A Different Way Of Comparing Canada & America

Since I’ve come back to Canada, I find myself continually (even subconsciously) comparing it with the US. I’ve noticed that while Canada is roughly 1/10th the size of the States, it feels like it’s way less complicated (and sophisticated). This also worries me, as I wonder how we’ll continue to compete in a more networked, global, insert your favorite 21st century economy cliche here world.

But I’ve struggled with how to best articulate this. Whats the language to reflect complexity? How does it affect nations? How can we have a data-driven conversation about this?

After listening to a podcast with Cesar Hidalgo I stumbled onto the Harvard MIT Atlas of Economic Complexity – and I finally think I’ve the tools to talk about this.

So what is “economic complexity” and why does it matter?

Well, economies produce things. Some of these can be produced by everyone (think dried fish) while some can only be produced by a few (think x-ray machines).

It takes a lot more knowledge to build an x-ray machine than dry a fish. It’s more complex (plus a lot of that knowledge is locked in people’s heads) ergo fewer countries can do it.

By comparing trade statistics amongst countries you can figure out the relative complexity of all goods produced and traded by all nations.

You can also use these comparisons to map out how products are related. By looking at what countries co-export, you can see the degree to which products are related. This lets us statistically demonstrate concepts that make sense like “countries that export textiles also export finished garments but not airplanes”.

Here’s what this map looks like:

In order to understand the rest of this blog post, let’s take a walk through it.

Start with the oil well in the upper middle of the map. There’s a big brown dot. This shows you that crude oil accounts for a lot of global exports. And it’s connected to the teardrop-shape that represents petrochemicals. This means that if you export crude oil it’s reasonable for you to move on to refining the oil and exporting it.

Since it’s only connected to the teardrop, it means that the only thing you can naturally move into is petrochemical refining (more on this later). If I was to propose that a country’s expertise in exporting crude oil should make them succeed at making airplanes, you can now explain why my logic intuitively feels flawed. Airplanes are off to the left of the map, several nodes removed and crude oil is an edge connected only to refining. Success at exporting oil does nothing to directly encourage the ability to build planes.

Conversely, if your country is good at manufacturing, you are thrown into the center of the map and you have more places to go. It wouldn’t be surprising to see you get into chemicals or metal products.

It’s also important to note that many countries have more than one industry, so Canada for instance exports both crude oil (oil sands) and airplanes (Bombardier). The mapmshowsnthat this is due to historical factors; the two aren’t “naturally” related.

The map above is weighted for global trade. The size of each ball (in jargon: a node) corresponds to that node’s share of trade. But what if we looked instead at how complex each product is?

Crude oil is simple stuff; an airplane is complex. If you redo the map based on complexity that crude oil dot is now tiny; the airplane is still big.

And here’s where things get really important. It turns out that if you want to predict how well countries are going to grow, you should look at their ability to export more economically complex products over time.

A great example comes from comparing Thailand with Ghana. In the mid-60s they had similar GDP capita. Adjusting for all other factors (read the paper), you see that the biggest reason Thailand is now wealthy is that they’ve invested in producing more complex things. This “movement up the value chain” and ability to export it is what has driven their increase in living standards.

So back to Canada and the US. How do we compare and why do I feel uncomfortable?

Let’s start by looking at the Canadian and US product maps. Here, we take the map from above and put a black box around the nodes on the map that matter to a country. Here’s Canada:

Good news: we’ve a lot of black boxes and several are on big circles. Bad news: these circles tend to be commodities with low complexity and off on the edge of the map.

Let’s look at how this is changing. Here’s how our exports break down over time:

We can see that oil and other commodities increasingly define our exports.

Now let’s look at America:

Dots everywhere; you’re looking at a complex, diverse economy that makes just about everything except clothing and electronics. And has no oil to export.

This shows up in the export statistics. Look at all that machinery, chemicals, aircraft and (relatively declining as a share) electronics. Clothing is nonexistent.

And this disparity is why I worry about Canada.

Living in Canada you constantly hear about two things:
1) Housing
2) The Oil Sands and other commodities

The Oil Sands and commodities live on the edge of the maps above and have low economic complexity. This means that they don’t do a lot to stimulate other industries and build a robust, diverse economy.

We’re building a less sophisticated, less dynamic economy that’s dependent on one thing we don’t control: commodity prices.

A commodity price collapse will be death to Canada; the map shows us that a lot of our major industries will literally have nowhere to go. In contrast, America’s have a lot of leeway. You can retool your factory to do something else; oil rigs only drill oil.

And what about housing? Well, I now have a sense of why so many Canadians “invest” I’m real estate. The map suggests how industries evolve and therefore where your expertise would be most likely to succeed.

If you’re an oil exec, you could invest in a refinery and legitimately contribute knowledge (and therefore reduce risk/make the venture more successful). You are “dumb money” if you invest in a tech startup or an airplane manufacturer.

But good luck opening up a new oil refinery. It’s way too capital intensive for any individual investor. Instead, you go buy another house or financial assets like stocks. And that’s probably part of the reason why every Canadian newspaper has large sections on commodities, housing and stocks.

