Household Debt in the UK

Yesterday I argued that if you want to discuss what changes to household debt might mean, you need a decent framework for assessing the fluidity of the microstructure of debt. To illustrate I set out two hypothetical countries with identically modest aggregate household debt loads – but with different sensitivities to changes in interest rates, and differing levels of financial stability. I called them Debtzania (where debt is concentrated and debt-to-income ratios for debtor was high) and Feudaland (where debt is thinly spread and a few households are creditors).

Which of these economies does the UK most resemble?

Using the NMG/ Bank of England survey data we can examine where it is in the income distribution that UK household debt falls.

Table 2 shows that most debt is owed by those in the top three deciles of the income distribution, and on sums below £175k. The mortgage-dominated nature of this debt and the untroubling LTVs with which it is associated may explain it to some as both rational and desired.

Table 2: Where household debt sits, by pre-tax income decile and total debt cohort

We can perform the same analysis for deposit balances held among our sample (knowing that at the macro level, total monetary assets and total monetary liabilities in the household sector are roughly the same), the output of which is shown in Table 3. It is perhaps not surprising that most of the money is owned by members of the upper half of the income distribution.

Table 3: Where household deposits sit, by pre-tax income decile and total deposit cohort

We could treat each income decile as a separate sector and look at the transfer of net monetary claims between them (in the form of savings and borrowings). This is shown in Table 4, and the data suggests that the flow of savings is on a net basis from lower income cohorts to upper income cohorts, typically to deliver mid-sized mortgages (we could, of course, reverse the causality in this description). This concentration of debtors might sound a bit more Debtzanian than Feudalish.

Table 4: UK households by pre-tax income decile and total debt cohort, estimated net saving/ borrowing flow

But debt sustainability is only really an issue for those in debt, and will correspond to their ability to service debt. And so in Table 5 we examine how debt-to-income ratios vary across the our matrix of UK household debt and income distribution. After all, small cohorts of high debt-to-income ratios was the thing identified as problematic when looking at otherwise untroubling Debtzanian aggregate debt statistics. Here we can see that debt-to-income ratios look pretty high for almost all debtors, but least troubling for highest income households where most nominal debt resides.

Table 5: UK household debt-to-income ratios, by pre-tax income decile and total debt cohort

And so the problems for policymakers seeking some rules of thumb to estimate sustainable or unsustainable levels of household debt appear twofold.

First, there is a data issue. Our sample size of around 6,000 households is small (as evidenced by @jmackin2’s outrage yesterday), even before we start to exclude households that refuse to give data in which we have interest. For the purpose of this blog I have used a snapshot, but I think that we need to compare datasets on a longitudinal basis. When the numbers on the matrix change, we need to understand whether this the result of movement on the income dimension or debt dimension.

Second, we have a question over the appropriate unit of analysis. In discussing Feudaland and Debtzania we argue against this being the overall economy, but as suggested by a comparison between the matrices of debt and income distribution on the one hand, and deposits and income distribution on the other, splitting the population into income deciles takes us not much closer to the issue at hand. There are meaningful flows between debtors and creditors within members of any given income decile. The appropriate unit of analysis is perhaps the household. But that statement seems a bit nuts.

The chart below looks at individual households in the NMG survey, rather than treating them as aggregated cohorts. It’s messy, but maybe messy is what works.

Putting aside human concerns for families struggling with the very real problems attached to sever indebtedness, why should policymakers care?

FPC members care because widespread unsustainable household debt can lead to problems for financial stability. But, through this lens, problems in debt service among low income households with small but unsustainable debt balances are just not worthy of deep interest: they are insufficiently likely to cause systemic financial problems.

MPC members care because changes in appetite for debt might tell them something about the location of the neutral real interest rate. Through this lens it might seem that it doesn’t matter what individual households are doing, but does matter what they collectively do. And I guess that if this is true, changes in the optimal level of debt which can only be inferred from an examination of its microstructure will be an important in any decision as to the wisdom of ‘leaning against the wind’. And they will care because cashflow impacts for indebted households following changes in interest rates are part of the transmission mechanism.

What to do?

Despite data shortcomings, there are some suggestions that we can draw out. First, in considering changes to household debt, policymakers should do well to treat aggregate debt statistics with caution. Having an idea as to whether they are dealing with Feudaland or Debtzania, and the degree to which there is stability in the microstructure of the debt distribution seems important. Second, examining the entire distribution of nominal debt-weighted debt-to-income ratios (and changes thereof) appears to be a step towards more capturing more meaningful household debt characteristics, changes in household behaviour and systemic debt problems.


Reverse ferret on household debt

A couple of blogs ago I posed the question as to how the sustainable level of household debt might be identified for an economy. I then reckoned that there might be a decent 30k ft way of addressing this question. This post serves as a bit of a reverse ferret. I’m not saying that the previous approach was wrong, but after some back-to-basics thinking I understand that its rightness is overly-dependent upon the stability of something that it may or may not be wise to assume. In my defence, I reckon *every* macro approach relies on this stability (comments section please to correct me). But I take little comfort in expressing a popular view based solely on its popularity.

Debt is an entirely distributional issue. Evenly distributed in a closed economy, no level of household debt is unserviceable or unsustainable. But evenly distributed, debt is also functionless. Every sustainability question rests upon the microstructure of debt loads across different households. And so, from a macro perspective, debt is tricksy to say the least.

