Earlier this year, Ricardo Hausmann, César Hidalgo and others at the Center for International Development at the Kennedy School at Harvard released a report Atlas of Economic Complexity. [See also a story in the New York Times Magazine earlier this year on The Art of Economic Complexity and their technical paper – “The Building Blocks of Economic Complexity” from the Proceedings of the National Academy of Sciences, June 30, 2009.]
This study looks at a nation’s economic structure and prosperity from an output base — as opposed to most other studies of competitiveness which look at inputs (i.e. years of education, R&D spending, etc.). Their concept of Economic Complexity is two-fold. First, it measures what they call the product complexity of a particular industry (based on its output). Crude oil production ranks the lowest while specialized machinery ranks the highest. Note: which crude oil is not a very “complex” product, lubricating petrol oils is the 4th highest in complexity. In this way they attempt to measure the knowledge intensiveness of any particular product. They then combines a country’ portfolio of products to get an overall indication of how advanced the economy is. Countries that make complex products, a greater variety of them and products that no one else can make score higher on their Economic Complexity Index (ECI):
According to our measures, Japan and Germany are the two countries with the highest levels of economic complexity. Ask yourself the question: If a good cannot be produced in Japan or Germany, where else can it be made? That list of countries is likely to be a very short one, indicating that Japan and Germany are complex economies. Now take an opposite example: if a product cannot be made in Mauritania or Sudan, where else can it be made? For most products this is likely to be a long list of countries, indicating that Sudan and Mauritania are among the world’s least complex economies.
They argue that “economic complexity reflects the amount of knowledge that is embedded in the productive structure of an economy.” It is the social accumulation of productive knowledge.
Most modern products require more knowledge than what a single person can hold. Nobody in this world, not even the saviest geek nor the most knowledgeable entrepreneur knows how to make a computer. He has to rely on others who know about battery technology, liquid crystals, microprocessor design, software development, metallurgy, milling, lean manufacturing and human resource management, among many other skills. That is why the average worker in a rich country works in a firm that is much larger and more connected than firms in poor countries. For a society to operate at a high level of total productive knowledge, individuals must know different things. Diversity of productive knowledge, however, is not enough. In order to put knowledge into productive use, societies need to reassemble these distributed bits through teams, organizations and markets.
Accumulating productive knowledge is difficult. For the most part, it is not available in books or on the Internet. It is embedded in brains and human networks. It is tacit and hard to transmit and acquire. It comes from years of experience more than from years of schooling. Productive knowledge, therefore, cannot be learned easily like a song or a poem. It requires structural changes. Just like learning a language requires changes in the structure of the brain, developing a new industry requires changes in the patterns of interaction inside an organization or society.
Expanding the amount of productive knowledge available in a country involves enlarging the set of activities that the country is able to do. This process, however, is tricky. Industries cannot exist if the requisite productive knowledge is absent, yet accumulating bits of productive knowledge will make little sense in places where the industries that require it are not present. This “chicken and egg” problem slows down the accumulation of productive knowledge. It also creates important path dependencies. It is easier for countries to move into industries that mostly reuse what they already know, since these industries require adding modest amounts of productive knowledge. By gradually adding new knowledge to what they already know, countries economize on the chicken and egg problem. That is why we find empirically that countries move from the products that they already create to others that are “close by” in terms of the productive knowledge that they require.
As they stress, much of this knowledge is tactic and relational:
For example, to make a shirt you need to design it, procure the fabric, cut it, sew it, pack it, brand it, market it and distribute it. In a firm that manufactures shirts, expertise in each of these knowledge chunks will be held by different people. And shirts require all of them. Moreover, you need to finance the operation, hire the relevant people, coordinate all the activities and negotiate everybody’s buy-in, which in itself require different kinds of knowhow. We can say that putting together this operation requires know-who and know-where. Know-who can be thought of as knowledge of who has the requisite chunks of knowledge, and know-where as knowledge of where the people and organizations that have this knowledge are located. To make shirts, you can import the fabric and access the knowledge about looms and threading that is embedded in a piece of cloth. Yet some of the knowledge required cannot be accessed through shipped inputs. The people with the relevant knowledge must be near the place where shirts are made.
Why does this matter? Because countries with more complex economies grow faster and become richer — since, as the previous explanation indicates, they have a competitive advantage. The key here is “become” — since countries with abundant natural resources (e.g. oil) can be rich but not have the capacity for economic growth. As they state, “economic complexity is not just a symptom or an expression of prosperity: it is a driver.”
The ability of the ECI to predict future economic growth suggests that countries tend to move towards an income level that is compatible with their overall level of embedded knowhow. On average, their income tends to reflect their embedded knowledge. But when it does not, it gets corrected through accelerated or diminished growth.
. . .
In short, economic complexity matters because it helps explain differences in the level of income of countries, and more important, because it predicts future economic growth. Economic complexity might not be simple to accomplish, but the countries that do achieve it, tend to reap important rewards.
How do countries develop?
countries expand their productive knowledge by moving into nearby goods. This increases the likelihood that the effort to accumulate any additional capability will be successful, as the complementary capabilities needed to make a new product are more likely to be present in the production of the nearby goods.
The more capabilities a country has over a range of products, the easier it is for that country to move into new products — or, as I would argue, create new products. While the authors don’t explicitly discuss innovation, their findings of the importance of economic complexity for innovation are clear.
I should note that the U.S. is right on the trend line – meaning its income level reflects it knowledge base. The correlation is slightly less when looking at the rate of growth — not just overall income. However, the U.S. has had a slower rate of growth between 1998 and 2008 than its Economic Complexity Index in 1998 would have indicated. Based on this analysis, they expect the U.S. to have an annual growth rate of about 2% in GDP per capita between 2009 and 2020 and an overall GDP growth rate of 2.84% (based on our 2008 level of economic complexity).
Unfortunately, the America’s Economic Complexity Index score has declined since 1964 — and especially in the past decade. It was 1.78 in 1964. Dipped to 1.35 by 1978. Rose again to 1.81 in 1998. Then dropped to 1.45 in 2008. The change between 1964 and 2008 was a decline of .33. Between 1998 and 2008, the drop was .36. This reinforces what others have been saying about the last 10 years being more of a lost decade. As such, the U.S. ranks 13th (as of 2008). By comparison, Japan has a 2008 ECI of 2.3 and Germany’s ECI in 2008 was 1.985.
What is missing from the analysis is the linkage back to policy. The report is silent on what the best policies are to develop the capabilities needed to expand their productive knowledge beyond what they already have or would gain from moving into nearby product. I’m sure as the data is reviewed more widely, policy prescriptions will emerge. Something I hope to look into myself in the new year.