Economic complexity & Australia’s troubling economic outlook

What is the economic outlook for Australia?

It’s the kind of question that gets thrown about often in the halls of Parliament House, at business council functions, or over golf-tees between retirees.

Historically, the answer to this question has relied on macroeconomic modelling, some econometric work, and a good dose of ‘economic judgement’.

Recently, a completely different toolset has emerged to answer this question by relying on something known as ‘economic complexity’.

Spoiler: By economic complexity, Australia’s outlook is not rosy.

So what’s economic complexity?

Introduced in a series of papers by Harvard economists César Hidalgo and Ricardo Hausmann during 2007 to 2011, [1] [2] [3] [4] economic complexity seeks to measure something of a ‘holy grail’ of economic growth modelling: a country’s underlying productive knowledge.

Productive knowledge is a mercurial concept encompassing the diversity and sophistication of a nation’s workers, the efficiency and sophistication of a nation’s productive infrastructure, and even the institutional environment which can act as a help or hindrance to developing new products or inventions. It’s a holy grail because at the heart of any economic prosperity engine are the methods to make or do things of value, and the skills to carry those methods out. The better your productive knowledge, the more value you can create with fewer inputs. Increasing productive knowledge should be the goal of any policy which aims at increasing long-run prosperity.

The catch is that there is no statistical series of ‘productive knowledge’. The reason being that you can’t simply measure the skill base of the working population, or the store of ideas it has at its hands (patents cover only a tiny subset of such knowledge), let alone the myriad institutional factors which help.

What to do?

Hidalgo and Hausmann’s answer was to take a rather unusual look at global trade data. Rather than look, as many economists do, simply at the flows of trade between countries (volumes and value), they conjectured that the network of these flows, and specifically the network of flows in related products, could be used to make visible the invisible: to get at a country’s productive knowledge.

The first step of their analysis was to construct what is called the ‘product space’: a novel network representation of global trade where nodes are products (not countries, like many previous trade network analyses). The product space is created by first compiling a list of all of the products traded between countries, and then drawing a link between two products if it is found that these two products are more often exported by the same country than you would expect by chance. For example, if bananas and passionfruit are exported more often than not by one nation (rather than two separate ones), a link would be drawn between bananas and passionfruit in the product space. Whereas if it is found that banana producing countries typically don’t also export, say, automotive hand-brakes, a link wouldn’t be drawn between bananas and automotive handbrakes. Eventually, by analysing all products in the trading data, a network is built from all such linkages.

Hidalgo and Hausmann crucially reasoned that this pattern of co-location of any two products must indicate that the two products rely on the same set of underlying productive capabilities. In other words, links in the product space network are interpreted as revealing common productive knowledge. If a country knows how to produce bananas then it’s likely that the same productive knowledge required to produce bananas also makes it relatively easy to produce passionfruit.

Put simply, the product space is then the revealed knowledge map of mankind’s productive enterprise.

The final trick is to identify which nodes (products) in the network a single nation produces. Are these nodes possessed by many countries? (that is, do many countries ‘know’ how to make this product?) Of these countries, do they typically produce vary scarce (and so we infer) sophisticated products, or are they typically producing very ubiquitous (and so we infer) simple products? By working through each nation’s export basket, we can now score a nation’s inferred Economic Complexity Index (ECI)—an index the authors created to score the inferred sophistication and diversity of a country’s export activity.

To get a feel for the ECI—have a look at an export break-down of Japan in the figure to the above right (Fig. 1). The larger the box, the larger the fraction of exports fall into that category. Categories are coloured by high-level class of export (like ‘electronics’, ‘transport’, and so on).

Fig. 1: Japan’s export value shares in 2013, coloured by major trade class. [5]

And now contrast this with Australia (Fig. 2).

Fig. 2: Australia’s export value shares in 2013, coloured by major trade class. [5]

The difference is pretty easy to spot.

Japan has a highly diverse set of export products (many small classification boxes within other boxes), and those that it produces are mostly highly sophisticated (electronics, cars, LCDs, optical fibres etc.).