This all becomes clear in an example. Let’s imagine two super-simple closed economies with no sectoral flows: the Kingdom of Feudaland and the Republic of Debtzania.

The Kingdom of Feudaland consists of 10m households, each of them with perfect equality of employment income (at £20k per annum), making its GDP worth £200bn per annum. Household debt sums to £20bn, and interest rates are 15%. Debt is owed by 9.625m households to the other 375,000 remaining households (evenly). Interest costs are eminently serviceable (£312 per annum per household in transfers from the debtors to the creditor households, each of whom receives a handsome £8,000 per annum in interest).

The Republic of Debtzania also consists of 10m households, each of them with perfect equality of employment income (at £20k per annum), making the GDP worth £200bn per annum. Household debt also sums to £20bn, and interest rates are also 15%. Debt is owed by 375,000 households to the other 9.625m households (evenly). Servicing this debt costs the debtor households £8,000 per annum. The creditor households each receive a modest £312 in interest income on their savings.

Table 1: Feudaland and Debtzanian debtmetrics

The aggregate debtmetrics for Feudaland and Debtzania are exactly the same (Table 1). At 10%, the aggregate household debt to income ratio looks pretty low, as do aggregate interest payments as a proportion of GDP at 1.5%. But the microstructure of the debt distribution is what matters in assessing the sustainability or otherwise of these two economy’s debt loads.

Feudaland, despite its wealth inequality, looks to be a beacon of financial stability. The debt-to-income ratios for debtors are pretty low at 10.4% of income, and servicing ongoing interest costs is universally manageable, absorbing 1.6% of income. Feudaland’s wealth inequality might eventually prove politically unsustainable, but it would be hard to call it financially unsustainable, even in an environment where interest rates doubled.

Despite exactly the same aggregate debt-metrics, Debtzania looks much more financially fragile than Feudaland. The majority of its citizens are savers, but they have amassed claims on the small minority of financially-fragile debtors who must pay out 40% of their incomes in interest payments to keep out of default. Debtzania debtors’ debt-to-income ratios are individually high at 267%, and it is probably fair to say that a doubling of interest rates might bring meaningful problems to Debtzania’s financial system (with associated morality stories).

Now imagine being a monetary policy maker in each of the two countries. At what point should you worry about changes to aggregate household debt, and at what point could you infer that household debt in your country is too high? If a policymaker looked to their countries’ respective histories for clues they would, in so doing, be making the assumption that the microstructure of debtor-creditor relationships would be relatively static (or at least semi-stable) over time.

With a static microstructure of debt in Feudaland we might expect to see household default rates relatively unresponsive to wild changes in aggregate debt loads or interest rates. Policy makers might draw the lesson that household debt – at only 10% of GDP – is far too low to worry about, and that rising debt could be a sign that households were moving towards their optimal debt load (and so should not be met by any ‘leaning against the wind’). One could even imagine the cultural trope of creditworthiness being assigned to Feudalanders (backed by the empirical evidence of minuscule historical credit losses).

With a static microstructure of debt in Debtzania, we might by contrast expect to see household defaults rise and fall with both real (and nominal) interest rates and debt-loads. Those 375k households who do the borrowing may – if cross-country evidence holds in our mythical land – do a disproportionate amount of the spending in the economy. And so monetary policy might prove a particularly speedy macro tool for demand management: introducing higher and lower pain thresholds to debtor households and transferring higher or lower amounts of debtor household incomes to creditor households with lower marginal propensities to spend. It would be unsurprising if Debtzanians acquired a reputation of being quick to default; concerned politicians might encourage them all to save more.

In short, if the structure of the distribution of debt was utterly unchanging in both countries, policymakers might reasonably rely on each country’s individual history of debtmetrics to determine policy pinch-points.

But the usefulness of any historical comparisons (in terms of aggregate debt-to-income ratios or debt-service ratios) rests on this microstructure of debt being pretty concrete. The more changeable the microstructure, the less useful any historical comparisons.

Imagine again that the microstructure of debt distribution in Feudaland shifted to match the distribution in Debtzania, perhaps as its demography shifted through population ageing and associated changes in savings habits. A policymaker following only aggregate debtmetrics might infer that the change in default patterns was associated with some cultural shift in the population. When in fact, aggregate debtmetrics had instead done a good job at masking this demographic sea change.

It is not clear that I, or anyone else who makes macro comments on matters of household debt, has a decent framework for assessing the fluidity of this microstructure.

In the next post I look at the data that I can see readily available for the UK.

In search of a better World Interest Rate

Financial market practitioners are, if nothing else, pragmatic in their approach to analysis. Finding something that works is great. And when something stops working it tends to be discarded. After a twitter convo last night with some smart folks I’m going to outline something that I have tried and discarded in the realm of seeking an answer to the question as to how the long dated real yields across the world might be determined by macroeconomic variables.

Some years ago when I was a full-time bond geek I asked my then CIO (the wonderful Michael Hughes of BZW Equity-Gilt Study fame) how he thought about assigning relative value in the inflation-linked bond market. (Please bear in mind that this preceded the ‘popular’ preoccupation with discerning the true r-star by some years.) 

