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Money and its impact on happiness

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They say money can't buy happiness. Of course, they're wrong.

A substantial body of economic research says otherwise: Statistically speaking, household income is strongly related to both emotional well-being and a person's evaluation of their own quality of life.

Will getting a raise this season make you less nervous, stressed or sad? It depends on how much you're already making, according to a recent study from an economist at the Weatherhead School of Management at Case Western University.

Each dollar makes a big difference in reducing negative emotions for people in the 20th income percentile, but those returns fall off by the 80th income percentile and disappear at around $200,000, according to the study.

The paper looked at the level of negative emotion reported by participants in the Panel Study of Income Dynamics - a long-running longitudinal survey of thousands of Americans. People were asked how often they experienced negative feelings like nervousness, hopelessness or restlessness in the last 30 days (K9 scores).

Previous studies have found a correlation between money and happiness, but the Case Western study used the data on individuals over time to demonstrate that income can cause a reduction in negative emotions. It also found that an increase in income can reduce the incidence of serious mental illness (defined as a score of 10 or higher).

"We know from the results that changes in family income are important drivers of people's emotional lives," said David Clingingsmith, the author of the paper and an associate professor of economics at the Weatherhead School of Management. While it may also be true that having high anxiety or sadness could lead to lower income, the study shows that that's not the whole story, said Clingingsmith.

The difference the marginal dollar makes in reducing negative emotions starts to fall off around $70,000, is very low by $160,000 and hits zero around $200,000.

Those results are remarkably similar to the correlations reported in a 2010 study that found that people don't get happier after $75,000 a year.

That study analyzed data from the Gallup Organization in the Gallup-Healthways Well-Being Index (GHWBI). It's a lower number because researchers in that case used a single person's income rather than the family income used by Clingingsmith.

While the 2010 study found that three different measures of positive emotion (or lack of negative emotion) saw rapid improvement that slowed to nothing around $75,000, it also looked at a fourth metric that continued to rise far after that point.

That metric was "life evaluation" - a scale that asks the respondent to rate his or her life from 0, "the worst possible life for you," to 10, "the best possible life for you." It can be thought of as a measurement of how a person judges the success of their own life, rather than the feelings they experience while living it.

That measure seems to be more sensitive to socioeconomic status, the authors noted. People naturally compare their lives and incomes to others', and even if they experience no negative emotions they may yearn for and be more satisfied with higher pay far beyond the level they would need to avoid pain or afford leisure.

"That comparison between yourself and other people is really important for one's sense of well-being," said Clingingsmith. "If I'm a trader and making $300,000 at a firm where everyone else is making $450,000, that may feel different."

So why do these studies matter? In one sense, people already know that their happiness is related to their income, but the details of how exactly that relationship works are important.

Income is often used as a convenient proxy for well-being in economics, but not every dollar of income has the same end effect. If the marginal dollar means nothing for the emotional well-being of an extremely wealthy person, but has a huge effect for a poorer person, then taking $1,000 from the top percentile and giving it to the lowest percentile will have a large net effect on happiness, based on these findings.

"A little bit of marginal taxation doesn't hurt people who are up high as much as it benefits those down low through redistribution or spending on programs," said Clingingsmith.

And for individual people, learning more about the relationship between income and happiness could help them find their own personal sweet spot - where income leads to life satisfaction without requiring additional sacrifices. There's still a lot of economics work that needs to done to fully understand that relationship.

"We earn income because there are things we want to do - we want to live our lives, support our families, have experiences and so forth," said Clingingsmith. "We need to keep in mind what people's ultimate ends are for the things they are striving to do, and what this paper shows is it's not as straightforward as you might think."

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Money can buy happiness, but only to a point

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They say money can't buy happiness. Of course, they're wrong. A substantial body of economic research says otherwise: Statistically speaking, household income is strongly related to both emotional well-being and a person's evaluation of their own quality of life. Will getting a raise this season make you less nervous, stressed or sad?

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Introduction

In the first World Happiness Report we surveyed a wide range of available data. The Gallup World Poll surveys covering 2005-2011 gave the widest international coverage. Now, seven years later, we have twice as many years of data from the Gallup World Poll, giving us a sufficient time span to consider how our principal measures of happiness, and their main supporting factors, have evolved from 2005 through 2018.

The chapter therefore starts with a presentation of the evolution of annual data at the global and regional levels for three key happiness measures - life evaluations, positive affect, and negative affect over the whole course of the Gallup World Poll from 2005 through 2018. For all our plots of annual data, we combine the surveys in 2005 and 2006, because of the small number of countries in the first year.

The title of this chapter is intentionally ambiguous, designed to document not just the year-to-year changes in happiness, but also to consider how happiness has been affected by changes in the quality of government. After our review of how world happiness has been changing since the start of the Gallup World Poll, we turn to present our rankings and analysis of the 2016-2018 average data for our three measures of subjective well-being plus the six main variables we use to explain their international differences. See Technical Box 1 for the precise definitions of all nine variables.

For our country-by-country analysis of changes, we report changes from 2005-2008 to 2016-2018, grouping years together to provide samples of sufficient size. We shall also provide estimates of the extent to which each of the six key explanatory variables contributed to the actual changes in life evaluations from 2005-2008 to 2016-2018.

We then complete the chapter with our latest evidence on the links between changes in the quality of government, by a variety of measures, and changes in national average life evaluations over the 2005-2018 span of years covered by the Gallup World Poll.

The Evolution of World Happiness 2005-2018

In recent previous reports, we presented bar charts showing for the world as a whole, and for each of 10 global regions, the distribution of answers to the Cantril ladder question asking respondents to value their lives today on a 0 to 10 scale, with the worst possible life as a 0 and the best possible life as a 10. This gave us a chance to compare happiness levels and inequality in different parts of the world. Population-weighted average life evaluations differed significantly among regions, being highest in North America and Oceania, followed by Western Europe, Latin America and the Caribbean, Central and Eastern Europe, the Commonwealth of Independent States, East Asia, Southeast Asia, The Middle East and North Africa, Sub-Saharan Africa and South Asia, in that order. We found that well-being inequality, as measured by the standard deviation of the distributions of individual life evaluations, was lowest in Western Europe, Northern America and Oceania, and South Asia; and greatest in Latin America, Sub-Saharan Africa, and the Middle East and North Africa.

This year we shift our focus from the levels and distribution of well-being to consider their evolution over the years since the start of the Gallup World Poll. We now have twice as many years of coverage from the Gallup World Poll as were available for the first World Happiness Report in 2012. This gives us a better chance to see emerging happiness trends from 2005 through 2018, and to investigate what may have contributed to them.

First we shall show the population-weighted trends, based on annual samples for the world as a whole, and for ten component regions, for each of our three main happiness measures: life evaluations, positive affect, and negative affect. As described in Technical Box 1, the life evaluation used is the Cantril Ladder, which asks survey respondents to place the status of their lives on a "ladder" scale ranging from 0 to 10, where 0 means the worst possible life and 10 the best possible life. Positive affect comprises the average frequency of happiness, laughter and enjoyment on the previous day, and negative affect comprises the average frequency of worry, sadness and anger on the previous day. The affect measures thus lie between 0 and 1.

The three panels of Figure 2.1 show the global and regional trajectories for life evaluations, positive affect, and negative affect. The whiskers on the lines in all figures indicate 95% confidence intervals for the estimated means. The first panel shows the evolution of life evaluations measured three different ways. Among the three lines, two lines cover the whole world population, with one of the two weighting the country averages by each country's share of the world population, and the other being an unweighted average of the individual national averages. The unweighted average is always above the weighted average, especially after 2015, when the weighted average starts to drop significantly, while the unweighted average starts to rise equally sharply. This suggests that the recent trends have not favoured the largest countries, as confirmed by the third line, which shows a population-weighted average for all countries in the world except the five countries with the largest populations - China, India, Indonesia, the United States and Russia. The individual trajectories for these largest countries are shown in Figure 1 of Statistical Appendix 1, while their changes from 2005-2008 to 2016-2018 are shown later in this chapter, in Figure 2.8. Even with the largest countries removed, the population-weighted average does not rise as fast as the unweighted average, suggesting that smaller countries have had greater happiness growth since 2015 than have the larger countries.

Figure 2.1 World Dynamics of Happiness

The second panel of Figure 2.1 shows positive affect over the same period as used in the first panel. There is no significant trend in either the weighted or unweighted series. The population-weighted series show slightly but significantly more positive affect than does the unweighted series, showing that positive affect is on average higher in the larger countries.

The third panel of Figure 2.1 shows negative affect, which follows a quite different path from positive affect. The population-weighted world frequency of negative affect in 2005-2006 is about one-third of the frequency of positive affect. Negative affect is lower for the weighted series, just as positive affect is greater. Both the weighted and unweighted series show significant upward trends in negative affect starting in 2010 or 2011. The global weighted measure of negative affect rises by more than one-quarter from 2010 to 2018, from a frequency of 22% to 28%. This global total, striking as it is, masks a great deal of difference among global regions, as will be shown later in Figure 2.4.

The four panels of Figure 2.2 show the evolution of life evaluations in ten global regions, divided into four continental groupings. In each case the averages are adjusted for sampling and population weights. The first panel has three lines, one each for Western Europe, Central and Eastern Europe, and the Commonwealth of Independent States (CIS). All three groups of countries show average life evaluations that fell in the wake of the 2007-2008 financial crash, with the falls being greatest in Western Europe, then in the CIS, with only a slight drop in Central and Eastern Europe. The post-crash happiness recovery started first in the CIS, then in Central and Eastern Europe, while in Western Europe average life evaluations only started recovering in 2015. CIS evaluations rose almost to the level of those in Central and Eastern Europe by 2014, but have since fallen, while those in Central and Eastern Europe have continued to rise, parallelling the post-2015 rise in Western Europe. The overall pattern is one of happiness convergence among the three parts of Europe, but with a recent large gap opening up between Central and Eastern Europe and the CIS.

