides a wealth of data and study results on happiness. It is
described by the authors as “an archive of research findings on
subjective enjoyment of life”.

In this archive you can find
measures of happiness inequality, as well as time series for a vast
range of countries. You also find correlational studies with just about
any measure you can imagine. It also provides a list of tools (‘apps’)
with which one can records one’s own happiness and compare with the
happiness of other people and/or track one’s own happiness over time.

References

   
Particularly important was the Stiglitz-Sen-Fitoussi Commission. It
also relates to Bhutan’s famous measurement of Gross National Happiness
(GNH) as indicator of progress (Wikipedia here).

    To
    be precise, in 27 out of 31 countries with data spanning longer than
    one decade, the estimate for 2016 is higher than the earliest available
    estimate.

    In some cases, the underlying data
    had multiple average estimates for the same territory (e.g. Baden and
    Württemberg were treated as different entities). In these cases the map
    shows the average between these two observations. Details can be found
    in this file

    Petrunyk,
    I., & Pfeifer, C. (2016). Life satisfaction in Germany after
    reunification: Additional insights on the pattern of convergence.
    Jahrbücher für Nationalökonomie und Statistik, 236(2), 217-239.

    The
    GSS asks people a very similar question to the World Value Survey:
    “Taken all together, how would you say things are these days—would you
    say that you are very happy, pretty happy, or not too happy?”

    Stevenson,
    Betsey, and Justin Wolfers. “Happiness inequality in the United
    States.” The Journal of Legal Studies 37.S2 (2008): S33-S79. An ungated
    earlier version of the paper is available here.

    These
    results have been discussed in various blogs. Freakonomics provides a
    quick and interesting overview of the debate, specifically with regard
    to gender gaps.

    Clark,
    Andrew E., Sarah Flèche, and Claudia Senik. “Economic growth evens out
    happiness: Evidence from six surveys.” Review of Income and Wealth
    (2015). An ungated earlier version of the paper is available here

    To
    be precise, the gradients correspond, country by country, to the
    regression coefficients between income quintiles and the related average
    life satisfaction reported by people within each income quintile.

    Stevenson,
    B. and Wolfers, J. (2008). Economic growth and subjective well-being:
    Reassessing the Easterlin Paradox. Brookings Papers on Economic
    Activity, 1-87. An earlier version is available online here.

    The
    dataset includes observations for Egypt. However, we have excluded
    these observations from our analysis. This is because the survey for
    Egypt in the wave labeled 2014 is from 2012, which was a year
    characterized by extreme political instability in that country.

    R.A.
    Easterlin and L. Angelescu – ‘Modern Economic Growth and Quality of
    Life: Cross-Sectional and Time Series Evidence’ in Land, Michalos, and
    Sirgy (ed.) (2011) – Handbook of Social Indicators and Quality of Life
    Research. Springer.

    Stevenson, B. and
    Wolfers, J. (2008). Economic growth and subjective well-being:
    Reassessing the Easterlin Paradox. Brookings Papers on Economic
    Activity, 1-87. An earlier version is available online here.

    Chart
    from Stevenson B, Wolfers J (2008) – Economic Growth and Subjective
    Well-Being: Reassessing the Easterlin Paradox. Brookings Paper Econ
    Activ 2008 (Spring):1–87. Underlying data source: Life in Nation
    surveys, 1958–2007. Note from the authors: “The series in each of the
    four panels reports responses to a different life satisfaction question,
    and therefore comparisons should be made only within each panel. GDP
    per capita is at purchasing power parity in constant 2000 international
    dollars.”

    Koivumaa‐Honkanen, H., Honkanen,
    R., Antikainen, R., Hintikka, J., Laukkanen, E., Honkalampi, K., &
    Viinamäki, H. (2001). Self‐reported life satisfaction and recovery from
    depression in a 1‐year prospective study. Acta Psychiatrica
    Scandinavica, 103(1), 38-44. Available at: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1600-0447.2001.00046.x.

