UK

G7 growth rates and austerity

Rob Calvert Jump and Jo Michell

In August 2022, revisions to official measures of UK output generated headlines because the new figures implied that the economic contraction during the pandemic was greater than previously thought. 

At the same time, however, substantial revisions were made to historical data, and these received far less attention. One outcome of these revisions is that the UK’s performance relative to other rich economies during the austerity period of 2010–2016 has been downgraded: growth in real GDP per capita over this period is now meaningfully lower. This means that some recent analyses relying on the older figures are misleading.

For example, in a recent FT article, Chris Giles includes data showing that the UK had the highest growth of real GDP per head in the G7 between 2010 and 2016. Inevitably, the article was circulated by defenders of austerity including Rupert Harrison and Tim Pitt, alongside a claim that the data “shows why the idea austerity has caused our growth problems post-GFC doesn’t stack up. During peak austerity (2010-6) UK had strongest GDP per capita growth in G7”.

The data used by Chris Giles are from the International Monetary Fund’s (IMF) October 2022 World Economic Outlook (WEO), and show average annual growth in real GDP per head of 1.4% in the UK between 2010 and 2016, compared with 1.3% in both Germany and the USA. But the October 2022 WEO uses data from the 2021 Blue Book, which were compiled before the most recent set of revisions were introduced.

The 2021 data imply that total per capita growth between 2010 and 2016 was 8.39% in the UK, compared with 8.36% in Germany and 8.27% in the USA. On these numbers, the UK is indeed the highest, albeit by a margin in the second decimal place: under a billion pounds separates the UK and Germany. (This very slim margin appears larger in the FT chart due to growth rates being annualised and then rounded to 1 decimal place, implying UK growth of 8.7% versus German growth of 8.1%, a difference of 0.6 percentage points rather than the actual difference in the IMF data of 0.03 percentage points.)

However, according to the revised figures, real per capita growth in the UK over this period was only 7.7%: total nominal GDP growth between 2010 and 2016 was revised down by around one percentage point in the 2022 data, culminating in lower cash GDP of around £17 billion by 2016.  Smaller adjustments to inflation estimates mean that real GDP growth was revised down by around 0.7 percentage points, from 13.4% to 12.7%. Alongside unchanged population estimates, the result is that official real GDP per capita was revised down by around £340 (in 2019 prices) by 2016 – an amount approximately equal to a third of the average household energy bill in that year.

Chart showing downward revisions to UK nominal GDP growth between 2011 and 2016

These revisions are summarised by the ONS here, and their sources are discussed here. The bulk of the revisions are due to the contribution of the insurance industry to GDP being revised down by the use of Solvency II regulatory data, as well as improvements to the way pension schemes are measured. In addition, and of particular relevance for the current exercise, part of the revisions are due to the ONS, “bringing through a package of sources and methods changes that improve the international comparability of the UK gross domestic product (GDP) estimates.” 

These revisions make a material difference to UK GDP, as well as its international ranking. On the basis of the latest official figures taken directly from national statistical agencies, real UK per capita growth of 7.7% during the austerity period compares with 8.4% for Germany and 8.2% for the US.

Chart comparing growth rates in US, UK and Germany between 2010 and 2016.

So, based on the most recent data, the UK did not have the fastest growth in GDP per capita between 2010 and 2016. 

Aside from this, as others have noted, focusing narrowly on the 2010-2016 period is potentially misleading. When austerity was implemented, the UK was in the process of recovering from the 2008 recession. It is likely that there was substantial spare capacity which, under strong demand conditions, could have been quickly reabsorbed into economic activity. If we start our comparison at the pre-crisis peak (2007 for the UK and US, 2008 for Germany), rather than 2010, the divergence is much greater: by 2016, real UK GDP per capita had increased by 2.8% on its pre-crisis level, compared with 5.5% for the US and 7.1% for Germany. Much of UK growth during between 2010 and 2016 was recovering losses from the recession: GDP per capita did not reach pre-2008 levels until 2014, compared to 2011 for Germany and 2012 for the US.

As Chris Giles notes, “Most economists now accept that the sharp reductions in public spending between 2010 and 2015 delayed the recovery from the financial crisis”. Comparing outcomes with pre-crisis levels is not, therefore, “baseline bingo” as claimed by Rupert Harrison. These outcomes are hard to square with Harrison’s claim that this is “what catch up looks like”.

