The social sciences are buzzing with excitement about network analysis: here at UCL’s Centre for Advanced Spatial Analysis (CASA), networks are being used in ways both intuitive, in the modelling of traffic flows around road networks and patterns in the use of social networking; to the highly counter-intuitive, such as in the study of where and when crimes occur in relation to other crimes, how rioters and the police play out a networked game of cat and mouse, or how ethnic conflicts are affected by access to, and use by the government of, media both new and traditional.
But Economics has been late to the game. Eric Beinhocker has written forcefully about the need for Economics to adapt, by doing nothing less controversial than simply to acknowledge the existence of the Second Law of Thermodynamics—the law of ever-increasing disorder which was pithily summarised by Jagger/Richards as “you can’t always get what you want”—and to embrace networks and the science associated with them: complexity.
The cost at which a particular government can borrow from investors is clearly related to how convinced the money markets are that other governments represent a safe bet, yet borrowing costs are routinely studied using only the variables of the country being studied. Similarly, you can’t expect to explain movements in macroeconomic variables such as exchange rates or foreign trade, without including the other countries in your analysis. It is this kind of old-world thinking, regress A against B to see if it has ‘an effect’, that has led Economics to consistently misunderstand the world as it really is today. (And, indeed, as it always has been.)
John Stuart Mill, in motivating previous generations of economists, described the booms and busts of macroeconomics as being like a stormy sea tossing and rolling a boat: “Would you advise those who go to sea to deny the wind and the waves — or to make use of them and to find the means of guarding against their dangers?” This is an analogy which seems immediately to require a global focus to the study of Economics, and an approach to the systems and interactions involved which is meteorological in scale. But too often we see attempts to predict the movement of the ship based on the actions of the crew, rather than an acknowledgement that bigger forces are at work.
So to the problem of applying the lessons of complexity to the global economic system: we must start with a network upon which to operate. But the systems involved are intimidatingly large. The European Union, the globe’s biggest single market, represents the economic activity of some 500 million inhabitants trading goods and services with a value of around $16 trillion. Any practicable representation of a network of systems of this complexity, and the global economy is surely the system of maximum-conceivable complexity, would be such a gross simplification as to iron out all of the interactions, chain reactions, bifurcations and time-lag effects which could potentially make a model useful for explaining or forecasting global events. And yet we must begin somewhere.
As with the meteorologist, the place to begin must surely be with data. Given the dismal state of Economics as a tool for predicting even the simplest of human interations, let alone something as subtle and sensitive as the famous but little-understood “market outcome”, we need to build inductive models based on as much data as we can gather. An inductive model is one which is driven, to the greatest extent possible, by what can actually be observed without relying on assumptions or postulated mechanisms through which one variable affects another.
So how can a network representation of the global economy be put together in a way which is rich in data and light on assumptions? Perhaps we might continue the meteorological analogy: just as the weather is something greater than the movement of warm and cold air around the world, and yet can be described and predicted by knowing about how, where and when the air moves, so the economy is a function of, but something more than, the global flow of goods and services. And just as storms and heatwaves are predicted by weathermen watching airflows, the phenomena of interest to the economist, wealth and growth, poverty and inequality, might be studied by watching these goods and services move around the world. Note that there’s no assumption about causality in weather forecasting, or any statement about the mechanisms by which certain air flows result in certain weather patterns. They simply trust the data to include these implicit relationships without ever attempting to specify them. This is why an inductive model like this is distinct from a deductive one, where theories are postulated to explain mechanisms and causal links.
We can start by describing the flow of goods and services around the global economy as taking place at just two distinct levels: within an economy, and between economies. These two levels can each be characterised by a simple question.
Within an economy: to what extent does the production of goods and services, the level of which is set in response to demand from consumers, require the production of other goods and services as inputs to the production process? The answer to this question can be represented by a network of dependency between the various goods and services an economy produces. Some goods require a great deal of input from other sectors of the economy, others are almost independent.
Between economies: to what extent does trade in goods and services occur between economies which produce a product, and economies which consume it? The answer again comes in the form of a network of trading relationships. Some countries have a history of trading certain products with one another, and other countries are largely self-sufficient. Critically, in both cases, the structure of the network, that is to say the relative importance of each interconnection, can be derived purely from data.
In both cases, we build a network representation of flows which take the available data as a starting point, and take that data seriously. It is the data which defines which countries are linked to which other countries, and the data which defines the relative significance of those links. Where traditional economic models attempt to say something insightful about the high levels of trade between, say, France and its former colonies, here we let the data do the talking. If France trades more with Senegal, a former French colony, than it does with Ethiopia, a former Italian colony, then the data will reflect this, and the network which is built on that data will include the bias in its construction, along with all other biases too numerous to hope to describe explicitly. We are taking the observable state of the trade network as the best possible proxy for all the subtleties and irrationalities of human interaction and, in doing so, explicitly abandon the attempt to include such unknowables in our model. It is in this sense that we are taking the data seriously.
Even with nothing but a static description of the world’s economy as viewed through the observed networks of production and trade, we can perform some interesting analysis. For example, which trade link—that is to say, which product, traded between which two countries—contributes most to global output? The answer may not be simply the trade link with the highest dollar value. For example, Belgium provides around a quarter of France’s imported iron and steel. If those two countries had a falling out which caused them to stop trading altogether then, all things being equal, France would have 25% less steel to use in manufacturing other goods. The French car industry uses 75% domestic metals and 25% imported metal, meaning that French output of cars would be down 12.5%. Since 50% of all cars produced in France are exported, and France exports over $20 billion-worth of cars a year, the spat between Belgium and France would cost the world over $1 billion in lost car exports alone.
The above simple example is constructed using trade data from the UN, and a sector-by-sector description of France’s economy, produced by the European Union, including which products are required to produce cars and how much of each of those products is imported. By combining these two data sets we build a picture of the movement of goods and services not only through economies but between economies and, hence, around the world. By adding some simple linearity assumptions—to make 1% more cars, France needs 1% more steel—we can even begin to do some dynamic analysis of the global economy, to answer questions such as: how will the world respond to a growth in demand for cars coming from China? Or: which products would most benefit Nigeria if imports were replaced with domestic production? And how would that affect Nigeria’s trading partners?
We describe this framework, a network of networks based on observable data, as a Global Demonstration Model: it is a least-assumptions description of the world’s economy designed to give a demonstration of the power of network and complexity science when applied to questions which are inescapably global in nature, such as those concerning migration, trade, international security and development aid. More subtle and realistic models are of course possible, but the more interpretation and insight we add to the model, the further we get from the data.
Here at CASA we’re currently assembling the data that will allow us to build our global network of networks. We’re expecting to see the first version of our demonstration model, and the first of the demonstration analyses based on it, to be completed in the autumn.
I’ll be posting the results of these explorations here on this blog. Stay tuned…