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2. UK and country-level personal well-being

Between the years ending March 2018 and March 2019, there was very little change in the ratings of personal well-being measures. The only slight improvement was in the average rating of happiness, which increased slightly across the UK from 7.52 to 7.56, measured on a scale from 0 to 10. For the other measures of personal well-being - life satisfaction, feeling that the things done in life are worthwhile, and anxiety - average scores remained level with no significant changes over this period. For further details about UK-wide headlines, please see Personal and economic well-being in the UK: August 2019.

At country level (Figure 1), there was an 11.9% increase in anxiety ratings in Northern Ireland, from 2.53 to 2.83 (out of 10), between the years ending March 2018 and March 2019. This was driven by a substantial decrease in people reporting very low ratings of anxiety from 47.0% to 41.1%. Looking at the long term, the anxiety scores in Northern Ireland continuously improved over the last seven years. The year ending March 2018 marked the end of a generally downward trend in anxiety in Northern Ireland since we began measuring in the year ending March 2012, with anxiety increasing markedly between 2018 and 2019.

As has been the case in previous years, people in Northern Ireland gave better average ratings for life satisfaction, feelings that things done in life are worthwhile and happiness than people in England, Scotland or Wales. The increase in average anxiety score seen in 2018 to 2019 brought the average for Northern Ireland closer in line with the rest of the UK. For more information on headlines 2018 to 2019 personal well-being data for Northern Ireland, please see Personal Well-being in Northern Ireland: 2018/19.

Figure 1: The average rating of anxiety has increased by 11.9% in Northern Ireland, bringing Northern Ireland into line with the other UK countries on this measure

Average ratings of anxiety, UK countries, years ending March 2012 to March 2019

Source: Office for National Statistics - Annual Population Survey

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5. Areas of persistently high and low well-being

In this section, we explore whether some areas of the UK persistently had high or low ratings of personal well-being. For this analysis, we considered local authorities to have persistently high or low well-being if their average personal well-being ratings were in the top or bottom 10% for the UK in at least five years since the year ending March 2012 (year ending March 2013 for Northern Ireland) and for at least two of the four well-being measures.

Figure 6: Several London boroughs persistently report low well-being across all measures of personal well-being

Local authorities consistently reporting low average personal well-being ratings, UK, between the years ending March 2012 to March 2019

Source: Office for National Statistics - Annual Population Survey

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Looking across the whole UK, several London boroughs (Lambeth, Hackney, Islington and Camden) persistently had some of the lowest personal well-being ratings reported across all measures since the year ending March 2012 (Figure 6). They were followed by Wolverhampton, Manchester, Lewisham, Greenwich and Nottingham which reported poor well-being scores for three measures. Although Wolverhampton scored poorly in most measures, it scored very well where anxiety is concerned, reporting consistently lower levels of anxiety compared to the UK average for five of the last eight years.

Figure 7: Areas of Scotland and Northern Ireland persistently report high well-being across all measures of personal well-being

Local authorities consistently reporting high average personal well-being ratings, UK, between the years ending March 2012 to March 2019

Source: Office for National Statistics - Annual Population Survey

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In the UK, the Scottish council areas of Orkney Islands, Na h-Eileanan Siar and Shetland Islands, and Fermanagh and Omagh in Northern Ireland consistently reported high ratings for all the four measures of personal well-being since the year ending March 2012 (Figure 7). They were followed by Causeway Coast and Glens in Northern Ireland, and the English local authorities of Ribble Valley and Eden, which reported high well-being scores for three measures. Additionally, as shown in previous research, people's well-being seemed to be highest in relatively rural areas across England, such as Chichester and North Devon.

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Wales, as a whole, generally reported personal well-being average ratings similar to the UK, with fewer variations across local authorities compared with the other UK countries. This could explain why there are no areas in Wales standing out as scoring consistently high or low in well-being when compared with the UK average.
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Personal well-being in the UK - Office for National Statistics

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2. UK and country-level personal well-being Between the years ending March 2018 and March 2019, there was very little change in the ratings of personal well-being measures. The only slight improvement was in the average rating of happiness, which increased slightly across the UK from 7.52 to 7.56, measured on a scale from 0 to 10.

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Official Statistics

Cumulative data showing the number of GP registered patients in England having the seasonal influenza vaccine.

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Details

Provisional monthly data for the uptake of the seasonal influenza vaccine in GP registered patients in England.

The most recent monthly data collection covers cumulative flu vaccinations administered from 1 September 2018 to 28 February 2019.