Chart showing real GDP per capita between 2007 and 2016 in US, UK and Germany

These data revisions highlight the dangers in drawing strong conclusions – particularly about politically loaded topics – from small differences in data that are subject to measurement error and revision. It is inevitable that an FT article claiming that UK growth per head was highest in the G7 during the main austerity years will be used as justification for austerity policies. But, on the basis of the most recent and accurate data available, the claim is false. UK GDP growth was relatively strong by international standards (and may yet be revised back to the top of the table) but this statement ought to be placed in its proper context, using a variety of data sources and an understanding of their strengths and weaknesses.

Nominal GDP (YBHA)Real GDP (ABMI)
Year2021 Blue Book2022 Blue Book2021 Blue Book2022 Blue BookIMF 2022 WEO
20101,612,1951,612,3811,884,5151,876,0581,884,515
20111,669,5091,664,2111,911,9831,896,0871,911,983
20121,721,3551,713,2411,940,0871,923,5511,940,087
20131,793,1551,782,2961,976,7551,958,5571,976,755
20141,876,1621,862,8272,035,8832,021,2252,035,883
20151,935,2121,920,9982,089,2762,069,5952,089,276
20162,016,6381,999,4612,136,5662,114,4062,136,566

Data are in millions of pounds (2019 pounds for the real data). Data downloaded from ONS and IMF websites on 20th March 2023. Note that the 2022 Blue Book dataset was only published on the 31st October 2022, too late for inclusion in the IMF’s October 2022 World Economic Outlook. The revisions were initially introduced (and reported on) in August 2022, the quarter before the Blue Book publication.

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Pension funds and liquidity spirals

Bruno Bonizzi and Jennifer Churchill

Falling prices in UK government bond (aka gilts) markets yesterday forced the Bank of England to intervene: “a material risk to financial stability” led the Bank to “carry out temporary purchases of long-dated UK government bonds” and to postpone the beginning of Quantitative Tapering, i.e. the sale of bond holdings accumulated over the past decade.

Falls in gilt prices are caused by both global factors – the strong dollar and rising global interest rates – and the large unfunded tax cuts announced in Kwasi Kwarteng’s budget. The most immediate worry is the risk of pension funds “falling over”. How do increasing bond yields pose a problem for pension funds?

Pension funds are widely assumed to function as large passive “containers” of long-term assets which engage in little short-term activity. This is incorrect: pension funds, especially large and mature ones, are sophisticated investors that use leverage and derivatives to achieve their financial objectives.

One such objective, for Defined Benefits (DB) pension funds (that still hold most UK pension fund assets), is best captured by the rise of the Liability Driven Investment (LDI) paradigm. According to LDI, the ultimate goal of pension funds is not the maximisation of returns per se, but performance against the commitments originating from pension liabilities. The key objective of LDI is the minimisation and stabilisation of the so-called “funding deficit”, the difference between the market value of assets and the discounted value of the future pensions to be paid (liabilities).

To achieve the stabilisation of the “funding deficit”, pension funds use a dedicated protection or liability-matching portfolio. This involves strategies that makes the value of assets move in the same direction as the valuation of liabilities. The most important influence on the funding deficit is movements in interest rates: if rates fall, the value of liabilities rises because bond yields are used as discount rates. But if pension funds invest in bonds with similar duration (i.e. sensitivity to interest rate changes) to their liabilities, their assets will also increase by a similar amount, leaving the funding deficit unchanged.

As well as bonds, these strategies also use interest rate swaps, which act in a similar way: pension funds pay a variable rate (e.g. the LIBOR or its recent replacement SONIA) in exchange for a fixed interest payment (the swap rate). By so doing they hedge against interest rate changes. Another LDI strategy is to use repos: pension funds can use their gilts to borrow in the repo market, and then buy more gilts, effectively doubling their exposure to gilts, and thus the degree of interest rate hedging.

The advantage of using repos and derivatives is that it frees up space to invest in other assets. Rather than fully investing their portfolio in bonds, pension funds typically hold a growth portfolio which is invested in all sorts of higher-risk assets, with the objective of increasing returns. This too can make use of derivatives, especially to hedge foreign currency risk. Data from the the ONS Financial Survey of Pension Schemes shows that interest rate swaps and foreign exchange forwards account for almost the totality of derivatives held by pension funds, and these sum (in gross terms) to over 10% of the value of their assets. And while LDI is only relevant to DB pension funds with debt-like liabilitiesall pension funds hedge their overseas assets.

These strategies all require collateral, often short-term bonds. A decline in the market value of collateral or the value of the derivative contracts can lead to margin calls on repo or on interest rate swaps, as explained by Toby Nangle. Similarly, if the value of the sterling falls, pension funds might face margin calls on their foreign exchange derivative positions.

