Which places in the UK are winning on wellbeing?

A lot of the “levelling up” debate has focused on employment and incomes, and rightly so: these are important elements of prosperity for any region. However, they are not the most important contributors to people’s life satisfaction; mental and physical health and social relationships are, on average, more consequential. And this is where the story gets interesting.

As the chart shows, at the local authority level in the UK, there is essentially no correlation between incomes and life satisfaction [left hand panel]. Indeed, people in London (light blue) are among the highest-income but least satisfied with their lives. There are many reasons for this, not least that Londoners are on average younger, and younger people are, on average, less happy; and that income and other inequalities in London are particularly stark. [See e.g., data visualisation here.]

The colour coding in the chart suggests that people tend to be happier in villages (purple) and small and medium towns (pink and red): these are further up in the graph than large towns (yellow), cities (green and acquamarine) or London. This is consistent with evidence that access to green spaces, and tight-knit communities (which are somewhat more likely in smaller places), are important contributors to wellbeing.

However, from an overall societal point of view, what matters is not just wellbeing at a point in time, but the amount of time that people are able to enjoy this level of wellbeing. In the World Happiness Report 2021, Richard Layard and Ekaterina Oparina advocate a quantitative metric for wellbeing that multiplies average life satisfaction with average life expectancy. The resulting metric is called a “wellbeing year”, or WELLBY, and is shown for each local authority in the right-hand panel of the chart.

Unlike life satisfaction, average life spans in the UK are highly correlated with incomes (middle panel). Here, London and other affluent areas (e.g., villages and towns in the South East and East of England) score significantly higher than poorer districts, especially cities in Scotland, North East and North West. Glasgow stands out with its very low life expetancy, at 76 — similar to the average in Serbia or Brazil (pre-pandemic). This appears to be driven, in particular, by the male life expectancy at less than 74.

Again, there are likely to be several reasons for the longer lives experienced in higher-income areas, some directly causal and others not. For example, higher levels of education are associated with both higher incomes and better health. Being lower-income — and, especially, living in poverty — is stressful and likely to encourage some unhealthy behaviours, such as smoking or drinking, while not giving access to healthier life style choices, such as healthy food.

What emerges, then, as an overall picture for wellbeing disparities across the UK is not a straightforward “high vs. low income” or “cities vs. towns” story. This is important for at least two reasons.

First, if you believe — like I do — that the ultimate goal of policy is to maximise individual life-satisfaction (times length of life), then focusing on GDP or incomes to “level up” regions is unlikely to be hugely effective on its own. Much more important would seem to be to understand, and tackle, the causes of shorter lives and ill-health everywhere, but especially in our populous cities in the North and Scotland. [And, as per my previous blogs (see #16 here), education is likely to be a critical component here.]

Second, low life expectancy and ill-health are major issues for the so called “left behind” areas. So, while people in these places would likely benefit from higher incomes, it may be that their life satisfaction can be increased further and faster (possibly at lower cost and while also contributing to incomes) by improving health outcomes. These observations underscore the importance investing in human capital to “level up” the UK’s local areas.

[Note: left-hand panel R2 = 0.01, P = .11 (linear); middle panel R2 = 0.62,
P < .0001 (polynomial); right-hand panel R2 = 0.25, P < .0001 (polynomial).]



I help complex organisations make the right strategic decisions through innovative, insightful and incisive analysis and recommendations.

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Tera Allas

I help complex organisations make the right strategic decisions through innovative, insightful and incisive analysis and recommendations.