The second panel of Figure 2.2 covers the Americas. The upper line shows the North America+ANZ country grouping comprising the United States, Canada, Australia and New Zealand, with about 80% of its population in the United States. The weighted average, heavily influenced by the U.S. experience, has fallen more than 0.4 points from its pre-crisis peak to 2018, about on a par with Western Europe. The lower line shows that average happiness in Latin America and the Caribbean rose without much pause until a peak in 2013, with a continuing decline since then.

Figure 2.2 Dynamics of Ladder in 10 Regions

The third panel shows quite different evolutions of life evaluations in the three parts of Asia, with South Asia showing a drop of a full point, from 5.1 to 4.1 on the 0 to 10 scale, driven mainly by the experience of India, given its dominant share of South Asian population. Southeast Asia and East Asia, in contrast, have had generally rising life evaluations over the period. Southeast and South Asia had the same average life evaluations in 2005-2006, but the gap between them was up to 1.3 points by 2018. Happiness in East Asia was worst hit in the economic crisis years, but has since posted a larger overall gain than Southeast Asia to end the period at similar levels.

Finally, the fourth panel of Figure 2.2 contains the Middle East and North Africa (MENA) and Sub-Saharan Africa (SSA), with MENA dropping fairly steadily, and SSA with no overall trend. In all regions there is a variety of country experiences underlying the averages reported in Figure 2.2. The country-by-country data are reported in the on-line statistical data, and the country changes from 2005-2008 to 2016-2018 shown later in Figure 2.8 will help to reveal the national sources of the regional trends.

The four panels of Figures 2.3 and 2.4 have the same structure as Figure 2.2, with life evaluations being replaced by positive affect in Figure 2.3 and by negative affect in Figure 2.4. Figure 2.3 shows that positive affect is generally falling in Western Europe, and falling and then rising in both Central and Eastern Europe and the CIS, achieving its highest levels at the end of the period. This pattern of partial convergence of positive affect between the two parts of Europe leaves positive affect still significantly more frequent in Western Europe. Within the Americas, the incidence of positive affect is generally falling, at about the same rates in both the NA-ANZ region (with most of the population weight being on the United States), and in Latin America. Positive affect is fairly stable and at similar levels in East and Southeast Asia, while starting lower and falling significantly in South Asia. There are no significant trends in positive affect in Sub-Saharan Africa, while in the Middle East and North Africa (MENA), it starts lower and follows a declining trend.

Figure 2.3 Dynamics of Positive Affect in 10 Regions

Figure 2.4 shows that negative affect is generally increasing in Western Europe, generally lower and falling since 2012 in Central and Eastern Europe, and also falling in the CIS until 2015, but rising thereafter. Negative affect thus shows divergence rather than the convergence within Europe seen for life evaluations and positive affect. There is a continuing post-crisis increase in the incidence of negative affect in Latin America as well as in the NA-ANZ region. Within Asia the frequency of negative affect rises most sharply in Southeast Asia, and by only slightly less in South Asia, while falling in East Asia until 2014 and then rising thereafter. In the Middle East and North Africa, the frequency at first falls and then rises, but within a narrow range. The biggest increases in the frequency of negative affect are found in Sub-Saharan Africa, with the 2018 frequency greater by half than in 2010. Thus all global regions except for Central and Eastern Europe have had significantly increasing negative affect in recent years, with some variations among regions in starting dates for the increases.

Figure 2.4 Dynamics of Negative Affect in 10 Regions

The Evolution of Happiness Inequality

In this section we focus our attention on changes in the distribution of happiness. There are at least two reasons for us to do this. First, it is important to consider not just average happiness in a community or country, but also how it is distributed. Second, it is done to encourage those interested in inequality to consider happiness inequality as a useful umbrella measure. Most studies of inequality have focused on inequality in the distribution of income and wealth, while in Chapter 2 of World Happiness Report 2016 Update we argued that just as income is too limited an indicator for the overall quality of life, income inequality is too limited a measure of overall inequality. For example, inequalities in the distribution of health have effects on life satisfaction above and beyond those flowing through their effects on income. We and others have found that the effects of happiness equality are often larger and more systematic than those of income inequality. For example, social trust, often found to be lower where income inequality is greater, is even more closely connected to the inequality of subjective well-being.

Figure 2.5 shows the evolution of global inequality of happiness, as measured by the standard deviation of the distribution of the individual life evaluations on the 0 to 10 scale, from 2005-2006 to 2018. The upper line illustrates the trend of overall inequality, showing a clear increase since 2007. We further decompose overall inequality into two components: one for within-country inequality, and another for between-country inequality. The figure shows that inequality within countries follows the same increasing trend as overall inequality, while between-country inequality has increased only slightly. In summary, global happiness inequality, measured by the standard deviation of Cantril Ladder, has been increasing, driven mainly by increasing happiness inequality within countries.

Figure 2.5 Dynamics of Inequality of Ladder (Standard Deviation)

Figure 2.6 Dynamics of Inequality of Ladder in 10 Regions

Figure 2.6 shows that the inequality of happiness has evolved quite differently in the ten global regions. The inequality of happiness rose between 2006 and 2012 in Western Europe, and has been falling steadily since, while in Central and Eastern Europe it has followed a similar path but starting from a higher starting point and falling faster. Inequality in the CIS region follows somewhat the reverse pattern, being stable at first and rising since 2013. In Latin America, inequality was steady until 2014 and has risen since, while rising until 2010 in the US-dominated NA+ANZ region and being fairly constant since. Inequality in Southeast Asia has been rising throughout the period since 2010, while in the rest of Asia rising much less. Inequality in Sub-Saharan Africa has risen on the steep post-2010 path similar to that in Southeast Asia. In the MENA region, inequality rose from 2009 to 2013, while being stable since.

Ranking of Happiness by Country

Now we turn to consider life evaluations covering the 2016-2018 period, and to present our annual country rankings. These rankings are accompanied by our latest attempts to show how six key variables contribute to explaining the full sample of national annual average scores over the whole period 2005-2018. These variables are GDP per capita, social support, healthy life expectancy, freedom, generosity, and absence of corruption. Note that we do not construct our happiness measure in each country using these six factors - the scores are instead based on individuals' own assessments of their lives, as indicated by the Cantril ladder. Rather, we use the six variables to explain the variation of happiness across countries. We shall also show how measures of experienced well-being, especially positive affect, supplement life circumstances in explaining higher life evaluations.

The results in the first column of Table 2.1 explain national average life evaluations in terms of six key variables: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and freedom from corruption. Taken together, these six variables explain almost three-quarters of the variation in national annual average ladder scores among countries, using data from the years 2005 to 2018. The model's predictive power is little changed if the year fixed effects in the model are removed, falling from 0.740 to 0.735 in terms of the adjusted R-squared.

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

As for the coefficients on the other variables in the final equation, the changes are material only on those variables - especially freedom and generosity - that have the largest impacts on positive affect. Thus we infer that positive emotions play a strong role in support of life evaluations, and that much of the impact of freedom and generosity on life evaluations is channeled through their influence on positive emotions. That is, freedom and generosity have large impacts on positive affect, which in turn has a major impact on life evaluations. The Gallup World Poll does not have a widely available measure of life purpose to test whether it too would play a strong role in support of high life evaluations. However, data from large samples of UK do suggest that life purpose plays a strongly supportive role, independent of the roles of life circumstances and positive emotions.

Table 2.1: Regressions to Explain Average Happiness across Countries (Pooled OLS)
Notes: This is a pooled OLS regression for a tattered panel explaining annual national average Cantril ladder responses from all available surveys from 2005 to 2018. See Technical Box 1 for detailed information about each of the predictors. Coefficients are reported with robust standard errors clustered by country in parentheses. ***, **, and * indicate significance at the 1, 5 and 10 percent levels respectively.
Technical Box 1: Detailed information about each of the predictors in Table 2.1

1. GDP per capita is in terms of Purchasing Power Parity (PPP) adjusted to constant 2011 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on November 14, 2018. See Statistical Appendix 1 for more details. GDP data for 2018 are not yet available, so we extend the GDP time series from 2017 to 2018 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 104 (Edition November 2018) and the World Bank's Global Economic Prospects (Last Updated: 06/07/2018), 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.

2. The time series of healthy life expectancy at birth are constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2005, 2010, 2015, and 2016. To match this report's sample period, interpolation and extrapolation are used. See Statistical Appendix 1 for more details.

3. Social support is the national average of the binary responses (either 0 or 1) to the Gallup World Poll (GWP) question "If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?"

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 do with your life?"

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

6. Perceptions of corruption are the average of 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 business corruption is used as the overall corruption-perception measure.

7. Positive affect is defined as the average of previous-day affect measures for happiness, laughter, and enjoyment for GWP waves 3-7 (years 2008 to 2012, and some in 2013). It is defined as the average of laughter and enjoyment for other waves where the happiness question was not asked. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? See pp. 1-2 of Statistical Appendix 1 for more details.

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

Our country rankings in Figure 2.7 show life evaluations (the average answer to the Cantril ladder question, asking people to evaluate the quality of their current lives on a scale of 0 to 10) for each country, averaged over the years 2016-2018. Not every country has surveys in every year; the total sample sizes are reported in the statistical appendix, and are reflected in Figure 2.7 by the horizontal lines showing the 95% confidence intervals. The confidence intervals are tighter for countries with larger samples. To increase the number of countries ranked, we also include three countries that did have surveys in 2015 but have not had one since.

The overall length of each country bar represents the average ladder score, which is also shown in numerals. The rankings in Figure 2.7 depend only on the average Cantril ladder scores reported by the respondents, and not on the values of the six variables that we use to help account for the large differences we find.

Each of these bars is divided into seven segments, showing our research efforts to find possible sources for the ladder levels. The first six sub-bars show how much each of the six key variables is calculated to contribute to that country's ladder score, relative to that in a hypothetical country called Dystopia, so named because it has values equal to the world's lowest national averages for 2016-2018 for each of the six key variables used in Table 2.1. We use Dystopia as a benchmark against which to compare contributions from each of the six factors. The choice of Dystopia as a benchmark permits every real country to have a positive (or at least zero) contribution from each of the six factors. We calculate, based on the estimates in the first column of Table 2.1, that Dystopia had a 2016-2018 ladder score equal to 1.88 on the 0 to 10 scale. The final sub-bar is the sum of two components: the calculated average 2016-2018 life evaluation in Dystopia (=1.88) and each country's own prediction error, which measures the extent to which life evaluations are higher or lower than predicted by our equation in the first column of Table 2.1. These residuals are as likely to be negative as positive.

