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Economic Complexity: Measurement and Implications for Growth and Development
Type
applied research project
Start Date
01 February 2018
Status
scheduled
Description
The world today sees unprecedented levels of global economic integration, yet dramatic cross-country differences in terms of incomes. Such differences have profound consequences on the overall well-being of people: their economic conditions, health status, life expectancy, and general satisfaction. A better understanding of growth prospects of developed countries in a globalized world therefore remains one of the most important research questions in economics.
A key driver of long-run economic growth is the division of labor. Increased specialization, however, goes hand in hand with a higher complexity of economic activities: the more narrowly defined individual tasks are, the more different inputs are needed for production and, hence, the more will economic success rely on the availability and coordination of these inputs. From the perspective of an individual country embedded in global networks we can therefore think of economic development as the process of building up the capabilities to provide increasingly sophisticated inputs for production. This, of course, raises the question of how to best support countries in this process and what hinders some countries from acquiring such capabilities.
In the course of this project, we will address this issue from two different angles. First, we will work on measures of economic complexity as developed by Hidalgo et al. (2009) and Hausmann et al (2011a). These measures have been shown to be informative about both countries’ current economic strength and their future growth prospects. They allow assessing countries’ overall performance in complex production and are therefore highly influential in policy debates. Yet, their economic foundations and how they are reflected in the equilibrium of international trade are less clear. We will substantiate the use of these measures in applied development work by means of a Monte Carlo study that demonstrates their merits in the context of a stochastic general equilibrium model of international trade. We will then develop alternative measures for product complexity that combine theoretical reasoning on sources of comparative advantages with data on the economic strength of countries.
Second, we will analyze countries' potential to grow in the network of industries directly. We will consider a multi-country multi-industry framework where industries differ in their input requirements of tasks. Countries differ in the tasks they are capable of performing. Countries grow into new industries by building up such capabilities. The associated costs and benefits critically depend on the required learning and on the competitive environment. In other words, the growth prospects of a country are shaped by its own and competing countries’ current position in the network of industries. To shed light on these interdependencies, we will take our model to the data exploiting information on the network structure of industries as entailed in inter-industry labor flows (Neffke et al 2017). We will then use our structural framework to perform counterfactual analyses of industrial and trade policies.
Second, we will analyze countries' potential to grow in the network of industries directly. We will consider a multi-country multi-industry framework where industries differ in their input requirements of tasks. Countries differ in the tasks they are capable of performing. Countries grow into new industries by building up such capabilities. The associated costs and benefits critically depend on the required learning and on the competitive environment. In other words, the growth prospects of a country are shaped by its own and competing countries’ current position in the network of industries. To shed light on these interdependencies, we will take our model to the data exploiting information on the network structure of industries as entailed in inter-industry labor flows (Neffke et al 2017). We will then use our structural framework to perform counterfactual analyses of industrial and trade policies.
A key driver of long-run economic growth is the division of labor. Increased specialization, however, goes hand in hand with a higher complexity of economic activities: the more narrowly defined individual tasks are, the more different inputs are needed for production and, hence, the more will economic success rely on the availability and coordination of these inputs. From the perspective of an individual country embedded in global networks we can therefore think of economic development as the process of building up the capabilities to provide increasingly sophisticated inputs for production. This, of course, raises the question of how to best support countries in this process and what hinders some countries from acquiring such capabilities.
In the course of this project, we will address this issue from two different angles. First, we will work on measures of economic complexity as developed by Hidalgo et al. (2009) and Hausmann et al (2011a). These measures have been shown to be informative about both countries’ current economic strength and their future growth prospects. They allow assessing countries’ overall performance in complex production and are therefore highly influential in policy debates. Yet, their economic foundations and how they are reflected in the equilibrium of international trade are less clear. We will substantiate the use of these measures in applied development work by means of a Monte Carlo study that demonstrates their merits in the context of a stochastic general equilibrium model of international trade. We will then develop alternative measures for product complexity that combine theoretical reasoning on sources of comparative advantages with data on the economic strength of countries.
Second, we will analyze countries' potential to grow in the network of industries directly. We will consider a multi-country multi-industry framework where industries differ in their input requirements of tasks. Countries differ in the tasks they are capable of performing. Countries grow into new industries by building up such capabilities. The associated costs and benefits critically depend on the required learning and on the competitive environment. In other words, the growth prospects of a country are shaped by its own and competing countries’ current position in the network of industries. To shed light on these interdependencies, we will take our model to the data exploiting information on the network structure of industries as entailed in inter-industry labor flows (Neffke et al 2017). We will then use our structural framework to perform counterfactual analyses of industrial and trade policies.
Second, we will analyze countries' potential to grow in the network of industries directly. We will consider a multi-country multi-industry framework where industries differ in their input requirements of tasks. Countries differ in the tasks they are capable of performing. Countries grow into new industries by building up such capabilities. The associated costs and benefits critically depend on the required learning and on the competitive environment. In other words, the growth prospects of a country are shaped by its own and competing countries’ current position in the network of industries. To shed light on these interdependencies, we will take our model to the data exploiting information on the network structure of industries as entailed in inter-industry labor flows (Neffke et al 2017). We will then use our structural framework to perform counterfactual analyses of industrial and trade policies.
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Eprints ID
247503