Category: Prep

As an academic discipline, Macroeconomics has been criticised for not predicting crises; for using simplistic, out-of-date models; for ignoring data that challenged stylised theories; and for failing to acknowledge that economic theory has little to offer without a clear, socio-political and historical context. The purpose of this course has been to counter such criticisms, not by reinventing the wheel but rather by showing that Macroeconomics, carefully and intelligently deployed, can offer helpful guidelines in addressing society’s key challenges for the 21st century.

In understanding the macro world around us we have deployed models. As we noted towards the beginning of the course, all models are wrong but some are useful. Models help citizens and their representatives to make better choices by organising thoughts, suggesting strategies, clarifying definitions and exposing assumptions. Accounting identities impose discipline as do long-run budget constraints – the issue of sustainability. Models require us to marshal facts and figures; more than that, to understand and interpret such data both critically and intelligently. In many cases, models can reveal unexpected consequences, forcing a rethink of strategies and policy options.

Initially we gained understanding of long-run growth processes through models developed by Solow and Romer. The classic Loanable Funds model provided additional insights. For business cycles, we have enlisted the help of multiplier-accelerator models, the Frisch-Slutsky paradigm and the AD-AS apparatus involving the IS curve, the Taylor Rule, the Phillips Curve as well as the Okun labour market relationship. Taking an international perspective we have extended our analysis to include exchange rates and capital flows. Specifically, we have deployed the Mundell-Fleming model and its extensions to embrace the realities of potential inflation and variable risk premia.

Models of expectations have extended our understanding of how the global economy works. Volatile beliefs, especially about an uncertain future, typically impact the present. Stability is no longer assured, a unique steady state might not exist; the world can be populated by multiple equilibria, bad as well as good. Efficient markets can become highly inefficient; recall Minsky’s ideas about the financial system. Crises can reflect bad luck, but often shocks are home-grown, endogenous responses to systemic frailties. Messy reality poses serious challenges for conventional macroeconomic analysis and these are explored in greater depth in two highly recommended books – Andrew Lo’s Adaptive Markets and William Janeway’s Doing Capitalism in the Innovation Economy.

A focus of our final session will be DSGE and structural models that are widely used in central banks and elsewhere for forecasting and simulating macroeconomic processes. They are by no means the only games in town and alternative approaches – such as Agent-Based Models and machine learning – are becoming increasingly popular. A key theme of the session is that you can never have enough models.

James Surowiecki’s Wisdom of Crowds and Scott Page’s The Difference speak to a particularly modern motif – the power of diversity. The diversity prediction theorem says that many models are better than single models; diversity can trump ability. As prosaic macro examples, Norway’s central bank has found that combining different forecasting models actually produces better inflation predictions. Weighting together the GDP and inflation forecasts of several models is often found to be a more robust procedure than relying on a single approach. The Bank of England is advocating interdisciplinary models that respect complexity, heterogeneity, networks, and heuristics.

At a more general level, the diversity prediction theorem implies that you should not be afraid to think and speak differently – you’re doing society a favour. More diversity is better than less; otherwise an undiversified “wisdom” leads to the badness of crowds; herds, manias and bubbles. A related issue is the benefit of humility: admitting ignorance is better than nursing an illusion of knowledge. Excess confidence can lead to bad decisions: the underestimation of true risk, the cultivation of complacency. In addition, do not overlook the value of simple rules of thumb that can work in the context of radical uncertainty. Better to be roughly right, than precisely wrong. Not a bad piece of advice, perhaps, for your finals!

SPH
3 Dec 2020

In our session on exchange rates, we introduced the concept of interest rate parity. The basic idea is that efficient market forces will ultimately combine to produce a global benchmark for safe real interest rates. Safe usually means the short-term rates paid by State institutions such as the Fed, the US Treasury and their equivalents in other parts of the world. And short-term means just that – the yield paid on debt maturities that are often significantly less than one year. For such financial instruments, any interest rate divergences will prove temporary and associated with real exchange rate disequilibria.

Once we move beyond these short-term official rates it gets more complicated. Market  rates, the longer-term interest rates – both official and private – that drive consumer and investment spending, typically involve risk. However, uncertainty is variable, not fixed, and depends on circumstances – not least the business cycle and mercurial expectations.

Moreover, beyond the short term, uncertainty is not confined to credit risk (where payment promises – if they exist – might not be honoured). Market rates will be buffeted by varying risk premia connected with liquidity, geopolitics, sentiment, inflation outlook and growth prospects. As we learnt in session 7, asset prices – even of high credit quality US Treasury Bonds – can change simply because of fluctuations in policy or the inflation outlook. For most advanced economies, governments are hardly likely to default on their borrowing. But, US Treasuries and other government bonds can be highly volatile if investors alter their views about the likely path of short-term policy rates, perhaps because the inflation target seems less likely to be achieved. Even prospective moves in the regulatory environment or tax treatment of government debt could shift market prices, creating headaches for investors despite secure cash flow promises.

