Graphic from infosthetics.com
As a follow-up to Eric Beinhocker’s The Origin of Wealth, I recently downloaded and read The Atlas of Economic Complexity by Ricardo Hausmann, Cesar Hidalgo et al. It was a good chaser after Beinhocker’s massive introduction to complexity economics. Hausmann and Hidalgo are influenced by the idea that the economy is a complex adaptive system. Therefore they reject the idea that you can easily sum up an economy in a single number like GDP. Instead, they try to analyze all the different products produced in the economy in an attempt to get a grasp on how complex it is rather than just how big it is in dollar terms. Their results are interesting.
Hausmann and Hidalgo assume that the economy is based on productive knowledge–bits of knowledge necessary to make the products that we consume. These units of productive knowledge they call capabilities. They also assume that many products have overlaps in the capabilities needed to make them. So if you make shirts, there is a high probability you can make blouses too. These relationships should show up in a visualization of the product space. Countries that makes shirts will show a tendency to produce products with overlapping capabilities: blouses, pants, etc.
Hausmann and Hidalgo calculate a country’s economic complexity by measuring the diversity of products a country is capable of producing, and by also calculating the ubiquity of the products they do produce. So if a country produces only a few products and those products are ubiquitous throughout the world it is pretty certain the country has a fairly non-complex economy. The amount of capabilities present in that economy must be pretty few. For example, here’s an infographic of the economy of Tajikistan:
Aluminum, raw cotton and dried fruit dominate the Tajik economy. And each of these items are ubiquitous enough in the world that it does not take a high degree of scarce knowledge to produce them. On the other hand, here’s Thailand’s exports:
Notice the increase in the number of products, but also of the kinds of products produced. It takes many more capabilities to produce electronics than it does to produce raw cotton. Here the difference between an emerging economy like Thailand and an underdeveloped economy like Tajikistan is pretty stark.
The interesting thing about the measure of economic complexity is that Hausmann and Hidalgo have found it to be a strong indicator of future economic growth. The economies which are highly complex but with a lower-than-expected current GDP can be expected to grow quickly, while countries that have a high GDP relative to their complexity can be expected to grow slowly, if at all. This gives a new dimension to the “resource curse” hypothesis in that it displays growth based on natural resource exploitation is unsustainable, unless it is invested in expanding other productive capacities.
According to the Atlas of Economic Complexity, the economies whose level of complexity most predict growth in GDP per capita are China, India and Thailand. Next in the rankings come Belarus, Muldova and Zimbabwe. All of these countries have economies which currently lag behind their potential.
Using GDP instead of GDP per capita, the top slots all go to Sub-Saharan Africa: Uganda, Kenya and Tanzania take the top slots, with Zimbabwe, Madagascar and Senegal following. Though their high levels of population growth keep income per capita down, these will all likely be fast-growing economies over the next decade.
The Atlas of Economic Complexity brings to life the incredible diversity within the world economy. It offers a new metric of development: the Economic Complexity Index, or ECI, which may prove to be a more important indicator than more simplistic metrics like GDP per capita. Time will tell how Hausmann and Ricardo’s predictions turn out. But their approach seems bound to be imitated as development theorists absorb and make use of the insights of complexity science.
For a brief introduction to economic complexity from Cesar Hidalgo himself, check out his talk at TEDx Boston:
And for more cool data visualizations visit the Observatory of Economic Complexity.