Economists should start doing themselves out of a job

The reason why teaching undergrads is the best job I’ve ever done is because interacting with intelligent, energetic people is not the once-in-a-while happy coincidence it is in most jobs, but it’s the central purpose of what you’re supposed to be doing.

Sure, there are the hours of marking, the jocks, the whingers (colleagues that is, not students), the disastrous classroom IT. But the main point and purpose of my being here is to come into regular contact with open-minded people, and try and draw them into interesting discussion. And to do this with no agenda other than to keep everyone involved entertained and intellectually stimulated. It’s like being at a daytime teetotallers’ high-brow cocktail party.

But some conversations are more interesting than others. And a conversation I was drawn into on my first day of teaching after a long, isolated Easter break left me in need of a strong drink and a sit down.

It involved a student of the most memorable Myers-Briggs category: TICL (Talkative; Intelligent; Cheeky; Lazy). TICLs are either a joy or a damnation depending on whether you like the student in question, whether they like you, and how you deal with the inevitable uproar they cause. My managing of this particular TICL has been an up-and-down affair, depending on our respective moods, but on this occasion they were in a particularly restive mode.

After some fairly meandering discussion of an arcane trade theory, the TICL interjected with “when are we going to get to the good stuff?” Foolishly drawn in, I said “This is the good stuff. We’re talking about the actual world here! Trade! You might wish you were back in this class when you’re doing statistical asymptotics in 2nd year econometrics!” To which they smartly replied “Yeah well, I might wish I’d studied something different.”

The basic complaint is that the student had imagined, when applying to do economics, they would learn about what makes the world tick. How do banks make their billions? Why are some countries so seemingly doomed to mishap? Just what is it about China that makes everyone want to talk about it? And come to think of it what ever happened to the US? Or Japan? Or Europe?  In other words, to come to understand the world. To see it as a set of explicable causes and effects, not just a random mishmash of historical happenstance. To know the proposed remedies for the ailments of the world, and why the remedies of the past were no good. To have a sense that somebody, somewhere knows what the heck we should DO!

And I couldn’t completely disagree with this student’s analysis of their undergraduate course. Where were the big ideas? Where were the theories that made all the seemingly insane stuff we see happening actually fall into place? Where was the sense that here, in the Economics department, was a team of geniuses plotting the salvation of global capitalism from its cannibalistic excesses?

But worse than my half-agreeing that we were teaching these undergraduates the wrong stuff was a deeper more gruesome realisation by far. What if there is no explanatory grand theory? What if there is no team of geniuses? Why are we still debating the reasons why people trade with each other, or why they don’t all gamble their savings away on the stock market? Or what governments should do when a recession hits?

It seems to me that 90% of economists are still stuck trying to understand the developed old world, forever peering into the black boxes of the Federal Reserve or poring over UK tax returns, and trying to guess how the various levers work, while studies of development, or the power of Google, or the end of manufacturing, or the dark web of global capital of which Panama is only the tip of the iceberg, are relegated to tiny departmental outposts or, horror of horrors, the social sciences.

Hopefully I have successfully convinced you by the above tirade that I was in a bit of a neg. And so I retreated to the only place I knew I was guaranteed to be undisturbed by man nor beast: the Economics staff common room, a place so lonely and ill-loved that even the old copies of the FT cling and heap together for company.

And so it was that I starting reading this Lucy Kellaway article about how young people are encouraged to think that their shiny graduate jobs at KPMG and Goldman Sachs are going to be fun, important and exciting, only belatedly to discover that they are working for a tax auditor and an investment bank respectively. Needlessly to say they’re pissed off, and feel betrayed:

When the penny drops like this, there are only two possible outcomes. Either you quit… or you silence your doubts and get sucked into the machine.

This seemed to sum up the situation facing my angry, disappointed student. But, of course, these are not the only options. There is a third choice.

You can seek out the parts of your job or course that you do like, and seek out the people you respect and who you would actively like to work and spend time with, then stick to them as tightly as you can and insist without shyness on being in their teams, on their projects or in their classes. And while you’re there learn the ways of the organisation you’re a part of.

And by doing this you can come to know the enemy you seek to undermine. You need to be fluent in the arguments you’re hoping to counter, and to understand the culture you’re so frustrated by before you can become an agent for change within that culture.

So here’s my manifesto: I want to become an economist so I can change what becoming an economist entails, and what being an economist is like. I want to work in a supportive, collaborative department where the common goal of understanding the economy, and hence the world, is palpable all around me. And I want economics to at least start to do itself out of a job, rather than creating the vacancies for a thousand more economists as we did in the years before 2008.

Or, to put it better, in the words of good old JM Keynes:

The day is not far off when the economic problem will take the back seat where it belongs, and the arena of the heart and the head will be occupied or reoccupied, by our real problems – the problems of life and of human relations, of creation and behaviour and religion.


How to make an economist

I’ve often asked myself, in self doubting moments and imposter-syndrome-rich night sweat events, what the difference is really between a person who says they are an economist and, well, just a person. Can I really lay any claim to be something other than the averagely well-informed news media-consuming citizen?

