Understanding Macroeconomics: A User-Friendly Guide

Macroeconomics is one of those disciplines where the ideas are simple, but the lingo is complicated.

Paul Krugman, in his New York Times blog, is usually great at communicating the ideas of Macroeconomics in a human-friendly way.

But sometimes the language gets ahead of Krugman—sadly, most obviously when the ideas he’s expressing are important and deep. This post is a perfect example. The ideas are incredibly important for understanding what governments are doing or not doing to manage the financial situation, but without some specialist knowledge, it’s pretty hard to understand.

Here, I’ve written a summary of the stuff you’ll need to know to understand Krugman’s excellent writing, and get a sense of the deep and subtle ideas he’s discussing.

It’s not particularly short: there’ll be plenty of “too long didn’t read”s I suspect, but for those who are keen to understand what on earth is going on with macroeconomics—inflation, interest rates and all that—I think it’ll be worth the while.

Here it is:
disclaimer: this is just my own understanding of the situation. Don’t use this summary to actually run an economy, because there’s a chance that parts of it are wrong or oversimplified. If you’re an actual central banker, best get a proper qualification on the subject before fiddling with any knobs or levers.

Rob Levy’s User-Friendly Guide to Macroeconomics

or, how to understand Paul Krugman and his economist pals


A bank traditionally earns a profit by taking the money which savers have deposited with it, called deposits, and lending it back out to people who are looking for a loan. These borrowers might be after a mortgage, or after funds to invest in a business venture.

The banks encourage people to deposit their money by offering them interest. In order for the whole thing to make a profit, they charge more interest to the borrowers than they pay to the savers.
A bank has a legal obligation to keep a certain amount of its deposits in ‘cash’, and is free to lend out as much of the rest as they fancy. We’ll call the amount of cash they have to keep a “cash cushion”, because its designed to stop them running out of money if lots of the depositors suddenly want their savings back at the same time.

The relationship between a central bank and normal banks is exactly the same as between banks and customers: banks stash excess deposits with the central bank and earn an interest rate. Banks even borrow money from the central bank, for which they’re charged an interest rate. It’s this last interest rate which determines how much of a bank’s deposits it want to lend out and how much it wants to cling onto. We’ll see why in a second.

The setting of this interest rate is called ‘monetary policy’ and it’s ‘loose’ monetary policy when the interest rate is low.

When monetary policy is loose, banks are keen to dish loads of their deposits out as mortgages and business loans, because they can borrow for cheap from the central bank to keep their cash cushion at the right level. But if the interest rate is high, the bank will be more tempted to keep hold of its deposits because it doesn’t want to have to borrow to maintain its cash cushion.
When a bank is doing lots of lending, lots of business investment takes place and lots of houses are bought. These things are (usually) good for the economy so the central bank wants to encourage lending.

But there are limits: if the banks are so keen to lend that they’ll offer mortgages and business loans at ridiculously low prices, then people will start buying homes faster than they can be built, or expanding their businesses faster than they can expand their customer base. In this case, inflation sets in: prices rise because businesses have to pass on the cost of all this wasted investment to consumers. It’s this kind of mania for house-buying and business investment that economists refer to as the economy “overheating”.

So, the central bank, via the interest rate it charges to banks, can control how much lending the banks do. It therefore has to set a balance between too much lending (overheating) and not enough (recession); both are bad for the economy. What it really wants to do is set the interest rate to just the right level, such that there is just enough investment in businesses to continue to match demand as it ‘naturally’ grows or shrinks. This is the so-called ‘natural’ interest rate.

Natural growth in demand comes from innovation (a new smartphone model), population growth, the exploitation of natural resources, and improved manufacturing processes which means companies can makes the same products more cheaply. It’s this natural rate of growth that the central banks are trying to second-guess.

But there are limits to what the central bank can do. If banks have some reason to feel negative about the future, they won’t want to lend money however cheaply they can borrow it from the central bank. In this case, the central banks interest rate could get down to zero (at which point the banks can borrow money ‘for free’) and the banks still won’t take the bait. Once interest rates are at zero, that’s it. The central bank is out of options! This is what Krugman refers to as the ‘zero lower bound’. This whole set of circumstances is called a ‘liquidity trap’ because banks get addicting to hoarding their money (and money, when it’s cash as opposed to loans you’re owed, is called ‘liquid’: an extra layer of jargon to worry about.)

Once the central bank reaches this zero point, where they’re lending money to banks for free and still the banks won’t lend, its role as a controller of the economy is stuffed. All it can do is wait for things to improve on their own. This is clearly not what the central banks ideally want.

With this knowledge in your armoury, go and read “Secular Stagnation, Coalmines, Bubbles and Larry Summers”. It’s both well-written and important to understand. If you can get to the bit right at the end about offering a positive interest rate on all savings even when the market doesn’t really want to, you’ll be rewarded with a real “ah-ha!” moment.