In Session 13, our penultimate teaching week, we explore the interactions of mobile capital, floating exchange rates, policy choices and international economic performance. As with our domestic AD-AS model we soon discover that fickle expectations play a key role. However, unlike goods and services, global financial asset prices are far from sticky. Valuations are highly sensitive to changes in beliefs. Moreover, such expectations are not always firmly rooted or classically rational. Bob Shiller’s article on potential reactions to the Fed’s widely-expected rate hike in December 2015 is highly recommended. His references to behavioural finance are worth noting with the theme of human psychology prominent in the literature on rational inattention. And take a look at the worked examples especially the topic of risk-on and risk-off dollar trading. Our session touches on the point that, when analysing shocks, it is important to assess whether that shock is expected to be temporary or permanent. Similarly key is whether markets see the “”shock” coming (an anticipated shock) or whether it comes completely out of the blue.

IMF analysis confirms the importance of risk premia and interest rate differentials. A July 2016 study presents a plausible narrative explaining 2014-16 US$ strength in terms of risk premia and yield differentials. There is also evidence of a dollar safe haven effect in the context of heightened global risk; a feature that has been amply confirmed in the Covid crisis.

A 2016 Fed study on international spillovers of monetary policy tackles another important theme. Specifically, changes in US monetary policy can significantly impact foreign economies.  Taking the example of GFC-related cuts in US interest rates, the analysis shows the quantitative importance of three distinct channels:

a) exchange rate: a softer dollar, reflecting lower interest rates, will boost US exports and so weaken foreign GDP
b) domestic demand: but, in the opposite direction, better US GDP performance – on the back of a weaker dollar – sucks in imports, so providing offsetting benefits to overseas producers
c) financial spillovers: lower US interest rates will pull down foreign asset yields so benefitting foreign GDP (although note BIS concerns about potentially greater risk-taking in emerging markets)

The Fed’s empirical work suggests that a) and b) net out. In other words, the overall effect of US monetary easing, represented by c), is GDP-positive for the world economy.

Of course, it is not always a one-way street as our feature video makes clear. Foreign developments can also independently impact the US. America is a large and powerful country, but we should never underestimate the importance of global networks and feedback effects. The dollar can change not just because of what happens in the US but also because of overseas shifts in asset prices and risk tolerance.

In quantifying these monetary spillover effects the Fed makes use of its SIGMA model, one of several that US officials use in framing interest rate and other policy decisions. As such, the empirical studies cited above are a useful prelude to our final session that focuses on large-scale macro models – both conventional DSGE models widely in use amongst central banks as well as increasingly popular Agent-Based Models (ABM).

Throughout the course we have gradually expanded our modelling expertise. But you will be aware that macro-analytical techniques faced extensive criticisms after the GFC. Are these models too pointy-headed, too remote from reality, to be of any use? Hopefully, I can persuade you that these quantitative techniques are worthwhile. But, as with their epidemiological equivalents, models can only get us so far. And, as with coronavirus, models can be unnervingly revealing about how much more we need to learn!

SPH
26 Nov 2020

For the next couple of sessions we turn our attention to foreign currency and capital flows. There will be something for everyone: accounting revision, trade and financing riddles, geeky equations, financial arbitrage, politics and institutional design, model simulations. What’s not to like?

Our feature video takes up just one of the threads covered; specifically, whether structural flaws are tainting the Eurozone (euro area). The video focuses on one particular view that not all economists would agree with. Some, for example, would say that the region’s problems reflect bad policy choices rather than poor structure. However, it is hard to deny that Economic and Monetary Union is not really a union at all. It is a dog’s dinner that serves countries with very different economic structures, languages, legislatures, customs and regulations.

For sure, there is a huge common purpose – primarily to bind social and political ties so as to prevent a rerun of European wars that have plagued the region for many centuries. However, the project has too often required large, and painful, sacrifices that undermine essential co-operation and trust. The GFC delivered severe recessions (via required internal devaluations) in the so-called European “periphery”, creating bitterness and fuelling old rivalries between the North and South. For many economists this is no surprise since the Eurozone is hardly a convincing optimal currency area.

As the interviewee makes clear, and I agree, it is not the euro per se that is the problem. Rather, the flaw arises from the institutional setup of the Eurozone itself, principally revolving around the lack of policy flexibility.

The key problems areas are,

• inadequate provisions for national central banks to support liquidity-strained governments
• no sign of fiscal integration
• legal and social constraints on labour mobility
• insufficient supply of euro safe assets

Perhaps there is too much impatience. It takes a long time to move from old boundaries to new frontiers. The US in its infancy was arguably in a similar place to where EMU is now. But, almost twenty years on since the euro’s launch, there is precious little sign of governments engaging in supportive fiscal transfers. Full political union is unlikely and possibly unwarranted. That said, it would be a catastrophe if EMU itself were dissolved – especially in the midst of social and/or economic stress. The only way now is to push forward and reform: but so much more needs to be done on banking unions, fiscal co-ordination and structural integration if further EMU crises are to be contained.

On a more light-hearted note, another problem with the Eurozone is that few are actually clear as to what and where it is! Formally, the euro area consists of those Member States of the European Union that have adopted the euro as their currency.

