The Eurozone, once celebrated as a ‘convergence mechanism’, has instead experienced economic and political divergence. Why have countries diverged in terms of economics and what can we do about it?
Complexity and development
Our project funded by Rebuilding Macroeconomics approaches these big questions with the concept of economic complexity developed by physicist and multi-disciplinary researcher Cesar Hidalgo and economist Ricardo Hausmann.
According to these authors socio-economic development is a collective learning process. Countries become wealthy if their members have developed the collective ability to engage in complex activities, such as, for example, producing and exporting complex products such as cars or computers. Countries without these collective abilities tend to remain poor, and export only simple products such as T-shirts or olives.
This ‘collective knowledge’ for producing complex goods is spread very unevenly across the Eurozone. For example, while Germany is able to produce complex products, such as sophisticated car engines, Greece has been particularly ‘successful’ in exporting very simple products like refined oil. In the long-run, the observed macroeconomic divergence can be traced back to differences in ‘technological capabilities’.
The obvious next question is: are poorer countries catching up in terms of capabilities? Unfortunately, empirical evidence suggests they are not. If we look at the trade patterns across Eurozone countries, we see that poorer countries simply import complex products, which are mainly produced and exported by richer countries.
How then should members in poorer countries learn to produce complex products if these products are simply largely imported at lower cost from abroad? Typically, richer countries have also shown little interest in shifting complex economic activities to poorer countries.
This ‘unequal exchange’ between richer and poorer countries has characterized Europe for decades, but it has become more and more pervasive – particularly after trade within the EU was liberalized (see here).
Evidence on the unequal distribution of capabilities and Hidalgo’s and Hausmann’s theory of complexity might help us answer the question posed at the start. That is, can the rising polarization in the Eurozone be explained by inequality of productive capabilities?
If the diagnosis is correct, policies are needed to address the polarization in terms of capabilities. But there are numerous challenges to developing the complexity approach so that it can supply concrete solutions and policy recommendations to the polarization problem in the EMU.
First, we still lack a macroeconomic theory of how countries accumulate the capabilities required to produce complex products. At a micro level we have a number of theories about how people and firms develop new ideas, but these theories have not yet been linked to a macroeconomic theory of development within a “consistent” model.
Second, the theory of economic complexity has been built exclusively on real trade flows, i.e., trade flows of goods. However, many economists have argued convincingly, that financial flows and the dynamics of current account imbalances are important when it comes to understanding the development patterns in the Eurozone.
These two kinds of flows are interlinked. For example, capital flows from rich to poor countries have boosted demand for products of the richer countries, thereby increasing real trade flows from the rich to poor countries. Therefore, it is desirable to consider both real and financial trade flows within one consistent model framework.
We try to address both challenges by developing a model of the EMU that helps to rationalize the relationship between long-run development and collective learning taking place within countries, while at the same time taking into account both real and financial flows among countries. This model will be a “stock-flow consistent and agent-based model” of economic development.
Our answer to these challenges
An agent-based model is a computational model of an artificial society composed of many heterogeneous economic agents interacting with each other. By simulating the interactions of these agents, one can conduct computational experiments and simulate, for example, the effects of industrial policies.
Why do we take this approach? As indicated earlier, we know that complexity is about how people with different knowledge pieces interact, and how these knowledge pieces are merged together to yield more powerful capabilities that are required for developing more complex products. We aim to model this process explicitly; hence, we need many different heterogeneous agents that are able to learn and to interact directly with each other. This is exactly what agent-based models were built for.
Why stock-flow consistent? This means that the origins and destinations of all financial transactions are recorded, and all financial assets belonging to one actor are equally recorded as a liability for another actor. This is important for many reasons (see here). For example, the learning activities of agents that are required to expand the capability set of a country require financial funding.
Moreover, it may be that the way such funds are raised is important: whether through external surpluses, via public or private debt, or mainly by attracting foreign investments. The long run outcomes may differ a lot. The same is true for the funds required for industrial policy. These differences can be compared within this framework.
Our approach will help us understand the conditions under which less developed regions can realize a sustainable catch-up process. It is the first of its kind and hopefully contributes not only to the academic discourse about complexity and development, but also points to practical policy solutions of one of the most worrying recent trends.