It might help to show in more detail how we calculate each factor's contribution to average life evaluations. Taking the example of healthy life expectancy, the sub-bar in the case of Tanzania is equal to the number of years by which healthy life expectancy in Tanzania exceeds the world's lowest value, multiplied by the Table 2.1 coefficient for the influence of healthy life expectancy on life evaluations. The width of these different sub-bars then shows, country-by-country, how much each of the six variables is estimated to contribute to explaining the international ladder differences. These calculations are illustrative rather than conclusive, for several reasons. First, the selection of candidate variables is restricted by what is available for all these countries. Traditional variables like GDP per capita and healthy life expectancy are widely available. But measures of the quality of the social context, which have been shown in experiments and national surveys to have strong links to life evaluations and emotions, have not been sufficiently surveyed in the Gallup or other global polls, or otherwise measured in statistics available for all countries. Even with this limited choice, we find that four variables covering different aspects of the social and institutional context - having someone to count on, generosity, freedom to make life choices and absence of corruption - are together responsible for more than half of the average difference between each country's predicted ladder score and that in Dystopia in the 2016-2018 period. As shown in Statistical Appendix 1, the average country has a 2016-2018 ladder score that is 3.53 points above the Dystopia ladder score of 1.88. Of the 3.53 points, the largest single part (34%) comes from social support, followed by GDP per capita (26%) and healthy life expectancy (21%), and then freedom (11%), generosity (5%), and corruption (3%).

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

To provide more assurance that our results are not seriously biased because we are using the same respondents to report life evaluations, social support, freedom, generosity, and corruption, we tested the robustness of our procedure (see Table 10 of Statistical Appendix 1 of World Happiness Report 2018 for more detail) by splitting each country's respondents randomly into two groups, and using the average values for one group for social support, freedom, generosity, and absence of corruption in the equations to explain average life evaluations in the other half of the sample. The coefficients on each of the four variables fall, just as we would expect. But the changes are reassuringly small (ranging from 1% to 5%) and are far from being statistically significant.

The seventh and final segment is the sum of two components. The first component is a fixed number representing our calculation of the 2016-2018 ladder score for Dystopia (=1.88). The second component is the average 2016-2018 residual for each country. The sum of these two components comprises the right-hand sub-bar for each country; it varies from one country to the next because some countries have life evaluations above their predicted values, and others lower. The residual simply represents that part of the national average ladder score that is not explained by our model; with the residual included, the sum of all the sub-bars adds up to the actual average life evaluations on which the rankings are based.

Figure 2.7: Ranking of Happiness 2016-2018

What do the latest data show for the 2016-2018 country rankings? Two features carry over from previous editions of the World Happiness Report. First, there is still a lot of year-to-year consistency in the way people rate their lives in different countries, and of course we do our ranking on a three-year average, so that there is information carried forward from one year to the next. But there are nonetheless interesting changes. The annual data for Finland have continued their modest but steady upward trend since 2014, so that dropping 2015 and adding 2018 boosts the average score, thereby putting Finland significantly ahead of other countries in the top ten. Denmark and Norway have also increased their average scores, but Denmark by more than Norway, so Denmark is now in second place and Norway third. There are no 2018 survey results available for Iceland, and their score and ranking remain the same, in 4th place. The Netherlands have slipped into 5th place, dropping Switzerland to 6th.The next three places contain the same three countries as last year, with Sweden's increasing scores raising it to 7th, with New Zealand remaining 8th and Canada now in 9th. The final position in the top ten goes to Austria, rising from 12th to 10th, with Australia dropping to 11th, followed by Costa Rica in 12th, and Israel in 13th.There are further changes in the rest of the top 20, with Luxembourg rising to 14th and the United Kingdom to 15th, Ireland and Germany in 16th and 17th, and Belgium and the United States in 18th and 19th. The Czech Republic rounds out the top 20 by switching positions with the United Arab Emirates. Both countries posted rising averages, with the Czech score rising more. Throughout the top 20 positions, and indeed at most places in the rankings, even the three-year average scores are close enough to one another that significant differences are found only between country pairs that are several positions apart in the rankings. This can be seen by inspecting the whisker lines showing the 95% confidence intervals for the average scores.

There remains a large gap between the top and bottom countries. The top ten countries are less tightly grouped than last year. The national life evaluation scores now have a gap of 0.28 between the 1st and 5th position, and another 0.24 between 5th and 10th positions, a more spread-out situation than last year. Thus there is now a gap of about 0.5 points between the first and 10th positions. There is a bigger range of scores covered by the bottom 10 countries. Within this group, average scores differ by almost three-quarters of a point, more than one-fifth of the average national score in the group. Tanzania, Rwanda and Botswana still have anomalous scores, in the sense that their predicted values, based on their performance on the six key variables, would suggest they would rank much higher than shown by the survey answers.

Despite the general consistency among the top country scores, there have been many significant changes in the rest of the countries. Looking at changes over the longer term, many countries have exhibited substantial changes in average scores, and hence in country rankings, between 2005-2008 and 2016-2018, as will be shown in more detail in Figure 2.8.

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

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

Our main country rankings are based on the average answers to the Cantril ladder life evaluation question in the Gallup World Poll. The other two happiness measures, for positive and negative affect, are themselves of independent importance and interest, as well as being, especially in the case of positive affect, contributors to overall life evaluations. Measures of positive affect also play important roles in other chapters of this report, in large part because most lab experiments, being of relatively small size and duration, can be expected to affect current emotions but not life evaluations, which tend to be more stable in response to small or temporary disturbances. The various attempts to use big data to measure happiness using word analysis of Twitter feeds, or other similar sources, are likely to be capturing mood changes rather than overall life evaluations. In this report, for the first time since 2012, we are presenting, in Table 2.2, rankings for all three of the measures of subjective well-being that we track: the Cantril ladder (and its standard deviation, which provides a measure of happiness inequality), positive affect and negative affect. We also show country rankings for the six variables we use in Table 2.1 to explain our measures of subjective well-being. The same data are also shown in graphical form, on a variable by variable basis, in Figures 16 to 39 of Statistical Appendix 1. The numbers shown reflect each country's global rank for the variable in question, with the number of countries ranked depending on the availability of data. The league tables are divided into a premier league (the OECD, whose 36 member countries include 19 of the top 20 countries) and a number of regional leagues comprising the remaining countries grouped in the same global regions used elsewhere in the report. Within leagues, countries are ordered by their 2016-2018 ladder scores.

Table 2.2 Happiness League Tables
Notes: The data are organized so that for negative affect a higher rank (i.e. a lower number in the Table) means fewer negative experiences and for corruption a higher rank means a lower perceived frequency of corruption. All other variables are measured in their usual scales, with a higher rank standing for better performance.
Changes in National Happiness and Its Main Supports

We now turn to our country-by-country ranking of changes in life evaluations. In the two previous reports, we concentrated on looking at recent changes in life evaluations. This year we take advantage of the ever-growing length of the Gallup sample to compare life evaluations over a longer span, averaging ten years, from 2005-2008 to 2016-2018. In Figure 2.8 we show the changes in happiness levels for all 132 countries that have sufficient numbers of observations for both 2005-2008 and 2016-2018.

Figure 2.8: Changes in Happiness from 2005-2008 to 2016-2018

Of the 132 countries with data for 2005-2008 and 2016-2018, 106 had significant changes. 64 were significant increases, ranging from 0.097 to 1.39 points on the 0 to 10 scale. There were also 42 significant decreases, ranging from -0.179 to -1.944 points, while the remaining 26 countries revealed no significant trend from 2005-2008 to 2016-2018. As shown in Table 32 in Statistical Appendix 1, the significant gains and losses are very unevenly distributed across the world, and sometimes also within continents. In Central and Eastern Europe, there were 15 significant gains against only one significant decline, while in Western Europe there were eight significant losses compared to four significant gains. The Commonwealth of Independent States was a significant net gainer, with eight gains against two losses. In Latin America and the Caribbean and in East Asia, significant gains outnumbered significant losses by more than a two to one margin. The Middle East and North Africa was net negative, with six losses against three gains. In the North American and Australasian region, the four countries had two significant declines and no significant gains. The 28 Sub-Saharan African countries showed a real spread of experiences, with 13 significant gainers and 10 significant losers. In South and Southeast Asia, most countries had significant changes, with a fairly even balance between gainers and losers.

Among the 20 top gainers, all of which showed average ladder scores increasing by more than 0.7 points, 10 are in the Commonwealth of Independent States or Central and Eastern Europe, five are in Sub-Saharan Africa, and three in Latin America. The other two are Pakistan and the Philippines. Among the 20 largest losers, all of which show ladder reductions exceeding about 0.5 points, seven are in the Middle East and North Africa, six in Sub-Saharan Africa, three in Western Europe, with the remaining significant losers being Venezuela, India, Malaysia and Ukraine.

These changes are very large, especially for the 10 most affected gainers and losers. For each of the 10 top gainers, the average life evaluation gains were more than would be expected from a tenfold increase of per capita incomes. For each of the 10 countries with the biggest drops in average life evaluations, the losses were more than twice as large as would be expected from a halving of GDP per capita.

On the gaining side of the ledger, the inclusion of four transition countries among the top 10 gainers reflects the rising average life evaluations for the transition countries taken as a group. The appearance of Sub-Saharan African countries among the biggest gainers and the biggest losers reflects the variety and volatility of experiences among the Sub-Saharan countries for which changes are shown in Figure 2.8, and whose experiences were analyzed in more detail in Chapter 4 of World Happiness Report 2017. Benin, the largest gainer since 2005-2008, by almost 1.4 points, ranked 4th from last in the first World Happiness Report and has since risen 50 places in the rankings.