It sounds straightforward but you do not have scratch far below the surface before confusion arises. Take Scandinavia…

• Finland is an EU member, uses the euro as its principal currency and so is a fully signed-up member of the euro area.
• Norway is not an EU member, retains its own currency and so is not in the euro area.
• Sweden is an EU member, retains its own currency and has not yet adopted the euro. That said, Sweden is obliged to use the euro once it fulfils the necessary conditions but, until then, it remains outside the euro area.
• Denmark is in a similar, but not identical, position as Sweden in that it is outside the euro area. Denmark is an EU member, retains its own currency but the krone shadows the euro. However, unlike Sweden, Denmark negotiated an opt-out from the euro and thus is not obliged to introduce it.

Are there European countries that are not EU members but who use the euro as their principal currency? Yes, the European microstates of Vatican City, Andorra, Monaco and San Marino all officially use the euro – with formal EU agreements – but are not part of the EU (hence, not part of the euro area). Kosovo and Montenegro both adopted the euro unilaterally in 2002 although, given the absence of an EU agreement, the currency is not de jure legal tender just de facto.

Then there is the issue of whether a country needs to be in Europe to be a euro user. Indeed, can you be outside Europe and yet still qualify as a euro area member?

Euro users such as the Canary Islands do not belong to Europe geographically but are an autonomous community of Spain and so qualify as part of the EU and the euro area. Similar considerations apply to the Azores and Madeira (in the Atlantic and belong to Portugal), Mayotte and Réunion (in the Indian Ocean and belong to France). French Guiana in South America uses the euro, qualifies as an EU member (via France) and is part of the euro area. But then Saint Pierre and Miquelon – just off the eastern coast of Canada – are formally part of France and use the euro but, as an “overseas collectivity” rather than “overseas departments”, do not qualify as members of the EU and thus are not formally part of the euro area.

So here’s a suggestion once Covid-related travel restrictions are lifted. Take a field trip to the Caribbean and determine the precise EU/euro area status of Guadeloupe, Martinique, Saint Martin and Saint Barthélemy. Research conclusions, please, on pretty postcards.

SPH
19 Nov 2020

In session 8 we explored a link between GDP spending and real interest rates called the IS curve.

It turned out that “IS” was a misnomer and would be better described as a WJ curve – the combination of output gaps and real interest rates where planned withdrawals W and planned injections J are (possibly only temporarily) in alignment.

In session 10 we look at another curve – often called the MP (monetary policy) or TR (Taylor Rule) curve. Like the IS curve it describes potential balancing acts of real interest rates and output gaps, but this time from the perspective of financial rather than non-financial products.

Unfortunately, like the IS curve, the name attached to our new line is also misleading. We are given the impression that the MP/TR curve is all about monetary policy and is primarily pushed around by central banks.

For sure, central banks play a role in guiding the economy’s interest rate/activity mix towards more friendly levels than what unsupervised markets might deliver. However, it is worth emphasising once again that real interest rates comprise three elements:

• a nominal policy rate (such as the fed funds rate)
• inflation expectations
• market risk premia

The central bank has most control over the first – the fed funds rate. Yet, even here, there can be exceptions. This becomes evident in crises such as the GFC and Covid-19, when, for all practical purposes, the Fed cannot reduce the rate below its Zero Lower Bound (ZLB). Moreover, with evidence that r-star is much lower than previously thought, this problem will reoccur in future periods of stress.

As for inflation expectations and risk premia, these are subject to market vagaries and sentiment swings. They are not levers that a central bank can easily pull. Yet we should not be too disheartened. The Fed (and Government) can gain traction in these areas if they work together, bolster confidence and enjoy credibility. The economy at large will then take on board that the State machinery will “do whatever it takes” to restore normality.

Such mind games require deploying the “dark arts” of central banking – forward guidance, communications, emergency liquidity, QE shock and awe. But to be credible, they also require governmental co-operation – supportive fiscal policy, new legal frameworks, recapitalisations. Central banks do a lot more than just tinker with interest rates but they are not all-powerful.

So where does this leave us and our new-found MP/TR curve? Certainly we shall need to tread carefully. The mechanistic Taylor Rule, adopted by many a textbook, cannot be taken too seriously with its dependence – explicit or implicit – on imperfectly measured and/or dubious concepts such as output gaps, Phillips Curves and natural real interest rates.

And surely the Taylor Rule is too narrowly focused. As well as monetary stability, central banks are charged with the task of financial stability, seeking to ensure the integrity of the banking and payments system. The Taylor Rule has nothing to say about credit growth, lending standards, asset prices, capital adequacy – all vital if benign business and financial conditions are to co-exist.

More fundamentally, the benefits of inflation targeting in ZLB-infested waters are again coming under scrutiny. Ben Bernanke’s suggestion about temporary price level targets looks interesting and has been considered before. The FRB San Francisco’s John Williams has also weighed in about the need for more policy space. Indeed, in August 2020, the Fed itself announced a new approach to monetary policy that will tolerate periods of higher inflation.

Going further, more commentators are coming round to the view that we ask too much of monetary policy. Fiscal and macro-financial policies, acting on both demand and supply, are vital tools to deploy.