Simple, he told me. Think of the real yield as the bid for international capital: there should be an inverse relationship with current account balances. I regressed 10yr real yields with current account deficits as a percentage of GDP, and there was an okay fit. Markets are forward-looking so I took consensus c/a balances (proxied by IMF forecasts). A better fit.

Here’s the chart* with and without the UK using today’s IMF forecasts:
It’s not the worst chart in the world at first glance (although @Barnejek might disagree). It gave me a speck of hope that there might be some useful stuff here that could improve on King & Low 2014.

King & Low sought to provide a ‘world interest rate’ series that can be used by all sorts of people. And it has since been heavily cited. They basically take an unweighted average of available ten year inflation-linked bond yields, and also a GDP-weighted average of the same instruments (finding them pretty indistinguishable). As far as papers by former G7 central bank governors go it is unextraordinary. But I like the idea of a global real yield and for play for having a stab at it.

As a practitioner, developments within one market appear to drive others, although the ‘aggressor’ market appears to change. (So, for example, it could be US Treasuries setting the tone for the world’s bond markets until the ECB decides to institute QQE and starts driving the car. And then the BoJ rocks up and declares Yield Curve Control. That sort of thing.) In financegeek language, developed market bond yields appear to be cointegrated. This is uncontroversial.

Finding a world interest rate that recognises adjustments for the international balance of payments (which looks from these charts like they might be a *thing*) could be interesting. Rather than looking at the simple average of the historically small number of issued bond yields and calling that the world interest rate, you could instead plot the course of the intercept of the regression line on the chart over time: a line not distorted by a country’s idiosyncratic balance of payment positions (or at least aggregating and controlling these idiosyncratic distortions).

When I tried this approach I came up with an answer that was not radically different from King & Low. The green line in the chart below is not far from King & Low’s blue line (which I extended by updating their dataset) and is modestly below (taking account of the degree to which King & Low’s issuers typically run current account deficits, and that they hadn’t made any credit spread adjustments to their data). Not that different. But better?

However, we now reach the disappointing part of the story. How stable is the relationship shown above in the nice scatter/bubble charts? Not very. As the next two charts show, the r-square of the relationship goes to pot 2008-2014 (although here I am using actual c/a balances rather than forecast ones). And the slope of the line actually flips from negative to positive in 2008 (admittedly, some really reallly crazy stuff was going on in international inflation-linked markets then – stuff that I still talk to people about today, so maybe that’s forgivable).

I haven’t GDP-, M3-, or duration-equivalent-bond-market-size-weighted the dots to find this r-square, so maybe things improve with a bit more work.

Bottom line: The cutting room floor is littered with ideas that don’t quite make the cut. I really like this way of thinking about international real yields, but am disturbed by the episodic nature of the relationship actually ‘working’. Pragmatism leads me to look in other directions if I want to link international real yields to international macroeconomic variables.

If anyone has had more success in this approach please do drop a comment in below.


* I take the average IMF c/a balance for 2017-2021 as a percentage of GDP and compare here to 10yr real yields on inflation linked bonds as at 28th November 2017. I have adjusted the UK for the RPI-CPI ‘wedge’, and the Eurozone real yields by the difference in their 5yr CDS and Germany’s 5yr CDS so that it doesn’t become a credit quality chart. The bubble sizes are common currency GDP; I was wondering whether they should be common currency M3 or bond market size in 10yr duration equivalents. Not sure.

Finding the *right* level of household debt

My last post reflected on the Borio/ Vlieghe idea that the Equilibrium Real Rates (ERR) should perhaps be defined not just by inflation, but also by changing debt levels. Since then I have been thinking about that list of tricksy questions I listed at the end of the post. I want to have a stab at one – specifically, whether it is possible to define the ‘optimal nonfinancial debt’ for an economy. Short answer: maybe, but I don’t have the skills to know*; in the meantime I’ve got a way to think about it from 30k feet. 

Thinking about optimal debt loads from 30k feet by cleverer people than me has had a frankly shocking record.** That said, my MPhil thesis on the geopolitics of emerging market finance, has given me familiarity with debt sustainability models that I think could maybe be helpful. The bottom line of debt sustainability frameworks tends to be that there is rarely an impossible level of debt, but debt does get meaningfully tougher to service as debt rises, rates rise, and growth slows. Big surprise.

Let’s look at the UK household sector as a whole through the lens of a super-simplified sovereign debt dynamics framework.*** We know what disposable household income growth and effective interest rates have recently been. And we know where the household debt stock has got to. As the chart below shows, it has risen to 138% of disposable income.

So what is the ‘primary surplus’ (or in the households’ case the net acquisition of financial liabilities minus interest payments as a percentage of household disposable income) required to stabilise household debt-to-disposable-income at 138%? The answer is around about 2% right now. And what is the actual ‘primary surplus’? Around -2%. So debt is growing, and is growing quickly. And the Bank of England appears to be worried about this worrying rise in household debt. (Again, read the last blog on this here.)

We can compare the primary surplus required to stabilise debt levels over time (blue line in the chart below) to the actual primary surplus recorded by households (grey line). You may notice that when the grey line is below the blue line, debt rises. When the grey line is above the blue line, debt falls. It’s a super-simplified little framework. The chart on the right compares the gap between the two lines to the change in debt-to-income: we can see that it doesn’t capture everything, but it sort of works(ish).

