Category: Prep

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

Welcome to London and I look forward to meeting you.

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, 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.

We start our first session with administrative issues which should take no more than 30 minutes:

• 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, 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 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, 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
30 Aug 2020