Q4 2020: The Exploring Happiness Index Methodology

At Exploring Happiness we have been building this index for some time. Last year we wrote two research articles that looked at the theory and evidence in relation to the determinants of life satisfaction. The detail included in these research articles played a role in the calibration of our index. In our default calibration, we use the same six determinants that we highlighted back then as the foundations of our index. The concepts that we used to build the index were simple. We look to capture the fact that everyone is different and what makes each of us happy is also different. Some people are career-driven, others live for the social scene and some are highly family-orientated. Despite this, we all also have a lot in common – we all value our health, both mental and physical, the quality of our personal relationships matters a lot and we all like to have something to do that makes us feel worthwhile.

We make our index unique for each user by creating the functionality to allow users to personalise the index, based on their own circumstances and preferences. Users are able to choose how important each of the components that make up the index are to them, and as a result the weights within the index will shift to reflect that.

In addition, our index considers the different stages and circumstances for each of us in our lives. Our base calibration is built towards the most common ‘individual type’ - an employed worker. However, when users create their accounts they will choose from a list of 13 different individual types such as parent/carer, student or retired and as a result of this choice the components that make up the index will change to reflect that individual types circumstances.

So thats the concept, but what’s the purpose? We see there as being three main benefits from using the index.

1. Being informed: If we assume that each individual’s objective in life is to maximise their own happiness, which we think is a fairly reasonable assumption, then it’s likely to be useful to know how happy you are now and how happy you have been in the past. Our decisions, big or small, will play an important role in determining our happiness and we are more likely to make the right decision when we have better information available to draw upon. The usage of this index provides users with this information.

2. Mental health tool: Using this index allows users time to reflect, to think about what has been going well and what has been more challenging. There is also a significant amount of research available which points to the benefits of self-reflection on mental health. This has been shown for self-reflection in a number of forms (e.g. from expressive writing to gratitude journaling), across various life stages and as an effective treatment for those with diagnosed mental illnesses. Our view, which we intend to robustly test in the future, is that users of the index will be able to build resilience to mental health illnesses through the method of self-reflection that this index requires.

3. The ultimate tracker: Nowadays, it is not uncommon to track several parts of our lives, from steps to sleep to calories. But whats the point in tracking these things? For most people, it’s because they believe if they do more steps or sleep better, they will end up feeling better. This index allows you to check whether thats true in practice.

The index methodology paper linked below outlines the evidence used to select the variables that make up the index, how this evidence feeds through to the calibration of the index and then how the base calibration is adjusted according to the users choices about their preferences and circumstances.

Q3 2020: Analysing the impact of COVID-19 on wellbeing

In our previous research article, we suggested policy solutions for the recovery period that were consistent with our overarching goal: increasing happiness and wellbeing in society in a sustainable and equal way. We think that this should be the main goal of governments in developed countries. However in order for this to be the main goal we need to be able to measure and track our progress against this goal. Fortunately, the Office for National Statistics (ONS) has been doing this in the United Kingdom since 2011. This means we have a highly valuable yardstick with which to measure how the UK is progressing. Huge amounts of attention have been paid to the financial impacts of COVID-19 and although they have been significant, this information only tells us part of the story. It is important to measure progress on a wider range of variables. All these variables should feed through to the overarching goal of sustainably and equally increasing wellbeing.

Our research article includes four main takeaways:

The initial impact of the pandemic on life satisfaction in the UK

Source: Office of National Statistics. Note: The frequency of the data in this chart changes from quarterly to weekly at the end of Q1 2020.

1. The initial impact of the pandemic on wellbeing was large. Even though it may not look like it, the decline life satisfaction scores shown in the chart to the right is large. Since 2011, the lowest quarterly life satisfaction score, on average, was 7.35 (the highest was 7.71 in 2018). These weekly scores were produced by the ONS during the pandemic to gauge an understanding of how the pandemic was influencing citizens wellbeing. The average life satisfaction score across these high frequency surveys is 7.00, which is 0.65 lower than in Q1 2020, almost double the range since the inception of this measure (range is 0.36). A note of caution is that the sample of these weekly surveys is much smaller than the quarterly surveys (approx. 1,500 vs. 30,000)

2. Assessing how wellbeing is likely to change in the future is useful for informing policymakers. In the research article we provided an illustrative example of how wellbeing may change going forward based on forecasts of financial variables and known historical relationships between these variables and life satisfaction. It is highly useful for policymakers to be aware of how wellbeing is likely to change in the future in response to both policies and market dynamics. Policies could then be tweaked accordingly in order to support citizens wellbeing.