While extremely simple, this framework highlights the degree to which the issue of changes in debt sustainability (a somewhat different and easier question to the optimal debt load question) is a function of income growth, interest rates and debt loads. And in seeking to answer the question as to why households have re-levered recently, we can quickly hypothesise an answer that doesn’t involve a spending splurge. (The incredibly smart Neville Hill at Credit Suisse, who kindly shared his data with me that I used to calculate the blue line, argues exactly that here.)

What pushes the blue line up and down so erratically? Well, the most volatile input to the little debt dynamics ‘model’ is household income growth. The spike up in the blue line in recent quarters appears to have been driven by a collapse in nominal household income growth through 2016. In other words, it sort of looks like households are expecting the sharp recent slowdown in income growth to be temporary.

If the slowdown in household income growth isn’t temporary and the Bank of England raise rates to try to control household debt growth, the blue line gets a double-whammy. The following projection shows the path of the breakeven primary surplus in an environment where household disposable income growth continues at the sluggish pace of the last year and three rate rises over the next three quarters:

Actual household consumption would have to drop consumption pretty darn quickly to stabilise debt levels. And I can see households interpreting this as monetary policymakers arguing that the beatings will continue until morale improves.

Where does this leave the idea that an understanding of the ERR should take into account debt growth? I’m still scratching my head on this, but it seems to introduce some qualifications at the least.

* Given the importance of this question for all sorts of things, it is frankly weird that there hasn’t been more research on it. I feel pretty confident that it might be advanced by some awesome econometricians who have experience in mining large panels of ONS household data, but is there anything to say on the matter before then?

** In the wake of the GFC, Ken Rogoff and Carmen Reinhart’s empirical work on debt at a sovereign level attracted sufficient policymaker attention to get them cited as the intellectual handmaidens of austerity. Indeed, ex-UK Chancellor George Osborne cited their work directly ahead of the fact as the rationale supporting austerity. But since the aspects of the R&R work looking for debt optimality has been shown to be uncharacteristically error-strewn.

*** Simply d[(r-g)/(1+g)]=pb, where d = debt/ disposable income; r = effective household interest rate; g = annual growth in household disposable income, and; pb = household net acquisition of financial liabilities minus interest payments as a percentage of household disposable income.

How to think about debt?

I attended Gertjan Vlieghe’s speech yesterday at the Society of Business Economists annual conference in London. The speech had a market impact because it challenged the consensus perception of Vlieghe as an uber-dove. It had an impact on me for another reason.

But first, a bit of context.

Back in May I was reflecting that many people seem to have become either really cross that monetary policy is way too tight (looking at low levels of inflation), or really cross that monetary policy is way too loose (reflecting either historical anchoring, or looking to debt growth). For a sample of this crossness, you could do worse than look at my compendium of tl;dr versions of submissions to the Treasury Select Committee’s inquiry into the effectiveness and impact if post-2008 UK monetary policy.

Anyway, I drew the following chart, to try to understand this bad-temperedness. 

It shows for each calendar year since 1983 the pace of US inflation (y-axis), the change in US private nonfinancial credit to GDP ratio, and then the bubble size relates to the real interest rate (blue is positive, white is negative; large is large, small is small). I can see that the debt folk (I’ve labelled as Austrians, but this is probably unfair) probably reckon that anywhere right of the y-axis means that rates have been too low, while inflation folk see anything below the x-axis as evidence that rates have been too high. In the top right and bottom left quadrants, debt and inflation folk would probably get on okay at a drinks party without coming to blows. The top left and bottom right quadrants are, by contrast, times when these folk are likely to spend most of the time either talking past – or slinging abuse – at one another. You will notice that we’ve spent a good deal of time in the bottom right quadrant in recent years: inflation has been scarce, but real interest rates low and increased leverage forthcoming.

Claudio Borio, Head of the Monetary and Economic Department of the Bank of International Settlements, wrote an interesting paper in 2013 arguing that the idea of the output gap had kind of gone astray. Typically, monetary policymakers seek to identify potential economic growth with reference to non-inflationary economic growth. Borio argued that this is, if not bogus, too restrictive: the pace of economic growth may be unsustainably strong, and the economy operating beyond capacity, if financial imbalances (aka leverage) are building up. In other words, Borio argues that changes in the price level (inflation) and changes in the stock of private nonfinancial credit (leverage) are each important in determining the sustainable pace of economic growth.

I’ve got some sympathy with Borio. If this sounds a bit abstract think of China today: inflation is not a problem, but credit growth is rampant,  perhaps to the extent that it might point to faster growth than is ultimately sustainable. Central bank mandates in many – perhaps most – developed economies are meaningfully oriented towards delivering low levels of price inflation, variously defined. Why? Because inflation is a form of monetary instability. And preventing monetary instability so that people can get on with their lives rather than obsess over the nature of money  – via the creation and execution of an inflation-targeting mandate – seems a pretty reasonable thing for a monetary sovereign to do.

But, as the Global Financial Crisis made baldly evident, inflation is not the only form of monetary instability. During the GFC, Central Banks resurrected their age-old response to this episodic type of monetary instability: by acting as Lenders of Last Resort and clearing up *after the fact* with super-easy monetary policy.