Certainly a lot of what I covered in my MSc was hard to learn but, in terms of sheer insight and understanding of how the world works, I usually think that you can explain all of economics to an attentive listener during one good session in the pub.

But if there is a difference between an economist and our hypothetical well-informed media consumer, it’s perhaps this: it’s an economist’s actual business to read massive, threatening-looking books about the economy. I won’t understand the content of such books differently (or, crucially, retain anything any better) than the average reader. It’s just that I’ll actually take the time to do the reading in the first place rather than tackling, say, an account of the British protectorates in the Middle East or a tome on Cubism. You’ve only got a certain number of reading hours in your life (particularly if you watch as much John Oliver on YouTube as I do) and what you choose to dedicate those hours to ends up shaping the adjectives you feel comfortable using to describe your actual self: I am an economist.

It is in this spirit that I begin what looks like your archetype of the no-fucking-around serious and weighty tome on economics currently threatening to destabilise the centre of gravity of my pretty low-slung coffee table:


Holy shit it’s big. And it’s serious looking. The unbelievably old-fashioned twisted flax motifs bracketing the title are the sleeve-designer’s equivalent of a bow tie on a 35-year-old debate show host: they’re designed to fill you with awe at the seriousness of what’s being presented.

But so far, and it’s very VERY early days by the way, I’m impressed and excited by the tone that’s been set and the direction it seems to be heading. What I hadn’t realised is that the whole thing’s a summary of a load of work Picketty and assorted colleagues did to gather as much historical data as they possibly could about wealth and income (which not the same thing) in as many countries as they could, going back as far in history as they could. The introduction gives some details about the monster database they’ve built, and then the rest of the book is going to be all about the things they’ve actually managed to find out, by looking at this data and asking questions of it.

This is exactly what my whole philosophy of data-driven economics was aiming to be about, although I never actually managed to articulate it in any coherent way. But it’s basically about trying to think of economics as the social science it is, wanting to imitate anthropology or political science, instead of physics as the discipline’s been doing for 120 years. And that involves careful observation of the thing in question, in our case the economy, and conclusions drawn on the basis of those observations.

It also has given me a big grin in the form a wicked (in both senses) quote which sums up perfectly exactly how I feel about the economics establishment I’ve recently become a card-carrying part of:

To put it bluntly, the discipline of economics has yet to get over its childish passion for mathematics and for purely theoretical and often highly ideological speculation, at the expense of historical research and collaboration with the other social sciences. Economists are all too often preoccupied with petty mathematical problems of interest only to themselves.

Boom! It’s reading statements like this that not only make me feel more justified in calling myself an economist, but that actually make me proud to assign myself the title.

The new “improved” UN job application website

Anyone who has every applied for a job at the UN will feel a sweat break out on the back of their neck at the mention of the 90’s-style faux-Latin-named job application site “Inspira”. (Remember when things were all called “Exceptimus” and “Ignitor” and stuff? Ugh.)

Anyway, the site’s been down for a few days for modernisation, and the new improved site came back up on Friday. (They had a FOUR DAY outage! This alone is enough to make me suspect that the people who designed the website didn’t know what they were doing. Outages should measured in minutes or hours, not days.)

Given my experience of large organisations and their ability to procure IT which isn’t grossly overpriced and absolutely terrible (for which see Britain’s NHS, Barclays’ several iterations of terrible online banking websites, any government department in the world ever) it’s no surprise to see that the fundamentally broken Inspira has been given little more than a lick of paint.

Still present are the absence of a “Back” functionality, because the site doesn’t use URLs to control location, visible meaningless Javascript all over the place (hovering over a link to my job opening reveals “javascript:submitAction_win4(document.win4, ‘HRS_CE_WRK_HRS_JOB_LINK$21$$0′” which gives just a hint of the horrors people meeting the website’s code for the first time must experience), Windows 98-style icons and the overall user-experience and look and feel of being dragged by chain up a muddy slope by a slow moving tractor.

But my personal favourite is the built-in spell-check function. (Note to developers: people don’t develop their own spell-check functions any more. Browsers do this now.) My entire cover letter passed the spell-check with flying colours bar one exception: It suggests I replace the word “in” in the opening sentence with the admittedly more emphatic, but perhaps overzealous “IN”.

I’m not even joking.

The UN Inspira spellcheck suggesting "IN" as a replacement for the word "in"

Modelling Africa’s trade routes

People talk a lot about how development aid might be used to improve a country’s attractiveness as a trade partner. (Mostly the World Trade Organisation, but not exclusively!)

“Aid for Trade” is a controversial project because it has a distinctly globalisation-friendly vibe about it, and a fundamental belief in the kind of trickle-down economics so beloved of market-oriented people and organisations.

But one thing that is never discussed when the possibility is raised of improving a country’s export competitiveness, is that in the absence of additional global demand, any increase in export due to an Aid for Trade programme must be accompanied by a reduction in exports for somebody else.

With the global economic model I’ve built as part of my PhD (and some fabulously bold assumptions about how trade works), I can have a stab at modelling which countries stand to gain and which to lose from a particular Aid for Trade project.

