Does More Inequality Make Us Less Kind?

Yes. Blog done!

Nah, not really, but inequality does makes us less kind and in what follows, I’ll briefly explain why.

Firstly, the evidence. In European nations with more inequality, people are less willing to contribute to the wellbeing of others. Figure 1 below shows how solidarity (the willingness to actively help others) toward neighbours is lower in nations that have higher levels of inequality.

Figure 1: Inequality and Kindness across 26 European Nations

Inequality neightburs

In American states with more inequality, people are, less trusting and, well, just less kind to each other, as shown in Figure 2 below (agreeableness here is a proxy for kindness).

Figure 2: Inequality and Kindness across US States


These are just two snapshots (and there are others) of a broader phenomenon – places that are more unequal are also less kind.

But why? Well, kindness (and morality more broadly) depend upon empathy and sympathy – the ability able to feel another’s pleasure or pain and then taking an active interest in their wellbeing.

But we don’t feel sympathy for everyone. Westermarck once wrote that, “We approve and we disapprove because we cannot do otherwise. Can we help feeling pain when the fire burns us? Can we help sympathising with our friends?”

Can we help sympathising with our friends? No. But can we help sympathising with everyone? Yes, and more than that, we are hard-wired not to.

We sympathise with those we recognise as being one of our group – those we recognise as one of “Us”. We do not sympathise with those in other groups – the “Thems”. We want those in our group – the “Us” to do well; we are at best indifferent to or, even worse, actively root for the misfortune of “Thems”.

This is a trait that appears to be older than humanity itself and was beneficial in our evolutionary past. Homo Sapiens (and other mammals) organise themselves into groups, and when these groups come into contact and then conflict with each other over scarce resources, you really want to only be sympathising with the “Us” in your own group rather than sympathising with the “Thems” who are running towards you with some rather pointy spears.

In modern nations, where we don’t have to fight off other groups with sharp sticks, expanding the number of people we sympathise with, the number who are part of “Us”, is unquestionably beneficial. You and I are much better off when we are looking out for and are kind to each other rather than when we are only looking out for ourselves and are at best indifferent or actively hostile toward each other.

Inequality, by increasing the social distance between people, reduces the number of people we recognise as being “Us” and increases the number we view as being “Them”. In more unequal nations, The Rich and The Poor come to live radically different lives and no longer belong to the same “Us” but instead to more and more “Thems”. The bonds of affection that bind us together become weaker, and then sever, as a nation becomes more unequal.

And that is how greater inequality undermines the basic tenant that underpins kindness: To treat our neighbour as we would like to be treated requires that we regard another as our neighbour in the first place.

Poverty isn’t (Yet) Widening but it is Deepening

I’ve been reading some of the latest poverty reports/statistics and scribbled down a few thoughts on the latest in UK poverty  – the key takeaway is that while the proportion who live in poverty is relatively stable, those who do live in poverty are becoming ever poorer. In short, poverty may not (yet) be getting wider, but it is getting deeper.

Rising Employment has Helped Keep Poverty Rates Stable

Below are the official measures of poverty, which have remained largely stable in the last few years even as working age social security payments have been frozen in nominal terms.


Even looking at other measures, like the minimum income standard (the amount needed to afford a certain basket of goods) or material deprivation (whether families can afford certain necessities), the story is much the same – the number living in poverty has been largely stable at the same time as working-age social security payments have been cut. This is largely because more of the poor are working and earning, which is helping to counteract the effect of the cuts – employment is, after all, at a record high.


However, the flip-side of this is that poor who aren’t working are becoming even poorer. Poverty may not be getting wider but it is getting deeper – as shown in the poverty gap measure from the OECD below – a higher poverty gap means that the depth of poverty is increasing.

Poverty Gap

Going forward, the good news story of rising employment reducing poverty may now be running out of road – it looks like both employment and wage growth have stalled, and Brexit certainly played a role.

Brexit is already making the poor poorer

Poorer families have been hit harder by Brexit than everyone else because their incomes have not kept up with the rise in prices caused by the post-Brexit depreciation of the pound. Poorer families are more dependent on social security payments whereas richer families are more dependent on earnings for their income. Now, as working age social security payments have been frozen while earnings growth have kept pace with inflation, poorer families who rely on these payments have been hit harder by Brexit than the rest.

The below two figures show how real income growth has grown for different sections of the population. The poorest 1/5 have seen their incomes fall due to cuts in social security payments as the rest have seen their incomes either remain stable or grow slightly as earnings have continued to grow.

Income Growth

Income poor growth

So the next time you hear, “Well you know that Blackpool voted for Leave so we’ve got to Leave”, remember that the people in Blackpool have already lost the most from Brexit and the harder the Brexit, the more they’ll lose in the future as well.

The Hidden Poor

I lived in Somaliland for two years between 2016 and 2018 and what shocked me on my return was the rise in homelessness not just on the streets but, for the first time in my life, on the trains and the tube.

The homeless are the very poorest among us and, (this is the important part so make sure you’re paying attention), these people aren’t captured in poverty statistics. Why? Because poverty is measured using a household survey and, by definition, those who don’t live in a house are not surveyed.