Whether central banks should act *before the fact* and ‘lean against the wind’ so preventing the build up of bubbles has been a live and heated debate probably for as long as they have acted as Lenders of Last Resort. There are good arguments on both sides, simplifying as:

  1. Pro-leaning: bubbles are dumb (aka lead to capital misallocation), and big bubbles bursting hurts (aka deliver large loss of welfare, can be associated with financial and monetary instability etc). If your whole job is to maintain monetary stability, going on and on about how hard it is to lean against the wind is a bad look.
  2. Anti-leaning: By definition, people won’t agree that something is a bubble until after it bursts. Furthermore, while bubbles might hurt a few people a lot, tightening monetary policy more than would otherwise be called for comes at a real cost for many (fewer jobs, slower investment, etc). Better to clear up after the event with some ultra-easy policy response if necessary. In other words, it might be really important to stop bubbles, but it’s also both practically impossible and trying to do do is likely to be pretty damaging. 

The Global Financial Crisis did highlight the arguments of the pro-leaners, but didn’t really challenged the arguments of the anti-leaners. I see Vlieghe’s speech as an elegant take on this debate: maybe even a way to square the circle.

In the speech Vlieghe introduced what he called the Finance Theory of the Equilibrium Real Rate (ERR). At it’s simplest it is an intuition that interest rates are low and the risk premia attached to equities are high when the world is risky. Few would disagree. ‘Risky’ in this context means consumption growth has a lot of volatility, and (importantly) negative skew and kurtosis. The intuition is demonstrated with an historical econometric analysis of a couple of hundred years of UK data, and different regimes are identified – some with a high ERR and some with a low ERR. The different regimes have some shared characteristics of credit growth, realised equity risk premia, realised nominal rates and inflation, as well as distributions of consumption growth (expressed in terms of mean, standard deviation, skewness and kurtosis). I would urge you to read it and make up your own mind whether it says more than real policy rates are very low and equity returns are very strong in periods following economic busts. I think that it does.

How is this linked to thinking about the current state of monetary policy? Importantly, Vlieghe’s Finance Theory of the ERR doesn’t actually help define where the ERR might be, ex ante. But one of the things he associates with high or low ERR regimes is the change in the stock of household credit. If households are deleveraging, chances are that you are in a low ERR regime, and even an ultra-low nominal Bank Rate might not be very far below the ERR. But if households are releveraging, maybe you’re moving *out* of a low ERR regime, and Bank Rate might be very far below the ERR. Over the past year or so, UK households have been releveraging, so a question is introduced as to whether the UK is moving out of a low ERR regime and into a higher ERR. Through this lens monetary policy in the UK may be becoming ever-easier unless Bank Rate is raised. And so Vlieghe argues, without a hint of Austrianism, that the easiness of a given monetary policy becomes defined by changes in the price level (inflation) and changes in the stock of private nonfinancial credit (leverage).

Vlieghe’s Financial Theory of the ERR, like Borio’s Financial Theory of Sustainable Economic Growth (OK, I made up that name), doesn’t argue ‘sure, inflation is important – but there’s this other thing too called financial stability/ leverage to worry about’. It isn’t a new target variable to chuck into your optimisation. Instead, it is a variable that reconfigures other terms in the optimisation process in a potentially unknown and whacky way.

If this understanding is right (and, to be fair, I would be surprised if Vlieghe even recognised it as such) I am left with some questions:

1. The circularity question.

Isn’t inter-temporal substitution supposed to be a pretty major transmission mechanism? And doesn’t this manifest in changes to credit stock? Maybe I should reread James Cloyne’s awesome 2016 paper that finds the transmission mechanism isn’t all about intertemporal transfers, but also about transfers between households. 

2. The reversion to *which* mean question.

But if we target stable debt to GDP, are we stabilising at a level that is above or below equilibrium level of debt at a whole economy level? What is equilibrium level of private non-financial sector debt-to-GDP? Is the equilibrium level of debt conditional upon the term structure of interest rates available to, and the whole shape of the prospective distribution of economic growth as experienced by, each debt-bearing demographically-unique cohort? Do we have confidence that we know what this is for any economy?

3. The FPC question.

The whole point of the FPC was supposed to be to a means of exerting control over stuff like leverage growth. This was a sort of nod to the Leaners (without actually going the whole way and saying that rates should be changed to lean against the wind). Is Vlieghe saying the whole project is just a bit rubbish? Or, to put it as a meme:

4. The globalisation question.

Isn’t the location of all those dots in the bottom right quadrant of the chart more easily explained by the idea that there is global overcapacity in labour owing to a global labour glut that will turn as Chinese governance-adjusted unit labour costs reach developed market governance-adjusted unit labour costs? This dovetails into the @rajakorman question (simply put, is @rajakorman right that the whole thing can be put to bed by adding an international flows dimension).

I didn’t really expect a really impactful speech. I was dead wrong.

20 years in

It was twenty years ago today that I began working in fund management. 
What advice would I offer someone starting out in the industry, based on my experience? I’ve spent a good few tube journeys pondering this question, and this is my best effort.

1. Treat the job as if you are lucky to have it, because you are.

    There are many wonderful people with brilliant minds who will never get the opportunity to sit in your seat. Somehow, you’re sitting here. This is an amazingly fabulous opportunity. Do something with it, and don’t waste it.