The other scary thing here is that it explains why Canada is so crappy at producing globally competitive companies outside the commodity space. When you’re at the edge of the network, there’s just not as many places to go. We don’t have the expertise at the center of the network necessary to create those companies in meaningfully large sectors like electronics, chemicals or vast swathes of manufacturing.

So, what can we do? Well, the map isn’t destiny; you can go anywhere on it, it just might be really hard to get there.

The first thing we should do is decide where we want to play on the map (hint: in the middle in heavily connected nodes like high end machinery, chemicals, electronics and construction equipment).

Then, we’d do all we could to encourage it. Kill any subsidy to other industries (why do we subsidize aircraft if they’re at the edge?), do everything reasonable to reduce red tape or obstacles for companies in the middle of nodes, better training for those folks (let the oil patch pay more if they need more workers in Fort Mac), etc.

It remains to be seen what will happen to the Canadian economy, but I’m betting on a very hard landing over the next five years. The map gives us one more tool to explain why – and shows just how different Canada is from America.

Cascading to Failure

So, unless you live under a rock, you heard that yesterday, at 2:45 pm, the stock market dropped like a rock:

We don’t know why this yet happened, but there are two likely culprits – and neither should make you happy.

The first hypothesis is that this mini-crash was caused by ‘fat fingers’ – an errant trader entered $16 billion instead of million (this trader needs a dialing wand).  Pause for a moment and think about that.  Is this even possible? A trader can place a trade for $16 billion – larger than the GDP of 81 countries – without someone checking it?  If this is even possible, then risk management is a joke and it’s time to shut down some of these trading companies.

The other hypothesis is based on programmed trading.  Massive automated trading means that Wall Street 2010 is part Skynet, where the machines are in control.  It’s possible that yesterday’s downward spike was caused by a computer-controlled model gone wrong.  It interpreted some combination of signals as meaning that the market was turning and it started to sell.  Hard.

This sell order was then picked up by other machines who then started to sell too. And the feedback loop began, rocketing prices downwards.

At this point the sell orders come in faster than a human can make sense of them and you get panic on the exchange.  Moreover, no market maker is able to stabilize the market because the trades are coming in faster than they can interpret them.

The irony is that another batch of computers are looking at the market and see this as a buying opportunity.  Proctor and Gamble is down by 30%.  A computer buys it because it knows that, due to mean reversion, it’s unlikely that P&G will not gain back part of that loss very soon.  And now the feedback loop kicks back in the opposite direction.

And the market stabilizes again, albeit down a few percentage points (which is a big deal).

If this turns out to be the reason for the spike, it’s very disheartening. These programmed trading models are proprietary and we have no idea what they interpret as sell signals. It could be some combination of statistics and trading data; it could be as esoteric as reading blogs for sentiment. Moreover, even if 99% of models are ‘correct’, the 1% that wrong can potentially set off a cascade.

Moreover, the fact that the NASDAQ has cancelled all the wild trades yesterday reduces some of the incentive to fix the system.  Don’t get me wrong-whoever’s system set this off is working the weekend to figure out what happened.  But, a lot of people lost a lot of money yesterday (and some made a fortune too) and having those trades cancelled sends a signal that market owners are going to smooth these things over. If you get carried away, they’ll just hit a big “undo” button (I wish I got this at my job).

The real reason this scares the living hell out of me, is that unpredictable volatility like this is typically a sign of a major instability in a complex system.  Our financial system is a terribly complex system.  When you see a major change like this, it’s a sign that your models of the system are wrong and you’re now flying the plane without a captain. I’m going on record as saying that no one on Wall Street really understands how the entire system works; the proof is in a thousand point decline -and 700 point rebound – in the span of 15 minutes.  Statistically, this should never happen.

So what’s going to happen? I’m putting my money on a huge increase in volatility over the coming months and, unless there’s a change in the regulatory system (more disclosure of modeling, caps on trading limits or sudden price changes), increasingly shorter cycles of stability/volatility.  It’s going to be an interesting ride; hold on.

Great Meals and Path Dependence

Wen and I are fortunate to have great friends.  A few of them (randomly) ended up giving us gift certificates to Gramercy Tavern when we got married and we finally made it the other night.  As we were basking in the glow of a ridiculously good meal, Wendy mentioned “how did we get here?”  I couldn’t help but think of how, at least for me, a couple of decisions that – at the time – seemed irrelevant have massively shaped who I am today.  (Note that this is not an original notion; complexity scientists call it path dependence).  Here are a couple of those events:

When I was in high school, you applied for three different university programs in descending preference.  I didn’t get into my first pick – computer engineering at the University of Waterloo.  Instead, I did engineering at Queen’s.  If I’d gone to Waterloo I likely wouldn’t know my current set of friends and almost certainly be married to Wen.  In fact, I’d argue that not getting into Waterloo is the best thing that ever happened to me (and that’s no knock to Waterloo as a school).

