NYU London

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

In session 3 we use the Solow growth model to help collect thoughts about the long-run dynamics of a competitive market economy. The implicit assumption is one of efficient firms operating at the production frontier. The model tells us that net investment adds to the capital stock which, in turn, adds to GDP. We abstract from pesky external and government sectors, ignore inflation and sweep finance under the carpet. Unemployment and under-utilisation of capital do not figure in our stories. And the emphasis is on smooth, continuous waves – no stochastics, small or epic, allowed!

Like all models it is both wrong and useful. Immensely instructive as a pedagogical tool but also glossing over too many harsh realities to carry us through this course. But we have to start somewhere and, at the very least, it will help exercise some maths muscles before we tackle thornier issues such as stagnation (slower trend growth), distribution (what growth there is seems unfairly spread), imperfect competition, radical uncertainty together with the mysteries of money and banking.

Please do not forget the lessons learnt in our first two sessions. Advanced economies are increasingly “intangible” in their production focus. This makes it very hard not just to measure what is going on with productivity but also whether growth disappointments really matter for society’s well-being.

That said, we shall take time to look at US data comparing the last 15 or so years with the half-century preceding it. The comparisons do not make for comfortable reading. Trend GDP growth has more than halved, despite all the techno-hype. Ok, part of it is a slowdown in the growth of the labour force. But digging deeper shows that total productivity growth has more than halved. 

We shall need to do algebra so make sure you dust off your notes on exponentials, natural logarithms and calculus. Hopefully, the colour-coordinated slides, cheat sheets and Excel playbooks will dull any pain.  We identify the links between so-called Loanable Funds analysis (presumably covered in your Principles course) and introduce a core concept in Intermediate Macroeconomics, namely R-Star.

R-Star is the long-equilibrium (or “natural”) real interest rate and, like growth and productivity, has displayed an unnerving slide in recent years. We shall come across R-Star many times through this course. For example, evidence that it has slumped to around zero (and the US is not alone) is creating huge headaches for central banks. As if financial crises and pandemics were not enough! 

Along the way we shall question many of the key assumptions of the Solow model. The idea that we live in perfectly competitive factor markets stretches credulity. And the exogeneity of total factor productivity is also problematic. We shall note that modern macroeconomists are taking such “defects” on board – the Romer model which introduces a distinction between ideas and objects is particularly instructive. But time is limited so we focus on basics whilst recognising that we can do better once we have mastered the essentials.

A key takeaway is that the mathematics of the Solow model should not blind us to the deeply political issues that supply-side macro touches on. Classical Economists such as Mill, Smith, Marx and Ricardo were fully aware that commerce does not exist in an institutional vacuum. We illustrate all that, albeit sketchily, by seeking to apply our basic Solow apparatus to explore topics such as immigration, climate change and pandemics.

SPH
17 Sep 2020

For session 2 there will be three big themes:

Theme 1 – More Accounting

We start by examining the output, expenditure and income methods for calculating and cross-checking GDP. This will help you in a number of ways

• understanding the concept of value-added
• recognising the importance of distinguishing transfers from productive activities
• noting the difficulties of measuring output in key sectors such as finance and health
• offering deeper insights into production boundaries
• appreciating the implications of globalisation for measuring income flows

The bad news is that you will come across further examples of arbitrary, sometimes puzzling, definitions that can confuse and deceive the unwary.  Measuring the economy is a messy and frustrating business.

Theme 2 – From Flows to Stocks

We  add depth to our GDP understanding by taking a look at flows of funds (financial accounts). For every buyer there is a seller. And when business takes place we should ask questions about how such transactions are financed; the “no free lunch” principle.

In addition to cross-checking GDP calculations, flow of funds analysis offers a vital bridge towards stocks and balance sheets. As we discussed last week, GDP growth and inflation can only be partial (and often flawed) measures of economic health. Equally important, if not more so, is examining the stresses and strains that might be occurring because of growing debt burdens and sector imbalances. If we learnt anything from the Great Financial Crisis (GFC) of 2007-09, it is that we should never take finance for granted.

By looking at the difference between assets and debt we arrive at a concept called “net worth” which, in national income accounting speak, is called national wealth. Effectively, it is a $ measure of a country’s total non-financial assets plus net claims on (or by in the US’s case) the rest of the world. At a macro level, financial assets always have a counterpart liability (debt or equity) so these all cancel out. At the sector level you can either focus on direct holdings of non-financial assets (as with the diagram below) or, in addition, you can record that sector’s holdings of, say, government debt and corporate equity.

In the Federal Reserve’s Z1 Financial Accounts series, data to support both approaches are available (notably tables B.1 and S.2.a). For a guide on interpretation US Net Wealth in the Financial Accounts of the United States, FEDS Notes, 8 Oct 2015 is recommended.

As you can see from the Fed’s chart (and the update below), US net wealth recovered sharply from GFC recession levels and reached new highs before hitting a Covid-19 wall. The rebound mainly reflected property and share price strength. Whether that underpinning proves resilient remains to be seen. The maths deliver an inconvenient truth: if asset values sink, so does your wealth!

Theme 3 – From Accounting to Economics

We then prepare ourselves for economic analysis by reviewing simple theories of production, productivity and distribution (wages and profits). This means some revision of basic micro stuff – production functions, diminishing returns, economies of scale, marginal productivity & competitive factor markets. It would help move things along more smoothly if you could dust off your notes on these, if necessary.

This might be a good place to mention a fantastic, free, online resource. It is the CORE economy textbook, supported by the Institute for New Economic Thinking. Anytime you need to review introductory economic material you could dip into it. A search for “production function” gives you this, for example.

We briefly pause to reflect on real-life complications: depletable natural resources, the value of leisure, the riches of human capital. For now, let’s fear to tread before rushing in.

SPH
10 Sep 2020