Data is presented by NHS England local team, clinical commissioning group ( CCG) and by local authority ( LA).

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Seasonal flu vaccine uptake in GP patients: monthly data, 2018 to 2019

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Tell us whether you accept cookies We use cookies to collect information about how you use GOV.UK. We use this information to make the website work as well as possible and improve government services. Official Statistics Cumulative data showing the number of GP registered patients in England having the seasonal influenza vaccine.

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Radio 4 More or Less on Measuring Happiness

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Radio 4 More or Less on Measuring Happiness

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Tim Harford investigates the economics of happiness.

Measuring Happiness
From political speeches to glowing prime-time television series, the idea that our "happiness" can be enhanced using public policy is more fashionable than ever.

But is it really plausible and useful to measure human well-being using numbers?

Professors Paul Ormerod and Richard Layard have been arguing, through the pages of Prospect magazine, about the extent to which governments can monitor and enhance our happiness.

We bring them together in the studio to debate the issues with presenter Tim Harford.

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BBC Radio 4 - More or Less, Measuring Happiness

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Tim Harford investigates the economics of happiness. Measuring Happiness From political speeches to glowing prime-time television series, the idea that our "happiness" can be enhanced using public policy is more fashionable than ever. But is it really plausible and useful to measure human well-being using numbers?

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Calculating glacier ice volumes and sea levels

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Calculating glacier ice volumes and sea levels

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How do we estimate the global volume of ice?

The IPCC and other outlets frequently indicate how much sea levels will rise under given climate change scenarios [1]. Other times, you might see that such and such volume of ice (km ³) is equivalent to so many millimetres of sea level rise (sea level equivalent (SLE); the amount of sea level rise on full melting of the ice). But how are these calculations made?
Table 1. Sea level equivalent (SLE) from various land ice sources. From IPCC AR5 (Vaughan et al, 2013).
The first complexity is in calculating the volume of ice in the world. This is complex, as we often do not have a complete picture of the bed of the ice sheet or glacier. For Antarctica, BEDMAP2 provides the most complete and up-to-date estimate of ice volume, and it is derived by combining thousands of radar and seismic measurements of ice thickness [2]. In fact, BEDMAP 2 is derived from 25 million measurements. Fretwell et al. 2013 estimated that the Antarctic Ice Sheet comprised 27 million km 3 of ice, with a sea level equivalent of ~58 m.

For glaciers, ice thickness datasets are sparse [3]. While we have a good estimate of global glacier ice surface area [4] from satellite measurements, direct observations of glacier ice thickness are available for only around 1100 glaciers [3]. These include radar measurements (both airbourne and from the ice surface) and seismic measurements. Unfortunately, these methods are time consuming and costly, and glaciers are often remote and difficult to access.

Yet glacier volume and ice thickness data are essential parameters required for understanding sea level rise, water resource management and assessment of future glacier changes [5]. Glaciers and small ice caps are also contributing rapidly to present-day sea level rise [6, 7]. Ice thickness data and bed topography are essential data required for numerical modelling of glaciers [8]. Other methods employed to calculate glacier volume include volume-area scaling [8-12], and calculations of ice thickness based on ice surface slope [13, 14] that assume a given basal shear stress. These methods, when correctly applied, do well against direct observations of ice thickness [8, 10].

Calculating the mass of ice

Calculating the sea level equivalent for a given volume of ice requires some simple maths and a knowledge of the densities and properties of ice and sea water. Ice volumes are usually given in km ³. The mass of ice is usually given in metric gigatonnes (Gt). 1 Gt = 10 ⁹ tonnes (where 1 tonne = 1000 kg).
Table 2. Densities of ice and water (at 1 atmospheric pressure and 4.3°C)

We can calculate the mass of something if we know the volume and the density:
Density = Mass / Volume Mass = Volume x Density Volume = Mass / Density
See this website for more information on these equations.

Because ice and water are different densities, 1 km ³ results in different masses. However, remember that 1 Gt of ice = 1 Gt of water! They take up different volumes but have the same mass. So, 1 Gt (whether ice or water) is equal to:

We can convert a given volume of ice (in km ³) to a mass of ice (in Gt) by using the following equation:

Mass of ice (Gt) = Volume of ice (km³) x Density of ice (Gt/km³)

If we have a calculated ice volume of 500 km ³, then:

The mass of ice here is 458.30 Gt. If it all melted, you would have 458.30 Gt of water.