Below is an estimate of “core” homelessness in England (those in the most extreme form of homelessness), which has risen by about 30% since 2010. This will be an underestimate of how many people are actually homeless (it is pretty hard to count those who do not have a fixed abode) and quantitatively, an extra 40,000 living in poverty isn’t large enough to show up as an increase in the poverty rate but it does represent an extra 40,000 living in utter destitution.

Core Homeless

On homelessness, there has been some good news with the passing of the Homelessness Reduction Act as well as some more funding, but I am sceptical that it will make much of a difference to the number of who live in utter destitution. For that, you need to increase the incomes of the poorest (by say, reversing cuts to social security) and/or reduce housing costs (not just by building more social housing but also by relaxing planning restrictions).

And that’s all I have for you right now. Poverty rates may not be rising, but that doesn’t mean that poverty isn’t getting worse.

The Story Of Poverty and Inequality is Written By Governments

Forty years ago, those in the top one per cent took home around three per cent of all income; today that share has more doubled to around eight per cent. Around thirty-five years ago, one in seven households could not afford three or more basic necessities; by 2012 this had risen to one in three and is likely to have risen further since. And today, around 2 million people in the UK are undernourished and one in five children lives in a household that is either moderately or severely food insecure making the UK perhaps the “worst performing nation in the European Union” when it comes to ensuring its citizens have enough to eat.

But the story of how and why inequality and poverty has changed in the UK is not solely, or even primarily, one in which impersonal economic forces are the main protagonists beyond the control of whichever government happens to occupy the stage at any point in time. Instead, the story is, at its heart, a political one.

For at least thirty years prior to the Great Recession there was a consistent trend for those at the top to earn increasingly more than the rest. But this consistent rise in earnings inequality did not translate into consistently rising income inequality or poverty because government policy was, and is, the final determinant of both.

Both inequality and poverty skyrocketed under the Thatcher government as she made clear that “opportunity means nothing unless it includes the right to be unequal” and then cut the top rate of tax from 83 to 40 percent. She believed the government should not “take money in taxes from those who work hard and pay it out to those who don’t”, and in due course, social security payments, including the state pension, were frozen (or cut) in real terms and grew slower than earnings.

Changing economic circumstances at the time did have an impact on inequality and poverty but it was the government’s policies on taxation and social security that were responsible for around half of the rise in inequality in the 1980s – an impact that was one and a half times greater than the impact of changing employment and three times greater than that of rising earnings inequality.

The pattern of income growth under the Conservative governments of 1979 – 1997 was straightforward: the richer you were, the faster your income grew (see figure one below). Those at the top 10 per cent saw their incomes grow by around 75 per cent compared to around 25 per cent for those at the bottom 10 per cent.

Figure 1

Source: IFS, Living standards, poverty and inequality in the UK

The story of inequality and poverty then changed markedly with the election of New Labour. Tony Blair was clear before he came to office that “the next Labour government [would raise] the living standards of the poorest” and, once in office, promised that he would “end child poverty” in a generation. Gordon Brown made a similar promise regarding pensioner poverty. When they came to office, they set out to do exactly that.

The introduction of tax credits would raise the incomes of low-income working age families; the introduction of pensioner credit would increase the incomes of low-income pensioners; and poverty rates (measured as the proportion of families with less than 60 per cent of median income before housing costs) for both groups subsequently fell (see figure two).

But the poverty rate for working age families without children actually rose in this period. Why? Because the ambition was not to end all poverty; it was to end child and pensioner poverty.

Figure 2
Source: IFS, Living standards, poverty and inequality in the UK

Then came the Great Recession and the post-2010 coalition and Conservative governments, who set out to move the UK from a “high tax, high welfare economy; to … [a] lower tax, lower welfare country”. They abolished the 50p tax rate on high incomes and increased the personal allowance – measures that disproportionately benefited richer households.

They did not believe that reducing poverty could be achieved by “income redistribution” and cut social security payments for low-income working age families. But not all of those on low-incomes faced social security cuts. In the Treasury, the Conservatives may not have had “much time for tax credits… [or] Housing Benefit” but made it clear, “that they will not touch pensioners”.

Now, as the more eagle-eyed will have noticed, figures one and two show that poverty and inequality have barely risen since 2010 despite regressive cuts to taxation and social security. This is because in the aftermath of the 2008 crash, both earnings growth and earnings inequality fell, counteracting these regressive policy changes. Today, as both earnings growth and inequality have begun to return, so too have the first signs of rising inequality and poverty.

The main protagonists in the story of inequality and poverty are governments. When governments change, so does the story. If, tomorrow, New Labour were to return to power and implement their taxation and social security policies, then the story would change once again, with certain subplots seeing dramatic twists.

This is not just idle speculation on my part. Using the EUROMOD tax and benefit microsimulation model, I have estimated what the difference in inequality and poverty rates would be if New Labour’s policies on taxation and social security were implemented tomorrow. I constructed this counterfactual by uprating social security payments & tax thresholds in line with New Labour policy when they left office and uprating the minimum wage in line with actual rises up to 2015 and in line with the 2015 Labour manifesto thereafter (figure three below).

Under New Labour’s policies, inequality would fall slightly while the proportion of the total population living in poverty would be around 2½ percentage points lower. But certain groups would see more dramatic falls – poverty would fall significantly for children (by around 5½ percentage points) whereas poverty would rise slightly for pensioners.