    Gehricke,
    J. G., & Shapiro, D. (2000). Reduced facial expression and social
    context in major depression: discrepancies between facial muscle
    activity and self-reported emotion. Psychiatry Research, 95(2), 157-167.
    Available at: http://www.psy-journal.com/article/S0165-1781(00)00168-2/abstract.

    Clark,
    A. E., Diener, E., Georgellis, Y., & Lucas, R. E. (2008). Lags and
    leads in life satisfaction: A test of the baseline hypothesis. The
    Economic Journal, 118(529).

    Brickman, P.,
    Coates, D., & Janoff-Bulman, R. (1978). Lottery winners and accident
    victims: Is happiness relative?. Journal of personality and social
    psychology, 36(8), 917. Chicago. Available online here.

    Oswald,
    A. J., & Powdthavee, N. (2008). Does happiness adapt? A
    longitudinal study of disability with implications for economists and
    judges. Journal of public economics, 92(5), 1061-1077.

    Diener,
    E., Oishi, S., & Lucas, R. E. (2009). Subjective well-being: The
    science of happiness and life satisfaction. Oxford handbook of positive
    psychology, 2, 187-194.

    To be precise, the
    Gallup World Poll asks: “In this country, are you satisfied or
    dissatisfied with your freedom to choose what you do with your life?”

    Inglehart,
    R., Foa, R., Peterson, C., & Welzel, C. (2008). Development,
    freedom, and rising happiness: A global perspective (1981–2007).
    Perspectives on psychological science, 3(4), 264-285.

    Johnston, W. M., & Davey, G. C. (1997). The psychological impact of
    negative TV news bulletins: The catastrophizing of personal worries.
    British Journal of Psychology, 88(1), 85-91.

    Combs, B., & Slovic, P. (1979). Newspaper coverage of causes of death. Journalism Quarterly, 56(4), 837-849.

    Riddle
    (2010), for example, found that people watching more vivid violent
    media gave higher estimates of the prevalence of crime in the real
    world. (Riddle, 2010, “Always on my mind: Exploring how frequent,
    recent, and vivid television portrayals are used in the formation of
    social reality judgments.” Media Psychology.)

    Kahneman,
    D., & Krueger, A. B. (2006). Developments in the measurement of
    subjective well-being. The journal of economic perspectives, 20(1),
    3-24.

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

    Van
    Praag, B.M.S. (1991). ‘Ordinal and cardinal utility: an integration of
    the two dimensions of the welfare concept’, Journal of Econometrics,
    vol. 50, pp. 69–89.

    Sutin, A. R.,
    Terracciano, A., Milaneschi, Y., An, Y., Ferrucci, L., & Zonderman,
    A. B. (2013). The effect of birth cohort on well-being The legacy of
    economic hard times. Psychological science, 0956797612459658.

    Sandvik,
    E., Diener, E. and Seidlitz, L. (1993). ‘Subjective well-being: the
    convergence and stability of self and non self report measures’, Journal
    of Personality, vol. 61, pp. 317–42. Diener, E. and Lucas, R.E. (1999).
    ‘Personality and subjective well-being’, in Kahneman et al. (1999)
    chapter 11.

    Scollon, C. N., Diener, E.,
    Oishi, S., & Biswas-Diener, R. (2004). Emotions across cultures and
    methods. Journal of cross-cultural psychology, 35(3), 304-326.

Citation

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Esteban
Ortiz-Ospina and Max Roser (2019) - “Happiness and Life Satisfaction”.
Published online at OurWorldInData.org. Retrieved from:
‘https://ourworldindata.org/happiness-and-life-satisfaction’ [Online
Resource]

BibTeX citation

@article{owidhappinessandlifesatisfaction,
    author = {Esteban Ortiz-Ospina and Max Roser},
    title = {Happiness and Life Satisfaction},
    journal = {Our World in Data},
    year = {2019},
    note = {https://ourworldindata.org/happiness-and-life-satisfaction}
}

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