This means that pressure in the short-term bond market can spill over into the market for long-term bonds. To meet margin calls, pension funds can be forced at the extreme to sell growth assets (such as equity, or long-term bonds) to raise the required liquidity to meet margin calls. This is what was seen in the wake of the budget, with pension fund managers reportedly “throwing the kitchen sink to meet margin calls”. If margin calls are not met then collateral could be seized and liquidated, further adding to the downward pressure on asset prices.

This is how we find ourselves in liquidity spiral territory – a situation of severe financial instability, as markets become one-sided, depressing asset prices and potentially provoking more margin calls. The risk of such instability lay behind the decision by the BoE to intervene.

More trouble could be on the horizon: similar liquidity spirals could originate in other derivative markets, such as foreign exchange derivatives as the Pound keeps depreciating against the dollar, or other financial institutions. The possibility of a broader “dash for cash”, requiring more BoE intervention, is still very much on the cards.

Strong and stable? The Conservatives’ economic record since 2010

In a recent interview, Theresa May was asked by Andrew Neil how the Conservatives would fund their manifesto commitments on NHS spending. Given that the Conservatives chose not to cost their manifesto pledges, May was unable to answer. Instead she simply repeated that the Conservatives are the only party that can deliver the economic growth and stability required to pay for essential public services. When pressed, May’s response was simple: ‘our economic credibility is not in doubt’.

Does the record of the last seven years support May’s claim?

The first statistic always quoted in such discussions is GDP growth. A lot has been made of the latest quarterly GDP figures, showing the UK at the bottom of the G7 league with quarterly GDP growth of just 0.2%. But these numbers actually tell us very little: they refer to a single quarter and are still subject to revision.

It is more useful to look at real GDP per capita over a longer period of time. This tells us the additional ‘real’ income available per person that has been generated. The performance of the G7 countries since the pre-crisis peak in 2007 is shown in the chart below, with the series indexed to 1 in 2007 for each country. (Data are taken from the most recent IMF WEO database.)

G7 GDP per capita, 2007-2016

GDP per capita in the UK only surpassed its pre-crisis level in 2015. By 2016, GDP per capita relative to the pre-crisis level was less than 2% higher than in 2007, putting the UK behind Japan, Germany, the US and Canada, slightly ahead of France, and well ahead of the Italian economy which remains mired in a deep depression. On this measure, the UK’s performance is not particularly impressive.

For most people, wages are a more important gauge of economic performance than GDP per capita. Here, the UK is an outlier. Relative real wage growth in the G7 economies is shown in the table below, alongside the changes in GDP per capita for the period 2007-2015.

Country

% change in GDP per capita, 2007-2015

% change in average real wage, 2007-2015
Canada 3.2 0.8
France -0.2 0.6
Germany 6.3 0.9
Italy -11.7 -0.7
Japan 3.0 -0.2
United Kingdom 0.7 -1.0
United States 3.7 0.5

Despite coming mid-table in terms of GDP per capita, the UK has the worst performance in terms of real wages, which have fallen by an average of 1% per year over the period. Even in depression-struck Italy, wages did not fall so far.

This translates into a fall of almost five percent in the real wage of the typical (median) worker since the crisis, as the chart below shows. This LSE paper, from which the chart is taken, finds that while almost everyone is worse off since the crisis, the youngest have seen the largest falls in income with 18-21-year-olds facing a fall in real wages of over 15%

Chart-3-LSE

With the value of the pound falling since the Brexit vote, inflation is once again eating into real wages and the latest figures show that, after a period of a couple of years in which wages had been recovering, real wages are now falling again and are likely to do so for the next few years. Average earnings are not projected to reach 2007 levels again until 2022 – by then the UK will have gone fifteen years without a pay rise.

A related issue is the UK’s desperately poor productivity performance. ‘Productivity’ here refers to the amount produced per worker on average. As the chart below from the Resolution Foundation shows, the UK has now experienced a decade without any increase in productivity — something which is historically unprecedented.

CHART-productivity

What causes productivity growth is a controversial topic among economists. Until recently, the majority view was that productivity is not affected by government macroeconomic policy. This position (which I disagree with) is increasingly hard to defend. As Simon Wren-Lewis argues here, evidence is mounting that the UK’s productivity disaster is the result of government policy: the Conservatives’ austerity policies have caused flatlining productivity.

Austerity — or, as it was branded at the time, the ‘Long Term Economic Plan‘ — was the central plank of Osborne’s policy from 2010 until the Brexit referendum vote in 2015.

As I and others have argued at length elsewhere, austerity was based on two false premises — ‘lies’ might be more accurate. The first was that excessive spending by Labour was a cause of the 2008 crisis. The second was that the size of the UK’s government debt posed serious and immediate risks that outweighed other concerns.

