Which places have the highest standard of living?

How much do living standards vary across the country? People care deeply about disparities across space, with surveys suggesting that individuals rank place-based inequalities as the most significant form of inequality in the UK (ahead of income, gender or intergenerational differences). But what makes households in one area more affluent and in another area less affluent?

The challenge of measuring living standards

Living standards across areas can be measured in many ways. Common approaches are to look at differences in local productivity (which reflect differences in the earnings of those working, but not necessarily of those living in an area) or local incomes. Past work shows that regional inequalities are much lower when you use local incomes rather than local productivity and lowest after accounting for differences in housing costs, which arguably is the better reflection of local living standards.

However, economists have long argued that consumption is a better measure of individuals’ long-run living standards than income, as it better reflects their ability to borrow, and draw on accumulated assets. Students, for example, might have low current incomes but relatively high consumption as they are able to borrow against their future incomes. Similarly, retirees might have low current incomes but nonetheless be able to achieve high living standards by spending wealth accumulated earlier in life. These differences in incomes across the life cycle are also likely to drive many of the differences in incomes across areas. Student towns – like Nottingham – will tend to appear poorer on measures of income than consumption, as will popular retirement locations.

New methods

A difficulty with measuring local consumption spending is that the UK’s main source of household spending data is a budget survey - the Living Costs and Food Survey (LCFS) - with relatively small sample sizes. Each year the LCFS interviews only around 5,000 households, which leaves small or non-existent samples in some local authorities. In our new ESCoE discussion paper, we solve this issue using a statistical estimator that combines information in the LCFS with information from additional sources. These include: a much larger dataset on local ages, family sizes, housing type and education levels; information on credit and debit card spending by households in different locations; and a measure or local energy consumption. When the sample sizes in an area in the LCFS are large, we can rely more on the survey to produce our estimates. When sample sizes are small, we can rely more on what the other data sources imply about local consumption. An additional advantage of our measure relative to average individuals’ incomes, is that it is based on average household consumption spending, adjusted for family size and their relative consumption needs (“equivalised”).

We use this method to estimate average consumption spending in 367 local authorities across Great Britain, using data from 2018 and 2019.  We focus here on consumption spending excluding housing-related costs. This ensures our results better reflect differences in consumption spending across areas, rather than differences in house prices or rental rates.

Key findings

Our estimates reveal large variation in weekly average equivalised consumption after housing costs across areas, with residents of the City of London (£407) spending more than twice as much as those in Leicester (£190). Furthermore, within-region gaps are often as important as differences across regions. Within London, Richmond upon Thames ranks among Great Britain’s highest-spending areas, while residents of Barking and Dagenham have the lowest average consumption spending nationwide.

We also compare our estimates of local consumption spending to local income measures derived from the ONS Gross Disposable Household Income (GDHI) series (adjusted here to make it more comparable with the ‘Households Below Average Income’ definition). Apart from the fact that households might tend to save and dissave (using savings for current expenses) more of their current incomes in some locations than others, there are other reasons to expect our consumption spending estimates to differ from this particular measure of average local incomes. For one, this series measures incomes before housing costs, whereas our preferred measure of consumption spending is net of these. In addition, our consumption spending estimates are based primarily on survey evidence, whereas our income data comes from administrative (tax) sources. If more affluent households in an area are less likely to respond to surveys, then the consumption data will tend to miss very affluent individuals while the administrative data on income will not. Given incomes tend to be highly skewed, the presence of even a few high-income individuals could exert a strong upward influence on average local incomes, whilst leaving our consumption spending estimates relatively unaffected.

Figure 2 shows how areas compare when ranked by average household income or by average household consumption after housing costs.  There are striking differences, particularly in the ranking of London boroughs. Areas like Islington and Tower Hamlets rank in the top decile of the nationwide distribution when looking at average income but drop to the bottom 5% of the consumption spending distribution.

To show how using average consumption instead of income affects our view of regional inequality, Figure 3 shows the distribution of consumption spending and income across local authorities in different regions. The results challenge the conventional wisdom that the typical household in London enjoys higher living standards than households in other regions. On average, London local authorities rank at the top of the income distribution when comparing across regions (39% above the Great Britain mean) but are bottom of the net-of-housing regional consumption spending distribution (7% below the Great Britain mean). Moreover, certain areas in London have very high incomes relative to the national average and are therefore not plotted to keep the scale manageable (for example average incomes in Kensington and Chelsea are 392% of the Great Britain mean). More broadly, we find that differences in consumption spending – both within and across regions – are smaller than differences in incomes, suggesting that regional disparities in living standards may be less extreme than income-based comparisons suggest.

Figure 3: Regional differences in average consumption spending after housing costs and average household income among local authorities

_{Notes: ‘Boxes’ show values for the upper quartile, median and lower quartile among local authorities with each region. Upper/lower whiskers extend to the largest/smallest value within IQR x 1.5 of the relevant quartile.  The population weighted mean index of each region is shown with a cross. We adjust the GDHI income measure in line with Judge and McCurdy (2022) as in the notes for Figure 2. The national mean of each variable (used to index regional values) is a weighted average using local-authority level population estimates for 2019. We omit outliers (the largest of which are all in London and for income), defined as values greater in absolute value than the region-variable IQR x 1.5. Author’s calculations using the LCFS, Annual Population Survey, Gross Domestic Household Disposable Income and population estimates data (from the Office for National Statistics), card transaction data from Fable and electricity consumption data from the Department for Energy Security and Net Zero.}

Challenges and limitations

Any measure of local living standards will have limitations – including ours.  Prior work has shown that LCFS respondents tend to underreport their consumption spending (with total spending implied by the survey having fallen relative to the National Accounts over time). Therefore, it is likely that our absolute values underestimate true local consumption spending. A further thing to bear in mind is that neither the income nor the consumption estimates account for differences in price levels across areas (besides in the case of our consumption spending estimates, subtracting housing costs). This reflects the lack of data on local price differences, which  are likely to be very important in understanding differences in living standards. Finally, whilst our work measures mean consumption spending more granularly than past efforts, significant within-area inequalities remain and simple averages may not tell the whole story.  As ever, it is important to consider a range of different indicators to determine which areas are more or less affluent.  

Why does this matter?

Understanding regional living standards through consumption spending rather than income offers a more nuanced perspective on economic well-being across the country. By incorporating a broader set of data sources and refining estimation methods, this research sheds new light on regional inequalities. As policymakers and researchers continue to explore ways to reduce place-based inequalities, adopting a more comprehensive approach to measuring economic well-being will be essential in ensuring that interventions are both effective and equitable.