The 10 countries with the largest declines in average life evaluations typically suffered some combination of economic, political, and social stresses. The five largest drops since 2005-2008 were in Yemen, India, Syria, Botswana and Venezuela, with drops over one point in each case, the largest fall being almost two points in Venezuela. Among the countries most affected by the 2008 banking crisis, Greece is the only one remaining among the 10 largest happiness losers, although Spain and Italy remain among the 20 largest.

Figure 42 and Table 31 in Statistical Appendix 1 show the population-weighted actual and predicted changes in happiness for the 10 regions of the world from 2005-2008 to 2016-2018. The correlation between the actual and predicted changes is only 0.14, and with actual changes being less favorable than predicted. Only in Central and Eastern Europe, where life evaluations were up by 0.6 points on the 0 to 10 scale, was there an actual increase exceeding what was predicted. South Asia had the largest drop in actual life evaluations (more than 0.8 points on the 0 to 10 scale) while it was predicted to have a substantial increase. Since these regional averages are weighted by national populations, the South Asian total is heavily influenced by the Indian decline of more than 1.1 points. Sub-Saharan Africa was predicted to have a substantial gain, while the actual increase was much smaller. Latin America was predicted to have a small gain, while it shows a population-weighted actual drop of the same size. The MENA region was predicted to be a gainer, and instead lost 0.52 points. The countries of Western Europe were predicted to have no change, but instead experienced a small reduction. For the remaining regions, the predicted and actual changes were in the same direction, with the substantial reductions in the United States (the largest country in the NANZ group) being larger than predicted. As Figure 42 and Table 31 show, changes in the six factors are not very successful in capturing the evolving patterns of life over what have been tumultuous times for many countries. Nine of the ten regions were predicted to have 2016-2018 life evaluations higher than in 2005-2008, but only half of them did so. In general, the ranking of regional predicted changes matched the ranking of the actual changes, despite typical experience being less favorable than predicted. The notable exception is South Asia, which experienced the largest drop, contrary to predictions.

On a country-by-country basis, the actual changes from 2005-2008 to 2016-2018 are on average much better predicted than on a regional basis, with a correlation of 0.50, as shown in Figure 41 in Statistical Appendix 1. This difference can be traced to the great variety of experiences within regions, many of which were predicted reasonably well on a national basis, and by the presence of some very large countries with substantial prediction errors, India being the largest example.

Changes in Governance

Government institutions and policies set the stages on which lives are lived. These stages differ largely from country to country, and are among primary factors influencing how highly people rate the quality of their lives. The importance of national institutions and living conditions was shown forcefully in World Happiness Report 2018, which presented happiness rankings for immigrants and the locally born, and found them to be almost exactly the same (a correlation of +0.96 for the 117 countries with a sufficient number of immigrants in their sampled populations). This was the case even for migrants coming from source countries with life evaluations less than half as high as in the destination country.

The evidence from the happiness of immigrants and the locally born suggests strongly that the large international differences in average national happiness documented in this report depend primarily on the circumstances of life in each country. These differences in turn invite explanation by factors that differ among nations, including especially institutions that are national in scope, among which governments are perhaps the most prominent examples.

It is natural, as public and policy attention starts to shift from GDP to broader measures of progress, and especially to how people value their lives, that there should be growing policy interest in knowing how government institutions and actions influence happiness, and in whatever changes in policies might enable citizens to lead happier lives.

What is Good Government

At the most basic level, good government establishes and maintains an institutional framework that enables people to live better lives. Similarly, good public services are those that improve lives while using fewer scarce resources. How can the excellence of government be measured, and how can its effects on happiness be determined? There are two main possibilities for assessment, one very specific and the other at the aggregate level. The more specific approach is adopted in the Global Happiness and Wellbeing Policy Reports, while here we shall take a more aggregate approach using the national happiness data that lie at the core of the World Happiness Reports.

Created in response to growing interest in the policy relevance of happiness, the Global Happiness and Wellbeing Policy Reports aim to find and evaluate best-practice examples from around the world on how government policies in specific areas could be redesigned to support happier lives. The just-released Global Happiness and Wellbeing Policy Report 2019, for example, contains surveys of happiness-oriented policy interventions in specific areas of public policy - in particular education, health, work and cities - as well as on topics of cross-cutting importance, such as personal happiness and the metrics and policy frameworks needed to support policies for well-being. These policy surveys show that what counts as good governance is specific to each policy area. Within each ministry or subject area there are specific targets that are the primary focus of attention, including mainly medical and cost outcomes in health care, academic achievement and completion in education, productivity and job satisfaction in the workplace, reduced crime and incarceration rates in justice, and a range of specific indicators of the quality of city life. The happiness lens is then used to find those policies that achieve their traditional objectives in the most happiness-supporting ways. This kind of specific focus is probably the most effective way to move from a general interest in using happiness as a policy objective to the development of cost-effective ways of delivering happiness. One major common element in the chapters of Global Happiness and Wellbeing Policy Report 2019 is the use of results from happiness research to establish the relative importance of a variety of outcomes long considered important but not readily comparable. As advocated by Chapter 6 in World Happiness Report 2013, developed in more detail in a recent paper for the UK Treasury, and exemplified by the happiness-based policy evaluation tool in Dubai, and in the health chapter of Global Happiness and Wellbeing Policy Report 2019, this involves expanding traditional methods for estimating the cost-effectiveness of policies to make happier lives the objective. Seen from this perspective, good governance would be defined in terms of the methods used and results obtained, both for traditional policy objectives and the happiness of all participants.

There is another way of assessing different government structures and policies. This is done at a more aggregate level by using a number of national-level indicators of the quality of governance to see how well they correlate with levels and changes in national average life evaluations. There are now many examples of this sort of research. We consider here some of the effects of government structure and behavior on average national happiness, while Chapter 3 considers how happiness affects voting behavior.

Our own analysis in Table 2.1 provides one example of the effects of government via its estimate of the links between corruption and life satisfaction, holding constant some other key variables, including income, health, social support, a sense of freedom and generosity, all of which themselves are likely to be affected by the quality of government. Unpacking these channels convincingly is not possible using the aggregate data available, since there is too much in play to establish strong evidence of causality, and many of the system features held to be of primary importance, for example the rule of law, tend to take long to establish, thereby reducing the amount of evidence available.

Hence any conclusions reached are likely to be suggestive at best, and have also been found to be more evident in some countries and times than in others. For example, a number of studies have divided the World Bank's six main indicators of governmental quality into two groups, with the four indicators for effectiveness, rule of law, quality of regulation, and control of corruption combined to form an index of the quality of delivery, and the two indicators for voice and accountability and for political stability and absence of violence combined to form an index of the democratic quality of government.

Previous studies comparing these two indexes as predictors of life evaluations have found that quality of delivery is more important than the democracy variable, both in studies across countries and in ones that include country-fixed effects, so that the estimated effects are based on changes in governance quality within each country. These latter results are more convincing, since they are uninfluenced by cross-country differences in other variables, and have the capacity to show whether significant changes in the quality of government can happen within a policy-relevant time horizon. These studies made use of data from the World Values Survey and from the Gallup World Poll, but were based on shorter sample periods. For this chapter we replicated earlier analysis based on the GWP data for 2005-2012 but now using the longest sample with available data for life evaluations and for the indicators of government quality, covering 2005-2017. The results are shown in Table 10 in Statistical Appendix 2. The core results continue to show that delivery quality has a significant positive effect on average life evaluations with or without accounting for the effects flowing through the higher levels of GDP per capita made possible by government regulations and services that are more efficient, more configured to match the rule of law, and less subject to corruption. The estimated magnitude of the more convincing results, which are the ones based on within-country changes in governance quality, is substantial. For example, a previous study found that "the ten most-improved countries, in terms of delivery quality changes between 2005 and 2012, when compared to the ten countries with most worsened delivery quality, are estimated to have higher average life evaluations by one tenth of the overall life evaluation gap between the world's most and least happy ten countries." In other words, the estimated effect of the divergence in governance quality on life evaluations was about 0.4 points on the 0 to 10 scale. We have been able to confirm that previous result with data now covering twice as long a time period, as shown in Table 22 in Statistical Appendix 2.

Tables 12 to 15 in Statistical Appendix 2 test whether changes in a variety of other measures of governmental quality contribute to changes in life evaluations. None show significant effects with one notable exception. Changes in the Gallup World Poll's measure of confidence in government do contribute significantly to life evaluations, as shown in Table 13 of Statistical Appendix 2. To some extent, this variable might be thought to reflect a measure of satisfaction with a particular life domain, much as was shown in Figure 1.1 for Mexico in Chapter 1.

Somewhat stronger results are obtained by using the Global Peace Index assessing 163 countries in three domains: the level of societal safety and security, the extent
of ongoing domestic and international conflict, and the degree of militarisation. The index (which is defined as if it were a conflict variable, so that a more peaceful country has a lower value) is negatively correlated with average life evaluations in both levels and changes from 2008 to 2016-2018. The effect of within-country changes in the peace index remains significant even when changes in GDP and the rest of the six key variables are included, with a change of 0.5 in the peace index (about 1 standard deviation) estimated to alter average life evaluations by 0.15 points on the 0 to 10 scale, a value equivalent to a change of more than 15% in per capita GDP.

Conclusions

This chapter has had a special focus on how several measures of happiness, and of its contributing factors, have changed over the 2005 to 2018 period covered by the Gallup World Poll. We started by tracing the trajectories of happiness, and its distribution, primarily based on annual population-weighted averages for the world as a whole and for its ten constituent regions. This was followed by our latest ranking of countries according to their average life evaluations over the previous three years, accompanied this year by comparable rankings for positive and negative affect, for six key factors used to explain happiness, and for happiness inequality. We then presented 2005-2008 to 2016-2018 changes in life evaluations, positive and negative affect, and the key variables supporting life evaluations. Finally, we considered different ways in which the nature and quality of government policies and institutions can influence happiness.