When I was at Queen’s every engineer did a common first year and then had to pick a discipline to specialize in over the next three years.  I had no idea what I wanted to do, but knew that I liked computers and math and physics.  Each discipline made a presentation and the Engineering Physics department invited a grad named Kamal Hassan to present.  He talked about how he had studied Eng Phys and learned lots of interesting math/physics/engineering but didn’t want to be an engineer and therefore became a management consultant.  I had no idea what a ‘management consultant’ was, but the program sounded like something interesting so I decided to do Eng Phys.  The training I received there continues to help me on a daily basis.  (And, in a weird twist of fate, I ended up becoming a consultant like Kamal and, freakishly, ended up at the same business school he went to)

After my 2nd year of school, I went overseas to London on a work exchange program.  There was a central organization that helped you find a job.  A list of positions were posted; you applied; and if you were to be interviewed, a notice was placed for you in a book (this was pre-cellphones).  This book had a very odd structure.  There were tabbed pages (by students’ last names), but the tabs weren’t rigid and you could open the book but it would be collapsing under its own weight if your last name started with a “w”.

One morning I went to check if I had any interviews and I had one-for a 150 quid/week job at Merrill Lynch. However, due to the collapsing book, I missed one for a 250/week at some publishing company. When I found out I missed out on a job that paid 66% more I was crushed (and I spent the summer living in pernury) – but years later I was  in a job interview and saw “Merrill Lynch” circled on my CV and knew that it had been worth it (if you ever meet me, ask me about that job at Merrill).

Finally, I didn’t get into any of the American grad schools I applied to.  Instead, I ended up at INSEAD.  Again, I met some truly unique people who I otherwise would not know.  More importantly, I got a special chance to work with a serial investor and startup in Silicon Valley.  This gave me the confidence to strike out on my own after school, and while that business wasn’t a success, it directly led to me getting my current job.

All of which led to us having dinner at Gramercy Tavern.  I don’t pretent that the only reason I was able to have dinner there was because of the decisions made above (after all, there are an infinite number of paths that could have led to me eating there), but at least I know which points in my life have made the biggest impact.

And as for the meal.  It was delicious.  The appetizer was a lamb papardelle with olives, lemon confit and swiss chard.

This was followed by venison loin and sausage in a Bourbon sauce and with a potato pancake:

We quaffed it down with probably the best bottle of wine I’ve ever have – a 2001 bottle of Oddero Barolo:

Finally, they gave us a little amuse bouche for the next morning – a piece of cocount cake with pear inside:

Simple is the New Complex

This weekend I walked into a gallery on 21st street and saw this sculpture:

Fan Sculpture

This photo captures the components of the system, but not the dynamics of it.  Basically, you’re looking at two fans facing each other and connected by four pieces of fishing line.  Around the fishing line are wrapped two circular streamers of magnetic tape.

This is fundamentally a simple system – the fans provide continuous input – but the outcome is unbelievably complex.  The two streamers bounce back and forth between the two fans.  At time they appear to stand still and then wildly gyrate in a new direction.  At no time can you predict where they are going to go next, nor do they ever take the same path twice.

This art installation is a fantastic visual example of what is talked about in a recent paper, The (Unfortunate) Complexity of the Economy, by Jean-Phillipe Bouchaud.  Bouchaud shreds the notion that our economy can be explained simply by supply and demand.  Instead, he outlines how many of the behaviours we see in our economy (bubbles, markets that never settle on an equilibrium, etc.) can be explained by different physical analogues.  For example, the fans above are an example of a system that is incredibly sensible to the slightest perturbation in its environment, meaning that is constantly and dramatically changing state (sound like the stock market of late ’08/early ’09?).  What’s more, the system above is also governed by  few simple actions (fans blow a tape wrapped around strings) yet incredibly complex action resuls (think about many people buying/selling a stock, yet prices gyrate madly).

If you read one academic paper this year, make it this one.

How We Got Here

Two recent articles I read have me thinking about path dependence.  For those who don’t know, path dependence basically means that we (either individuals or institutions) are the sum of historical experiences – history matters.  A corollary is that small events can build up over time to have large historical impacts (read the Wikipedia entry above for the story of VHS vs. Betamax).

The first article comes from the New Yorker and is about how we arrived at the current U.S. healthcare system (The author, Atul Gawande, is a pretty fascinating guy – read his recent NEJM paper on how simple checklists can significantly improve patient outcomes).  The synopsis could be: nobody designed this system, rather many little decisions have now led us to what it is.  This is classic path dependence (and his article calls it out).  Anyone who wants to change the system is going to have to accommodate this and show that their solution is able to deal with all the challenges that got us here in the first place.

The same dialogue is going on right now in the world of finance.  Check out Alan Blinder’s recent article in the New York Times.  He outlines the six retrospectively obvious mistakes that we made to lead us into the current financial crisis we’re in.  This again, is classic path dependence: a few independently made mistakes combines to create one massive mistake that was much great than the sum of its parts.

You might be thinking that path dependence is a bad thing, but that’s not true.  In fact, it can lead to great outcomes.  Before the Euro, one of the reasons that Germany consistently had a high standard of living was the Bundesbank’s focus on low inflation.  They were adamant about keeping inflation low as the Bundesbank’s early governors had lived through the hyperinflation of the Weimar Republic and were obsessed with making sure that it never happened again.