Production is shaped by capability requirements of products and availability of these capabilities across locations. We propose a capabilities based production model and an empirical strategy to measure product sophistication and location’s production ability. We apply our framework to international trade data, and employment data in the US, recovering measures of production ability for countries and cities, and sophistication of products and industries. We show that both country and city level measures have a strong correlation with income, and economic growth at different time horizons. Product sophistication is positively correlated with measures like education and training needed in the industry. Our model-based estimations also predict the diversification patterns through the extensive margin.
Income per capita in Uganda has doubled in the last 20 years. This remarkable performance has been buoyed by significant aid flows and large external imbalances. Economic growth has been concentrated in non-tradable activities leading to growing external imbalances and a growing gap between rural and urban incomes. Future growth will depend on achieving sufficient export dynamism. In addition, growth faces a number of other challenges: low urbanization rate, rapid rural population growth and high dependency ratios. However, both the dependency ratio and fertility rates have begun to decline recently. Rural areas are also severely overcrowded with low-productivity subsistence agriculture as a pervasive form of production. Commercial agriculture has great possibilities to increase output, but as the sector improves its access to capital, inputs and technology it will shed jobs rather than create them.
These challenges combined tell us that future growth in Uganda will require a rapid rate of export growth and economic diversification. The country faces the prospect of an oil boom of uncertain size and timing. It could represent an important stepping stone to achieve external sustainability, expanded income and infrastructure and a greater internal market. However, as with all oil booms, the challenges include avoiding the Dutch disease, managing the inevitable volatility in oil incomes and avoiding inefficient specialization in oil. Policies that set targets for the non-oil deficit could help manage some of these effects, but a conscious strategy to diversify would still be needed.
The best strategy is therefore to use the additional oil revenue and accompanying investments to promote a diversification strategy that is sustainable. To determine how to encourage such a transformation, we draw on a new line of research that demonstrates how development seldom implies producing more of the same. Instead, as countries grow, they tend to move into new industries, while they also increase productivity in existing sectors. In this report, we analyze what those new industries might be for Uganda.
To do so, we first look to those products which balance the desire to increase the diversification and complexity of production, while not over-stretching existing capabilities. These include mostly agricultural inputs, such as agrochemicals and food processing. In addition, Uganda should concurrently develop more complex industries, such as construction materials, that are reasonably within reach of current capabilities and will be in great demand in the context of an oil boom. Here, the fact that Uganda is landlocked and faces high import costs will provide natural protection to the expanding demand in Uganda and neighboring countries. We conclude with a discussion of the government policies that will support Uganda in developing new tradable industries.
The comparative advantage of a location shapes its industrial structure. Current theoretical models based on this principle do not take a stance on how comparative advantages in different industries or locations are related with each other, or what such patterns of relatedness might imply about the underlying process that governs the evolution of comparative advantage. We build a simple Ricardian-inspired model and show this hidden information on inter-industry and inter-location relatedness can be captured by simple correlations between the observed patterns of industries across locations or locations across industries. Using the information from related industries or related locations, we calculate the implied comparative advantage and show that this measure explains much of the location’s current industrial structure. We give evidence that these patterns are present in a wide variety of contexts, namely the export of goods (internationally) and the employment, payroll and number of establishments across the industries of subnational regions (in the US, Chile and India). The deviations between the observed and implied comparative advantage measures tend to be highly predictive of future industry growth, especially at horizons of a decade or more; this explanatory power holds at both the intensive as well as the extensive margin. These results suggest that a component of the long-term evolution of comparative advantage is already implied in today’s patterns of production.
How does an economy grow? What is economic complexity? How do we determine where countries can start to diversify their production? The growth theories of Ricardo Hausmann and others at CID are explained in this 5 page briefing.
It has been two years since we published the first edition of The Atlas of Economic Complexity. "The Atlas," as we have come to refer to it, has helped extend the availability of tools and methods that can be used to study the productive structure of countries and its evolution.
Many things have happened since the first edition of The Atlas was released at CID's Global Empowerment Meeting, on October 27, 2011. The new edition has sharpened the theory and empirical evidence of how knowhow affects income and growth and how knowhow itself grows over time. In this edition, we also update our numbers to 2010, thus adding two more years of data and extending our projections. We also undertook a major overhaul of the data. Sebastián Bustos and Muhammed Yildirim went back to the original sources and created a new dataset that significantly improves on the one used for the 2011 edition. They developed a new technique to clean the data, reducing inconsistencies and the problems caused by misreporting. The new dataset provides a more accurate estimate of the complexity of each country and each product. With this improved dataset, our results are even stronger.
All in all, the new version of The Atlas provides a more accurate picture of each country’s economy, its "adjacent possible" and its future growth potential.
Over the past two centuries, mankind has accomplished what used to be unthinkable. When we look back at our long list of achievements, it is easy to focus on the most audacious of them, such as our conquest of the skies and the moon. Our lives, however, have been made easier and more prosperous by a large number of more modest, yet crucially important feats. Think of electric bulbs, telephones, cars, personal computers, antibiotics, TVs, refrigerators, watches and water heaters. Think of the many innovations that benefit us despite our minimal awareness of them, such as advances in port management, electric power distribution, agrochemicals and water purification. This progress was possible because we got smarter. During the past two centuries, the amount of productive knowledge we hold expanded dramatically. This was not, however, an individual phenomenon. It was a collective phenomenon. As individuals we are not much more capable than our ancestors, but as societies we have developed the ability to make all that we have mentioned – and much, much more.
This paper establishes a robust stylized fact: changes in the revealed comparative advantage of nations are governed by the pattern of relatedness of products at the global level. As countries change their export mix, there is a strong tendency to move towards related goods rather than to goods that are farther away. The pattern of relatedness of products is only very partially explained by similarity in broad factor or technological intensities, suggesting that the relevant determinants are much more product-specific. Moreover, the pattern of relatedness of products exhibits very strong heterogeneity: there are parts of this ‘product space’ that are dense while others are sparse. This implies that countries that are specialized in a dense part of the product space have an easier time at changing their revealed comparative advantage than countries that are specialized in more disconnected products.
In this paper we examine the product space and its consequences for the process of structural transformation. We argue that the assets and capabilities needed to produce one good are imperfect substitutes for those needed to produce other goods, but the degree of asset specificity varies widely. Given this, the speed of structural transformation will depend on the density of the product space near the area where each country has developed its comparative advantage. While this space is traditionally assumed to be smooth and continuous, we find that in fact it is very heterogeneous, with some areas being very dense and others quite sparse. We develop a measure of revealed proximity between products using comparative advantage in order to map this space, and then show that its heterogeneity is not without consequence. The speed at which countries can transform their productive structure and upgrade their exports depends on having a path to nearby goods that are increasingly of higher value.