Figure 3
Source: EUROMOD Tax-Benefit Microsimulation Model

Beveridge, the architect of the welfare state, understood the political nature of the inequality and poverty story. In the closing of his blueprint for the post-war welfare state, he declared that “freedom from want cannot be forced on a democracy or given to a democracy. It must be won by them”. That remains as true today as it was three-quarters of a century ago when he wrote it. The story of inequality and poverty is written by governments and, in a democratic society, the people who elect them.

This post originally appeared on the Political Quarterly blog

Islands of Prosperity and Penurity (Part IIb: Inequality of Place in the United Kingdom)

The easiest way I find to think about the UK’s spatial inequality, and the tendency for prosperous local economies to cluster together, is with a simple thought experiment that I call the “Islands of Prosperity and Penurity” story. This thought experiment is a modified core periphery type model described by Krugman technically and in plain English, with a human capital twist from Florida.

In this thought experiment, each local economy in the UK is an island. These islands border other islands and, when islands have good transport links between them (as in London), they form a continent. A continent can be thought of as a commutable area, where it is relatively easy for workers to commute between the islands in that continent. Transport links are assumed to appear spontaneously depending on demand.

There are two types of islands. Prosperous islands, where high productivity firms, that pay higher wages, exist and Poor islands, where low productivity firms, which pay lower wages, exist. The UK is also made of up two types of people – high and low education people. The wage people earn depends solely on their education and the productivity of the firm they work for.

In this thought experiment, people and firms can move. The productivity of high productivity firms increases with the number of high education people that can commute to where a high productivity firm is. This is because high education people see their productivity rise when there are more of them – perhaps they feel the heat of competition from other high education people in the area and/or they get benefits from working with other high education people. I label this the agglomeration benefit.

Agglomeration benefits increase as travel times and costs to a local economy fall. The easier it is for more high education people to commute to a given local economy, the greater the agglomeration benefit within that local economy, precisely because it is easier for high education people to commute there. Crucially, this agglomeration effect is not available to low productivity firms and low education people – they produce the same amount of output, and earn the same wage, regardless of where they live.

Because travel will always cost something, and wages for low education people will be constant regardless of where they work, only high education people will commute. Any continent will then have, at the very least, a prosperous “centre” island that high education will commute to and work within, even if they do not live there. By contrast, there will be no purely Poor continents because there will be no demand for transport links between poor islands.

Housing costs are assumed to increase with the prosperity of the local economy. This means that low education individuals will not want to live on a Prosperous Island because the higher living costs will lead them to have lower after housing cost income.

High education people will earn higher wages the more prosperous an island is, but will also find housing costs increase with prosperity. At a certain point, no more high education people will want to move to a given local economy because the higher costs of living there wil exceed the extra income they would earn by living there.

This will leave us with two types of islands: Prosperous islands with high education people, high productivity firms and high housing costs, and Poor islands with low education people, low productivity firms and low housing costs.

This thought experiment uses a number of simplifying assumptions and is not as a perfect description of spatial inequality within the United Kingdom. Nevertheless, it does leave us with a set of propositions that could help to describe and explain the UK’s spatial inequality:

  1. Prosperous Islands and Continents will have higher housing costs and higher house price growth
  2. Prosperous Islands and Continents will have more high education people and seen faster growth in the number of high educated people
  3. The Level/Growth of Output per Head will be positively related to level/growth of well-educated people within a commutable area
  4. The Level/Growth of Output per Head will be positively related to the level/growth of wages within a commutable area
  5. Each Continent will have a prosperous centre island
  6. Larger Continents will be more prosperous and have more people living within them


The Black Death and Today’s Disappearing Natural Rate of Unemployment

We used to be certain that wage growth would rise when unemployment was low, and fall when unemployment was high. But today, wages are still not growing despite us having a 44-year low unemployment rate. In this post, I am going to explain why the wage growth-unemployment link may have disappeared by describing what happened to wages and unemployment during and after the Black Death.

Most economists used to (and probably still do) believe that wage growth would rise or fall depending on whether unemployment was above or below its “natural” rate. The “natural” rate of unemployment is the unemployment rate that would prevail in the absence of short term shocks.

When unemployment is above its natural rate, then wage growth and inflation is supposed to fall (as employers find it easy to hire workers and so offer lower pay rises). When unemployment is below its natural rate, then wage growth and inflation is supposed to rise (as employers find it more difficult to hire workers and so offer higher pay rises). And when unemployment is equal to its natural rate then both wage growth and inflation are supposed to remain stable.

This is known as the natural rate hypothesis and is described in the equation below:



Where w = wage growth, θ = worker bargaining power, b = constant, U­n = natural unemployment rate, U = unemployment rate and g = per person growth rate.

Wages grow depending on how far unemployment is from its natural rate, (U­n – U), the per person growth rate, g, and, crucially, θ. In fact, whether wages grow at all depends on θ. θ is a measure of worker bargaining power and it is very important for what follows. So pay attention to it.