SPH
5 Nov 2020

In the early part of our course we explored some of the structural problems challenging policymakers. Specifically, we identified evidence of stagnation in productivity growth and thus a slowing in the growth of living standards. Not unrelated, we have seen clear signs that the “normal” real interest rate, r-star, has fallen in recent decades.

In our sessions dealing with business cycles, the implications of a low r-star for demand management are clear. For any given inflation target, a lower “normal” real interest rate must mean that a central bank’s “normal” nominal interest rate must also be lower. In the US’s case, for instance, even before Covid-19 struck, the FOMC indicated 2.5%-3.0% as the new normal for the fed funds rate rather than 4% as was assumed pre-GFC.

This downgrading of normal policy rates is important since it suggests that, in crises such as this pandemic, central banks have limited headroom as regards conventional monetary policy. The effective lower bound, not significantly different from zero, is on our doorstep.

So it comes as no surprise that fiscal policy is alreading bearing more of the burden of demand management. Governments, as well as central banks, need to be more proactive.

But fiscal policy also has its problems.

• Automatic stabilisers are not that large and discretionary shifts in tax & spend programs can take time – too much time – to plan, agree and implement in modern democracies. In addition, even when fully implemented, fiscal policy may fail to gain to gain traction.
• Multipliers, even if they are positive in the short run, often fade to nothing within a short space of time.
• As we discussed in session 8, crowding out is not always a problem – especially in large recessions – but the potential for failure in the transmission mechanism cannot be denied.

In session 9 we explore the challenges that arise when fiscal policy is more actively used to cushion a weak economy. In particular, after becoming more familiar with budget accounting measures, we address the question of long-run fiscal sustainability.

Unless matched by productive assets, high debt can hamper growth, diverting resource from more productive uses and so undermining the long-run tax base. High debt also means high debt payments ( INT of T = TX – TR – INT fame). But high debt payments mean higher budget deficits which mean more debt in future. If you’re thinking “this sounds like a doom loop” – well, you’re right!

If the economy is stagnating, and thus not producing much growth in tax revenues, then interest payments will feed a vicious cycle leading to ever increasing debt. But ever increasing debt will get investors twitchy who will then sell that country’s debt – forcing interest rates even higher! Like so many financial cycles, we get into a nasty, self-fulfilling prophecy of doom. Fiscally weakened governments, recessionary economic conditions and failing banks – a toxic brew indeed.

The bottom line is that fiscal largesse – albeit launched with noble, anti-depression motivations – can lead to problems of their own unless carefully managed.

In response to Covid, the US administration has already committed almost $3trn of budget measures, nearly 15% of GDP. Such a huge sum is comparable to a wartime manoeuvre, which effectively it is. Of course, Uncle Sam enjoys an “exorbitant privilege” in that it has a captive audience as regards creditors. America’s perceived safe haven status, reflected in the primacy of US$ in international finance, means that it can preside over widening deficits and mounting debt burdens without necessarily suffering a crisis of credibility. For sure, it is doubtful that the dollar and US Treasury bonds are about to collapse any time soon. But, as history and playground antics teach us, you can only stretch the elastic so far before it snaps back in your face!

SPH
29 Oct 2020

Models are a key tool in macroeconomics and we need to invest time in learning the key tricks, checking that,

• we know the difference between exogenous and endogenous variables
• the number of equations equals the number of endogenous variables to solve for
• a solution actually exists
• where models are dynamic, stability is feasible

Models are useful. They help us to focus on key issues at hand, removing unnecessary detail and sometimes revealing conclusions that were not immediately obvious. But models can also lead us astray if we are not careful. Key pitfalls include

• catching partial derivative disease; assuming all other things equal (ceteris paribus) when, in real life, they are not
• thinking that the whole is simply the sum of the parts; not always so as we move from micro motivations to macro consequences, with the Paradox of Thrift a great example
• believing that the model will work in all circumstances; parameters and behaviour often change, for example, when an economy moves from boom to bust
• assuming that all driving forces are objective, measurable variables; our case studies will touch on the power of expectations – how beliefs about the future shape the present and how uncertainty (the degree of comfort with the expectations formed) can play an additional role in spending decisions
• supposing that governments (and central banks) invariably get matters under control; it is not always obvious what fiscal and monetary levers to pull, nor are the impacts easy to predict given limited information about the future and the vagaries of human behaviour 

The standard workhorse model of domestic business cycles comprises three equations. One is the Phillips Curve – the relationship between the output gap and inflation that we tackled in Session 6. Another is the TR/MP curve that examines one relationship between real interest rates and expenditure, operating through monetary policy and financial markets. That we shall leave to Session 10 where we draw on material from our Money & Finance session, recognising that real interest rates are not simply an outcome of monetary policy but also inflation expectations and risk premia.

For session 8 we focus on another relationship between real interest rates and expenditure – called the IS curve – that operates through non-financial goods and services markets, notably consumption, investment and government spending. We shall follow convention, and the textbook, in calling that relationship an IS curve even though the name often confuses and misleads. Strictly speaking it is the combination of real interest rates and the output gap that ensures planned withdrawals (W) equal planned injections (J). As you know from our accounting identities, W=J does not require I=S so the properties of the IS line would be clearer if we all called it the WJ line!