One thing that almost all macroeconomists agree on is that when recovering from a severe downturn such as 2008 — and with interest rates at nearly zero — the deficit should not be the target of policy. Instead, it should be allowed to expand until the economy has recovered.

Simply put, the deficit should not be used as a yardstick for successful management of an economy in the aftermath of a major economic crisis such as 2008. But since eliminating the deficit was the single most important target of the Conservatives’ so-called Long Term Economic Plan, we should examine the record.

In 2010, Osborne stated that the deficit would be eliminated by 2015. Two years after that deadline passed, the current Conservative manifesto states — in a passage that would not pass any undergraduate economics exam — that they will ‘aim to’ eliminate the deficit by 2025.

Even on their own entirely misguided terms, they have failed completely.

FIG-LTEP

While the dangers of the public debt have been vastly exaggerated by the Conservatives, they have had little to say about private sector debt. It is now widely accepted that the only remaining motor of economic growth is consumption spending. But with wages stagnant, continued growth of consumption cannot be sustained without rising levels of household debt.

This is the reason given when economists are asked why their predictions of post-referendum recession were so wrong: they didn’t anticipate the current credit-driven consumption burst. But this trend has been apparent for at least the last two years. It shouldn’t have been too hard to see this coming.

Chart-Credit-Cards

Just as the Tories tend to stay quiet on private debt, they also have little to say about the ‘other’ deficit — the current account deficit. This is a measure of how much the country is reliant on foreigners to finance our spending. The deficit expanded from 2011 onward to reach almost 5% of GDP. This is an important source of vulnerability for a country which is about to try and extricate itself from economic integration with its closest neighbours.

CHART-BoP- current account balance as per cent of GDP

Overall, the Tories economic record is far from impressive: stagnant wages and productivity, weak investment and manufacturing, rising household debt, and a large external deficit.

Now, a reasonable response might be that these are long-standing issues with the UK economy and are not the fault of the Conservatives. There is some truth to this. But if this is the case, Theresa May should identify and acknowledge these issues and provide a clear outline of how her policies will address them. This is not what she has done. Instead, she simply repeats her mantra that only the Conservatives will deliver on the economy, without providing any evidence to support her claim.

And then there is the decision to call a referendum on Brexit. It is hard to think of a more economically reckless move. Household analogies for government economic policy should be avoided — but I can’t think of an alternative in this case.

Following up on an austerity programme with the Brexit referendum is like sending the children to school without lunch money for six years and allowing the house to fall into serious disrepair in order to needlessly over-pay a zero-interest mortgage — and then gambling the house on a dice game.

Given this record, it is astonishing that the Conservatives present themselves, with a straight face, as the party of economic competence — and the media dutifully echoes the message. The truth is that the Conservatives have mismanaged the economy for the last seven years, needlessly imposing austerity, choking off growth in productivity, wages and incomes. They then called an entirely unnecessary referendum, gambling the future prosperity of the country for political gain.

Theresa May is correct — there is little doubt about the economic credibility of the Conservatives. It is in short supply.

What do immigration numbers tell us about the Brexit vote?

A couple of weeks ago I tweeted a chart from The Economist which plotted the percentage increase in the foreign-born population in UK local authority areas against the number of Leave votes in that area. I also quoted the accompanying article: ‘Where foreign-born populations increased by more than 200%, a Leave vote followed in 94% of cases.’

00-economist

This generated lots of responses, many of which rightly pointed out the problems with the causality implied in the quote. These included the following:

  • Using the percentage change in foreign-born population is problematic because this will be highly sensitive to the initial size of population.
  • Majority leave votes also occurred in many areas where the number of migrants had fallen.
  • Much of the result is driven by a relatively small number of outliers while the systemic relationship looks to be flat.
  • The number of points where foreign-born populations had increased by more than 200% were small relative to the total sample: around twenty points out of several hundred.

Al these criticisms are valid. With hindsight, the Economist probably shouldn’t have published the chart and article – and I shouldn’t have tweeted it. But the discussion on Twitter got me interested in whether the geographical data can tell us anything interesting about the Leave vote.

I started by trying to reproduce the Economist’s chart. The time period they use for the change in foreign-born population is 2001-2014. This presumably means they used census data for the 2001 numbers and ONS population estimates for 2014. My attempt to reproduce the graph using these datasets is shown below. The data points are colour-coded by geographical region and the size of the data point represents the size of the foreign-born population in 2014 as a percentage of the total. (The chart is slightly different to the one I previously tweeted, which had some data problems.)