As I said before, we used to believe that the natural rate hypothesis could explain wage growth and inflation. Today, however, we aren’t so sure. This is because the relationship between wage growth and unemployment has become weaker in each decade since the 80’s, and there now appears to be little if any relationship between the two (as shown in Figure 1 below).

Figure 1.png

So is there a natural rate of unemployment? Is it a useful concept anymore? Well, yes, but only when workers have the bargaining power to bid for higher wages (i.e. when θ > 0). If we are living in a time when θ is close to zero, then the unemployment rate will no longer have a material effect on wage growth and the natural rate of unemployment will, likewise, no longer be a useful concept.

Luckily for us (but unluckily for those who had to live through it), The Black Death shows us how a decline in unemployment only leads to a rise in wages when workers have the bargaining power required to bid for higher wages.

The Black Death was a plague that led to widespread death in Europe and the Middle East, killing over half of the population between 1346 and 1353, as well as striking repeatedly over the next 150 years. Figure 2 below shows how the population in England and Europe changed in the period before, during and after the Black Death.

Figure 2.png

The Black Death and its repeated aftershocks led to a dramatic fall in unemployment (as less workers were alive) and, in England at least, a large rise in rural wages, as shown in Figure 3 below (taken from here).

Figure 3: Medieval Farm Wages in England

Figure 3.png

But this decline in unemployment did not lead to a rise in wages everywhere. In Eastern Europe, rural wages fell even as unemployment fell. To understand why this happened, we need to take a look at how worker bargaining power evolved before, during and after the Black Death in England and Eastern Europe.

At the beginning of the 1100’s, most of the peasants (or workers) in England were serfs. Serfs were tied to the land and had to do service for their lord (or employer). They could not bargain for higher wages, they could not move without the permission of their employer and they certainly were not free to try and bargain for better wages elsewhere. In other words, they had almost no bargaining power and θ was close to zero.

Serfdom began to disintegrate in England in the 1200s and this process accelerated in the wake of the Black Death. After the Black Death struck, employers needed more workers (as unemployment was so low), they could not collude to keep wages low (although this did not stop them from trying) and workers also had the option of moving to urban areas for higher wages. In other words, the bargaining power of workers, or θ, had risen in England.

As θ rose, so too did the dependence of wage growth on unemployment. As unemployment fell in the aftermath of the Black Death, wages began to rise. When population rates began to recover, and unemployment began to rise in turn, wages began to fall.

In Eastern Europe, however, the Black Death led to the imposition of serfdom on what was a relatively free peasantry. Eastern European employers were able to reduce worker bargaining power after the Black Death struck – workers were forced to undertake more free labour for their employers, their ability to move freely was curtailed even further and, as a result, their bargaining power, θ, fell toward zero.

Rural wages fell in Eastern Europe despite the fall in population and unemployment caused by the Black Death. In other words, as θ was close to zero, there was little relationship between the unemployment rate and wage growth.

But what does all this have to do with today’s disappearing natural rate of unemployment? Well the point is that, if we think that θ (i.e. the bargaining power of workers) is falling today (and there are good reasons to think that it is), then this can explain why the relationship between unemployment and wage growth is breaking down. If workers have less bargaining power today than they did in the 1970s, in the same way that workers in Eastern Europe had less bargaining power than those in England after The Black Death, then unemployment will also have less effect on wage growth.

And if θ is approaching zero, then the natural rate of unemployment will effectively disappear and will then, crucially, also no longer be a useful concept for monetary policy. I will be coming back to this in the future so watch this space.


I used the Total Wages and Salaries growth (KGQ3) series from the ONS to construct the quarterly nominal pay growth series and adjusted this figure to account for population growth. The Unemployment series is the LF2Q series, available here.



Prosperous Local Economies Tend to Have Prosperous Neighbours (Part IIa: Inequality of Place in the United Kingdom)

The United Kingdom is one of the most geographically unequal nations in Europe and, since the onset of the Great Recession in 2007, spatial inequality has increased.

We might think we know where the rich and poor areas within the UK are. The South of England is rich whereas the North of England is poor and the Midlands are, well, somewhere in the middle.

But, actually, that is not quite correct. The South is not just one prosperous economy and nor is the North just one poor economy. Each region is made up of a mixture of relatively prosperous and relatively poor local economies.

You can see this in Figure 1 below, where I have shown the percentage of local economies that fall into each output per head quintile by NUTS1 region. (In this post, I define a local economy as a local authority district and not as an NUTS3 region for reasons described in the annex and, don’t worry, it does not affect the results.) As before, I will drop the “per head” suffix from here on out – all output figures refer to output per head. Figure 1

There is a clear regional pattern – richer regions contain more prosperous local economies and vice versa. Unsurprisingly, London and the South East (as the nation’s richest regions) have the highest percentage of local economies in the top quintile (45% each). Around 70% of the UK’s local economies in the top decile are in London and the South East, which suggests that a significant portion of the top 10% of local economies that are pulling away from the rest of the UK are in these two regions (see annex for the equivalent NUTS3 figure). Wales, the nation’s 2nd poorest region, has the highest percentage of local economies in the bottom quintile (60%) and neither Wales, the North East nor Yorkshire and the Humber have any local economies in the top quintile.

However, around 10% of local economies in both London and the South East are in the bottom quintile. London and the South East may be the nation’s richest regions, but they do still contain poor local economies.

