Indiana Jones does Economic Models

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 (as well as financial markets but we leave that until Session 7). Some tentative thoughts will be offered on the characteristics of the Covid recession – not least on the issue of scarring (also called hysteresis).

Our first task is to understand what Jones (textbook author) means by “short-run” output and what everyone else calls the output gap. This gap is central to effective macro analysis and policymaking. The gap acknowledges that, contrary to Solow Land, actual GDP can over- or under-shoot its long-run potential. This, is turn, can give rise to inflation volatility and swings in employment prospects that typically require evasive policy action from governments and/or central banks.

Another task is to explore whether disturbances (shocks/stochastics) around the equilibrium path  burn themselves out or whether they lead to explosive/implosive behaviour. Typically, macro models assume long-run stability even though we might need fiscal and monetary policy to hurry things along.  The issue of stability is not without controversy and we touch on this with the aid of the famous Multiplier-Accelerator model, developed by Nobel Prize winner Paul Samuelson in the 1930s.

We quickly learn that, like R-Star, potential output (and hence the output gap) is a “hidden” – often called latentvariable. The same is true of its labour market equivalent, the “natural” rate of unemployment, sometimes referred to as NAIRU (the non-accelerating inflation rate of unemployment). To help us, we deploy a useful relationship between the jobless rate and the output gap called Okun’s Curve.

There is nothing out there that tells you what the values of these latent variables are. You have to estimate them, with models, from observed data that themselves are volatile, imperfect and subject to revision. Needless to say, different models give different answers. One thing applied macroeconomists learn is humility in the face of such uncertainties. Point projections are out, confidence intervals are in.

And, given the importance of R-Star, output gaps and employment gaps to policy, there is clearly plenty of room for argument. There have been numerous bust-ups in Europe about fiscal austerity – often centred around differing views of output gaps. Recently, the controversy surrounding the $1.9trn Biden package – will it let the inflation genie out of the bottle? – offers yet another example.

So, for the next few weeks we 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 herehere and here.

SPH
4 Mar 2021
Bill Phillips, MONIAC (Monetary National Income Analogue Computer) and a cigarette!

Bill Phillips with Moniac

MONIAC as represented in a Punch cartoon in 1953

Punch cartoon of MONIAC

Inequality & Institutions

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 can help organise thoughts although we need to break free from its many restrictions for deeper insights.

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 and may find time to discuss 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
25 Feb 2021

Innovation & Sustainability

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 recalling 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 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 recent Covid responses – 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 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 overdone. 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
18 Feb 2021

Growth Models

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 factor productivity (TFP) 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
11 Feb 2021

Exploring GDP

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!

US net wealth decomposition

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
4 Feb 2021

The First Session

We start our first Zoom session on Monday 1 Feb (10.00am GMT) with introductions and administrative issues which should take no more than 20-30 minutes:

  • introductions
  • course overview and syllabus
  • textbook and online resources
  • attendance and participation
  • virtual office hours
  • session formats
  • 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. 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.

I cannot over-emphasise how important data gathering, analysis and interpretation are. This course is all about macro models. Good models need the best data we can muster; data are always imperfect but we should work hard to ensure that foundations are as solid as they can be. Without that, it’s just garbage in, garbage out.

So no apologies for devoting early sessions to GDP data – both the practical issues and the conceptual pitfalls that we shall need to avoid. We shall look at the latest, Covid-infected, data from the US and UK to assess how 2020 has gone and what we can expect this year and next. 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.  Accounting may seem tedious but it’s essential for cross-checking and ensuring data coherence. 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.

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 as well as increasingly popular “fast” data from Google, Twitter, etc . We shall also touch on the need for informative and professional visualisations.

Yet, armed with the “right” growth numbers, what do they actually tell you? Even mature and pandemic-free economies 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. 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 UK Statistics Office article featuring the dangers of misreading GDP in the Age of Covid.

If you have any questions or concerns, please email me as soon as possible. Again, the email link is available in NYU Classes, in the firewalled section of the website and in the syllabus already circulated to you.

SPH
27 Jan 2021

Welcome

Welcome everyone and 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, Covid and Brexit Britain will make guest appearances 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, statistics, 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 government finances are out of control 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 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 – pandemics, climate change, trade wars, emerging market woes, currency gyrations and any other topics we collectively decide upon.

Looking forward to seeing you all next week.

SPH
25 Jan 2021