I. Empirical View
I.1 Cross-country evidence
Happiness around the world, country by country
The World Happiness Report is a well-known source of cross-country data and research on self-reported life satisfaction. The map below shows, country by country, the 'happiness scores' published in the World Happiness Report 2017.
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 below 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 2016 figures, Nordic countries top the ranking: Finland, Norway, Denmark, the Netherlands and Iceland have the highest scores (all with averages above 7). In the same year, the lowest national scores correspond to Central African Republic, South Sudan, Tanzania, Rwanda and Haiti (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.)
Changes in happiness over time—Findings from the World Value Survey
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". The visualization below 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.
Changes in happiness over time—Findings from Eurobarometer
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 below 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.
More than averages—the distribution of life satisfaction scores
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'. The below 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 the happiness of others
We tend to underestimate the average happiness of people around us. The following visualization shows this for countries around the world, using data from Ipsos' Perils of Perception—a cross-country survey asking people to guess what others have answered to the happiness question in the World Value Survey.
The horizontal axis in the chart below 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 significantly below the 45-degree line. In other words, people in every country underestimated self-reported happiness. The most extreme deviations are in Asia—South Koreans tend to 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 argument 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 also assuming that others do not misreport theirs.
Alternately, there is substantial evidence showing that ratings of one's happiness made by friends correlate with one's happiness (discussed in more detail below), and that people are generally good at evaluating emotions from simply watching facial expressions (also discussed below). Hence, a more likely explanation is 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.
I.2 Within-country evidence
Life satisfaction inequalities between East and West Germany
National aggregates of self-reported life satisfaction, such as those discussed above, mask within-country inequalities. In the map below we focus on regional inequalities within Germany—specifically the gap in happiness between West and East Germany.
The map below plots self-reported life satisfaction in Germany (using the Cantril Ladder), aggregating averages scores at the level of Federal States.3 The first thing that 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.
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 has been narrowing in recent years—and this is true both for the raw average differences, as well as for the 'conditional differences' that can be estimated after controlling for socioeconomic and demographic differences. You can see how the gap has been narrowing since reunification in these charts from Petrunyk and Pfeifer (2016).
And second, the differences in household income and unemployment status are important factors that contribute to the observed differences in self-reported life satisfaction—yet even after controlling for these and other socioeconomic and demographic differences, the East-West gap remains significant.
The fact 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. (More on this in the section on social environment.)
Happiness inequality in the US and other rich countries
The General Social Survey (GSS) in the US is a survey administered to a nationally representative sample of about 1,500 respondents each year since 1972, and is an important source of information on long-run trends of self-reported life satisfaction in the country.5
Using this source, Stevenson and Wolfers (2008)6 show that while the national average has remained broadly constant, inequality in happiness has fallen substantially in the US in recent decades.
The authors further note that this is true both when we think about inequality in terms of the dispersion of answers, and also when we think about inequality in terms of gaps between demographic groups. They note that two-thirds of the black-white happiness gap has been eroded (although today white Americans remain happier on average, even after controlling for differences in education and income), and the gender happiness gap has disappeared entirely (women used to be slightly happier than men, but they are becoming less happy, and today there is no statistical difference once we control for other characteristics).7
The results from Stevenson and Wolfers are consistent with other studies looking at changes of happiness inequality (or life satisfaction inequality) over time. In particular, researchers have noted that there is a correlation between economic growth and reductions in happiness inequality—even when income inequality is increasing at the same time. The visualization below, from Clark, Fleche and Senik (2015)8 shows this. It plots the evolution of happiness inequality within a selection of rich countries that experienced uninterrupted GDP growth.
In this chart, happiness inequality is measured by the dispersion—specifically the standard deviation—of answers in the World Value Survey. As we can see, there is a broad negative trend. In their paper the authors show that the trend is positive in countries with falling GDP.
Why could it be that happiness inequality falls with rising income inequality?
Clark, Fleche, and Senik argue that part of the reason is that the growth of national income allows for the greater provision of public goods, which in turn tighten the distribution of subjective well-being. This can still be consistent with growing income inequality, since public goods such as better health affect incomes and well-being differently.
Another possibility is that economic growth in rich countries has translated into a more diverse society in terms of cultural expressions (e.g. through the emergence of alternative lifestyles), which has allowed people to converge in happiness even if they diverge in incomes, tastes and consumption. Steven Quartz and Annette Asp explain this hypothesis in a New York Times article, discussing evidence from experimental psychology.
II. Correlates, Determinants, and Consequences
Higher national incomes go together with higher average life satisfaction
If we compare life satisfaction reports from around the world at any given point in time, we immediately see that countries with higher average national incomes tend to have higher average life satisfaction scores. In other words: People in richer countries tend to report higher life satisfaction than people in poorer countries. The below scatter plot shows this.
Each dot in the visualization below 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.
Higher personal incomes go together with higher self-reported life satisfaction
Above we point out that richer countries tend to be happier than poorer countries. Here we show that the same tends to be true within countries: richer people within a country tend to be happier than poorer people in the same country. The visualisation below shows this through a set of plots connecting income and happiness by income quintiles.
Each panel in this visualization 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).
The next visualisation presents the same data, but instead of plotting each country separately, it shows 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.
A snapshot of the correlation between income and happiness—between and within countries
Do income and happiness tend to go together? The visualization below 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 below 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 following chart 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.
Let us begin with the case of Japan. There, the earliest available data on self-reported life satisfaction came from the so-called 'Life in Nation surveys', which date back to 1958. At first glance, this source suggests that mean life satisfaction remained flat over a period of spectacular economic growth (see for example this chart from Easterlin and Angelescu 2011).12 Digging a bit deeper, however, we find that things are more complex.
