NYU London

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
12 Oct 2019

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 inflation, labour markets, finance. Maybe venture further into the world of exchange rates, cross-border capital flows, fiscal sustainability? Easy, tiger – we’ll deal with that later on.

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
4 Oct 2019

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 distribution. We already know about growth under-performance in recent decades. Now we learn that what extra scraps there are may have been 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 has eroded and, to understand that, we 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 a summary of Simcha Barkai’s work on a Chicago Booth blog (Nov 2016). Following is another picture to whet your appetite from The Rise of Market Power and the Macroeconomic Implications, De Loecker & Eeckhout, Aug 2017.

If you need it, background revision on rents, imperfect competition and market power can be found in this section of the CORE Economics textbook.

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, for example, the 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. If we have time we might also touch on the view that, in the 21st century, the battleground is not so much between sovereign nations but between sovereign nations collectively and the major tech corporations.

SPH
27 Sep 2019

For the next couple of sessions we tackle some of the most pressing issues facing 21st century economies: stagnation and inequality. 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.

For session 4 we start with looking at the evidence of so-called secular stagnation. Our focus is on the United States but we shall touch on similar trends for other advanced economies. We make frequent reference to recent, cutting-edge research to help gain insights.

We soon find that a principal concern is the slowdown in total factor productivity growth – the fountainhead of rising living standards. We discover that 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 recent estimates suggest that over three-quarters of that trend represent transitional factors. “Core” (steady state) US growth may already be close to zero.

Yet surely something is wrong. Every day we hear stories about the latest gizmos that make up the 4th Industrial Revolution. AI, 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 of a particularly nasty recession that we call the GFC (Great Financial Crisis of 2008 onwards)? 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 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, 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. 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 of interest 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; it is disappointing 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 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 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; 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
20 Sep 2019

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.

Along the way we shall highlight evidence that challenges key model assumptions. For example, there are widespread doubts about the benefits of China’s investment boom. These concerns are partly based on a 2016 study from Saïd Oxford which argues that over half of the infrastructure investments in China have destroyed, not generated economic value.
China Infrastructure Investments Threaten Economic Growth, Sep 2016

In anticipation of our upcoming analysis of growth stagnation and lack of inclusivity, we shall underscore the need for better ideas and improved total factor productivity if better living standards are to be enjoyed (hopefully by the many and not just the few).

Unfortunately, a recent study suggests that good ideas are becoming increasingly difficult to come by. One of the authors is Chad Jones, your Macro textbook hero!? For example, it is claimed that the number of researchers required today to achieve the famous doubling every two years of the density of computer chips (Moore’s Law – itself a focus of controversy) is more than 18 times larger than the number required in the early 1970s. Yikes!
Are Ideas Getting Harder to Find? NBER Working Paper, Sep 2017

SPH
13 Sep 2019

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
• 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 shall 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 were 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 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 you are strongly recommended to read US Net Wealth in the Financial Accounts of the United States, FEDS Notes, 8 Oct 2015.

As you can see from the Fed’s chart (and the update below), US net wealth has recovered sharply from recession levels and has reached new highs. This largely reflects the rebound in property and share prices. Whether that rally continues 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 shall 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. Any time you need to review introductory economic material you could dip into it. A search for “production function” gives you this, for example.

SPH
6 Sep 2019

Welcome to London and I look forward to meeting you next week.

Well done for choosing “Intermediate Macroeconomics” – a phrase that sets the pulse racing and breaks the ice at parties.

Seriously, while the course title is perhaps uninspiring, I am sure you will find the content fascinating, relevant and challenging. Throughout the semester we shall be touching on core challenges facing global society.

Initially, following our GDP data adventure, we take a super-long term focus exploring growth trends, industrial revolutions, technology, productivity, stagnation, trade and inequality –  themes that are both current and ancient. Of course, Brexit Britain will make a guest appearance but do not expect definitive answers! In these sessions you will soon appreciate that Macroeconomics will not just be testing your economics skills (micro as well as macro). We shall need to draw on many other disciplines: history, philosophy, maths, psychology, politics, accounting – to name just a few! It will be the start of your journey to become “the rarest of birds“. 

As the course progresses we place increasing emphasis on models – maps to guide us through the complexity of economic life. Models will not help us to pinpoint the “truth” (whatever that is). But models can be useful in marshalling our thoughts – what Keynes called “vigilant observation” – and to make better decisions.

Complexity arises partly because expectations of an inherently uncertain future influence the present. Human behaviour, that often belies simplistic concepts of “rationality”, fuels the endemic volatility of spending. And modern corporate life is much closer to game-theoretic oligopoly than perfectly competitive price-taking. In such an environment markets do not always work well; as such, official intervention – demand and/or supply oriented, fiscal and/or monetary  – can prove essential.