In Figure 2 below, I have constructed a map that shows which output decile each local economy falls into. Deeper blues indicate poorer local economies and deeper reds indicate richer ones, with yellow colours showing the local economies in the middle. London and the South East are made up of lots of prosperous local economies whereas Wales and Northern Ireland are full of penurious ones. But, again, these regions are not homogenous in terms of how prosperous the local economies within those regions are. Wales and Northern Ireland may be the 2nd and 3rd poorest regions in the United Kingdom but Belfast and Cardiff are the 52nd and 88th richest local economies in the United Kingdom (out of 391).

Figure 2

In Figure 3 below, I have enlarged London to show that the capital is not a homogenous blob of rich local economies.

Figure 3.png

Looking visually at the above maps, there also appears to be a relationship between how prosperous a local economy is and how prosperous its neighbours are. Local economies that are relatively prosperous tend to border other prosperous local economies (as in London and the South East) and poorer local economies tend to border other poorer local economies (as in Wales and Northern Ireland). As Tobler did not quite put it, “Everything is related to everything else, but near things appear to be more related than distant things”.

We can test whether there is a relationship between how prosperous a local economy is and how prosperous its neighbours are using a spatial similarity statistic known as Moran’s I measure of global spatial autocorrelation. I have calculated Moran’s I for the UK in Figure 4 below, categorising local economies by output per head percentiles and omitting the UK’s islands (for what I hope is the obvious reason that they don’t have neighbours).

Each dot represents a local economy (local authority district) and the relationship between its own output per head and that of its neighbours. The diagonal line running through the points measures the degree of national spatial similarity – the average relationship between the output of a local economy and the output of its neighbours for the whole of the UK. It is positive, meaning that prosperous local economies tend to have prosperous neighbours and vice versa.

The x-axis shows how prosperous a local economy is and the y-axis shows how prosperous a local economy’s’ neighbours are. A value that lies to the right of the red line on the x-axis indicates that a local economy has greater than average output per head (i.e. it is in the 50th percentile or above) and a value that is above the red line on the y-axis indicates that a local economy’s neighbours have greater than average output per head.

Figure 4: Global Moran’s I Scatterplot of UK Local Economies’ (LA Districts) Output per Head

Figure 4

This national relationship is highly significant and positive but the relationship is far from perfect. Generally speaking, but not always, local economies that produce more than average per person tend to also have neighbours that produce more than average and vice versa.

Now, let us dig down a little bit further. We can see from Figure 4 that each local economy can fall into one of 4 quadrants:

Table 1: Quadrants of Spatial Similarity Statistics for Output Per Head in UK Local Economies


Each local economy falls into one of those four quadrants and has its own local measure of spatial similarity. The local spatial similarity statistic is not, however, always statistically significant. In Figures 5 and 6 below, I have mapped where a local economy’s spatial similarity statistic is significant (i.e. where the p-value <0.05) and also shown which of the above four quadrants it belongs to.

The map shows us both clusters of local economies that are of similar prosperity and islands that are unlike the surrounding areas. In Figure 5 below, we see large areas of prosperity in London, the East and the South East and penurious areas in Wales, Northern Island and the South West. But there are prosperous local economies that are surrounded by poorer neighbours such as Cardiff, Belfast, Glasgow and Edinburgh. It is not a coincidence that these are all major cities (I will return to this point in a later post).

Figure 5.png

London shows a similar pattern as before, with a swathe of wealthy areas (surrounded by other wealthy areas) in the Centre and West of London, while the rest of London shows no significant association with the surrounding local economies.

Figure 6

There is a relationship between how prosperous a region is and the type of significant local spatial similarity within those regions. As shown in Table 2 below, richer regions tend to have prosperous local economies surrounded by other prosperous local economies (more High-High relationships) and the opposite is true for poorer regions (more Low-Low relationships). I have highlighted cells that have a value greater than or equal to 50% to show the pattern more clearly but, again, the pattern is not uniform and, in poorer regions, there is often a major city that is relatively prosperous and is surrounded by relatively poor neighbours.

Table 2: Locally Significant Spatial Associations by NUTS1 Region

Table 2

In the next post, I am going to construct a thought experiment (based on the core periphery model) to describe why it could be that more prosperous local economies cluster together and begin testing how well this thought experiment holds up as an explanation for the UK’s spatial inequality. It was originally going to go in this post but, as you can see, this post has gotten rather long as it is.

Annex: Local Economy Definitions – NUTS3 Regions and Local Authority Districts

If you have read the last post, you would have seen that I used NUTS3 regions to examine local economy inequality whereas here I used local authority districts. I have used local authority districts here because it is more useful to use smaller geographic areas when analysing and describing spatial inequality. For example, the NUTS3 region of Essex Thames Gateway contains the Castle Point, Rochford and Basildon local authority districts which are the 5th, 41st and 213th poorest local authority districts respectively. There is a lot of spatial inequality that you would miss if you just looked at NUTS3 regions.

You may reasonably ask why, in that case, I did not examine local authority districts in the last post if I prefer smaller geographic areas. The simple reason is that, because of the way the data is constructed, the time series becomes less accurate the further back you go.