Stevenson and Wolfers (2008)13 show that the life satisfaction questions in the 'Life in Nation surveys' changed over time, making it difficult—if not impossible—to track changes in happiness over the full period. The visualization below 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 the visualization below, we provide evidence of the cross-country relationship.
Each dot in the scatterplot below 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 below 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
Above we showed that countries with better national health outcomes tend to have higher self-reported life satisfaction. In the visualization below, 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 below 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.
II.3 Life events
How do common life events affect happiness?
Do people tend to adapt to common life events by converging back to a baseline level of happiness?
Clark et al. (2008)15 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 following visualization 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).16
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)17 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 below, 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)
II.4 Social environment
The relationship between 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 below 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".18
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.
The relationship between 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.
The visualization below shows the relationship between self-reported sense of freedom and self-reported life satisfaction using data from the Gallup World Poll. The variable measuring life satisfaction corresponds to country-level averages of survey responses to the Cantril Ladder question (a 0-10 scale, where 10 is the highest level of life satisfaction); while the variable measuring freedom corresponds to the share of people who agree with the statement "In this country, I am satisfied with my freedom to choose what I do with my life".19
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)20 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.
The link between media and gloominess
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),21 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 197922).
Of course, mood is not the same as life satisfaction. Yet, as we discuss below in the section on measurement and data quality, surveys measuring happiness often do capture emotional aspects of well-being. And in any case, people’s perceptions of what it means to lead a meaningful life are heavily influenced by their expectations of what is possible and likely to occur with their lives; and this has also been shown to depend on media exposure.23
As I kid I loved Snakes and Ladders. I don’t even know if you can find that board game anywhere anymore, but I enjoyed it. (Okay, I just found it on Ebay for $2).
It was pretty simple. Basically you roll the dice to see how many spaces you can move forward. The first person to the end wins. You hope to land on a ladder (which lets you skip a bunch of steps). And you want to avoid the snakes (which make you slide back, giving up a lot of your progress).
I realized today that I’ve been using Snakes and Ladders as a guiding allegory for my life. Since I was a kid I’ve been using this game to make tactical decisions about my life.
Life isn’t slow and steady
Think of ladders as things that can rapidly advance you ahead of the crowd. These are things that help you to “arrive” faster –to be financially secure, happy, successful, self-actualized earlier than you would otherwise.
You’re probably familiar with the 80/20 rule, which states that 80% of our returns in life come from just 20% of our efforts. The 80/20 rule suggests that we should focus on the things that give us the most bang for our buck. The Snakes and Ladders idea is similar to this: avoid unproductive behavior and invest the time into building yourself an advantage of some kind.
Examples of Ladders:
Making property investments made when you’re young. There are several investment strategies you can use. Property is the one that’s always made the most sense to me. The key is starting early. (And I suppose the key to that is being disciplined about savings from a young age).
Getting a university degree. Aside from the increased feelings of personal effectiveness and being more interesting at parties, a college master’s degree is worth $1.3 million more in lifetime earnings than a high school diploma (and about another million on top of that if you get a PhD). (You could debate this, on the grounds of correlation versus causation, but if you thought of that you probably already have a degree and therefore you don’t want to).
Having a trade. Very much like a degree, having an in-demand trade sets you up well to branch out on your own and really cash in. However, it also seems like those who really benefit from having a trade are those with some business acumen as well, so they can fully cash-in when the moment is right.
Being a well-known brand. Personal branding is one of the new next-big-things. I encourage people to take some actions to dominant their ‘name space’ online, to make it so that when someone types in their name in Google, they are the first one that comes up and that they’re happy with the information being shared. (If you type in Tim Woods in Google you’ll find that I still come behind a Tim Woods who was also known as “Mr Wrestling” and Tim Woods, an Australian composer). But I’m getting there. And, even at #3 in my name space, I’ve had a lot of positive feedback from my efforts online. (I got a job, was chosen to judge a national environmental competition and become an internationally recognized expert on slang… don’t ask).
Being happily married. Research shows being in a marriage that lasts correlates strongly with sustained career success and better health in old age. Also it’s nice to have someone to share clean-up duties. Long-term, stable friendships can have similar benifits.
Understanding risk.People have money personalities. Some people avoid debt (even good-debt) like the plague. And those poor people don’t leverage their money. So they miss-out on long-term benifits –such as being able to retire.
Learning a language. This one is actually more of an ‘alleged ladder’. I don’t actually know any bilingual people who credit their success to their bilingualism. However, I’ve always suspected that if I spoke another language I’d be unstoppable, so I’m keeping this one in the Ladder category.
Examples of Snakes:
Going bankrupt. Even with bankruptcy protection, there can be lingering effects from bankruptcy that will snake you back down a few steps.
Getting arrested. In these days of ubiquitous information, it’s hard to hide mistakes from your past.
Getting divorced. For some people divorce goes smoothly. But for others it can put an enormous emotional and financial strain on your life for a very long time.
Can you think of any more snakes or ladders? Please add them in the comments.
(Update: Just before posting this, I’ve learned the game Snakes and Ladders was originally called Moksha-Patamu. “Of Hindu origin, it taught the players that virtuous behavior would aid your progression to Nirvana, but evil would make the journey difficult.” I told you this game was deep, didn’t I?)
Did you enjoy this article?Subscribe for free by RSS or email and you’ll always know when I publish something new. (What’s RSS?)
This entry was posted in Life and tagged Adaptation, choices, distractions, Investing. Bookmark the permalink.