Cities and countries undergo constant structural transformation. Industries need many inputs, such as regulations, infrastructure or productive knowledge, which we call capabilities. And locations are successful in hosting industries insofar as the capabilities that they can provide. We propose a capabilities-based production model and an empirical strategy to measure the Sophistication of a product and the Production Ability of a location. We apply our framework to international trade data and employment data in the United States, recovering measures of Production Ability for countries and cities, and the 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 indicators of human capital and wages. Our model-based estimations predict product appearances and disappearances through the extensive margin.
We study the effects of local tariff drops for Mexican exports to the US on the local electoral performance of Andrés Manuel López Obrador (AMLO) in Mexico’s 2006 presidential election. In an effort to appeal to his rural base, AMLO proposed to unilaterally retain tariff exemptions on imported corn and beans, which were scheduled to drop under NAFTA by the end of 2008. This elevated protectionism in the public agenda during the campaign. We find that local economic gains due to export tariff drops under NAFTA between 1994 and 2001 led to a drop in AMLO’s local vote share gains in 2006. These effects are contingent to the 2006 election, as similar effects on local vote for the left are not found in previous or later elections. Results are robust to controls for local grain growing and Chinese competition. We predict that AMLO would have been elected in 2006 had protectionism not been a salient electoral issue. Our findings suggest export access gains due to globalization undermine local political preferences over national protectionist platforms.
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.
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.
Countries seldom grow rich by producing more of the same. Development implies changes in what countries produce. Structural transformation is the process by which countries move into new economic activities. In turn, new economic activities are the ones that are able to achieve higher levels of productivity, pay higher wages and increase the level of prosperity of a country’s population. Structural transformation is crucial for economic growth: countries that are able to upgrade their production and exports by moving into new and more complex economic activities tend to grow faster.
In economic systems, the mix of products that countries make or export has been shown to be a strong leading indicator of economic growth. Hence, methods to characterize and predict the structure of the network connecting countries to the products that they export are relevant for understanding the dynamics of economic development. Here we study the presence and absence of industries in international and domestic economies and show that these networks are significantly nested. This means that the less filled rows and columns of these networks' adjacency matrices tend to be subsets of the fuller rows and columns. Moreover, we show that their nestedness remains constant over time and that it is sustained by both, a bias for industries that deviate from the networks' nestedness to disappear, and a bias for the industries that are missing according to nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. We interpret the high level of nestedness observed in these networks in the context of the neutral model of development introduced by Hidalgo and Hausmann (2009). We show that the model can reproduce the high level of nestedness observed in these networks only when we assume a high level of heterogeneity in the distribution of capabilities available in countries and required by products. In the context of the neutral model, this implies that the high level of nestedness observed in these economic networks emerges as a combination of both, the complementarity of inputs and heterogeneity in the number of capabilities available in countries and required by products. The stability of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of the study of network properties in the evolution of economic networks.
Decades of research in ecology have shown that nestedness is a ubiquitous characteristic of both, biological and economic ecosystems. The dynamics of nestedness, however, have rarely been observed. Here we show that the nestedness of both, the network connecting countries to the products that they export and the network connecting municipalities to the industries that are present in them, remains constant over time. Moreover, we find that the conservation of nestedness is sustained by both, a bias for industries that deviate from the networks' nestedness to disappear, and a bias for the industries that are missing according to nestedness to appear. This makes the appearance and disappearance of individual industries in each location predictable. The conservation of nestedness in industrial ecosystems, and the predictability implied by it, demonstrates the importance of industrial ecosystems in the long term survival of economic activities.
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.