At a global level, population-weighted life evaluations fell sharply during the financial crisis, recovered completely by 2011, and fell fairly steadily since to a 2018 value about the same level as its post-crisis low. This pattern of falling global life evaluations since 2011 was driven mainly by what was happening in the five countries with the largest populations, and especially India, which has had a post-2011 drop of almost a full point on the 0 to 10 scale. Excluding the five largest countries removes the decline, while an unweighted average of the country scores shows a significant rise since 2016. Positive emotions show no significant trends by either weighted or unweighted measures. Negative emotions show the most dramatic global trends, rising significantly by both global measures. Global inequality of well-being has been fairly constant between countries while rising within countries.

These global movements mask a greater variety of experiences among and within global regions. There continues to be convergence of life evaluations among the three main regions of Europe. In Asia, divergence among the regions is more evident. All three parts of Asia had roughly comparable life evaluations in the 2006-2010 period, but since then life evaluations have generally risen in East and Southeast Asia and fallen in South Asia, with a gap building to more than 1 point on the 0 to 10 scale by 2018. Since 2013, life evaluations have risen by 0.4 points in Sub-Saharan Africa and fallen by 0.4 points in the Middle East and North Africa, finishing in 2018 at roughly equal levels. In Latin America, life evaluations rose by half a point to 2013, and have fallen slightly more than that since, while in the North America plus Australia and New Zealand group, with population dominated by the United States, life evaluations have fallen by roughly 0.3 points from the beginning to the end of the period.

What about well-being inequality? Since 2012, the mid-point of our data period, well-being inequality has fallen insignificantly in Western Europe and Central and eastern Europe, while increasing significantly in most other regions, including especially South Asia, Southeast Asia, Sub-Saharan Africa, the Middle East and North Africa, and the CIS (with Russia dominating the population total).

The rankings of country happiness are based this year on the pooled results from Gallup World Poll surveys from 2016-2018, and continue to show both change and stability. As shown by our league tables for happiness and its supports, the top countries tend to have high values for most of the key variables that have been found to support well-being: income, healthy life expectancy, social support, freedom, trust and generosity, to such a degree that year to year changes in the top rankings are to be expected. With its continuing upward trend in average scores, Finland consolidated its hold on first place, ahead of an also-rising Denmark in second place.

Then for each country, we showed that average changes in life evaluations from the earliest years of the Gallup World Poll (2005-2008) to the three most recent years (2016-2018). Most countries show significant changes, with slightly more gainers than losers. The biggest gainer was Benin, up 1.4 points and 50 places in the rankings. The biggest life evaluation drops were in Venezuela and Syria, both down by about 1.9 points.

We turned finally to consider the ways in which the quality of government, and the structure of government policies, influence happiness. The effects were seen to be easier to trace in specific policy areas, but also showed up in aggregate measures of governmental quality, whether based on citizen perceptions or the quality indicators prepared by the World Bank. Among these latter measures, the greatest impact still appears to flow from the quality of policy delivery, including the control of corruption. Finally, making use of international data measuring peace and conflict, countries able to reduce conflict and achieve peace were estimated to become happier places to live.

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Endnotes
Back to the 2019 report

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The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by data from the Gallup World Poll, and supported by the Ernesto Illy Foundation, illycaffè, Davines Group, Blue Chip Foundation, the William, Jeff, and Jennifer Gross Family Foundation, and Unilever's largest ice cream brand Wall's.

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Money and happiness ,an enriching relationship_

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How does money relate to life satisfaction_

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The world happiest country is all coffee and saunas

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Forbes, why Finland is the happiest country

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Why African countries are among the world’s unhappiest 

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Full reading time: 15 minutes.
Introduction

It's a cliché that "you can't buy happiness", but at the same time, financial security is among most people's top career priorities. Moreover, when people are asked what would most improve the quality of their lives, the most common answer is more money. What's going on here? Who is right?

A lot of the research on this question is of remarkably low quality. But there have been some recent major studies in economics that allow us to make progress. In particular, we now finally have survey data from hundreds of thousands of people all around the world. We've sifted through the best studies available to figure out what's really going on. The truth seems to lie in the middle: money does make you happy, but only a little. And this has many important implications about trade-offs you face in your life and career.

Summary of main points

Recent surveys of hundreds of thousands of people, in over 150 countries, show that richer people report being more satisfied with their lives overall, but that the richer you become, the more money you need to increase your satisfaction further. This is because people spend money on the most important things first. Someone earning $100,000 per year is only a little more satisfied than someone earning $50,000. The best available study found that each doubling of your income correlated with a life satisfaction 0.5 points higher on a scale of 1 to 10.

If you look at how 'happy' people say they are right now, the relationship is weaker. One large study found people in countries with average incomes of $32,000 were only 10% happier with their lives than those in countries with average incomes of just $2,000; another within the US could find no effect above a $40,000 income for a single person.

Moreover, some and maybe even most of this relationship is not causal. For example, healthier people will be both happier and capable of earning more. This means the effect of gaining extra money on your happiness is weaker than the above correlations suggest. Unfortunately, how much of the above relationships are caused by money making people happier is still not known with confidence.

Once you get to an individual income of around $40,000, other factors, such as health, relationships and a sense of purpose, seem far more important than income. So our recommendation is not to focus on earning more than this (insofar as you want to become happy, anyway).

However, you may gain from earning more than that if: you have dependents, you care about money more than other people, or you live in an area with an unusually high cost of living.

Giving money to someone living on $1,000 per year in the developing world will do far more to improve their lives than giving the same amount to someone earning $25,000. The correlations above suggest that it will be about 25 times more valuable. If you want to help people, this is a major reason to focus on international poverty rather than helping the relatively poor in richer countries.

Giving some money to charity is unlikely to make you less happy, and may well make you happier.

Are richer people more satisfied with their lives?

Thinking about it for a moment, you'd expect that the richer you are, the more extra money you need to further increase your happiness.

If you're earning $10,000 a year, and you get an extra $1,000, you're probably going to use it on something pretty important, like making rent, which will make a big difference to your happiness. But if you're earning $100,000 per year, you'll hardly notice an extra $1,000. Maybe you'll just use it to eat out a bit more. In other words, you'd expect the relationship to be diminishing. If you draw out a graph of income against happiness, it'll look a bit like the graph below. This is what every economist, philosopher and psychologist who works on this topic expects to see.

At some point you end up spending money on stuff that doesn't make much difference. For example:

(That's literally a roll of toilet paper made of gold that some people bought.)

The interesting question is how fast that happens. It may be that at middle-class incomes extra money still makes you significantly happier. Or perhaps after that point extra income has no discernible impact at all.

One way to figure this out is to ask lots of people all around the world how much they earn and how satisfied they are with their lives. A typical question of this kind from the 'World Values Survey' is:

"All things considered, how satisfied are you with your life as a whole these days?: 1 (dissatisfied) - 10 (very satisfied)."

In the 70s and 80s, it was widely thought by psychologists that after a certain point, there was no relationship between income and life satisfaction, at least in wealthier countries.

Today, larger and more rigorous studies haven't borne out that result. As you get richer, you need a lot more money to make you more satisfied, but there's no maximum level of income beyond which more seems to contribute nothing.

The best study we could find is this one by famous economists Betsy Stevenson and Justin Wolfers. It draws on polling data from hundreds of thousands of people in 166 countries and found that people in richer countries reported being more satisfied with their lives than those in poorer countries, and that within a country, richer people also reported being more satisfied than those with lower incomes.

As you can see, this survey found a clear straight-line relationship between income and happiness both within and between countries. The lines are straight rather than curved because each increment on the bottom of the axis indicates a doubling of income.

Roughly, what this means is that if you double your income, you gain about half a point on a scale of 1 to 10 of life satisfaction. More precisely, this is a called a logarithmic relationship.

Note that this is just an association at this point - we discuss whether higher income is actually causing people to become more satisfied below.

According to this survey data, a typical person with a household income of $2,000 rates their life satisfaction at around 4.2 out of 10. A typical person with a household income of $64,000 rates their life satisfaction at 7.2 out of 10.

In the past, with only inconsistent polls available in a small number of countries, this relationship was much less clear, causing researchers to think there was no relationship between satisfaction and income. For more on the controversy about this today you can skip to Appendix I.

OK, but are richer people happier?

There's more evidence for a maximum useful level of income if instead of asking people how their life is going overall, we ask them how they feel right now or felt yesterday.

For instance, this study by Nobel prize winners Daniel Kahneman and Angus Deaton, relied on a phone poll that asked hundreds of thousands of Americans how they felt in the following ways:

Positive affect - "were you happy yesterday?"

Low stress - "did you feel stressed yesterday?"

Not blue - "did you feel sad yesterday?"

Ladder - "how satisfied are you with your life overall?"

Here's the result:

Again, the scale at the bottom doubles with each increment.

You can see that the "ladder" of life satisfaction is roughly straight all the way up, just as we found before.

However, the other three lines start to flatten around $50,000, and are completely flat by $75,000. This means that extra income had no relationship with how happy, sad and stressed people felt after this point.

Moreover, note that this is $50-75,000 of household income. That's equivalent to an individual income of more like $26-40,000 if you're single and not supporting kids.

Not all studies find that money stops having any impact. For example, another enormous data analysis by Daniel Sacks, Justin Wolfers and Betsy Stevenson found that happiness continued to improve in countries with higher incomes - or at least there was no clear levelling off (see figure below).

People in richer countries were more likely to recall feeling 'enjoyment' or love yesterday, and less likely to experience 'depression', or 'physical pain' despite being older (see the figure below).

People in richer countries were also a bit more likely to report being consistently treated with respect, having good tasting food, smiling or laughing a lot, and being free to choose how they spend their time (see the figure below).

But simply scanning the data you can see that these associations, while real, are quite weak considering the enormous range of income across the sample. Raising income 16-fold, from $2,000 to $32,000, moved reported happiness from 3.0/4 to 3.3/4. A 64-fold increase in income, from $500 to $32,000, increased the probability of recalling feeling enjoyment or eating tasty food yesterday from around 60% to 80%. A 64-fold increase in income only raised the likelihood of feeling 'love' yesterday from 63% to 73%. Much of our everyday human experiences are just not affected much by money. In other words:

In other studies we looked at, overall life evaluation always showed the strongest relationship with income. If you ask people how happy they feel today, or felt yesterday the relationship becomes more tenuous.