Now, there is a cost to examining relative prosperity at the local authority level. And that is that it can lead to a perceived bias in the output per head statistic. This is because output per head is calculated as the output for a local authority divided by the resident population. As people commute to work, it could be the case that the output per head statistics becomes biased as the denominator is lowered due to the difference between the working and resident population in each local economy.

I don’t really consider this to be a problem for two reasons. Firstly, when you think about whether you live in a rich or poor area, you think about the place where your home is and not where the place you work is. I used to live in Luton and work in Westminster and I did not think I lived in one of the richest parts of the country because I worked in Westminster. Secondly, as people prefer shorter commutes, large differences between the resident and working population show that an area is relatively more prosperous. I am sure many people who work in Westminster would like to live there but cannot due to its prosperity and associated higher living costs.

In any case, I have recalculated all the above analysis using NUTS3 data to show that the relationships displayed above are not being driven by this NUTS3/Local Authority difference in Figures A1 to A4 below and, as you can see, all the main results still hold. Annoyingly, I can only show this for Great Britain due to inconsistent shape data.


In addition, 75% of NUTS3 regions in the top decile are in London and the South East showing that that a significant portion of the top 10% of local economies that are pulling away from the rest are likely to be in these two NUTS1 Regions.

Finally, you might want to know what the difference between NUTS1 and NUTS3 regions actually are, given I have used these terms so liberally in this post, and so I have shown the definitions in the table below (from the Wikipedia page here). On an off topic side note, I adore Wikipedia and think it is an excellent resource and it is why I have used the table below. There is too much Wikipedia related snobbery in this world for my liking.

NUTS Definition.png

Data Sources

GVA per Head by Local Authority:

GVA per Head by NUTS3: 

Stata Code

2a Code

NUTS3 Code

The United Kingdom is Growing Apart Because the Richest Areas are Pulling Away (Part I: The Inequality of Place in the United Kingdom)

The UK economy only produces around 2% more per person than it did in 2007. This is not good news. In the decade up to and including 2007, the economy grew by around 25% per person – the UK has gone through a decade of stagnation and, yes, austerity is largely to blame.

(All references to growth rates or the size of an economy in this post refer to per person measures and I will drop the “per person” suffix from here on out.)

Now for the really bad news. The UK economy may have grown by around 2% as a whole, but this does not mean that every area of the UK has grown by 2%. Some areas have grown much faster than 2% whilst many local economies have shrunk since the Great Recession. Half of the UK’s population lives in a local economy (defined as an NUTS3 region in this post) that shrank between 2007 and 2016. If this does not shock you, then it should. It means that around half of the country lives in an area that is poorer than it was a decade ago.

The UK is one of the most geographically unequal countries in Europe. Talk of a growing economy doesn’t give you, me, (or anyone), a good idea of how different places are actually faring. Since 2007, Croydon’s economy shrank by 20% while Milton Keynes’ economy grew by 20%. Does the fact that the UK economy grew by 2% tell you much about how Croydon’s or Milton Keynes’ local economies have performed over the last decade?

In this post, I am not going to explain why some areas of the country are poorer than others (that will have to wait for a future post) but instead am going to describe how inequality between the UK’s local economies has grown over the past two decades.

Inequality between local economies in the UK, measured using Theil’s T index, is shown in Figure 1 below. Inequality grew by about 30% between 1998 and 2016 and most of this rise occurred from the onset of the Great Recession in 2007. You don’t need to know how Theil’s T index is calculated in order to understand the below; all you need to know is that higher values indicate higher inequality. (As stated above, a local economy is defined here as an NUTS3 region and inequality was calculated using output per person in each NUTS3 region.)

Theil T NUTS3

But the fact that inequality between local economies is rising does not tell us how it is rising. Is inequality rising because richer areas are pulling away? Is it because poorer areas are falling behind? Or is it some combination of the two?

I constructed three inequality ratios to assess which of the above three statements is correct:

  1. The 90:50 ratio measures top-bottom inequality – how much richer the top 10% of local economies are compared to those in the middle
  2. The 50:10 ratio measures middle-bottom inequality – how much richer local economies in the middle are compared to those in the bottom 10%
  3. The 90:10 ratio measures top-bottom inequality – how much richer the top 10% is compared to the bottom 10%.

These inequality ratios are shown in Figure 2 below. I have plotted the 90:10 inequality ratio against the right-hand side axis (i.e. the secondary y-axis) to make things a little clearer. An increasing ratio indicates growing inequality, a stable ratio means that inequality is stable and a falling ratio means that inequality is falling.

NUTS3 Inequality Ratios

As you can see from Figure 2 above, both the 90:10 and 90:50 ratios have grown since the onset of the Great Recession, while the 50:10 ratio was pretty stable for the whole period; top-middle and top-bottom inequality grew while middle-bottom inequality barely moved. In other words, the United Kingdom is growing apart because the richest areas are pulling away.


