Publications

Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18th century, well-being in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to augment historical GDP per capita estimates. We publish our estimates with CI together with all collected source data in a comprehensive dataset.

We study the role of immigrants, emigrants, and locals in the historical formation of European knowledge agglomerations. We find that immigrants and emigrants are more likely to be associated with knowledge agglomerations than locals. We also find that immigrants and emigrants are more likely to be associated with knowledge agglomerations than locals. These findings suggest that migration can play an important role in the formation of knowledge agglomerations.

Despite the growing importance of the digital sector, research on economic complexity and its implications continues to rely mostly on administrative records, e.g. data on exports, patents, and employment, that fail to capture the nuances of the digital economy. In this paper we use data on the geography of programming languages used in open-source software projects to extend economic complexity ideas to the digital economy. We estimate a country's software economic complexity and show that it complements the ability of measures of complexity based on trade, patents, and research papers to account for international differences in GDP per capita, income inequality, and emissions. We also show that open-source software follows the principle of relatedness, meaning that a country's software entries and exits are explained by specialization in related programming languages. We conclude by exploring the diversification and development of countries in open-source software in the context of large language models. Together, these findings help extend economic complexity methods and their policy considerations to the digital sector.

Despite global efforts to harmonize international trade statistics, our understanding of digital trade and its implications remains limited. Here, we introduce a method to estimate bilateral exports and imports for dozens of sectors starting from the corporate revenue data of large digital firms. This method allows us to provide estimates for digitally ordered and delivered trade involving digital goods (e.g. video games), productized services (e.g. digital advertising), and digital intermediation fees (e.g. hotel rental), which together we call digital products. We use these estimates to study five key aspects of digital trade. We find that, compared to trade in physical goods, digital product exports are more spatially concentrated, have been growing faster, and can offset trade balance estimates, like the United States trade deficit on physical goods. We also find that countries that have decoupled economic growth from greenhouse gas emissions tend to have larger digital exports and that digital exports contribute positively to the complexity of economies. This method, dataset, and findings provide a new lens to understand the impact of international trade in digital products.

Urban outputs, from economy to innovation, are known to grow as a power of a city's population. But, since large cities tend to be central in transportation and communication networks, the effects attributed to city size may be confounded with those of intercity connectivity. Here, we map intercity networks for the world's two largest economies (the United States and China) to explore whether a city's position in the networks of communication, human mobility, and scientific collaboration explains variance in a city's patenting activity that is unaccounted for by its population. We find evidence that models incorporating intercity connectivity outperform population-based models and exhibit stronger predictive power for patenting activity, particularly for technologies of more recent vintage (which we expect to be more complex or sophisticated). The effects of intercity connectivity are more robust in China, even after controlling for population, GDP, and education, but not in the United States once adjusted for GDP and education. This divergence suggests distinct urban network dynamics driving innovation in these regions. In China, models with social media and mobility networks explain more heterogeneity in the scaling of innovation, whereas in the United States, scientific collaboration plays a more significant role. These findings support the significance of a city's position within the intercity network in shaping its success in innovative activities.

In recent years economic complexity has grown into an active field of fundamental and applied research. Yet, despite important advances, the policy implications of economic complexity can remain unclear or misunderstood. Here I organize the policy implications of economic complexity in a framework grounded on 4 Ws: what approaches, focused on identifying target activities and/or locations; when approaches, focused on the timing of related and unrelated diversification; where approaches, focused on the geographic diffusion of knowledge; and who approaches, focused on the role played by agents of structural change. The goal of this paper is to provide a framework that groups, organizes, and clarifies the policy implications of economic complexity and facilitates its continued use in regional and international development.

To achieve inclusive green growth, countries need to consider a multiplicity of economic, social, and environmental factors. These are often captured by metrics of economic complexity derived from the geography of trade, thus missing key information on innovative activities. To bridge this gap, we combine trade data with data on patent applications and research publications to build models that significantly and robustly improve the ability of economic complexity metrics to explain international variations in inclusive green growth. We show that measures of complexity built on trade and patent data combine to explain future economic growth and income inequality and that countries that score high in all three metrics tend to exhibit lower emission intensities. These findings illustrate how the geography of trade, technology, and research combine to explain inclusive green growth.

Economic diversification—the process by which locations enter new economic activities—is known to be a combination of related and unrelated diversification. Related diversification is—on average—more frequent, but unrelated diversification is nevertheless considered important to avoid economic lock-in. Here, we study the frequency and timing of unrelated diversification using two international trade datasets at the country level. We find that related diversification is more frequent for countries at low levels of development but becomes less frequent as countries climb the complexity ladder. These findings contribute to our understanding of the role of relatedness in the diversification of economies at different levels of complexity.