Through sessions 6 – 10 we shall tackle cycles, inflation, finance, monetary policy and fiscal stances. This will lead us into cutting-edge debates as to whether the Fed is doing the right thing, whether austerity is an appropriate reaction to excessive public deficits and whether the banks have finally got their houses in order. We are over a decade away from the Lehman Bros bankruptcy; a defining moment in a defining period of 21st century history – the so-called Great Financial Crisis (GFC for short). Yet the macroeconomic legacy of that crisis is still very much with us and there are signs that the right lessons have still not been learnt.

Later on in the semester, session 11 will pull many threads together and we explore simulations of a small domestic macro model. You will all be playing significant roles in presenting the results. The final sessions of the course – weeks 12 to 14 – add more global flesh to the bone. Exchange rates and global capital flows will be stirred into the mix and we conclude with debates about the large-scale models (mainly DSGE and Agent-Based) that central banks, think-tanks and private corporations use to navigate the choppy waters of our macro world.

Along the way, and not just in the specifically “global” sessions, we spice things up by highlighting contemporary events – trade wars, emerging market woes, currency gyrations and any other topics we collectively decide upon.

We start our first session with administrative issues which should take about 30-40 minutes:

• course overview and syllabus
• textbook and online resources
• attendance, registers & class etiquette
• office hours
• session formats & student presentations
• assessment and grading

The remainder of Session 1 will introduce Gross Domestic Product – the key “go-to” number for summarising a country’s economic performance. GDP data are rarely out of the news, real and “fake“. GDP data can win or lose elections, are central to key policy decisions and have enormous influence on private sector behaviour, not least global capital investment decisions. Little surprise then that statisticians have been threatened with jail, even executed, if their numbers upset political masters.

One thing we have all learnt in recent years is that data impart information but not always in a good way. Information can feed both risk and an abuse of the power it bestows. Used responsibly, data can boost profits but reputational damage can undo hard-earned revenues  – just ask Facebook.  So data can be good but it can also be evil – a threat to democracy and privacy. Some view banks and FAANGS – with their vast information riches – as the robber barons of the 21st century. Many commentators cite data as the new oil – arguably a misleading comparison.

So no apologies for devoting two sessions to GDP data – we can only scratch the surface but the lessons learnt will put us in good shape for the rest of the course. We start with the easy stuff that you’ve probably come across already: Y = C+I+G+X-M. Be careful though. The circular flow and its related accounting identities give the impression that measuring GDP is pretty straightforward. I wish it were that simple.

Getting the GDP numbers “right” really matters – not just for policy but for politics. Visibly getting the numbers “wrong” undermines trust in authority. Left to fester, that eroding trust can swiftly lead to socio-political disruption. But what do we mean by “right” numbers? Truth is generally not absolute. As John Keane, Professor of Politics, writes, “reality is multiple and mutable….what counts as truth is a matter of interpretation“.

On a practical level there are so many types of GDP measures out there: real, nominal, $-valued, index-based, per capita, seasonally adjusted, unadjusted.

Once you have chosen the “right” series you might want to calculate how quickly it has grown over any given period. How are you going to do that? It might sound easy but the potential for confusion is huge. There are many different ways of calculating growth rates.

Time will be taken to build technical skills by exploring the use of FRED,  OECD and IMF data sources. We shall also touch on the need for informative visualisations and finessed calculations of growth.

Yet, armed with the “right” growth numbers, what do they actually tell you? Even a mature economy like the US can demonstrate unnerving volatility over time. So, if a quarterly GDP growth number is weak, should we panic? Or do we just put it down to statistical noise? Data and information are not the same as knowledge and wisdom!

We also need to remember that GDP figures are built up from millions of individual households and companies. That means that the aggregate data may not be representative of “typical” firms or people. Higher productivity and new technology sound good-to-have but what if it means you, your friends, and your family are thrown out of work? “Expert” narratives welcoming modern development will no longer chime with a broad swathe of experience – a dangerous development that could seriously mislead readings of economic (let alone political) trends.

Moreover, data are not perfect. Even if there are no blatant lies, fresh information can come to light and new methods of putting the numbers together may be adopted. So, once data is published, the “truth” can change, sometimes dramatically.

Deeper conceptual problems also need to be addressed. GDP – dubbed one of the great inventions of the 20th century  – started life in the 1930s as a way of measuring “stuff”. But advanced 21st century economies are now dominated by services rather than goods. This generates a lot of problems for bean counters with difficulties mounting in the wake of digital technology and intangible, sometimes “free”, services.  Indeed, setting the production boundaries  – what’s in, what’s out of GDP measurement – has been a perennial problem, prompting historian Adam Tooze, to state that “GDP at its neatest captures a narrow and somewhat arbitrary slice of reality“. Certainly US Presidential candidate Bobby Kennedy, speaking in 1968, had serious doubts about GDP as a metric.

To round up our selective intro to macro data we shall pose this question: does more GDP make you happier? I look forward to hearing your thoughts.

For class prep I recommend the BEA’s primer on national economic accounts – it contains a nice (and thankfully brief) overview of some key concepts you will need to master. Also have a look at this video featuring Diane Coyle’s excellent book on GDP.

SPH
26 Aug 2019