    2. You can never truly *know* the mixture of luck and judgement attached to your results. 

    As @xkcd puts it:

    This is a big big deal, and will likely gnaw away at your insides if you are at all reflective, no matter how successful you are. (If it doesn’t, check that you haven’t got Dunning-Kruger syndrome.) Use this doubt to humble you, especially when things are going very well. Make sure that you do a really good job on the things that are absolutely within your control (the careful implementation of your investment process; well-prepared, respectful and unpatronising interactions with, and attitudes towards, clients; strong risk management, etc). And read this piece by @ericlonners.

    3. Despite #2, learn quickly that Every Job is a Sales Job.

    This was an absolute shock to me on leaving university. Nicely expressed in this thread.

    But also worth recalling one of my boss’s mantras here: 90% of life is about managing expectations. You live with the consequences of your sale (to others and to yourself); by over-selling you raise the bar that you must clear in order to not fail.

    4. Pick a good boss.

    This is hard when you’re starting out and literally know nothing about your boss and know nothing about what makes a good boss. So while your first boss will be a bit pot luck, choose your second carefully. Think about the values you have and how these are reflected in your boss; think about the way your boss succeeds or doesn’t succeed. You absolutely don’t need to share the same political, social or religious views, but you must be able to respect them professionally. If your boss doesn’t get some *core* stuff (personal integrity, centrality of the client etc) then change your boss. 

    5. Don’t (over)-job hop.

    You need a really good reason to not stick out your first job for at least two years. It is possible that you will be given mindlessly basic grunt work for two years. No biggie. You are being paid to learn: take every opportunity to do so.

    You may find an earnings or opportunity jump occurs each time you do change firms, but your ability to jump diminishes over time. No one will want to employ someone who has a history of not sticking around. Try moving internally rather than externally if it’s a role thing. (Once you’ve spent a long stretch with one firm this doesn’t apply so much.)

    6. When you have worked out what you think, make sure that you take enough risk on your view.

    You have the potential to lose your job with every decision* that you make, but it helps no-one if you make good judgements and their impact is insufficient to really make much difference.

    *(Don’t worry, you won’t be allowed to make many decisions until that time when you are competent to make them if you have a half-decent boss.)

    7. There is a part of you that will *become* your job/ profession.

    We don’t come out of the womb as accountants, psychiatrists, fund managers, journalists, etc. And we spend our formative years building an identity which may not ‘fit’ with our target profession. But after years of pretending to be the sort of person who is a finance professional you will find that you aren’t pretending anymore. If you can make peace with this early, you will have a better time of things. Incidentally, Hashi Mohamed did an awesome Radio 4 documentary on social mobility which I thought was a masterclass on extreme adaptability. Listen to it here

    8. Never Ever say that we live in unprecedented times.

    Stuff happens all the time. And the incidence of stuff is no excuse for doing a bad job for your clients; in fact it is the successful navigation of these that will add value to your clients. In my 20 years the following stand out: the Asian crisis, the Russian default, LTCM, Brazilian depeg, Dotcom boom/ bust, Argentina crisis, 9/11 and War on Terror, Brazilian electoral crisis, Worldcom/ Enron/ Anderson client crisis, Gulf War, all that stuff that is generally wrapped up into the Global Financial Crisis, the European sovereign debt crisis, Commodity meltdown/ deflation, Anglo-Saxon populist electoral wave. Each of these (and many others) felt like they could be fairly existential for markets on which I was professionally focused. Each was heralded as unprecedented. And each was. But navigating frequent unprecedented events is … er … the job. I don’t buy that the last twenty years is particularly challenging in the broader sweep of history. If a particular couple of decades further back in time look relatively calm I would suggest that this probably signals a lack of curiosity of the past.

    9. Read books.

    My job involves reading a lot of documents. Downtime consists in reasonable part of reading documents that I don’t have time to read during work time but feel I should read, or think might be useful to read. I could fill every hour of every day doing this and not read all that I think I should read. The idea of squeezing in time to read some professionally irrelevant book is not always appealing. (In fact, there seems to be no time.) But make room for books. Books get inside your head, and great books will touch on themes that are recurrently relevant. There are people way more professionally successful than I will ever be who find time to fit in reading a book or two a week. I read at least twelve a year. It’s a start.

    10. Don’t stop asking silly (but relevant) questions.

    The more you know, the more you know you don’t know, as the saying goes. But try to work out where you can get away with asking them.

    And finally, some advice I would offer, not based on experience? Learn to code.


    A quick post to say that I was lucky enough to be invited onto the Odd Lots podcast by Joe Weisenthal and Tracy Alloway to talk about money and my kids savings.

    Joe and Tracy managed to weave the chat such that we covered the establishment of the Nangle Household Bank, and the evolution of its monetary policy, the basics around inside and outside money, and the complications that target saving poses to monetary policy (where interest rate elasticity of demand has the potential to become small or perversely negative). And they did this in a way that is still fun to listen to, which is quite an achievement! If you are into podcasts and like this blog, it may be of interest.

    We didn’t quite get on to the nature of money Venn that I trialled on Twitter the other day (below), but that’s probably good because I have had to rethink the whole Moon thing (with help from Twitter).

    And if you want a more worky outline of all of the above, here is a document I wrote a while back that tries to pull it together.