In constructing the IS curve and examining its slope and shift factors we focus on consumption (C), investment (I) and government spending (G). Net exports are dealt with only briefly since we shall delay discussion of external trade and cross-border capital flows to Sessions 12 and 13.

The Jones textbook (notably chapters 16 and 17) devotes a lot of space to the micro foundations of domestic private sector spending,  so we do not need to repeat that in the lecture. In any case, the empirical evidence supporting micro/neoclassical models is somewhat patchy.

• For example, the so-called Euler Equation (Jones, pp 464-467) – derived from intertemporal models of utility-maximising consumption – appears not to fit the facts. The Euler Equation predicts that, for any degree of impatience, higher real interest rates are associated with stronger planned consumption growth – in other words, present consumption is restrained in favour of more consumption in the future. In practice, it seems that the opposite is the case. This could be a mirage (the statistical problems of separating out causes and effects are notoriously hard) but it could reveal more fundamental problems such as the absence of assumed perfect financial markets and, even more damning, the wholly misleading use of a representative individual to reflect decisions by a heterogenous mix of consumers who build habits and sometimes “act dumb”. Even if individuals do act rationally, outcomes can still end up in a collectively irrational “bad equilibrium”.
• As for investment, we examine evidence that accelerator (output gap) influences, together with expectations and uncertainty, appear much more important than variations in posted interest rates. The past affects the present but, arguably, the future is even more important; and no-one knows for sure what is going to happen. Hence, a huge role for sentiment, confidence and speculation – fickle stuff that is hard to capture or model. Such Keynesian-style thinking is nonetheless well suited to exploring scarring (hysteresis = cycle-trend interactions) and the stagnation themes revealed in Session 4. Indeed, problems of self-fulfilling prophecies – and thus the possibility of persistent deviations from the full-employment steady state  – underpin the rationale of contra-cyclical fiscal and monetary policy. Moreover, the policy instruments can be extended from classic “hard” tools (spending, taxes, interest rates) to “soft” brushes (nudges, guidance, artful communications).
• Government spending is another focus of Session 8. This leads us into a discussion of the so-called “crowding out” phenomenon, automatic stabilisers and the concept of the Keynesian expenditure multiplier (topics to be examined more deeply in Session 9). Again, we learn that elementary textbook “rules” are often invalid – the real world does not easily fit into simple theoretical boxes. Sometimes government action works as intended, sometimes not; the state of the economy and the degree to which private sector behaviour is influenced are key variables that can be tricky to pin down. “Partial derivative disease” is particularly dangerous; assuming other things equal (ceteris paribus) is often misleading, sometimes just plain wrong. On Planet Earth, “fixed” rarely happens.

A key conclusion from our session is that there are no decisive right answers about the impact of government spending on the economy. You should dip further into that debate but not get discouraged by the enormously wide range of views on offer. Your opinion is as good as anyone else’s but the important point is that you should see where the arguments are coming from and what assumptions are most in need of further investigation.

Macroeconomics is full of such belief-challenging controversies and intellectual fisticuffs. It is what makes the subject so interesting and relevant. As Banksy said about his own trade, “People say graffiti is ugly, irresponsible and childish… but that’s only if it’s done properly.”

SPH
22 Oct 2020

Money is a special product in that it performs several must-have functions. Yet money comes in different shapes and sizes – physical, digital and in various denominations. Essentially money is a promise, a debt. We no longer inhabit a world of commodity-backed money, ye olde gold standard. Money is what is generally accepted as money and the principal suppliers are private sector banks that have licences to “print” deposits, typically as counterparts to loans. A big helping hand that gives money its moneyness is, of course, official blessing. Namely, State-backed legitimacy and the generous provision of government/central bank support for the private banking system.

So, for fiat money,

• how much is the promise worth?
• what, exactly, is the price of money?
• how will money’s value change if the promise is suspect?

For this exercise, let’s focus on money in its “top quality” format, that is a liability of a government, or more typically, a central bank. This “top quality” money – the best that money can buy – is called the monetary base or high-powered money and FRED is at hand with data. Now let’s illustrate, with the help of simple demand-supply diagrams, the four prices of money, namely

• the interest rate price
• the foreign exchange price
• the goods price (inverse of the CPI), and,
• the par price (a $ is always a $, right? Wrong!)

Interest rate price. A price is an opportunity cost and the most frequently quoted price for money is an interest rate, reflecting the fact that money (as a demand deposit or cash) typically earns no yield. By having wealth in money format, you are forgoing the opportunity of earning interest on, say, a bill or bond. Using a standard demand and supply diagram we can illustrate this “price” and how it might change, if, for example, the Fed tightened policy by raising interest rates (necessarily being supported by a reduction in the monetary base).

Forex price. An alternative to holding a dollar is to hold another currency, say, sterling. Say the Fed supports a decision to slash interest rates by pumping up money supply, perhaps via Quantitative Easing. Other things equal, the price of a dollar – in sterling terms – will go down. You get less sterling for a dollar. Note you need to be careful as to how you express the exchange rate. Typically, “cable” – the nickname for the dollar-sterling exchange rate – is expressed as $ per £. That’s not going to work in our diagram since we are interested in the price of a dollar, NOT the price of a pound! More of this in sessions 12 and 13.