Calculating sea level equivalent

To convert a mass of ice into the total amount global sea levels would rise if the ice all melted (i.e., the sea level equivalent), we need to know how much area the oceans cover. This is usually given as 3.618 x 10 8 km ². A 1 mm increase in global sea level requires 10⁻³ m ³ (10⁻¹² km ³) of water for each square metre of the ocean surface, or 10⁻¹² Gt of water.

We can calculate the volume of water required to raise global sea levels by 1 mm:
Volume = area x height
We can convert km ³ of water to Gt of water as we did above; 1 km 3 water = 1 Gt water. In the same way, 1 Gt of ice = 1 km 3 water. So, 361.8 Gt of ice will raise global sea levels by 1 mm. 361.8 Gt of ice is equivalent to 394.67 km ³ ice.

If we took our 458.30 Gt of ice (as calculated above), then we could calculate the global sea level equivalent by:
SLE (mm) = mass of ice (Gt) x (1 / 361.8) SLE = 458.30 x (1 / 361.8)
However, we should note that some of the world's glaciers have parts that are below sea level. This ice will not affect sea level if it melted. The volume of glacier ice below the surface of the ocean should therefore be subtracted from the total volume of glaciers and ice caps when calculating sea level equivalents [15].

Also remember that ice that is floating (like ice shelves, sea ice and icebergs) does not contribute to sea level rise upon melting. Only land ice above sea level will contribute to sea level rise.

Useful websites

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Calculating glacier ice volumes and sea level equivalents - AntarcticGlaciers.org

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The IPCC and other outlets frequently indicate how much sea levels will rise under given climate change scenarios [1]. Other times, you might see that such and such volume of ice (km 3) is equivalent to so many millimetres of sea level rise (sea level equivalent (SLE); the amount of sea level rise on full melting of the ice).

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All questions submitted via google form above will appear here.

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OCR Core Maths Preliminary Material (Responses)

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Over the winter months, many people catch flu. The NHS offers free vaccination to children, the over-65s and people at particular risk, like pregnant women and those with certain diseases. Yet less than two thirds of those eligible for the vaccination actually have it.

So why are people worried about the vaccine - and should you be?

A common reason for people declining the vaccine is that the previous year they had the jab and they actually believe it gave them flu.

This is not possible as there is no live virus in the vaccine. However, it takes about 7 - 14 days for your body to develop protection after the vaccine, so if you contract the flu during this time, or even in the week before, then you might not be fully protected.

Protection is important, because flu kills up to half a million people worldwide every year and during a pandemic, this can escalate to millions.

However, the flu virus presents huge challenges for health professionals tasked with selecting which vaccine to deploy in any given year, and for scientists developing new vaccines against it. This is because the virus is constantly changing, which makes it one of the most difficult diseases to predict and control.

When we catch flu, what's actually happening in our bodies is that the flu virus infects a cell, multiplies within the cell, then goes on to infect more cells. Over time our immune system learns to recognise the virus and starts to destroy it.

The flu vaccine offers protection by priming the immune system in advance. It does this by 'training' it to recognise particular protein molecules that exist on the outside of the virus (a bit like wrapping on a package), and destroy it.

However, the problem is that the proteins that make up this external 'wrapper' change frequently. When viruses multiply and copy their genetic material, sometimes tiny changes or errors occur, and this results in the virus mutating. The immune system may not recognise the altered proteins made by this mutated virus, and so it escapes detection and destruction.

So a new strain of the virus emerges, which can then infect cells and multiply, leading to more people catching it, and the cycle repeats.

To keep up with these mutations requires a huge ongoing research effort. Scientists have to constantly monitor the latest flu strains so they can update the vaccine.

This involves predicting which strains will be most common each winter, which is more successful some years than others. If the prediction is wrong, this can result in a less effective vaccine being deployed, leading to more cases of flu among the population.

However, it is always worth having the flu jab, though, because some protection is better than none at all.

Thanks to cutting edge research in virology, however, in future we may not need to be vaccinated every year. Researchers are working to create a new broad-spectrum or "Universal" flu vaccine, which will protect against more versions of the flu virus and last longer.

The principle behind the new vaccine is to target not the external 'wrapper' but instead specific protein molecules that lie deep inside the virus, which mutate much less frequently. If the immune system can learn to target proteins inside every strain, then it will be able to spot and destroy more types of flu. This could mean that even if a new strain comes along in the future, the vaccine should continue to work.