    A Bad Week in the UK

    I was shown a really interesting App (Explain Everything) when looking around a school today. The classroom seems to be ahead of the boardroom as far as interactive technology goes.

    I thought I would play around with it on my phone and test it to give my personal view of things going on in the UK this past week.

    This is a corrected version after the first one erroneously said that Marine Le Pen had tweeted in approval of Theresa May’s conference address. Like the Huffington Post, I had been fooled by a supporter’s account.


    Still an awesome chart!

    Today the Resolution Foundation released an excellent and fascinating paper looking at data behind Branco Milanovic’s ‘elephant chart’, and asking what conclusions can be realistically drawn from it.

    My tweets may have played some small role in popularising the chart – the reporting of an opinion I tweeted that it is ‘the most important of the last decade’ is footnoted a couple of times in the paper, and it even appeared in a Dutch Trade Minister’s speech a couple of weeks ago.

    Here’s the tweet, which I drew on my iPad of a picture that I snapped while reading Milanovic’s terrific book:

    I have been thrilled to have had a role in drawing attention to the work that Milanovic’s and Lakner have conducted, a summary of which can be read here.

    But judging from my Twitter feed this morning you would think that the Resolution Foundation paper has found major methodological flaws that makes a nonsense of the chart.

    Not so.

    To reiterate, the elephant chart is an amazing chart. It paints in one graphic a picture of the global economy that is (in my opinion) unrivalled. It is so good precisely *because* it combines income changes and demographic changes; it tells the story of recent global history and the degree to which the rise of China (and India) have changed the world. It also illustrates that lower income households in the West have largely not participated in the remarkable global growth over the two decades. The original Milanovic & Lakner paper  dwelt at length on the compositional issues behind the chart, and there was even an animated gif that @MaxCRoser put together using country-specific compositional data contained therein which is pretty stunning, and was widely distributed before inexplicably disappearing from Twitter. Luckily I have been able to find and embed a copy below: global-income

    The Resolution report does the world a service by further drawing out the data behind the chart, discussing what policy implications can and cannot be drawn from the chart, and reminding people of the compositional issues.

    Compositional issues are hard. And this is probably the most striking chart in the report for people interested in the impact of compositional issues. It even makes its way into the FT.


    This chart is complicated. As its title suggests, it shows ‘Growth in average per capita household income of each percentile group (rolling average) if there had been no income growth and changes were solely due to uneven population growth’.

    I think that this means that it imagines a situation where:

    1. every worker is put into an country-specific income decile in 1988, and the mean income of the decile is then fixed in real terms;
    2. net new workers per country are distributed evenly across these country-specific deciles;
    3. all countries for which there is data have their country-specific deciles chucked into a spreadsheet in 2008;
    4. the income required to make it into each percentile in 2008 is then compared to the income required to make it into each percentile in 1988.

    The outcome is then charted. (Apologies if I’ve misinterpreted.)

    If this understanding is correct, the chart then shows the impact on the elephant chart of

    1. poorer countries having had faster population growth than richer countries;
    2. starting the exercise in 1988 when the global income distribution had the following shape:


    Or, as the author of the Resolution report, Adam Corlett, puts it:

    “For example, the poorest decile of people in the US were in the 70-75th percentile range in 1988. But population growth among poorer countries would have pushed those Americans up into the 75th-80th percentile range by 2008. The bottom US decile would be replaced in the 70-75th percentile part of the global distribution by the richest urban Chinese, but the latter’s average income was around $1,500 compared to the former’s $2,600: producing a fall in the average income of those percentiles.”

    And so, when holding constant the country population share the elephant chart looks as follows (red line):


    Is this a ‘truer’ version of the original elephant chart? Not at all. In fact, holding constant population shares stable is a nonsense: it no longer shows a picture of profound changes that have been experienced across the global economy as a whole. The author both understands this and makes no attempt to conceal it, because he is trying to do something important and useful to which this new chart is particularly well-suited: to correct a misperception as to what the original elephant chart showed.

    Specifically, it issues a corrective to claims that working class developed market incomes have *stagnated* in real terms during the period. And here, despite being one of the people who actually read Milanovic & Lakner, I am at least partly culpable. In a blog for voxeu among other places, I have used the elephant chart and stated “there is a large section of people who are well-off in global terms who have largely not participated in global growth over the past 20 years. That section is populated largely by the Western lower middle and working classes”.  The words may not be technically incorrect, but to say that they fail to draw attention to the compositional issues underlying the chart is more than fair. Furthermore, they mask the fact that while I have looked at US income distribution in some depth, and think myself relatively familiar with the data that is published in the UK, the impact of Japanese income data on the overall chart had passed me by.

    When we look at the constant country population share chart from the Resolution paper we can see that my claim that a large section of Western lower middle and working classes largely not participating in global growth might more accurately (but not massively more accurately) be described as having experienced a cumulative real income growth of c25% (although this will vary *meaningfully* by country – with Japanese lower income deciles experiencing contraction, US lower income deciles experiencing low but positive growth, and Western European lower deciles experiencing c45% cumulative income growth). These levels of cumulative income growth have been lower than the income growth at the top of each of the income distributions for the respective developed market block (leading in many developed countries to higher levels of income inequality), and lower than the income growth of the global median or global poor (leading to lower levels of income inequality across the globe, principally due to the rise of China). And so while real incomes have risen for lower middle and working classes in absolute terms, the bottom 80% labour share of GDP in the UK and US has declined as a proportion of GDP (defined as the labour share of GDP multiplied by the proportion of labour income received by the bottom 80% of the income distribution, see chart below), while the relative cost of labour in the West vs the rest of the world has reduced. (It is also notable that the big decline in the UK occurred in the 1980s, with an evening out thereafter.)