This picture, drawn using ESRI’s flashy new ArcGIS Pro shows the modelled results of improving Ethiopia’s export infrastructure. (It’s “inspired” (for which read, pinched) from this nice flight paths visualisation.)

Flows which increase are in blue, and those which decrease are in red.


Here are the boring but very necessary caveats:
– Only those between African countries are shown.
– Because the increased flows are much bigger in magnitude than the decreased ones (at least within Africa) I’ve had to compromise on the line thicknesses, leading to an overstatement of the decreases!! Caveat emptor!!
– This is based on a gravity-type trade model. Their use in predicting trade is controversial.
– The economic descriptions used in this model are based on estimates, since most African countries don’t publish the kind of economic data you’d need to build a proper description.

Fun with projections

Everybody loves a good map projection, none more so that the nerds here at UCL’s CASA.

I made a little toy visualisation of survey responses per global region for a pal of mine at Kings College, but knew he’d be unhappy with my choice of projection. So I decided to take the decision out of my hands and give control to the user.

The result is this fun little way of playing with map projections, relishing the smooth animations from one projection to the next. Go on, pick your favourite!


mercator transverse


The uncomprehending, blinking gaze: this is the default response when I tell people I’m “into” data.

It’s like being into electrical wiring, or urban sewer systems – yes, we’re glad they’re there, and yes, we’re certainly glad they work as expected, but yes, aren’t we also rather glad it’s someone else who has to worry about them and not us?

Well, I can see where these people are coming from. Data, as I’ve said on this blog before, looks like this:
Screenshot from 2015-10-21 15:06:59
That’s about as boring as it’s possible for something to look I’d say.

So it’s little wonder perhaps that no one knows, cares or thinks about data in this world of exciting stuff to see and do.

Screenshot from

But data is the one abstract concept that could feasibly be said to run the world. It’s being gathered in every imaginable context, from your pocket, to the furthest reaches of the solar system, and it’s being used to make decisions on how we deal with subjects ranging from refugees to pirates, to city planning to arms trading.

On Thursday of next week, I’ll be giving a lunchtime lecture here at UCL on the subject of data, why it’s informative, when it’s misleading and why on earth I love it so much. It’ll have examples of data visualisation so beautiful they’ll make you want to quit your job, and examples of the misuse of data so scurrilous they’ll make you wish other people would quit theirs. It doesn’t get more exciting than that…

Visualising similarity

A model of the global economy is, by its very nature, an unwieldy object to work with. There are 40 countries (we want more; that’s coming next) and the economy of each country is described by the economic activity of 35 sectors.

Each sector in each country interacts with each other sector in each other country creating close to two million interactions.

This is great for wowing potential users of the model with the sheer scale and size of thing, but it makes life pretty hard if you want to ask a question like “what effect has a certain change had on… well, everything?”

This is hard because “everything” here encompasses two million numbers some of which will have gone up and others of which will have gone down.

If you don’t put any effort into visualisation, the output of the model looks absolutely horrible:

Output of a World Input-Output Table

Needless to say, picking interesting information out of such a mass of numbers involves some careful thought. (For the interested, what you’re seeing here is dollar-valued commodity flows between sectors within the Australian economy, the sectors being numbered 1 to 35.)

The paper I’m writing at the moment asks an even trickier question than “what’s going on?”. I’m trying to work out how our model compares with other, more standard, ways of doing this kind of thing. This means making the same change in two models and comparing the results.

One way to boil down lots of information into a far smaller number of ‘things’ is to rank the numbers you’re analysing. This just means putting the numbers into order then saying which number is biggest, which is second-biggest etc.

So in our case, if we make a change to the global economy, instead of looking at a horrifying table of numbers we can just say “Australia was the country most affected by the change. Netherlands was second, Spain tenth, Bulgaria 39th…” and so on.

The advantage to this approach is that, when comparing the results of two models, you can just compare the ranks of the countries and see if they’re similar. If they are, you might be justified in concluding that the models are doing more-or-less the same thing.

It also allows for some nice visualisation. If we write down all the countries in one column in the order of their rank (most-affected by some change we’ve made, to least-affected) using one model, and make a second column where the countries are ordered according to their rank using the other model, we can quickly see where the differences are, particularly if we draw nice lines between the countries to show how their position has changed.

Here’s the outcome of such an experiment:

The design for this visualisation was inspired by a similar thing in the work of Hidalgo and Hausmann, see here on p4!

It shows the results of reducing demand for Chinese vehicles by $1M on the global economy in 2010. The left-hand column shows the results using a traditional model (for the interested: it’s called a Multi-Region Input-Output model, or MRIO). The most-affected countries are at the top and the least-affected at the bottom. The right-hand column is the same but for our model.

With the exception of Slovakia, the results look pretty good. The ranks are generally pretty similar which is encouraging. We’re currently trying to find out what’s going on with Slovakia, and I’ll post here if we ever find out!

(Note that Taiwan is not in our model, because the UN doesn’t report trade data for it, as it deems it to be a part of China. I won’t be delving into this international controversy here!)