Goods price. Instead of holding value as $ money you could buy goods and services – we’ll use the catch-all term “Widget” for products in general. Our diagram this time shows a demand-side shift, a weakening of demand for money perhaps because people are worrying that product prices will go up. Buy now, before stuff gets more expensive! The effect is to weaken the price of money (in Widget terms). Expectations of inflation have actually become a self-fulfilling prophesy. We’ll be returning to that issue before too long.

Par. Remember that our focus here is “top quality” money. Cash, such as Federal Reserve notes, falls into this category as do electronic deposits at the central bank (usually only available to a select group of banks).  FRED can show you that high-powered money increased sharply during the Great Financial Crisis, representing a surge of demand for safe, as opposed to risky, money. We illustrate this surge in the relevant diagram below. In 2008 some types of money – such as funds placed in Money Market Mutual Funds (MMMF) – suddenly “broke the buck“. For a while, an MMMF dollar was worth only 97c of a “proper” dollar (eg cash). Par was broken, a sure sign that the financial system was close to meltdown. Little wonder that the public authorities acted swiftly and aggressively to avoid the apocalypse. Restoring par, the litmus test of a functional banking system, required the authorities both to boost “top quality” money supply as well as contain the loss of confidence in substitute money (sometimes called quasi-money or shadow money). The GFC reminded us of a very important historical lesson: not all moneys are born equal.

SPH
15 Oct 2020

Our focus is about to shift to the shorter-term; that is to say, 5yr-7yr periods where real GDP fluctuations show recurrent, persistent and variable patterns that economists call business cycles. Why do they occur, do they matter, should policymakers get involved? And we need to tackle the links with inflation and labour markets. Some tentative thoughts will be offered on the characteristics of the Covid recession – not least on the issue of scarring (also called hysteresis).

For the next few weeks we shall add more analytical kit; new problems, different tools. To aid analysis of the inflation-activity relationship we explore the so-called Phillips Curve, named after Bill Phillips who “discovered” the inverse relationship between UK wage inflation and unemployment in the late-1950s. Although we shall cast some doubt about the existence of such a relationship in modern times, we acknowledge its importance in model-building.

Arguably, more interesting is the story of Bill Phillips himself – often portrayed as the Indiana Jones of economics. He was born in New Zealand and trained as an electrical engineer in the 1930s. He then moved to Australia, making a living as a busker, gold miner, cinema manager and crocodile hunter. He came to London, via the Trans-Siberian railway, at the start of World War II. He joined the Royal Air Force at the outbreak of war but was subsequently captured in Asia, spending most of the war in a Japanese POW camp, where he learned Chinese and some Russian from fellow prisoners.

After the war, Phillips returned to London (to the London School of Economics) and studied Sociology. However, he also developed an interest in Keynesian economics and resorted to his engineering skills to build a physical hydraulic model of the economy (using parts of a Lancaster bomber aircraft); coloured water, tubes, pumps, the works! When Phillips presented his model to the London School of Economics the impact was huge. Nicholas Barr’s tribute picks up the story…

“Over the following year [1949], with encouragement from James Meade, he completed the machine and demonstrated it to Lionel Robbins’s seminar. Everyone who mattered was there. They gazed in some wonder at this large, 7 foot high ‘thing’ in the middle of the room. Phillips, chain smoking, paced back and forth explaining it in a heavy New Zealand drawl, in the process giving one of the best lectures on Keynes and Robertson that anyone in the audience had heard….”

“Phillips rise thereafter was meteoric. He became an Assistant Lecturer in Economics in 1950, Lecturer in 1951, Reader in 1954 (the year his PhD was awarded and the year he married) and Tooke Professor in 1958.”

“His subsequent work at LSE was broadly of two sorts. He is best‑known for his 1958 paper on what later became known as the ‘Phillips Curve’ (a name he would never have given it himself), which explored the connection between the UK unemployment rate and wage inflation over the business cycle. That work was a progenitor of important later theoretical developments, in particular the analysis of expectations in macroeconomics. The second strand was the application of dynamic control theory to economic processes, so as to strengthen the ability of the economy to return to macroeconomic stability.”

Around fourteen of Phillips’ machines were built, one of which was used as a teaching aid at the London School of Economics until 1992. It now graces the new maths wing in London’s Science Museum although, regrettably, is not in working order. As far as I know there are only two machines left that are still working. One at the University of Cambridge (see this hilarious presentation by an engineer in 2010) and the other in The Reserve Bank of New Zealand’s museum (and you can turn it on, virtually, here). Further background on the fascinating story of hydraulic economic models is available here, here and here.

SPH
8 Oct 2020
Bill Phillips, MONIAC (Monetary National Income Analogue Computer) and a cigarette!

MONIAC as represented in a Punch cartoon in 1953

In session 5 we tackle the thorny issue of inequality. We already know about growth under-performance in recent decades. Moreover, if anything, Covid has made the outlook look even gloomier. Now we learn that what extra scraps there are get pocketed by the rich, leaving the vast bulk of society no better off, maybe even worse off in real terms. This is hardly a sustainable base from which to build better productivity and living standards all round. Politically, of course, non-inclusive income gains have already proved toxic.