By Hidalgo and Hausmann’s Economic Complexity Index, it turns out that Japan has the highest score of any country at around 2.3.

Australia on the other hand is made up of both low diversity (a massive fraction of our exports are occupied by a few goods such as iron ore, coal, petroleum), and low sophistication (raw minerals and gasses). Australia has an ECI of negative 0.43, ranking it at 78th, just above Kenya, and below Zambia—not exactly what we’d consider worldbeater economies. In ECI terms, it seems the caricature is true: Australia is digging up rocks and buying back computers.

For Australia, it gets worse.

Recall that Hidalgo and Hausmann started with the idea that lasting economic prosperity is linked to underlying (unobserved) productive knowledge (now proxied by the ECI). It turns out that this idea has been strongly confirmed: the ECI predicts the economic growth prospects for a country remarkably well, and many times better than existing forward-looking growth models. [5]

Indeed, a one standard deviation increase in ECI is found to be associated with an increase in a country’s long-run growth rate of 1.6% per year.

In May this year, the Harvard’s Centre for International Development updated their growth predictions up to 2023 based on the ECI.

Australia’s long run annual growth rate is given as 1.36%, or a ranking of 118, sitting just above Cuba. Not surprisingly, countries that have been found to be making strong progress in ECI with a lot of head-room for growth in economic prosperity come out on top: India leads the pack at a remarkable expected annual growth rate of 7.89%!

To put Australia’s prospects in context, strong, already prosperous countries such as the UK (3.36%), Finland (3.19%), Canada (3.02%), and the Netherlands (2.84) all outpace Australia’s estimated growth rate by more than a factor of two.

The issue is not necessarily a resource-curse. Authors of the most recent ECI report stress that resource wealthy nations such as Libya and Venezuela have been able to make gains by, “focusing on productive capabilities and strategically increasing the complexity of one’s exports, starting from nearby products that rely on similar capabilities to those currently present in the country.” [6] That is, a dramatical fall in ECI is not an inevitable outcome of resource wealth.

From the perspective of economic complexity, Australia starts this next phase of economic development behind the eight-ball: it has not managed to convert resource wealth into initiating (or keeping) diverse industries, nor in investing in a sophisticated skills agenda. Unfortunately, incentives for political leaders are weak since resource-led windfalls appear to generate great prosperity, but economic complexity analysis shows that this phenomenon is a chimera—the true health of our productive knowledge has been, for a time, masked.

Furthermore, investment in productive knowledge doesn’t often lend itself to smart photo opportunities or ribbon cutting—it’s far less dramatic than that.

So with an election looming, let’s hope we hear less of, “Go for growth”, and a whole lot more of, “Now for know-how!”

Dr Simon Angus is a Senior Lecturer in the Department of Economics. He has an interest in economic networks and is currently the co-ordinator of the Honours program at Monash for economics.

[1] Hidalgo, C. A., Klinger, B., Barabási, A.-L., & Hausmann, R. (2007). The Product Space Conditions the Development of Nations. Science, 317(5837), 482–487.

[2] Hidalgo, C. A., & Hausmann, R. (2009). The Building Blocks of Economic Complexity. Proceedings of the National Academy of Sciences of the United States of America, 106(26), 10570–10575

[3] Hausmann, R., & Hidalgo, C. A. (2011). The Network Structure of Economic Output. Journal of Economic Growth, 16(4), 309–342.

[4] Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Chung, S., Jimenez, J., Simoes, A., Yıldırım, M. A. (2011). The Atlas of Economic Complexity. Hollis, NH: Puritan Press.

[5] Figures and data taken from MIT Media Lab & Center for International Development. The Atlas of Economic Complexity. from http://atlas.cid.harvard.edu/

[6] Center for International Development (2015). New Growth Projections Predict the Rise of India, East Africa and Fall of Oil Economies [Press release]. Retrieved from http://atlas.cid.harvard.edu/rankings/growth-predictions/

Image: ‘Old key chain in the shape of a small Earth globe’ by Horia Varlan, https://flic.kr/p/7vkgwu. Licence at http://creativecommons.org/licenses/by/2.0.