Wellbeing differences between localities: the role of amenities

Having now skim-read several hundreds of pages of academic papers and books on what constitutes “wellbeing”, or how we could or should measure “social welfare”, I’m still of the opinion that life satisfaction, aggregated across every individual (including future generations) and each year of their lives, is one of the best ways of measuring how well we as a society are doing. There are some big philosophical questions that I still need to contemplate, but in the meanwhile, I have obviously been looking at the data…

One of the challenging features of the UK data on life satisfaction by local authority is that it’s quite hard to understand and explain the patterns. I think I did a decent job in my earlier data visualisation here, but it is a very incomplete explanation. Nevertheless, it’s by now uncontroversial that absolute incomes have only a small effect. [Note: incomes are, however, strongly correlated with longevity. I’ve not looked at whether there is a causal link, but I suspect so.] Now, thanks to Demos and Legal & General, there’s a new dataset that helps add a bit more colour and definition to the picture.

The attached chart shows how. The different rows represent ranges of life satisfaction, from the least satisfied local authority (on average) in England and Wales* (Wolverhampton, average life satisfaction of 6.74, on a scale of 0–10) to the most satisfied (Chichester, average life satisfaction of 8.16). The blue bars on the left show number of people the 16+ population that falls into each life-satisfaction range (when measured at local authority average level). The colourful stacked bars to the right show what percentage of respondents in the corresponding local authorities cited each of the listed issues (in the legend) as the “most urgently in need of improvement”.

[* Unfortunately, I had started doing this analysis for England and Wales only, because some of the other variables I wanted to look at (such as equivalised household income at parliamentary constituency level) were only (easily) available for those two countries. The data used in this chart would be available for Scotland and Northern Ireland, too, but I haven’t had time to go back and amend the original spreadsheet. So E&W will have to do for now.]

As identified in the “Everyday Places: Creating strong locations to support daily life in Britain” report by Demos and Legal & General, on average, the biggest issues that people raise about their local areas tend to be “local shops” and “transport services”. Indeed, the most important differentiator between local areas where people are most negative, and least negative, appears to be “local shops” .[As I’ve claimed previously, few people say the top issue is improving broadband speeds — and it’s worth noting this survey was run while there were still lockdowns going on.]

That’s the overview result. However, it is really interesting how this picture changes when one buckets the data in different ways. In particular, it’s reassuring that there are some recognisable patterns that seem to link to life satisfaction. [I’ve also pivoted the data by region, by latitude, by local authority district income, by type of locality (city vs. town vs. village vs. rural), by average age, etc. and will probably post about some of those in due course. The more you look at it, the more clear it becomes that most of the buckets we tend to use (see my chapter here) are quite unhelpful and much too aggregated to really understand the differences.]

For the areas with the lowest levels of life satisfcation (top rows in the chart), the issues that residents (disproportionately) raise as being in most urgent need of improvement are supportive communities, pleasant streets and access to fresh air and nature**. Even though not stated explicitly, it’s likely that the first category indicates a priority around safety, low crime rates, and cleanliness of the local streets or roads. [** It’s worth noting that the survey did not ask about quality of public services (such as health and education) locally. Would be interesting to see how they would compare to the more “amenities” approach here.]

The findings also speak to social capital: more and more evidence is being accumulated to understand what makes some neighbourhoods more community-oriented than others — but I would hazard a guess that it’s a complex interaction between things like level of deprivation, presence of strong religious or other communities (that go beyond core families), trust in public services (and other residents— but these two seem linked), and luck***. One of the chapters in the World Happiness Report in 2020 concluded that these were certainly some of the ingredients that have allowed Nordic countries to top the league tables on life satisfaction for a while now.

[*** For the life of me, I can’t now remember the name of the book which covered this, but it used historical case studies to make the argument that there is one factor that is most predictive of whether a particular stressful situation (e.g., lack of food, threat to security) escalates into some kind of violence (where people selfishly only look after themselves, Lord-of-the-Flies-style) or whether people realise that they are collectively better off if everyone shares in both the pains and gains of a group of people.

This is of course another example of the classic prisoner’s dilemma and there are typically many factors that determine whether people end up “cheating” or “collaborating”. However, the book (which might have been “Humankind” by Rutger Bregman) argues — in my view, quite compellingly — that leadership is key. If the people with the most power (to start with) behave selflessly, others will do the same, resuting in a better outcome for all; and vice versa. I also seem to recall from another brilliant book, “The Origins of You: How Childhood Shapes Later Life”, that the kindness of neighbours or other supportive adults (e.g., at a local church) had a significant positive impact on children’s future life chances.]

Anyway, returning [in my mind, from Preservation Island in Tasmania in south-eastern Australia] to England and Wales, the data reinforces what has now (finally) become received wisdom: every place is different, and within each place, every person is different. The brilliant news is that there’s plenty of data, and many channels, to understand and engage with the priorities of locals. Using such data should be, in my view, at the heart of modern democracy. [Which is, partly, why I keep doing these blogs. I genuinely think they could be helpful! If you agree, do please “like”, or share or comment on my Tweets and LinkedIn posts, as the algorithms will then make them visible to a larger audience.]