Good luck!

A model of global trade

Months of blood, sweat and tears (or rather, those all-too modern equivalents coffee, RSI and eye-strain) have been spilled in the last six months. I have an absolutely non-existent ability to concentrate on more than one thing at a time, and the one thing I’ve been thinking, dreaming and going on about non-stop for pretty much all that time reached fruition late last night.

This is the first ever output from what we’re calling a ‘Global Demonstration Model’:

Modelled trade flows and economy shapes for the three countries, based on 2008 input-output data and 2010 trade data
Modelled trade flows and economy shapes for the three countries, based on 2008 input-output data and 2010 trade data

The picture above doesn’t really make much sense yet, in that it’s pretty hard to tell what’s going on and some of the colours are repeated, but it gives you a sense of the sheer magnitude of the model I’ve been building. It shows the economies of India, the UK and the US and the trade flows between them. The economy shapes are from 2008, and the trade flows are from 2010.

There’s a proper paper coming out soon (as if I were an actual academic) but before then, and for the reader who has no interest in reading a literature review, here’s an sketch of the Global Demonstration Model in human-readable terms:

  • Countries are represented by data on the shape of their economies, as split into economic sectors by the World Input-Output Database.
  • Trade between countries is modelled from actual trade data from the UN.
  • Countries are assumed to trade with one another in fixed proportions; for example, the UK gets around 12% of its agricultural imports from the Netherlands. This percentage then remains fixed when exploring the model. (Since you ask, the 12% is mostly tomatoes, flowers, onions, peppers and cucumbers.)
  • Each sector is imported in a fixed amount in each country. For example, the US only imports 11% of its total fuel requirements, against the UK’s 45%. These ratios also don’t change.

These assumptions allow us to ‘mess about’ with the world as we see it today, and test how sensitive the global economy is to particular changes, or how the world might have looked if trade patterns had been different.

And this is just the beginning: we’re going to be putting several more social science models around this trade-based core to model the effects of, for example, migration on the global economy.

I’ll be working on the diagram to make it (a) more readable, and (b) more interactive, and we’ll be producing some interesting analysis based on the model pretty soon.

Watch this space…

China, the world’s great agriculture consumer

The great thing about having access to the entirety of the UN’s commodity trade database is that you can ask any kind of question you wish of the data.

For example, here’s a network representation of trade flows throughout the entire world in agricultural products in 2010.

COMTRADE 2010 agriculture sector
A network representation of global trade in agricultural products in 2010. Country size and colour is the exporter-ness of the country (purple = very exporter-y)

Bigger, more purple nodes are the ones with the biggest average export (simply the total export divided by the number of trading partners. Network scientists insist on calling this the ‘Weighted Out-Degree’. Don’t blame me.) When viewed from inside a country’s circle facing towards a particular trading partner exports curve out to the left, meaning, conversely, that imports curve in from the right. The colour of the lines is an average of the colours of the two countries.

Most interesting to note, are the following features:

  • The USA is by a huge margin the world’s biggest exporter of agricultural products ($67 billion compared to second-place Brazil’s $29 billion. The biggest products are soya, maize, cotton and wheat.)
  • China is by a huge margin the world’s biggest importer ($56 billion compared to second-place USA’s $35 billion. Since you ask, it imports soya beans from the US, Brazil and Argentina, cotton from the US and India, and wood from Russia. Now you know.)
  • Only Canada, Mexico, Ireland, the Netherlands (NLD) and Morocco (MAR) have a visually obvious trade balance. All other countries are either clear net importers or clear net exporters.

Also very interesting—but this ones requires a bit more concentrated looking if you’re not willing to take my word for it—you can definitely see geographic clusters. Look at the star around Japan (JPN); it includes Thailand, Vietnam, Australia, Indonesia and the Philipines. Europe is very clearly at the bottom (note that it’s purely fortuitous that Britain and Ireland have ended up on the Western fringe of Europe, but it’s not chance that they’re together on a fringe.) Israel (ISR) sits at the border of Europe and the Middle East/North Africa (Egypt, Syria, Jordan, and Tunisia are all nearby.)

The products which are categorised as ‘agricultural’ are according to the categorisation used by the World Input-Output Database (WIOD), the subject of at least one blog post here. The trade flow data is from a massive (200+ million rows) database of trade flows which I’ve spent the last hundred and fifty years assembling from the UN’s commodity trade database, COMTRADE. The visualisation is from a piece of open-source software called Gephi with a heavily-tweaked Force Atlas 2 layout.

This is just one of an unimaginable number of interesting analyses and visualisations I’ll be able to do, now that I’ve got my very own copy of UN’s COMTRADE database to play with and ask questions of as I see fit. If you’d like to see any interesting analyses in future blog posts, let me know on Twitter @aid_complexity.

The fun is only just beginning…