What can make sense of these results?

We guess the key factor is the one we noted at the beginning - you take the best opportunities to invest in your happiness first, so as you get more money, it becomes harder and harder to buy more happiness. Eventually the effect of additional income of happiness becomes negligible relative to other factors.

There could be other reasons for a weak relationship. For instance, one way to earn more money is to work longer hours in a job few other people want to do. Maybe the unhappiness caused by these extra hours at work offsets whatever you gain from the extra income. It's a case of mo' money, mo' problems.

There's some evidence for this idea. This meta-analysis of over 100 studies found only a very weak relationship between pay and job satisfaction. Some kinds of jobs are low-paying precisely because they are satisfying. For example, if teaching weren't fulfilling, salaries would have to be higher to convince enough people to become teachers.

Another factor is that we readily adapt to having more money. If you only have champagne once a year, it's a special occasion. But to quote more Biggie, if "we sip champagne when we thirst-ay", it's no big deal. This is called the " hedonic treadmill". This is particularly true when we spend money on material goods, like fancy clothes, which we quickly get used to. Moreover, we persistently underestimate how much we can adapt, so expect money to matter more than it does.

However, there are some things we can't adapt to, which explains why there remains some relationship between income and happiness even among the rich. One example is that long commutes make people unhappy - and they never get used to them (see the figure below). More money can also help you have more interesting, varied experiences and relationships, which are important too.

How come life satisfaction seems to increase more steeply with income than day-to-day happiness? Here's a likely explanation. If someone asks you whether you are in physical pain, it's easy to check and give a meaningful answer. But if someone asks you on the phone how satisfied you are with your life, all things considered, on a scale of one to ten... it can be hard to say. As you don't really know how satisfied you are on average, and you have to answer quickly, people are inclined to substitute in its place a related question that is easier to answer. A natural option is 'how much money am I making relative to others?', or 'how well is my career going?'. This widely observed phenomenon is called attribute substitution by psychologists.

In this respect experience sampling is superior because it avoids a range of possible biases in people's perception and recollection of their life. Nonetheless, life satisfaction passes several tests for being a good psychological measure (for example, it is stable over time and predicts future behaviour) so shouldn't be disregarded.

So would making more money make you happier?

So far we've just looked at the correlation between income and happiness, but correlation doesn't imply causation. As Stevenson and Wolfers remark:

We should note that we have focused on establishing the magnitude of the relationship between subjective well-being and income, rather than disentangling causality from correlation. The causal impact of income on individual or national subjective well-being, and the mechanisms by which income raises subjective well-being, remain open and important questions.

It could be that there's some other factor that causes both happiness and income. If this is true, boosting your income won't boost your happiness. For instance, maybe healthier people are both happier and able to earn more because they have more energy. Or maybe happiness increases your income because happier people make better colleagues.

If these other connections exist, and they probably do, making an effort to earn more money won't increase your happiness as much as you'd hope from the above correlations alone.

So, if the question is "if I earn more money, will I be happier?", then the relationship is likely weaker than what we've seen above.

And the relationship above was already pretty weak: If you already earn over $40,000, then you need to gain an extra $40,000 per year just to gain 0.5 on a 10 point scale of life satisfaction. You can expect little if any noticeable effect on day-to-day happiness, stress or sadness. That's a lot of income for a limited gain.

What about the possibility that people who earn more are happier because of their money, but this is counteracted by them having to work longer hours in less pleasant jobs? If that's what's going on, winning the lottery would make you happier, but choosing a higher paying job wouldn't.

So, what about lottery winners? When people write about income and happiness they always mention this study that supposedly shows lottery winners were no happier a year or two after winning. This would be good evidence that there's almost no relationship between income and happiness, even if you could get the money without having to do any extra work.

However, we went and read the original study, and found that actually the lottery winners were happier - they reported their happiness as 4 (out of 6) compared to 3.82 for the control group.

But the real problem is that the study had a tiny sample: there were only 22 winners. This was so small - and the control group so inappropriately selected - that the authors themselves didn't believe they had yet discovered much. Unfortunately, the story was too good for people to bother fact checking.

(This is also the paper you might have heard cited saying paraplegics are no less happy than anyone else - this is nonsense for the same reason. In fact paraplegics rated their general happiness as 2.96, much lower than others.)

Newer studies with larger samples have generally found that lottery winners seem a little better off - at least after their family and friends stop asking them for money. Unfortunately, it's hard to say much because i) the samples are usually small, ii) the winnings are often also small, and iii) the outcome measures all differ from one another, and from the papers above.

So in the end, what evidence we can get about lottery winners supports what we already thought: more income makes you happier, but only a little.

How do these figures apply to me?

The figures above are based on surveying a cross-section of people in a country. To customize the $40,000 threshold for yourself, you might want to make the following adjustments:

The $40,000 figure was from 2009. Due to inflation, it's more like $45,000 in 2016.

Add $20,000 per dependent who does not work that you fully support.

Add 50% if you live in an expensive city (e.g. NY, SF), or subtract 30% if you live somewhere cheap (e.g. rural Tennessee). You can find cost of living calculations online, like this one.

Add more if you're especially motivated by money (or subtract some if you have frugal tastes).

Add 5-10% in order to be able to save enough for retirement (or however much you personally need to save in order to be able to maintain the standard of living described above). It's true the people in the surveys above were saving for retirement, but we suspect not enough.

The average college graduate in the United States earns $77,000 per year over his or her life, while the average Ivy League graduate earns over $110,000. The upshot is that if you're a college graduate in the U.S. (or a similar country), then you'll likely end up well into the range where more income has almost no effect on your happiness.

Are there exceptions to this general rule?

The story might be different if you care about money more than most people. If that's true there could be opportunities to gain money that wouldn't be worth it for most people, but are for you.

There's empirical support for this. A small percentage of people say making money is a top priority for them at the start of their careers, and these people do turn out to be significantly more satisfied if they go on to make a lot of it. Maybe this is because they enjoy spending money more than others, or maybe it's just that checking their income is how they track their success. Unfortunately, people whose main goals require earning money are also less satisfied with their lives on average.

If you want to support more financial dependents, you will need to earn more before the income-happiness relationship weakens in the way described above. Likewise, if you live somewhere with an unusually high cost of living, you can scale up the figures at which money stops helping. Finally, if your friends are becoming wealthy and you want to continue to socialise with them in expensive places, money may also be more valuable for you, though we don't know of any specific studies on this.

Conversely, if none of those apply, extra income may do even less for your happiness than these aggregate surveys suggest.

How much does income matter relative to other factors?

If you're educated and in a rich country then there are other factors that will affect your happiness and satisfaction much more than extra income.

Ball & Chernova calculate that for the median single individual, the happiness boost produced by marriage is matched only by a 767% increase in absolute income, or by an increase in relative income from the 50th to the 88th percentile.

They also found the boost associated with moving from a health rating of 3 to 4 (when health is scored from 1 to 5) is matched only by a 6,531% increase in absolute income, or by a move from the 50th to the 100th percentile in relative income.

Being widowed (as a woman) or losing your job (as a man) appears to reduce life satisfaction by about 0.5 points, on a scale of 1-7. Using our estimates above, this would be the same as having your income fall or rise by two thirds.

Having a close friend become happy also seems to increase your chance of being happy a great deal (at least 10%).

What does this mean for your career choice?

We think the message is clear: if you want a satisfying career, once you're earning above about $40,000, don't focus on earning more money. Instead, focus on the factors we recommend in our article on how to find fulfilling work.

This is widely accepted by experts in the field. For instance: Timothy Judge, professor of management at the University of Notre Dame, suggests that if you ultimately care about having a job that's satisfying:

You would be better off weighing other job attributes higher than pay.

(Though note the possible exceptional conditions above.)

What does this mean for having a positive impact on the world?

Money can go much further in the poorest countries. It's clear that higher incomes benefit people in serious poverty. If the relationship between income and satisfaction is logarithmic, or even more sharply declining, you need 50-100 times as much money to increase the satisfaction and happiness of an educated American as that of someone in the poorest billion people.

This is one of the main reasons we think that if you want to help people alive today, it's important to focus on the effects your actions have on those in the developing world. That's true whether we're talking about giving to charity, enacting policy reform, or setting up a social enterprise. Their welfare is simply much more responsive to changes in income.

You may have greater knowledge of your local community, but that's probably not enough to make up for the fact that your resources could go one hundred times as far if you focus on the very poorest people. And fortunately there is high quality research you can rely on to know what really works in the developing world. One of the top opportunities is just directly giving money to the very poor.

If you gave money to charity, would it make you more satisfied or less?

The results above suggest that if you're a professional in a rich country, having a lower income won't make you much less happy. As a result the personal costs of donating to charity are also likely small.

Moreover, donating money is not at all the same as not earning it in the first place. Someone with an income that's low relative to what they aspire to may feel unsuccessful, and therefore unsatisfied with their life. But if you earn a good salary and donate a big chunk, you probably won't feel that way. On the contrary - you'll see yourself as successfully striving to make your mark on the world.

There's considerable evidence that acts of altruism make us happier, more satisfied and even healthier. This includes acts of charity, as well as other ways of helping people such as buying gifts for friends and family.

This means that donating money could easily make you happier than spending it on yourself.

For instance:

Imagine the following scenario. You are a participant in a psychological experiment: you are given an envelope containing a small sum of money, which you are asked to spend within 24 hours. The experimenter can assign you to one of conditions: she can require that you spend the money on yourself (paying a bill or buying yourself a treat) or she can require that you spend the money on others (buying a present for someone or donating the money to charity).