The researchers carrying out this work hope that people would need to be vaccinated less frequently than is currently the case, perhaps every 3 years and potentially up to every 5 years.

It is estimated that a Universal flu vaccine is still 5 - 10 years away. In the meantime, health professionals recommend that people should continue to use the currently available vaccine, especially for those at higher risk. (pregnant women, children, over-65 and those with certain medical conditions)

So if your GP recommends it, take the opportunity as it will help protect you, and those around you, and will not give you the flu.

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BBC Two - Trust Me, I'm a Doctor, Series 7, Episode 5 - Should I have the flu jab every year?

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Over the winter months, many people catch flu. The NHS offers free vaccination to children, the over-65s and people at particular risk, like pregnant women and those with certain diseases. Yet less than two thirds of those eligible for the vaccination actually have it.

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Our World in Data - Happiness and Life Satisfaction

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Our World in Data - Happiness and Life Satisfaction

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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?

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Whether spotting them from outer space, or standing on our coastline, IcebergFinder.com is your premier place for finding bergs in Newfoundland and Labrador.

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Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Saif M. Mohammad. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, July 2018.
Paper (pdf) BibTeX Interactive Visualization Key Ideas Presentation Video Poster

Summary

Words play a central role in language and thought. Several influential factor analysis studies have shown that the primary dimensions of word meaning are valence, arousal, and dominance (VAD) (Osgood et al., 1957; Russell, 1980, 2003).

valence is the positive--negative or pleasure--displeasure dimension;

arousal is the excited--calm or active--passive dimension; and

dominance is the powerful--weak or 'have full control'--'have no control' dimension.

The NRC Valence, Arousal, and Dominance (VAD) Lexicon includes a list of more than 20,000 English words and their valence, arousal, and dominance scores. For a given word and a dimension (V/A/D), the scores range from 0 (lowest V/A/D) to 1 (highest V/A/D). The lexicon with its fine-grained real-valued scores was created by manual annotation using Best--Worst Scaling. The lexicon is markedly larger than any of the existing VAD lexicons. We also show that the ratings obtained are substantially more reliable than those in existing lexicons. (See associated paper for details.)

NRC-VAD Lexicon for Various Languages

Despite some cultural differences, it has been shown that a majority of affective norms are stable across languages. Thus we provide versions of the VAD lexicon in over one hundred languages by translating the English terms using Google Translate (July 2018).

Applications

The NRC VAD Lexicon has a broad range of applications in Computational Lingustics, Psychology, Digital Humanities, Computational Social Sciences, and beyond. Notably it can be used to:

study how people use words to convey emotions.

study how different genders and personality traits impact how we view the world around us.

study how emotions are conveyed through literature, stories, and characters.

obtain features for machine learning systems in sentiment, emotion, and other affect-related tasks and to create emotion-aware word embeddings and emotion-aware sentence representations.

evaluate automatic methods of determining V, A, and D (using NRC VAD entries as gold/reference scores).

study the interplay between the basic emotion model and the VAD model of emotions (Mohammad, 2018: LREC paper).

study the role of high VAD words in high emotion intensity sentences, tweets, snippets from literature.

Note:

This study was approved by the NRC Research Ethics Board (NRC-REB) under protocol number 2017-98. REB review seeks to ensure that research projects involving humans as participants meet Canadian standards of ethics.

An Interactive Visualization of the NRC VAD Lexicon

Key Ideas

Some slides about this work are shown below. You can also acess the full presentation by clicking here.

Terms of Use:

The lexicons mentioned in this page can be used freely for research purposes.

If you use a lexicon, then cite the associated paper.

If interested in commercial use of any of these lexicons, send email to the contact. Use of the lexicons in commercial applications without express consent from NRC is strictly prohibited.

If you use a lexicon in a product or application, then please credit the authors and NRC appropriately.

Do not redistribute the data. Direct interested parties to this page.

National Research Council Canada (NRC) disclaims any responsibility for the use of the lexicons listed here and does not provide technical support.

# data[].attributes.attachment_link.description
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Saif M. Mohammad. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, July 2018. Paper (pdf) BibTeX Interactive Visualization Key Ideas Presentation Video Poster Summary Words play a central role in language and thought.

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Facts about glaciers | National Snow and Ice Data Center

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Surveillance Atlas of Infectious Diseases

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Surveillance Atlas of Infectious Diseases

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Coronavirus Simulations in Washington Post

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Coronavirus Simulations in Washington Post