3. Non-financial indicators are likely to have had a larger influence on wellbeing than financial indicators. By combining the data from the chart above with our forecast of how life satisfaction is likely to change in response to changes in financial variables, we were able to conclude that non-financial indicators (e.g. mental/physical health, trust in government, personal relationships) played a larger role in the recent decline in life satisfaction than financial variables (e.g. income or employment).

4. Wellbeing inequality is likely to increase as a result of the pandemic. Data suggests that those on lower incomes are likely to have a larger financial hit as a result of the pandemic than those on higher incomes (with the majority of those on higher incomes actually able to increase their savings this year). It is also well known that changes in income matter more for life satisfaction at lower incomes. Therefore, we should expect that the distribution of life satisfaction will widen as a result of the pandemic, meaning wellbeing inequality has increased. This should be a key concern for policymakers.

Please click on the link below to read about this in more detail. Comments are welcome.

Q1 2020: How much do government finances influence wellbeing?

At Exploring Happiness, at the end of 2019 we made the decision to reduce the frequency of our research article publications from monthly to quarterly, with the aim being that this would allow more time to produce unique and insightful research. This article is the first of these quarterly publications, and it is focusing on assessing the relationship between public finances and countries wellbeing.

Some governments believe that it is necessary to have a high level of government spending in order to provide a helping hand when things don’t go so well. In order for this to remain sustainable over a long period of time, government taxation also needs to be quite high in these countries, as this is used to fund the spending. However, in other countries they believe that is not the place for governments to get too involved and that the market will sort itself out in the end. In this article we are looking to assess which of these two approaches tend to lead to a greater level of wellbeing for its citizens, based on the data we have available. Importantly, we are not suggesting that there is a clear direction of causation in our analysis but we are looking to bring together the data and make inferences based on the relationships we observe.

Additionally, before we get into the detail our of study there is one key point that we would like to make clear: policy design matters much more than absolute spending or taxation. This matters now more than ever, as at the time of writing this, advanced economy governments all around the world are announcing significant stimulus packages in order to help combat the COVID-19 pandemic. As a result, we should expect to observe a significant increase in government expenditure as a percentage of GDP. These stimulus packages are currently necessary, in order to restore confidence, reduce the amount of people being laid off and the number businesses going into insolvency. However, whilst necessary, they are not likely to be well designed. These policies are more a case of throwing significant sums of money at the problem than anything else. It will not be efficient and much of this spending will have limited knock-on effects on the economy. Well-designed government policies considers how the government can play a role in the market in order to allow innovation to develop, in an environment that is stable and secure.

The relationship between countries wellbeing scores and their governments finances

Source: UN World Happiness Report Data, IMF WEO (October 2019) & EH calculations. Note: This chart shows correlation coefficients between countries' wellbeing scores and public finance metrics.

In order to complete our analysis, we needed to merge two datasets. To get wellbeing scores for all countries around the world we used the recently updated data from the UN’s World Happiness Report.  To get public finances data, we used the data published in the IMF’s October World Economic Outlook. We then needed to filter for all countries that had both sufficient public finances data (>10 years) and that are included in the UN’s World Happiness Report and as such our final sample came to 146 countries

We chose to also complete the analysis on a smaller sub-group of advanced economies (AE) in order to remove some of the income effect and allow for a more consistent comparison across countries. It is well known that more developed economies have higher wellbeing scores, meaning it is difficult to draw accurate conclusions when comparing countries with vastly different economic circumstances. Our main goal is to find a large enough group of countries that are similar in nature and wealth but choose to take different approaches for their public finances in order to observe how these different approaches may affect their citizens wellbeing.

We have summarised our key findings from our data analysis in the following points:

1. Countries with larger public debt-to-GDP ratios tend to report lower wellbeing scores. There is a small and positive correlation equal to 16% for the all country sample compared to a much stronger negative correlation equal to -63% for the AE sub-sample. More developed countries are able to accumulate greater amounts of debt as a proportion of GDP than developing ones and this has created the positive correlation in the all country sample. This is an example of the income effect dominating, due to richer countries reporting higher wellbeing scores. The income effect is removed in the AE sub-sample and the correlation coefficient becomes significantly negative.