01-chart-f-inc-hybrid-trans

Despite the problems described above, the significance of geography in the vote is clear – this is emphasised in the excellent analysis published recently by the Resolution Foundation and by Geoff Tily at the TUC (see also this in the FT and this in the Guadian).

Of the English and Welsh regions, it is clear that the Remain vote was overwhelmingly driven by London (The chart above excludes Scotland and Northern Ireland, both of which voted to Remain). Other areas which have seen substantial growth in foreign-born populations and also voted to Remain are cities such as Oxford, Cambridge, Bristol, Manchester and Liverpool.

A better way to look at this data is to plot the percentage point change in foreign population instead of the percentage increase. This will prevent small initial foreign-born populations producing large percentage increases. The result is shown below. For this, and rest of the analysis that follows, I’ve used the ONS estimates of the foreign-born population. This reduces the number of years to 2004-2014, but excludes possible errors due to incompatibility between the census data and ONS estimates. It also allows for inclusion of Scottish data (but not data from Northern Ireland). I’ve also flipped the X and Y axes: if we are thinking of the Leave vote as the thing we wish to explain, it makes more sense to follow convention and put it on the Y axis.

02-chart-f-pp-ons

There is no statistically significant relationship between the two variables in the chart above. The divergence between London, Scotland and the rest of the UK is clear, however. There also looks to be a positive relationship between the increase in foreign-born population and the Leave vote within London. This can be seen more clearly if the regions are plotted separately.

03-chart-f-region-pp-ons

The only region in which there is statistically significant relationship in a simple regression between the two variables is London. A one percent increase in the foreign-born population is associated with a 1.5 percent increase in the Leave vote (with an R-squared of about 0.4). The chart below shows the London data in isolation.

04-chart-f-pp-ons-london

The net inflow of migrants appears to have been greatest in the outer boroughs of London – and these regions also returned highest Leave votes. There are a number of possible explanations for this. One is that new migrants go to where housing is affordable – which means the outer regions of London. These are also the areas where incomes are likely to be lower. There is some evidence for this, as shown in the chart below: there is a negative relationship – albeit a weak one – between the increase in the foreign-born population and the median wage in the area.

05-chart-london-wage-pp-inc

Returning to the UK as a whole (excluding Northern Ireland), the Resolution foundation finds that there is a statistically significant relationship between the percentage point increase in foreign-born population and Leave vote when the size of the foreign-born population is controlled for. This is confirmed in the following simple regression, where FB.PP.Incr is the percentage point increase in the foreign-born population and FB.Pop.Pct is the foreign-born population as a percent of the total.

Coefficients:
 Estimate Std. Error t value Pr(>|t|) 
(Intercept) 57.19258 0.71282 80.235 < 2e-16 ***
FB.PP.Incr 0.90665 0.17060 5.314 1.87e-07 ***
FB.Pop.Pct -0.64344 0.05984 -10.752 < 2e-16 ***
---
Signif. codes: 0 ~***~ 0.001 ~**~ 0.01 ~*~ 0.05 ~.~ 0.1 ~ ~ 1

Residual standard error: 9.002 on 363 degrees of freedom
Multiple R-squared: 0.2475, Adjusted R-squared: 0.2433 
F-statistic: 59.69 on 2 and 363 DF, p-value: < 2.2e-16

It is clear that controlling for the foreign-born population is, in large part, controlling for London. This is illustrated in the chart below which shows the foreign-born population as a percentage of the total for each local authority in 2014, grouped by broad geographical region. The boxplots in the background show the mean and interquartile ranges of foreign-born population share by region. The size of the data points represents the size of the electorate in that local authority.

06-chart-f-ons-fp-electorate-boxes

This highlights a problem with the analysis so far – and for others doing regional analysis on the basis of local authority data. By taking each region as a single data point, statistical analysis misses the significance of differences in the size of electorates. This is important because it means, for example, that the Leave vote of 57% from Richmondshire, North Yorksire with around 27,000 votes cast is given the same weight as the Leave vote of 57% in County Durham, with around 270,000 votes cast.

This can be overcome by constructing an index of referendum voting weighted by the size of the electorate in each area. This index is constructed so that it is equal to zero where the Leave vote was 50%, negative for areas voting Remain, and positive for areas voting Leave. The magnitude of the index represents the strength of the contribution to the overall result. Plotting this index against the percentage point change in the foreign population produces the following chart. Data point sizes represent the number of votes in each area.

07-chart-leave-weighted

Again, there is no statistically significant relationship between the two variables, but as with the unweighted data, when controlling for the foreign population,  a positive relationship does exist between the increase in foreign-born and Leave votes.