There’s a lot more to look at when it come to the inequality of place in the UK – and here are just some of the topics that I might cover in the future :

  • Agglomeration, Automation and Globalisation –Can the spatial pattern of growth and output in the United Kingdom be explained by some combination of Agglomeration, Automation and Globalisation?
  • Well-Being across the UK – What does well-being look like in different local economies in the United Kingdom? Is there a clear link between, for example, economic growth and mental health outcomes across the UK?
  • Local Labour Markets – What does the Labour market look like in each local economy? What is the variation in employment performance across the UK?
  • Political Economy of Place – What has the effect of all of the above been on the politics of each place? Have the political preferences of local areas changed as a result of decline? Are people less likely to vote if they live in a declining area?

I want to look at this because, in part, I just am interested in how different areas of the UK are faring. But mostly it is because I think we need to think more in terms of places instead of people when making public policy. There is a problem with concepts like social mobility and poverty when they only see individuals and are blind to communities and places.

For example, a national poverty rate of 20% could mean that:

  1. In 1/5th of the country, everyone lives in poverty; in the remaining 4/5ths of the country no-one lives in poverty
  2. Across the whole nation, 20% of people live in poverty

The implications of those two identical national poverty rates have radically different implications for the places within (and the entire social fabric of) those two hypothetical nations. Similarly, increasing social mobility can simply mean that bright but poor students move to more prosperous areas (read: London), while the places they leave behind stagnate and decline.

It is going to take me a bit of time to get through all of this given that I both have a day job and am also looking at some other stuff (related to the Natural Unemployment Rate) but I will, eventually, get around to examining all of this.


In the annex below, I describe the data sources used for the above analysis. Excel and Stata do-files are also attached below for reference.

Data Sources

The main data source for this post was the Regional GVA (balanced) per Head figures released by the ONS, which can be found here:

The GDP per Head growth figures from the first paragraph were calculated using the ONS’ series IXHW. All GVA per Head figures were deflated using the GDP deflator to give real income and real growth figures. The GDP deflator was the L8GG series from the ONS (currently here: ).

Dataset and Stata Code:

The dataset I built for all calculations and the Stata code is also attached below:

NUTS3 Dataset

Part 1 Inequality of Place code




Trump, Tariffs and The Truth About Trade

This post originally appeared on the Young Fabian’s Blog:

On the 8th March, Donald Trump signed an executive order imposing substantial tariffs on imports of steel, amongst other products. The Economist rightly described this as, “an act of senseless self-harm” – Trump’s signature move on tariffs will hurt the US economy as a whole but (and this is a very important but) his actions will also help the American steel producers and, possibly, American steel workers as well.

Those who work in the American steel industry argue that it could do with some help. As one American steel worker put it, “The tariffs [Trump] wants to put on other countries? I love it… It’s about time. Because they’ve been doing it to us since what, the 80s?”. There is little doubt that some steel workers have lost their jobs due to foreign competition. In the 1960s, US steel factories employed around 650,000 people, whereas today they only employ around 140,000.

 But to think that the precipitous fall in the number of US steel jobs is solely due to lower tariffs and more trade would also be a mistake. Machines, who have replaced humans in the production of steel, should also take a large share of the blame. And what is true for the US steel industry in particular is true for US manufacturing as a whole: one in three US manufacturing jobs disappeared between 1989 and 2016. Machines may have been  responsible for taking most of these jobs away from American workers but some of these jobs went overseas as a result of lower tariffs and greater imports.

Trump’s answers to these problems are simplistic and wrong but his rhetoric is effective because he alludes to a truth about trade that is rarely acknowledged – lower tariffs and increased trade have caused significant harm to some individuals and communities. Areas in the United States that saw more Chinese imports also saw lower wages and significant falls in the number of manufacturing jobs. It was not a coincidence that these areas were also more likely to vote for Trump in 2016 – his tale of decline and betrayal struck an oddly harmonious chord in the Rust Belt states of Michigan, Wisconsin and Pennsylvania that subsequently handed him the Presidency.

The problem with trade is not that that developed nations like the US or the UK lose out from it. Overall, developed nations do benefit from lower import tariffs and more trade: the goods we buy are cheaper, the costs for firms fall, and more trade helps to reduce the likelihood of war as well. The problem with trade is that the costs and benefits are unevenly distributed. The former factory worker who lost his job due to low-cost imports has lost a lot more than you or I have gained from cheaper goods.

Lower tariffs and more trade have given us more growth but also more inequality; the economic pie is larger but the pieces are more unequal. It does not have to be this way. As a nation, we can harness the benefits of cheaper imports, more trade and more growth to reduce poverty and raise living standards for everyone.

The government can ensure that everyone benefits from trade through taxation and spending. If a worker loses his or her job because of trade, the government can guarantee them a job elsewhere; if their wages fall or stagnate because of low-cost foreign competition, the government can subsidise their earnings to increase their living standards; and if entire swathes of the country see stagnation and decline as factories begin to shut down then the government can invest in those areas to help transform the structure of their local economies.

None of this will happen by chance. Government policy needs to change to ensure that as our nation grows richer from trade, we all become individually more prosperous as well. Otherwise, the demagogic siren calls of nativism, protectionism and xenophobia will only grow ever louder, resonating with those whose lives have become more precarious with more trade, and, perhaps, reaching a crescendo with our own Trump-like Prime Minister.

Why Aren’t (Average) Wages Growing Faster?

