Growth Modelling

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
15 Sep 2018

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
  • 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!

US net wealth decomposition

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
8 Sep 2018

Welcome

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 and official intervention – demand and/or supply oriented, fiscal and/or monetary  – proves 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 approaching the 10yr anniversary of the Lehman Bros bankruptcy; a defining moment in a defining period of 21st century history – the so-called Great Financial Crisis (GFC for short). The macroeconomic legacy of that crisis is still very much with us today and there are signs that the right lessons have still not been learnt.

Following the mid-semester break, 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 – emerging market woes, the Greek debt saga, currency gyrations in Latin America and Turkey, the potential implosion of China’s shadow banking system 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 I make 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 and your friends and 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 2018