The outliers are different to those seen in the unweighted voting data, however – particularly in areas with a strong leave vote. This can be seen more clearly by removing the two areas with the strongest Remain votes: London and Scotland. The data for the rest of England and Wales only are shown below.08-chart-leave-weighted-nss

There is a clear split between the strong Leave outliers and the strong Remain outliers. The latter are Bristol, Brighton, Manchester, Liverpool and Cardiff. When weighted by size of vote, The previous outliers for Leave – Eastern areas such as Boston and South Holland – are replaced by towns and cities in the West Midlands and Yorkshire and with the counties of Cornwall and County Durham.

Overall, while there is a relationship between net migration inflows and Leave votes – at least when controlling for the size of the foreign-born population – it is only a small part of the story. The most compelling discussions I’ve seen of the underlying causes of the Leave vote are those which emphasise the rise in precarity and the loss of social cohesion and identity in the lives of working people, such as John Lanchester’s piece in the London Review of Books (despite the errors), the excellent follow-up piece by blogger Flip-Chart Rick, and this piece by Tony Hockley. As Geoff Tily argues, the geographical distribution of votes strongly suggests economic dissatisfaction was a key driver of the Leave vote, which pitted ‘cosmopolitan cities’ against the rest of the country. This is compatible with the pattern shown above, where the strongest Leave votes are concentrated in ex-industrial areas and the strongest Remain votes in the ‘cosmopolitan cities’.

The chart below shows the weighted Leave vote plotted against median gross weekly pay.09-wages

Scotland as a whole is once again the outlier, while much of the relationship appears to be driven by London, where wages are higher and the majority voted Remain. Removing these two regions gives the following graph.

10-wages

Aside from the outlier Remain cities, there is a negative relationship between median pay and weighted Leave votes. The statistical strength of this relationship is relatively weak, however.

Putting all the variables together produces the following regression result:

Coefficients:
 Estimate Std. Error t value Pr(>|t|) 
(Intercept) 80.98722 12.18838 6.645 1.12e-10 ***
FB.PP.Incr 2.46269 0.57072 4.315 2.06e-05 ***
FB.Pop.Pct -1.61904 0.21781 -7.433 7.72e-13 ***
Median.Wage -0.12539 0.02404 -5.216 3.08e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 29 on 362 degrees of freedom
Multiple R-squared: 0.2977, Adjusted R-squared: 0.2919 
F-statistic: 51.15 on 3 and 362 DF, p-value: < 2.2e-16

Leave votes are negatively associated with the size of the foreign-born population and with the median wage, and positively associated with increases in the foreign-born. The R^2 value of 0.3 suggests this model has some predictive power, but could certainly be improved.

Coefficients:
 Estimate Std. Error t value Pr(>|t|) 
(Intercept) 107.61139 13.30665 8.087 9.97e-15 ***
FB.PP.Incr 2.92817 0.49930 5.865 1.04e-08 ***
FB.Pop.Pct -2.34394 0.27140 -8.636 < 2e-16 ***
Median.Wage -0.14360 0.02313 -6.210 1.50e-09 ***
RegionEast Midlands -9.07601 5.44978 -1.665 0.09672 . 
RegionLondon 9.44698 8.34896 1.132 0.25861 
RegionNorth East -4.11112 8.02869 -0.512 0.60893 
RegionNorth West -16.69448 5.51048 -3.030 0.00263 ** 
RegionScotland -61.65217 5.76312 -10.698 < 2e-16 ***
RegionSouth East -4.60717 4.64123 -0.993 0.32156 
RegionSouth West -18.73821 5.55187 -3.375 0.00082 ***
RegionWales -27.65673 6.53577 -4.232 2.96e-05 ***
RegionWest Midlands 4.06613 5.83469 0.697 0.48633 
RegionYorkshire and The Humber 4.72398 6.61676 0.714 0.47574 
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 24 on 352 degrees of freedom
Multiple R-squared: 0.5323, Adjusted R-squared: 0.515 
F-statistic: 30.82 on 13 and 352 DF, p-value: < 2.2e-16

Adding regional dummy variables improves the fit of the model substantially – increasing the value of R^2 to around 0.5. This suggests – unsurprisingly – there are differences between regions which are not captured in the three variables included here.

2015: Private Debt and the UK Housing Market

This report is taken from the EREP’s Review of the UK Economy in 2015.

In his 2015 Autumn Statement, Chancellor George Osborne gave a bravura performance. He congratulated himself on record growth and employment, falling public debt, surging business investment and a narrowing trade deficit. He announced projections of continuous growth and falling public debt over the next parliament.