Economic growth is supposed to be the “rising tide that lifts all boats”  but, in the UK, this tide appears to be leaving most boats behind. Since the early 2000s, average wage growth has tended to be lower than economic growth and, today, average wages are no higher than they were 13 years ago. Figure 1 below shows how GDP per person and average weekly earnings (AWE) have evolved since the year 2000 and, as you can see, GDP per person tended to grow faster than average wages over this period.

Figure 1: Economic and Real Wage Growth – UK (Index: 2000 =100)

GDP Wages Deflated

This suggests that the default setting for the UK economy is for average wage growth to lag behind economic growth. Even before the Great Recession, average wages were growing more slowly than the economy and the same pattern reasserted itself when growth returned after the Great Recession.

So what is going on here? Well, I think the easiest way to think about this is in terms of how total income in an economy is divided. Total income in an economy accrues to owners of either Labour or Capital. Owners of Labour (i.e. employees) are paid income for their services and the owners of Capital are paid income for the use of their capital.

One possibility, then, is that average wages may not have kept pace with economic growth because the share of total income paid to Labour could have fallen. In other words, the proceeds of growth may be increasingly going to owners of Capital rather than Labour, and this could account for the fall in average wage growth relative to economic growth.

However, looking at the data in Figure 2 below, a falling Labour share does not appear to be the main culprit for low wage growth. If we ignore the spike around the Great Recession (where I assume that wages rose as firms were unwilling to give nominal wage cuts) then the share of total income going to Labour has been pretty stable since the early 2000’s.

Figure 2: Labour Share of National Income – UK

Labour Share

If a falling share of income going to Labour is not the main cause of slow wage growth then rising Labour income inequality must be the reason that average wages are not growing as quickly as GDP per person.

Importantly, the Labour share of income does not just include earnings, it also includes other non-wage benefits such as private pension payments. These non-wage benefits have increased over time, and have led to a reduction in average wage growth (as the Van Reenen paper in this publication describes).

The other factor that could be reducing average wages is earnings inequality. Even if total earnings were growing at the same rate as the economy, rising earnings inequality could lead to stagnating or falling average wages.

Earnings inequality does appear to be reducing average wage growth but, importantly, it appears to be the growth of incomes for the richest that is suppressing average wage growth. When we look at measures of the original Gini coefficient (a measure of total income inequality, which can be insensitive to changes in top incomes) in Figure 3 below, we can see that this has either been stable or falling since 2003.

Figure 3: Original Gini Coefficient – UK

Orig Gini

But when we look at the top 1% and top 10% income shares (i.e. the amount of income that goes to the top 1% and top 10% of population) in Figure 4 below, we can see that these shares rose from around 2003, fell in the Great Recession, and subsequently rose again from 2010. Data from the IFS also suggest that the top 1% income share may have risen further since 2014.

Figure 4: Top 1% and Top 10% Income Shares – UK


In short, earnings inequality, along with a rise in non-wage benefits, is the reason that average wages are not growing faster. For earnings inequality, it appears that it is the growth of the very highest incomes that is reducing average wage growth, and this has been a feature of the UK economy since before the Great Recession. The rising tide of economic growth has not lifted up all boats evenly; the largest boats have risen with the tide whilst the rest have been stuck in the harbour.

Young Cities and Older Towns: The Rise of Political Segregation in the UK

Each year, tens of thousands of graduates move to cities (and London, in particular) as part of the UK’s Great Graduate Migration. This is helping to fuel economic growth in these cities – they are growing much faster than the towns and villages that surround them. But while young graduates are moving to growing cities (where the best jobs with the highest pay are), older non-graduates are moving out or staying away.

This is having an effect on how people and places vote. The fact that age is a new political fault line in British politics has been well covered elsewhere. What has been less commented upon, however, is that there is another political fault line that encircles cities, separating them from the surrounding towns and villages. It is not just the young and the old that are voting in increasingly different ways; cities and non-cities are also voting in increasingly different ways as well. Age-based political polarisation has been mirrored by political segregation.

In order to show this, I have constructed a simple Political Segregation measure (Figure 1 below) using constituency voting data for England and Wales. Values that are further from zero indicate a greater level of political segregation.

Political Segregation is calculated as the percentage point difference between the average Labour share of the vote in each city-type constituency and the average Labour share of the vote in all English and Welsh constituencies. For example, in 2010, an average of 39.5% of those in large city constituencies voted for Labour. For all English and Welsh constituencies, an average of 30% voted for Labour. Political Segregation is the difference between the two: 9.5 percentage points.

Figure 1

Looking at Figure 1, it is clear that cities have become significantly more pro- Labour and non-cities have become significantly less pro-Labour since 2010. In other words, political segregation has increased.  Looking at Figure 2 below, we can see that this has been mirrored by an increase in age-based political polarisation. Young people have become significantly more likely to vote for Labour whilst older people have become significantly less likely to vote for Labour since 2010.

Figure 2.jpg

Source: Ipsos Mori “How Britain Voted”

The political fault lines of age and place are growing wider. In part, this appears to be driven by an unbalanced economy: cities take the lion’s share of growth and provide the best jobs which, in turn, attract young graduates. This trend of economic concentration is also likely to continue as firms and graduates go to the cities where the largest pools of potential employees and employers already exist. The political fault lines of age and place may appear wide now but they could grow much wider yet.