Q3 2021: The determinants of national wellbeing

In our previous research, we have dedicated much of our time to studying the determinants of wellbeing at the individual level. We used the main findings from this research to build the Exploring Happiness Index, that allows individuals to track their wellbeing over time. We are now turning our attentions to wellbeing on a national level. That is, we are looking to identify what exactly it is that causes citizens in one country to be happier than another.

The best source of data regarding countries wellbeing is the Gallup World Poll and these data are summarised each year in the World Happiness Report. In the survey, Gallup asks people to imagine a ladder, with the lowest rung representing the worst possible life and the highest rung representing the best possible life. The scale of this ladder is from 0- 10. The results show a large amount of variation across countries.

Through data analysis, it is possible to identify a range of factors that explain a significant proportion of the variance in average wellbeing across countries. The World Happiness Report identifies six factors, which when taken together, explain 76 per cent of the variation in average wellbeing across countries. Those six factors are as follows:

• Trust: which can be measured in a number of different but typically by using measures of perceptions of corruption. See full definition.

• Generosity: the proportion of people who have donated money in the present month.

• Social support: the proportion of people who have relatives or friends they can count on to help them whenever they need them.

• Freedom: the proportion of people who are satisfied with their freedom to choose what they want to do with their life.

• Health: years of healthy life expectancy.

• Income: GDP per capita.

The findings from this research tend to be supported by other empirical analyses that consider the main determinants of national wellbeing (e.g. OECD (2012). However, this topic is much less well researched than the topic of wellbeing at the individual level. There remains scope for additional empirical analyses that test whether additional variables or different groups of variables could be more effective at explaining the variation in national wellbeing.

In this research article we also discuss wellbeing inequality. We reiterate that progress should be measured against the goal of both a high average level of wellbeing within a country and a low level of variation around this mean.

We conclude the article by discussing two areas where further research would prove useful. The first is the extent to which cultural factors play an important role in determining national wellbeing. As shown in the chart above, Latin American and Caribbean countries have a high level of wellbeing, given their income level. This may be due to cultural factors but further research is necessary to confirm this hypothesis.

The second is the relationship between environmental quality and wellbeing. The implications of climate change are likely to increasingly impact our daily lives and therefore we could expect this to become an increasingly important factor in determining national wellbeing in the future. This remains speculative for now, but will be an important area of research in the years ahead.

Q1 2021: Policy analysis to improve human lives

The COVID-19 pandemic has shone a spotlight on how governments formulate their policies and has led to a widespread discussion about the social contract and what really matters. Increasingly, people are keen to know what and who is being considered when the government makes policy decisions that affect the lives of its citizens. In addition, there have been a number of calls from social scientists more recently to place wellbeing at the centre of policy analysis.

Traditionally, policies will be considered using cost-benefit analysis. Put simply, this is the process of measuring the benefits of a policy relative to its costs. This includes costs and benefits that are easily measured directly in monetary units, as well as less tangible costs and benefits, such as nature or health, which are then converted into monetary units where possible. Policies with the best benefits to costs ratios are the ones that are chosen to be put into action.

Most policymakers will agree that the objective of public policy is to maximise human welfare across the population. Since this is the case, measuring wellbeing more directly in policy analysis is likely to an improvement as compared with more traditional approaches which essentially look to proxy welfare . The new methods being proposed focus on producing a metric to measure how we can live long, healthy and happy lives. This combines life expectancy with measures of subjective wellbeing to produce wellbeing-adjusted life years (either known as WELLBY’s or WALY’s). In this way cost-benefit analysis can be used in very similar ways to the current approaches, except with wellbeing units playing the central role.

Both the current approach and the new wellbeing approach are not without their challenges. We outline these in detail in the paper but we briefly summarise some of the challenges below:

+ Methodological challenges in policy analysis

• Valuing statistical lives: Since policies affect human lives, it is necessary to try to value each persons life. Depending on the method used, the valuation figure can vary widely.
• Equality: Policies will impact different groups of society unevenly and it is important to capture this in the analysis.
• Sustainability: Policies may impact both current generations and future generations. It is a challenge to consider the extent to which this should be considered in the analysis. For example, a policy could reduce wellbeing today, in order to reap wider wellbeing improvements in future years.
• Isolating impacts: Often, a policy change will impact wellbeing across a number of dimensions that might be inter-related. It is important to isolate the individual impact of each of these dimensions on wellbeing, to avoid double-counting in the analysis.
• Uncertainty: Due to uncertainty about the future, often, assumptions need to be made in the analysis, and forecasts may prove to be inaccurate. These inaccuracies could significantly impact the policy proposal and lead to unintended consequences that weren’t considered in the analysis.

We conclude our article by giving our views on how best to structure policy analysis in order to improve lives. We split this into three categories.

+ A new policy framework for improving lives

The policy strategy

We believe that a dashboard of economic and social indicators should be created, which will require the government to form a policy strategy that is both values and data driven. The data production would be managed by the national statistical office, and governments would be required to create transparent objectives for policy, based on the trends in the data. This would need to be updated periodically, in order to respond to changes.

How to structure policy analysis

We believe that the following filtration process should be used for sorting policies (in order of preference).

1. First, where the wellbeing effects can be estimated, this should be used above any other approach. This should be considered the gold standard in policy analysis.

2. Second, where wellbeing effects are difficult to estimate, but we are confident that the monetary approach accurately captures the welfare effects of a policy, then we should use this method instead.

3. Third, where neither approach is adequate, this should be flagged as a future area of wellbeing research. We support the recent suggestion to create a wellbeing policy agency that will help to generate new evidence to be used in policy analysis.

+ Creating an environment for progress in policy

Outside of the policy analysis we believe there are a few ways that should help to create an environment for progress in policy.

• Effective transparency: Public officials should be transparent about the indicators and method that is used when making policy decisions. Effective transparency matters in a world of increasing information. This means layered information for different audiences and being clear aboutwhere the uncertainties lie.
• Evaluation and feedback: The policy process should produce a discussion paper where experts are able to comment on a policy action before it is put into place. This will reduce the likelihood of unintended consequences and refine the quality of the proposal.
• Diversity: The group of people who produce policy proposals should be as representative of the population they are producing these proposals for as possible. Greater diversity of thought, background and experiences will help to create more sophisticated, balanced and fair policy proposals.

+ Facing methdological challenges

• The question of valuing statistical lives is a difficult one. Current approaches using wellbeing units better match the observed valuation that can be inferred during the pandemic. These methods will need to be refined further however. Importantly, we believe that valuing Life years is a more proportionate and fair approach than valuing lives. This view is supported by the general public.
• We believe that the empirical approach during the policy analysis process should be adjusted for instances where investments today have large impacts on future generations (e.g. climate change). In these cases, the discount rate should be near to zero.
• Regarding distributional concerns, it is our view that these should be included within the empirical framework of the policy analysis where it is possible to do so. In cases where this is not possible, it is important that these remain considered qualitatively within the analysis. For example, in a case where a policy only has a small net social welfare benefit but is likely to negatively effect a marginalised group in society, this qualitative analysis could shift the policy to be rejected.

The slides from the presentation in the video above are available here. Or, to read the full research article, please click on the link below.

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.