While much of this was a straightforward misrepresentation of the facts – capital investment has yet to recover from the 2008 crisis and the current account deficit continues to widen – other sound bites came courtesy of the Office for Budget Responsibility. The OBR delivered the Chancellor an early Christmas present in the form of a set of revised projections showing better-than-expected public finances over the next five years.

When, previously, the OBR inconveniently delivered negative revisions, the Chancellor responded by pushing back the date he claims he will achieve a budget surplus. In response to the OBR’s gift, however, he chose instead to spend the windfall.  This is a risky strategy because any negative shock to the economy means he will miss his current fiscal targets – targets he has already missed repeatedly since coming to office.

As it turns out, these negative shocks have materialised rather quickly. Since the Chancellor made his statement a month ago, UK GDP growth has been revised down, the trade deficit has widened and estimates of borrowing for the current year have increased.

ca-forecasts

In reality, the OBR projections never looked plausible. The UK’s current account deficit – the amount borrowed each year from the rest of the world – is at an all- time high of around 5% of GDP. Every six months for the last three years, the OBR forecast that the deficit would start to close within a year; every time they were proved wrong (see figure above).  Their current assertion – that the trend will be broken in 2016 and the deficit will steadily narrow to around 2% of GDP in 2020 – must be taken with a large pinch of salt.

The current account deficit measures the combined overseas borrowing of the UK public and private sectors. In the unlikely event that George Osborne was to achieve his stated aim of a budget surplus, the whole of this foreign borrowing would be accounted for by the private sector. This is exactly what the OBR is projecting. Specifically, they predict that the household sector will run a deficit of around 2% per year for the next five years. They note that “this persistent and relatively large household deficit would be unprecedented”.

This projection has been the basis of recent stories in the press which have declared that the Chancellor has set the economy on a path to almost-certain financial meltdown within the current parliament. This is too simplistic an analysis. Financial imbalances can persist for a long time. The last UK financial crisis originated not in the UK lending markets but in UK banks’ exposure to overseas lending.

But the Chancellor’s strategy entails serious financial risks. Even though there is no real chance of achieving a surplus by 2020, further cuts to government spending will squeeze spending out of the economy, placing ever more of the burden on household consumption spending to maintain growth.

The figure below shows the annual growth in lending to households. While total credit growth remains subdued, unsecured lending has, in the words of Andy Haldane, chief economist at the Bank of England, been “picking up at a rate of knots”.

debt-growth

Moderate growth in the mortgage market may conceal deeper problems: household debt-to-income ratios have fallen since the crisis but, at around 140% of GDP, remain high both in historical terms and compared to other advanced nations. The majority of new mortgage lending since 2008 has been extended to buy-to-let landlords. These speculative buyers now face the prospect of rising interest rates and tax changes that will take a large chunk out of their property income. Many non-buy-to-let borrowers are badly exposed: a sixth of mortgage debt is held by those who have less than £200 a month left after spending on essentials.

The Financial Policy Committee has noted that these trends “… could pose direct risks to the resilience of the UK banking system, and indirect risks via its impact on economic stability”.

What is often left out of the more apocalyptic visions of a coming credit meltdown is that underlying all this is an unprecedented housing crisis in which an entire generation are locked out of home ownership. Instead of tackling this crisis, Osborne is using the housing market as a casino in the hope of keeping economic growth on track during another five years of austerity. It is a high-risk strategy. His luck may soon run out.

The report’s authors include:

John Weeks on fiscal policy

Ann Pettifor on monetary policy

Richard Murphy on taxation

Özlem Onaran on inequality and wage stagnation

Jeremy Smith on labour productivity

Andrew Simms on climate change and energy

Jo Michell on private debt

The full report is can be downloaded here.

Information on EREP is available here.

Happy Christmas from the Office of Budget Responsibility

Image reproduced from here

The sectoral balances approach to economic forecasting has come under scrutiny recently. It is certainly the case that when used carelessly, projections based on accounting identities have the potential to be either meaningless or misleading. This will be the case if accounting identities are mistakenly taken to imply causal relationships, if projections are presented without a clear statement of the assumptions about what drives the system or if changes taking place in ‘invisible’ variables such as the rate of growth of GDP are not identified (balances are usually presented as percentages of GDP).

Used with care, however (or luck, depending on your perspective), the approach is not without its merits – as I have argued previously. If nothing else, the impossibility of escaping from the fact that in a closed system lending must equal borrowing imposes logical restrictions on the projections that can be made about the future paths of borrowing in a ‘closed’ macroeconomic system.