...the experimenters found that subjects in the prosocial spending condition reported greater happiness after spending their windfall than did those in the personal spending condition. This was not an isolated result. Dunn et al. also conducted a longitudinal study of 16 employees at a Boston based company who received a profit-sharing bonus, finding that those who devoted more of their bonus to prosocial spending experienced greater happiness as a result of spending their windfall; a cross-sectional study of a representative sample of Americans also found greater prosocial spending correlated with significantly greater happiness, while personal spending turned out to be unrelated to happiness.

Aknin et al. examined survey-data from 136 countries gathered by the Gallup Organization, to see whether ratings of subjective wellbeing were positively correlated with donating to charity. Controlling for household income, it was found, in 122 of the 136 countries, that there is a positive correlation between subjective wellbeing and answering Yes to the question 'Have you donated money to charity in the last month?' On average, it was found, "donating to charity has a similar relationship to subjective wellbeing as a doubling of household income."

We worry that last effect is confounded by religion: membership of a church both predicts charitable giving and higher welfare. But the evidence is good that giving away money will lower your subjective well-being significantly less than not having it in the first place.

The bottom line

You have probably heard both from people who say earning a good income is both incredibly important, and not important at all. As is often the case when you look carefully at the evidence, the truth seems to be somewhere in between.

Unfortunately, existing research is not good enough to say for sure what impact a randomly assigned increase in income has on someone's welfare. Hopefully more thorough research on lottery winners will answer this question in the future. But until then we at least have a lot of data on how people who earn both a lot and a little report feeling about their lives.

If you're poor, having even small amounts of extra money is associated with significant gains in welfare. People in very poor countries report low levels of satisfaction with their lives, though their day-to-day happiness is surprisingly resilient.

But most of our readers are university graduates in rich countries, the group that is least likely to benefit from higher income. For them, making a meaningful contribution to their society and having good relationships with friends and family are likely to do them more good than a higher paying job. Inasmuch as earning more means sacrificing these things it's a very questionable trade.
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You can also continue reading our guide to finding a career that makes you truly happy.

Appendix I - But I've always been told we just look at relative rather than absolute income?

This remains the source of some controversy, but we think the answer is that we care about both absolute and relative income.

You may have heard of the 'Easterlin Paradox': why don't people get much happier when their country becomes richer? The popular explanation in the 70s and 80s was this: people are briefly happy when their income rises, and happier when they are richer than others around them, but don't value it in the long term when their country as a whole becomes richer.

The findings in the post above cast serious doubt on whether there is any paradox to explain. People in richer countries are somewhat more satisfied.

But Easterlin, who is now 90 and has spent much of his life studying this apparent paradox, was not convinced by this data. He published two papers in response, attempting to show that how quickly a country gets richer doesn't correlate with how quickly it gets satisfied. Easterlin believed the error being made by others was:

In the present analysis we demonstrate that these conflicting results arise chiefly from confusing a short-term positive happiness - income association, due to fluctuations in macroeconomic conditions, with the long-term relationship. We suggest, speculatively, that this disparity between the short and long-term association is due to the social psychological phenomenon of "loss aversion".

In response, Wolfers and Stevenson updated their paper to look at how economic growth relates to satisfaction growth over the longest timescales they could analyse. Examining the figures in their paper, the two do seem to move together, though there's a lot of other variation. This is to be expected. When you try to relate growth rates in two things, there is a lot of room for measurement error: in baseline income, final income, baseline satisfaction, and final satisfaction. You measure all of these imperfectly, creating a lot of noise that obscures any shared movement they have. Furthermore, no one claims economic growth is the only, or even most important thing, determining shifts in happiness.

Wolfers and Stevenson explain the disagreement this way:

...you should never confuse absence of evidence with evidence of absence. Easterlin's mistake is to conclude that when a correlation is statistically insignificant, it must be zero. But if you put together a dataset with only a few countries in it - or in Easterlin's analysis, take a dataset with lots of countries, but throw away a bunch of it, and discard inconvenient observations - then you'll typically find statistically insignificant results. This is even more problematic when you employ statistical techniques that don't extract all of the information from your data. Think about it this way: if you flip a coin three times, and it comes up heads all three times, you still don't have much reason to think that the coin is biased. But it would be silly to say, "there's no compelling evidence that the coin is biased, so it must be fair." Yet that's Easterlin's logic.

Nobel Prize winner Angus Deaton, by contrast, finds some evidence that relative income matters for 'happiness', but doesn't for satisfaction. He comments:

The most famous [unresolved puzzle] is the Easterlin paradox that in spite of the positive effect of income on life evaluation and on happiness, there appears to have been little effect of economic growth. That there is a paradox at all has been robustly challenged by Daniel Sacks, Betsey Stevenson, and Justin Wolfers (2012), and Easterlin's counter-evidence rests heavily on long-run Chinese data of dubious comparability. This literature typically does not make the distinction between evaluative and hedonic measures that is so important here.

As an aside, one paper attempts to explain the failure of Chinese happiness to rise with reference to much higher air pollution.

This question isn't fully resolved, but we're more convinced by Wolfers and Stevenson's latest (draft) paper on the topic, which shows a combination of positive and neutral relationships and offers several explanations for why others have not found the same results. The arguments come down to methodological details that are tricky to explain, so if you'd like to explore them I recommend reading the discussion section of the paper.

There's also a common sense argument that we find compelling: if richer countries are more satisfied than poorer ones, which seems to be the case, it would be remarkable if countries didn't get eventually more satisfied after they got richer, whatever the cause of the relationship between them. I suspect past studies were just not good enough to pick up the effect.

Unfortunately almost all of this appendix concerns income and satisfaction. Given that the income and happiness relationship is weaker to start with, it wouldn't surprise me if the unpleasant aspects of economic growth, such as pollution, made economic development a pretty ineffective way to increase day-to-day happiness - at least until countries had been wealthy long enough to fix up those problems and invest their higher income in other changes that make them happy. Unfortunately, the data on this question is more limited and hasn't been the focus of so much research.

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Everything you need to know about whether money makes you happy - 80,000 Hours

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Full reading time: 15 minutes. It's a cliché that "you can't buy happiness", but at the same time, financial security is among most people's top career priorities. Moreover, when people are asked what would most improve the quality of their lives, the most common answer is more money. What's going on here?

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By Ma Feiya
Malaysia ranked 80th in the 2019 World Happiness Report (covering the overall performance from 2015 to 2017) and dropped 45 places compared to 35th in 2018.

The World Happiness Report is a United Nations survey of global well-being. It ranks 156 countries on the basis of per capita GDP, life expectancy of citizens, satisfaction with life, per capita consumption of resources, environmental pollution, generosity, and corruption.

In the 2018 report (covering the overall performance from 2015 to 2017), Malaysia's performance is very good, ranking 35th in the world, second only to neighboring Singapore (ranked 34th), a ranking of happiness index in Southeast Asian countries. Second, the happiness index in Asian countries ranks third.
But why did the ranking fall sharply this year?
We all know that Malaysia is a multi-ethnic and pluralistic country. But it also means that there are always conflicts. Even in the streets, there will be gunfights between police and robbers.

According to the 2019 World Crime Rate Ranking, Malaysia's crime rate in Southeast Asia is the highest. Even in the past few years, it has ranked in the top 20 in the world crime rate ranking. This is simply too unbelievable. To know that Malaysia was once one of the safest countries in Asia, and now as a developing country, the crime rate is comparable to that of a third world country.

Large cities with complex social environments are more likely to breed crime than in the countryside. Theft, robbery, murder, drug trafficking, rape, destruction of official duties, etc., urban crimes are varied.

Although it is an indisputable fact that crime rates are high in all parts of the world, it is rare for "popular" looting cases such as breaking windows, robbing property and killing in Malaysia nowadays. So, if we travel to countries with high security rates, even in the middle of the night, strange streets are safer than walking in the streets of the familiar local big cities?

According to Numbeo, the Klang crime rate is as high as 68%. As a capital city, the crime rate in Kuala Lumpur is as high as 66.55%, Johor Bahru is 58.58%, and Kuching and Penang are 47.1% and 41.62% respectively.

Why is the crime rate high?
The high crime rate is basically not related to human nature, rather it is economic problems and lack of police force, but the former is more critical. If the economy is not improving, inflation, adultery and looting will always plague local citizens, and tourists will be discouraged, further dragging down the economy.

Moreover, as a neighbour of Malaysia, Thailand, which allows citizens to hold guns, is the country with the largest number of guns owned by civilians in Southeast Asian countries. This has led to the influx of many illegal guns into Malaysia, making the bandits more daring! It's also a great headache for the police.

Second, the gangsters specifically took time to commit crimes. The gangsters are very familiar with the police patrol time, and the residential areas are well-connected, with no obstructions, which is extremely convenient for theft and escape.

On the other hand, with the changes in the socio-economic structure, the community system is gradually changing. People are no longer familiar with each other, and everyday life relies on modern means of transportation, leading to an ever-expanding range of crimes. With the advent of the Internet age, communication is more convenient, which opens the door for criminal groups, smuggling drugs, human trafficking, online fraud and so on.
Other Reasons
As the survey shows, the World Happiness Report uses global voting data to measure how satisfied people are about their lives. Over the years, the Nordic countries have occupied almost the top 10 positions. Last year, Norway ranked first and Finland this year. This also shows that a happy society must also be supported by a good healthy social environment.

Therefore, happiness index should not only focus on crime rate or safety issues, but also on environmental changes and people's growing needs, such as psychological and physical health problems, environmental pollution, loss of community awareness, and a decline in social trust.

When life safety is threatened, how do we talk about happiness? From 2015 to 2019, reports of environmental pollution in various parts of Malaysia have caused public health problems. On October 4, 2015, the air pollution index was severe in many parts of Malaysia, and the smog continued, causing the school to suspend classes. On March 7, 2019, Pasir Gudang' s chemical waste polluted the river water, causing the river to become turbid, many of fish died, and the odour continued. A total of 35 victims were vomiting and fainting and having breathing difficulties after inhaling methane gas. And the catch was reduced. The fishermen suffered economic losses.