    I don’t think that all these things are disconnected, and I’m pretty sure that the Resolution Foundation report isn’t arguing that they are disconnected. Instead, it is seeking to quash a meme that real incomes have stagnated for developed market middle class workers on account of globalisation. It does so with aplomb. As Torsten Bell, also of the Resolution Foundation, blogsboth those saying globalisation automatically benefits everyone and those saying that developed world middle classes have seen no income growth are wrong. Perhaps most crucially, where individual countries lie in between those extreme positions is to a significant degree down to policy choices.”

    Given that I read Milanovic & Lakner’s compositional discussions in the original paper, why did I not major on these compositional discussions? Well, I didn’t think that they change the use to which I put the chart (discussing the global labour glut that came about with China joining the global trading system, the associated substitution of capital for labour, and the loss of labour bargaining power in a variety of developed market economies). Although I was not aware of the contribution the Japan had made to the distribution until reading the report.

    I tend not to be accused of oversimplifying things. But given the reaction to the Resolution Foundation report, I certainly feel complicit in propagating the end-product of a complex piece of analysis without due caveats.

    However, it’s still an awesome chart.

    You break it, you pay for it

    So we’re a month into the post-EU referendum period and the lights have not gone out. Yes, we’ve gone through a pretty shaky political patch for the Conservative Party leadership which was unnerving to say the least. And the Labour Party is either nearing the point of exuberant rebirth or self-immolation depending on your perspective. But things seem somewhat more stable than they did a month ago.
    From an economic data perspective we are still an essentially pre-data period, or at most a period of anecdata, collected/ assembled with various levels of formality. The Economist put together a piece about data it had scraped from the web which was interesting, but really illustrated how little data we really have. 
    We have seen data from a few forward-looking surveys come out, the sort of which market-folk like me digest when trying to understand likely developments at a macro level. They are, by definition, snapshots taken at a point in time. 

    Here is a quick recap of what we have seen:

    • The GfK consumer survey data, which was dire. However, this was taken during the period of peak national political insanity. So perhaps it should be taken with a big pinch of salt.
    • The RICS survey fell sharply, and tends to lead house prices by around six months, a point made by Sam Tombs here:

    • The Bank of England Agents’ summary of business conditions which was widely interpreted as being more positive, painting a picture as it did of firms somewhat in shock but trying to get on with life. This bullet point is a fair summary of the post-Referendum part of the report, but you might as well read the whole report. It is only three pages in total, so shame on you if you discuss it without having bothered to read it.

    • The Markit survey of Purchasing Managers (the UK PMI) released today, which was pretty awful, although contained an important caveat from its chief economist Chris Williamson that signs of confidence began to lift later in the month as the new government took shape.

    • The CBI Business Optimism index fell sharply, although the orders component was decent and the survey was taken between 27th June and 13th July (recall that May became PM on 13th). [This bullet added 25th July.] Here’s Samuel Tombs again:

    But to reiterate. This is not hard data. And without hard data we risk projecting our priors onto the straws in the wind that so far exist, as Tim Harford wrote most eloquently here
    And so it is perhaps natural to see a variety of commentators seizing on individual bits of information that *proves* that the UK is either booming or collapsing. Perhaps the whole thing looks rather emotional to the outside observer, and perhaps it is.
    But the truth is that we simply don’t know. We each have guesses. Furthermore, we can infer from market prices the degree to which our guesses are more or less consensus. But we don’t know and we won’t know (unless the bottom falls immediately out of the economy) for a little while.
    But the reason I’m writing this blog is that I’ve started seeing snippets of commentary from folks who supported Leave that I find somewhat disturbing. I’m happy for them to point to data that they reckon proves a UK boom. I’m happy for them to change their mind and say that they were wrong (although would be surprised if this should happen since we are still in the pre-data period). But I am not happy for them to say that the economy was already broken before the Referendum. And this is what I’ve started to see sneak in.

    Similarly, I’ve seen folks who supported Remain appearing to relish the bad pre-data that we’ve seen, perhaps seeing it as enhancing their reputation for analysis. This is really distasteful. There is nothing to celebrate about a downturn.

    I was one of the 288 signatories of the letter organised by Paul Levine, Tony Yates and Simon Wren-Lewis.  I stand by its wording, but I very much hope that I’ve been wrongly pessimistic on the impact on the UK of voting to leave the EU. The prospect of eating humble pie is wildly attractive because I want the UK economy to blossom. In fact I am working bloody hard to play my part in ensuring that it thrives. Economic recessions are frankly awful. They carry a distinctly human cost.

    But if it does turn out that we have scored an economic own goal by voting to Leave I’m simply not having those who belittled the prospective economic costs of Leaving wash their hands of their responsibility and telling the rest of us that the UK economy was already broken. It wasn’t.