Which brings us to the Chancellor’s Autumn Statement and the OBR’s rather helpful projections. As Duncan Weldon notes, the OBR are likely to receive a rather warmly written card from the Chancellor’s office this Christmas. While true that the OBR have, in the past, been less than helpful to the Chancellor, one can’t help but wonder about the justification for announcing the OBR projections at the same time as the Chancellors’ statements. Why are the OBR projections not made known to the public at the same time that they are made available to the Chancellor?

But back to sectoral balances. The model used by the OBR produces projections which comply with sectoral balance accounting identities. Four are used: those of the public sector, the household sector, the corporate sector and the rest of the world. The most closely watched is of course the public sector balance. The headline result of the OBR forecasts is that the public sector will run a surplus by 2019. What has so far received less attention (at least since Frances Coppola examined the projections from the March 2015 OBR forecasts) is the implication of this for the other three balances. The most recent OBR projections are shown below.

Fig-1-November-2015

Since the government is projected to run a small surplus from mid-2019, the other three sectors must collectively run a deficit of equal size. The OBR projects that the current account deficit will fall from its current level of around five per cent of GDP to around two per cent of GDP. The UK private sector must be in deficit. Interesting details lie in both the distribution of this deficit between the household and corporate sectors, and in the changes in figures since the last OBR reports in March and July.

In order to show how the numbers have changed since the previous forecasts, I have collected the data series from all three releases into individual charts.

The OBR series from these three releases for the public sector financial balance are shown below. Other than postponing the date at which the government achieves a surplus (and some revisions to the historical data) there is little difference between the three releases.

Fig-2-Public

Changes to the projections for the current account deficit are more significant. The latest projections include improvements in the projected deficit of between 0.5% and 1% of GDP, compared with the July predictions. With the current account deficit at record levels in excess of 5% of GDP, I think it is fair to say the projections look optimistic. I note that in each of the three OBR series, the deficit starts to close in the first projected quarter. Put another way, the inflection point has been postponed three times out of three.

Fig-3-ROW

Things start to get interesting when we turn to the corporate sector. Here the projections have changed rather more significantly. Whereas the previous two data series showed the corporate sector reversing its decade-long surplus in 2014 and finally returning to where many think the corporate sector should be – borrowing to invest – the new series contains significant revisions to the historical data. As it turns out, the corporate sector has remained in surplus, lending one per cent of GDP in Q2 2015. The corporate sector is not now projected to return to deficit until Q3 2018.

Fig-4-Corporate

Since the net financial balance for any sector is the difference between ex post saving – profits in the case of the corporate sector – and investment, these revisions imply either falling corporate investment, rising profits, or both.

The data series for corporate investment are shown below. The historical data have been revised down significantly. Investment in Q2 2015 is 1% of GDP lower than previously recorded. (This is hard to square with Osborne’s statement that ‘business investment has grown more than twice as fast as consumption’.) The reduction compared to previous forecasts widens in the projection out to 2020. Nonetheless, it is hard to escape the conclusion that the projections are extremely optimistic. By 2020, business investment is expected to reach twelve per cent of GDP, higher than any year back to 1980.

Fig-5.Investment

What of business profits? These are shown in the table below, taken from the OBR report. It seems that corporate profit grew at 10% year-on-year in 2014-15, despite GDP growth of around 2.5%. While projected growth rates decline, corporate profit is expected to grow at over 4% annually in every year of the projection out to 2021 (in a context of steady 2.5% GDP growth). There is not much sign of Goodhart–Nangle in these projections.

Fig-6-Profits

So, to recap: by 2020 we have government running a surplus just under 1% of GDP, a current account deficit of 2% of GDP and a corporate sector deficit around 1% of GDP. Those with a facility for mental arithmetic will have already arrived at the punchline – the household sector will be running a deficit of around 2% of GDP. In fact, given data revisions, the household sector appears to be already running a deficit close to 2% of GDP – a deficit which is projected to remain until 2021 (see figure below).

Fig-7-HHAs a comparison, note that in the period preceding the 2008 crisis, the household sector ran a deficit of not much over 1% of GDP, and for a shorter period than currently projected.

The OBR has this to say on its projections:

Recent data revisions have increased the size of the household deficit in 2014 and we expect little change in the household net position over the forecast period, with gradual increases in household saving offset by ongoing growth of household investment. Available historical data suggest that this persistent and relatively large household deficit would be unprecedented. This may be consistent with the unprecedented scale of the ongoing fiscal consolidation and market expectations for monetary policy to remain extremely accommodative over the next five years, but it also illustrates how the adjustment to fiscal consolidation assumed in our central forecast is subject to considerable uncertainty.  (p. 81)

Perhaps there is something to the sectoral balances approach approach after all. One can only wonder what Godley would make of all this.

Jo Michell