Science and technology are more and more developed, new technologies are constantly updated, and new apps are constantly on the shelves. In shops, restaurants, and even schools, there are people who bend their heads and concentrate on mobile phones. According to the latest figures from 'Hootsuite' and 'We are Social', the Internet penetration rate in Malaysia is 80%, and the average online time of users is 8 hours and 5 minutes per day.

People are keen to praise, comment and respond to strangers on social media, but they rarely communicate with their families and friends in real life, gradually alienating and decreasing their trust in each other.

Whether it is economic, social or human psychological reasons, it is hoped that the people living in Malaysia will become happier. ***

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Malaysia's happiness index: Why the massive drop?

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By Ma Feiya Malaysia ranked 80th in the 2019 World Happiness Report (covering the overall performance from 2015 to 2017) and dropped 45 places compared to 35th in 2018. The World Happiness Report is a United Nations survey of global well-being.

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How happy are people today? Were people happier in the past? How satisfied with their lives are people in different societies? And how do our living conditions affect all of this?

These are difficult questions to answer; but they are questions that undoubtedly matter for each of us personally. Indeed, today, life satisfaction and happiness are central research areas in the social sciences, including in 'mainstream' economics.

In this entry, we discuss the data and empirical evidence that might answer these questions. Our focus here will be on survey-based measures of self-reported happiness and life satisfaction. Here is a preview of what the data reveals.

1. Surveys asking people about life satisfaction and happiness do measure subjective well-being with reasonable accuracy.

2. Life satisfaction and happiness vary widely both within and among countries. It only takes a glimpse at the data to see that people are distributed along a wide spectrum of happiness levels.

3. Richer people tend to say they are happier than poorer people; richer countries tend to have higher average happiness levels; and across time, most countries that have experienced sustained economic growth have seen increasing happiness levels. So the evidence suggests that income and life satisfaction tend to go together (which still doesn't mean they are one and the same).

4. Important life events such as marriage or divorce do affect our happiness, but have surprisingly little long-term impact. The evidence suggests that people tend to adapt to changes.

Interactive charts on Happiness and Life Satisfaction

Happiness across the world today

The World Happiness Report is a well-known source of cross-country data and research on self-reported life satisfaction. The map here shows, country by country, the 'happiness scores' published this report.

The underlying source of the happiness scores in the World Happiness Report is the Gallup World Poll -a set of nationally representative surveys undertaken in more than 160 countries in over 140 languages. The main life evaluation question asked in the poll is: "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?" (Also known as the "Cantril Ladder".)

The map plots the average answer that survey-respondents provided to this question in different countries. As with the steps of the ladder, values in the map range from 0 to 10.

There are large differences across countries. According to the most recent figures, European countries top the ranking: Finland, Denmark, Iceland, Switzerland and the Netherlands have the highest scores (all with averages above 7). In the same year, the lowest national scores correspond to Afghanistan, Lebanon, Zimbabwe, Rwanda and Botswana (all with average scores below 3.5).

You can click on any country on the map to plot time-series for specific countries.

As we can see, self-reported life satisfaction correlates with other measures of well-being-richer and healthier countries tend to have higher average happiness scores. (More on this in the section below.)

In addition to the Gallup World Poll (discussed above), the World Value Survey also provides cross-country data on self-reported life satisfaction. These are the longest available time series of cross-country happiness estimates that include non-European nations.

The World Value Survey collects data from a series of representative national surveys covering almost 100 countries, with the earliest estimates dating back to 1981. In these surveys, respondents are asked: "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". This visualization plots the share of people answering they are Very happy or Rather happy.

As we can see, in the majority of countries the trend is positive: In 49 of the 69 countries with data from two or more surveys, the most recent observation is higher than the earliest. In some cases, the improvement has been very large; in Zimbabwe, for example, the share of people who reported being 'very happy' or 'rather happy' went from 56.4% in 2004 to 82.1% in 2014.

The Eurobarometer collects data on life satisfaction as part of their public opinion surveys. For several countries, these surveys have been conducted at least annually for more than 40 years. The visualization here shows the share of people who report being 'very satisfied' or 'fairly satisfied' with their standards of living, according to this source.

Two points are worth emphasizing. First, estimates of life satisfaction often fluctuate around trends. In France, for example, we can see that the overall trend in the period 1974-2016 is positive; yet there is a pattern of ups and downs. And second, despite temporary fluctuations, decade-long trends have been generally positive for most European countries.

In most cases, the share of people who say they are 'very satisfied' or 'fairly satisfied' with their life has gone up over the full survey period.² Yet there are some clear exceptions, of which Greece is the most notable example. Add Greece to the chart and you can see that in 2007, around 67% of the Greeks said they were satisfied with their life; but five years later, after the financial crisis struck, the corresponding figure was down to 32.4%. Despite recent improvements, Greeks today are on average much less satisfied with their lives than before the financial crisis. No other European country in this dataset has gone through a comparable negative shock.

The distribution of life satisfaction

Most of the studies comparing happiness and life satisfaction among countries focus on averages. However, distributional differences are also important.

Life satisfaction is often reported on a scale from 0 to 10, with 10 representing the highest possible level of satisfaction. This is the so-called 'Cantril Ladder'. This visualization shows how responses are distributed across steps in this ladder. In each case, the height of bars is proportional to the fraction of answers at each score. Each differently-colored distribution refers to a world region; and for each region, we have overlaid the distribution for the entire world as a reference.

These plots show that in sub-Saharan Africa-the region with the lowest average scores-the distributions are consistently to the left of those in Europe. In economics lingo, we observe that the distribution of scores in European countries stochastically dominates the distribution in sub-Saharan Africa.

This means that the share of people who are 'happy' is lower in sub-Saharan Africa than in Western Europe, independently of which score in the ladder we use as a threshold to define 'happy'. Similar comparisons can be made by contrasting other regions with high average scores (e.g. North America, Australia and New Zealand) against those with low average scores (e.g. South Asia).

Another important point to notice is that the distribution of self-reported life satisfaction in Latin America is high across the board-it is consistently to the right of other regions with roughly comparable income levels, such as Central and Eastern Europe.

This is part of a broader pattern: Latin American countries tend to have a higher subjective well-being than other countries with comparable levels of economic development. As we will see in the section on social environment, culture and history matter for self-reported life satisfaction.

If you are interested in data on country-level distributions of scores, the Pew Global Attitudes Survey provides such figures for more than 40 countries.

(Mis)perceptions about others' happiness

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.

An alternative explanation is that this mismatch is grounded in the well-established fact that people tend to be positive about themselves, but negative about other people they don't know.It has been observed in other contexts that people can be optimistic about their own future, while at the same time being deeply pessimistic about the future of their nation or the world. We discuss this phenomenon in more detail in our entry on optimism and pessimism, specifically in a section dedicated to individual optimism and social pessimism.

East and West Germany

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:

First, the gap is partly driven by differences in household income and employment. But this is not the only aspect; even after controlling for socioeconomic and demographic differences, the East-West gap remains significant.

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 observation that socioeconomic and demographic differences do not fully predict the observed East-West differences in self-reported happiness is related to a broader empirical phenomenon: Culture and history matter for self-reported life satisfaction-and in particular, ex-communist countries tend to have a lower subjective well-being than other countries with comparable levels of economic development.

Trends in life satisfaction for East and West Germany, 1992-2013

Happiness inequality

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

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.

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.

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.

The resulting connected scatter plot may be messy, resembling a 'spaghetti' chart, but it is helpful to confirm the overall pattern: despite kinks here and there, lines are by and large upward sloping.

Looking across and within countries

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.

These findings have been explored in more detail in a number of recent academic studies. Importantly, the much-cited paper by Stevenson and Wolfers (2008)¹⁰ shows that these correlations hold even after controlling for various country characteristics such as demographic composition of the population, and are robust to different sources of data and types of subjective well-being measures.

Economic growth and happiness

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).

An important point to note here is that economic growth and happiness growth tend to go together on average. Some countries in some periods experience economic growth without increasing happiness. The experience of the US in recent decades is a case in point. These instances may seem paradoxical given the evidence-we explore this question in the following section.

The Easterlin Paradox

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.

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.

In the US, the explanation is different, but can once again be traced to the underlying data. Specifically, if we look more closely at economic growth in the US over the recent decades, one fact looms large: growth has not benefitted the majority of people. Income inequality in the US is exceptionally high and has been on the rise in the last four decades, with incomes for the median household growing much more slowly than incomes for the top 10%. As a result, trends in aggregate life satisfaction should not be seen as paradoxical: the income and standard of living of the typical US citizen has not grown much in the last couple of decades. (You can read more about this in our entry on inequality and incomes across the distribution.)

GDP per capita vs Life satisfaction across survey questions, Japan, 1958-2007 - Stevenson and Wolfers (2008)¹⁴

Life expectancy and life satisfaction

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.

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

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.

In general, the evidence suggests that adaptation is an important feature of well-being. Many common but important life events have a modest long-term impact on self-reported happiness. Yet adaptation to some events, such as long-term unemployment, is neither perfect nor immediate.

Does disability correlate with life satisfaction?

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.

As we can see-and as the authors show more precisely through econometric techniques-those entering disability suffer a sudden drop in life satisfaction, and recover only partially. This supports the idea that while adaptation plays a role for common life events, the notion of life satisfaction is indeed sensitive to tragic events.

Life satisfaction of those entering serious disability, BHPS 1996-2002 - Oswald and Powdthavee (2006)

Culture and life satisfaction

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

To our knowledge, there are no rigorous studies exploring the causal mechanisms linking culture and happiness. However, it seems natural to expect that cultural factors shape the way people collectively understand happiness and the meaning of life.

Sense of freedom and life satisfaction

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.

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).

To our knowledge there are no rigorous studies exploring the causal mechanisms linking freedom and happiness. However, it seems natural to expect that self-determination and absence of coercion are important components of what people consider a happy and meaningful life.

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).

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 with the eyes ("unfakeable smile")

Ratings of one's happiness made by friends

Frequent verbal expressions of positive emotions

Sociability and extraversion

Happiness of close relatives

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.

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.

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: 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

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.

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Self-reported life satisfaction differs widely between people and between countries. What explains these differences?