2. Countries that are able to manage their public finances better, through fiscal surpluses, or small fiscal deficits tend to report higher wellbeing scores. The correlation coefficient for the all country sample is equal to 25% compared to 39% for the AE sub-sample.

3. Countries with higher government revenues and expenditure, as a proportion of GDP, tend to report higher wellbeing scores. The correlation coefficients are slightly larger for the all country sample, which is likely due to the income effect playing some role here too. AE’s tend to find it easier to extract greater amount of tax revenues from their citizens, due to low income countries shadow economies being proportionally larger and that their government institutions are less sophisticated. Nevertheless, there is still a relatively strong positive correlation in the AE sub-sample, meaning the income effect doesn’t significantly change this relationship.

4. AE’s countries that have run larger fiscal deficits over the last 10 years have reported greater increases in wellbeing scores over that same time period than AE’s who have put in place more stringent fiscal policies. This is a small amount of evidence to suggest that countries that have put in place austerity policies since the previous financial crisis may have constrained their citizens wellbeing during this time.

This is all discussed in greater detail in the full article below and we have explained some of the key concepts in the video above too.

Mental Health and the UK Economy

In this article we attempt to convince you that mental health creates a big enough problem that economic policies need to be designed to address the issue, and that doing so, would benefit the UK economy. Mental health sits at the centre of what happiness economics is trying to achieve. Our view is that there are two ways that policymakers should look to influence the happiness distribution in a country: by either shifting the bottom end upwards or by shifting the whole distribution upwards. The former aims to make the most miserable in society happier and the latter aims to improve happiness for all in society. 

Mental health affects a lot of people in the UK – 17.6% of the UK population have been diagnosed with either a mental health or substance use disorder. And there are many more people that go undiagnosed. In addition, according to a recent poll completed by YouGov for the Prince’s Trust, the number of young people in the UK who say they do not believe that life is worth living has doubled in the last decade. In 2009, 9% of 16-25 year-olds disagreed with the statement that “life is really worth living”, but that has now risen to 18%. Moreover, 27% of young people do not feel that their life has a sense of purpose – which is one of the most important ingredients of a happy life.

The main way that mental health impacts the economy is through the labour market. By the labour market, we refer to those who are in work (employees), those who are looking for work (unemployed people) and those who offer work (employers). Employment is central to many people’s live and identities. The probability of developing a mental health illness increases the longer an individual is unemployed. This can develop into a vicious cycle, which we have named the unemployment trap. It’s similar to a poverty trap theory, often used for developing economies. Essentially, the idea is that the longer an individual is unemployed, the more likely they are to develop a mental health illness, they then begin to lose motivation and confidence, which reduces the probability of finding a new job further as time passes. Policies aimed developing short term employment contracts to break this cycle could have large benefits to the UK economy, as well as lead to more efficient use of government resource.

On the employment side, the onus is on the employer to make the right decisions, leading to benefits for their firm and the wider economy. The current view is that the best approach is a flexible one from the employer. Different employees require a different response as mental health illnesses and people are not the same. Through firm-wide destigmatisation and taking a flexible approach employee loyalty will increase in the firm, leading to longer tenures and therefore an increased likelihood that employees will become experts in their role.

In order to create conditions within society to generate the greatest possible happiness and the least possible misery across the population, policymakers need to know the causes of happiness and misery. Therefore, we have chosen to study the correlates of life satisfaction and mental health, comparing across two methods of doing so, using within-country and cross-country data. The results showed that within country data performs better than cross country data. Although the results using cross country data did improve when we reduced the sample to a subset of countries that are more comparable to each other. The correlates using within country data were calculated by the UN as part of their World Happiness Report. The correlates using cross-country data were calculated by us and we have linked the data we used and results tables here. There is plenty more detail on this section of the paper in the full article available below, where we explain how to interpret the correlations. We also explain some of the interesting trends we have already been able to pick up regarding what makes a happy life.

Policies aimed at providing solutions to the challenges posed by mental health will have economic benefits that go beyond the labour market. In economics, we call these second order effects, since the majority of the first order effects come through the labour market. There are many second order effects and these will be explored further in a future article focused on policy design.

For more detail on mental health and the UK economy, please click on the link below or check out the video above.