In reviewing what is one of the top problems facing 21st century society we look at inequality from three angles (given limited time we shall focus primarily on income flows rather than wealth stocks).

• Functional distribution: the share of GDP going to wages and profits
• Personal distribution: the distribution of income across individuals and families within any given country
• Global distribution: the distribution of GDP (typically on a per capita basis) across different countries at any point in time

Functional distribution

The Solow model predicts stable income shares and assumes the existence of perfect competition. However, recent data paint a very different picture. Labour’s GDP share appears to have eroded. Again, measurement problems abound, so the the numbers need careful interpretation. However, statistical doubts do not detract from the need to recognise the imperfectly competitive nature of markets – a feature which has become more prominent with the rise of so-called superstar firms. Dipping into the latest research we examine the links between imperfect competition, higher mark-ups and rising supernormal profits (rent).

Useful, easy reads on this topic are available from Bob Solow himself, writing for Pacific Standard in Aug 2015, as well as an IMF blog article from 2018. If you need it, background revision on rents, imperfect competition and market power can be found in this section of the CORE Economics textbook.

Excess corporate power is a reminder of the importance of institutional context when developing economic models. The problem is not new. We have been here before when the immense power of the East India Company eventually led to its nationalisation in the mid-19th century. Also Theodore Roosevelt’s trust-busting in the early part of the 20th century proved, for a while, a corporate gamechanger.  Will today’s giant tech companies suffer the same fate? 

Personal distribution

With the aid of Gini coefficients and quantile data we shall explore the dramatic rise of within-country equality over recent decades. General interest in the topic soared in 2014 when Thomas Piketty’s Capital in the Twenty-First Century was published in English. A 700-page economics tome topping the Amazon sales list!  Just one word – RESPECT.

Not everyone is convinced by the Piketty thesis which blends modern growth theory with history and Marxism. But the writing is beautifully crafted and the book is full of fascinating data that are supported by painstaking research. As part of his historical contextualising, Piketty makes reference to some great works of literature (it inspired me to revisit Balzac and Austen). Taking grand sweeps of gilded ages and updated Great Gatsbys we tackle his two “laws” of capitalism and place them in the context of the Solow model that you know and love.

Global distribution

Comparing GDP across countries can trip up the unwary. A key problem is exchange rates which do not always properly reflect purchasing power. So we take the opportunity to learn about international dollars and apply the concept to comparisons of the US with India and China.

We also focus on the issue of convergence. Can we reasonably expect poor countries to catch up with their rich neighbours? Or are regions like Sub-Saharan Africa doomed to poverty? The Solow model is partly useful in organising our thoughts although we need to break free from its many restrictions to get to the heart of the matter.

As underlined in session 3, TFP is not simply about technology but also about institutions and politics. If you are not convinced then maybe we need to talk about Britain’s surge in GDP per capita since the mid-18th century. Was it just tech know-how (steam engines and all that)? Or maybe the extraordinary public-private partnership with the aforementioned East India Company had something to do with it.

Military domination is undoubtedly part of the reason as to why Asia – at the tech frontier in the middle ages – went into sharp relative decline after the various voyages of “discovery” in the 16th and 17th centuries. If you feel inclined to pursue, Niall Ferguson’s Empire and Shashi Tharoor’s Inglorious Empire offer plenty of uncomfortable truths about politics, power and growth. Combinatorial forces really come into play when you mix armies with steamships, cables, railways, theodolites and maps. Commerce, conquest, colonisation – hard to disentangle when they are working in such harmony.

And we should also recognise, ahead of session 7, that financial institutions are critical to TFP. Indeed many historians cite British and Dutch innovations in the banking, insurance, security and derivatives markets as principal drivers of economic (and military) success from the 17th century onwards; innovations that generally preceded the first Industrial Revolution.

In class we refer to Acemoglu and Robinson’s work on Why Nations Fail as well having a discussion about topical geopolitical-TFP overlaps such as Brexit and China’s Belt and Road Initiative. Further evidence that macroeconomics cannot escape the grip of institutional context.

SPH
1 Oct 2020

For the next couple of sessions we tackle some of the most pressing issues facing 21st century economies. The Solow model will help set the scene but we shall need to break out of its restrictive assumptions to start finding answers. Recall that the Solow model emphasises perfect competition and smooth, continuous efficiency; effectively, finance and politics are pushed out of sight. Needless to say, the model does not make much of a case for State intervention, except perhaps for nudging the savings rate towards its Golden Rule level. However, it is hard to see much success in tackling 21st century headwinds without more centrally-driven policies.

We begin session 4 by looking at the incredible rise in material living standards in recent centuries. It is a startling picture, resembling a hockey stick. The first reaction is a WOW; look what innovation has achieved. But then  a WOAH: surely this pace is unsustainable?

Material progress has certainly come at a price. Climate change, pandemics, social tensions are not totally random events. Resource depletion, rapid population growth, the growth of megacities, diverging fortunes and globalisation. These are human-driven processes that have contributed to our own misfortunes. Welcome to the Anthropocene.

Sustainability is possible but becoming more questionable. How we deal with long-run economic growth raises deep socio-political issues. We quickly turn to what is one of the most puzzling paradoxes of modern times. If we are so clever, with all our techie stuff, artificial intelligence and so on then why does the data suggest that we have hit a growth and productivity wall? It is a phenomenon that some call Secular Stagnation. We focus mainly on the United States but we also touch on similar trends for other advanced economies. Frequent reference is made to recent, cutting-edge research to help gain insights.

The principal concern is the slowdown in total factor productivity growth – the fountainhead of rising living standards. And the problem may be far worse than previously thought. Since the end of the Second World War, US GDP growth has averaged around 2% per annum. But some estimates suggest that over three-quarters of that trend represent transitional factors. “Core” (steady state) US growth may have already hit zero, well before Covid struck.

Yet surely something is wrong. Every day we hear stories about the latest gizmos that make up the 4th Industrial Revolution. Driverless cars, biotech advances – surely these must count for something? Taking a brief historical sweep of innovation it seems reasonable to argue that we are witnessing the emergence of yet another General Purpose Technology that could transform society’s future.

So we shall meander through several arguments to try and explain this apparent paradox of technological advance and GDP stagnation. Firm conclusions are hard to come by, understandably. But, while our destination is unclear, enjoy the scenery, it’s really worth soaking in.

Argument #1: GFC & policy legacies

Are we just seeing the after-effects (exacerbated by the pandemic shock) of a particularly nasty recession in 2008-09 called the GFC (Great Financial Crisis)? We note that easy monetary policy may have over-cushioned inefficient (and unproductive) zombie companies. Successful innovation often requires creative destruction; you can’t make omelettes without breaking eggs. So, as soon as Covid allows, maybe the central banks’ well-meaning policy of low interest rates needs to be toughened up – clear out the dead wood and all that.

Moreover, as we shall discuss, the State has hitherto played a huge role in supporting innovative activity (especially DARPA’s “starring” role in making the iPhone smart). Yet the State’s increased debt burden – following its 2008 rescue of the finance system and the 2020 response to Covid, may have weakened its risk appetite. That would be a shame because, left to its own devices, private markets will typically underinvest in projects that are necessarily infected by radical uncertainty.

It is hard to come to firm conclusions but there may well be something in this line of argument. The key problem, however, is that the evidence of a slowdown in TFP growth significantly predates the GFC, let alone Covid. For a full story we need to look further.

Argument #2: Measurement Error & Aggregation Issues

Superficially there is something to be said for the idea that stagnation reflects GDP measurement shortfalls. But scratching deeper below the surface suggest that while mis-measurement could lead to an understatement of the level of per capita GDP there is a compelling view that measured growth has not been significantly biassed.

What does emerge is that once aggregate data are unbundled into various sectors and firms there is clear evidence of sharp divergence in productivity performance between the frontier and the laggards. This is interesting because it suggests that any tech pessimism (see below) does not extend to all companies. Some firms are doing very well; shame that there are not more of them!

Argument #3: Tech Pessimism

Bob Gordon’s famous comparisons of new technology with flushing toilets and paved roads cannot be ignored. He makes an entertaining case for the view that innovation is not what it used to be and is certainly not strong, or impressive enough, to counter macro headwinds of climate change, unhelpful demographics, the peaking of educational attainment, excessive debt and growing inequality. We supplement Gordon’s thesis with evidence of a deceleration in Moore’s Law and a marked slowdown in ideas productivity growth. We also consider the view that diminished industrial competitiveness has dulled incentives to research new ideas.

The pessimistic view has plausibility but, in my view, is a bit too pessimistic. So, in the interest of balance, we turn to a more optimistic thesis – you ain’t seen nothing yet!

Argument #4: Diffusion Dynamics

Using historical evidence on railroads, the steam engine and electrification a convincing argument can be made that the path from ideas to diffusion (implementation in wider technologies) does not happen quickly. Indeed it can take several generations. The fourth industrial revolution is just in its infancy. And, arguably, the full benefits of its IT predecessors has still not been fully embraced.

Looking at the rise in R&D spend by chip-makers and others it is hard to escape the conclusion that there is still plenty of room for optimism.

Introducing Risk, Challenging Convention

We conclude this session’s journey through secular stagnation by talking about R-Star and its slow motion “crash” in recent decades. The story is undoubtedly linked to the (apparent) slowdown in trend GDP growth – we saw it in our Solow steady state equation. But we can also put forward a richer narrative that introduces risk and its impact on both savings and investment.

We’ll be coming back to that risk topic in future sessions, especially in trying to understand business cycles, the nature of finance and the challenges posed for monetary policy.

Hopefully, our discussion has also encouraged you to think about models and their assumptions. We have found that there is strong evidence to support active engagement by government in the supply-side of the economy. That said, in examining innovation and entrepreneurship we should also question conventional views that advise stability and the absence of bubbles. Maybe we should live life dangerously (pandemics and climate disasters may not give us a choice); risk-taking is arguably the feedstock of TFP and the future growth of living standards. So does demand management smoothing conflict with a buccaneering supply-side spirit? Hold those thoughts please for future sessions.

SPH
24 Sep 2020