The principle of relatedness allows us to explore the likelihood that territories diversify their current technological portfolios based on the global co-occurrence patterns of technologies. Countries that excel at developing semiconductors should develop mobile phones because both technologies require similar endogenous capacities, including scientific knowledge...

Industrial diversification depends on spillovers from related industries and nearby regions, yet their interaction remains largely unclear. We study economic diversification in China during the period 1990–2015 and present supportive evidence on both spillover channels. We add to the literature by showing that these two channels behave as substitutes when explaining new entries and exits, and by using acceleration campaigns of high-speed rail to address some endogeneity concerns with regional spillovers. Our findings confirm the role of relatedness and geographical distance in the diffusion of economic capabilities and support the idea that improvements in transportation can facilitate the diffusion of productive capabilities.

Economic complexity methods have become popular tools in economic geography, international development and innovation studies. Here, economic complexity theory and applications are reviewed, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions and income inequality.

Advances in computational urbanism have stimulated the rise of generative and parametric approaches to urban design. Yet, most generative and parametric ap- proaches focus on physical characteristics, such as a neighborhoods walkability, energy efficiency, and urban form.

During the last four decades, digital technologies have disrupted many industries. Car control systems have gone from mechanical to digital. Telephones have changed from sound boxes to portable computers. But have the firms that digitized their products and services become more valuable than firms that didn’t? Here we introduce the construct of digital proximity, which considers the interdependent activities of firms linked in an economic network. We then explore how the digitization of products and services affects a company’s Tobin's q—the ratio of market value over assets—a measure of the intangible value of a firm. Our panel regression methods and robustness tests suggest the positive influence of a firm’s digital proximity on its Tobin’s q. This implies that firms able to come closer to the digital sector have increased their intangible value compared to those that have failed to do so. These findings contribute a new way of measuring digitization and its impact on firm performance that is complementary to traditional measures of information technology (IT) intensity.

Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most inno- vative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less--complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that com- plexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and tech- nologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings sug- gest that the growth of spatial inequality may be connected to the increasing complexity of the economy.

During the last two decades, two important contributions have reshaped our understanding of international trade. First, countries trade more with those with whom they share history, language, and culture, suggesting that trade is limited by information frictions. Second, countries are more likely to start exporting products that are related to their current exports, suggesting that shared capabilities and knowledge diffusion constrain export diversification. Here, we join both of these streams of literature by developing three measures of bilateral relatedness and using them to ask whether the destinations to which a country will increase its exports of a product are predicted by these forms of relatedness.

How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry-, occupation-, and location-specific knowledge carried by workers from one establishment to the next, using a dataset summarizing the individual work history for an entire country. We study pioneer firms—firms operating in an industry that was not present in a region—because the success of pioneers is the basic unit of regional economic diversification. We find that the growth and survival of pioneers increase significantly when their first hires are workers with experience in a related industry and with work experience in the same location, but not with past experience in a related occupation. We compare these results with new firms that are not pioneers and find that industry-specific knowledge is significantly more important for pioneer than for nonpioneer firms. To address endogeneity we use Bartik instruments, which leverage national fluctuations in the demand for an activity as shocks for local labor supply. The instrumental variable estimates support the finding that industry-specific knowledge is a predictor of the survival and growth of pioneer firms. These findings expand our understanding of the micromechanisms underlying regional economic diversification.

Technological innovation seems to be dominated by chance. But a new mathematical analysis suggests we might be able to anticipate when seemingly useless technologies become keystones of more complex environments.

The idea that skills, technology, and knowledge, are spatially concentrated, has a long academic tradition. Yet, only recently this hypothesis has been empirically formalized and corroborated at multiple spatial scales, for different economic activities, and for a diversity of institutional regimes. The new synthesis is an empirical principle describing the probability that a region enters—or exits—an economic activity as a function of the number of related activities present in that location. In this paper we summarize some of the recent empirical evidence that has generalized the principle of relatedness to a fact describing the entry and exit of products, industries, occupations, and technologies, at the national, regional, and metropolitan scales. We conclude by describing some of the policy implications and future avenues of research implied by this robust empirical principle.

Countries and cities are likely to enter economic activities that are related to those that are already present in them. Yet, while these path dependencies are universally acknowledged, we lack an understanding of the diversification strategies that can optimally balance the development of related and unrelated activities. Here, we develop algorithms to identify the activities that are optimal to target at each time step. We find that the strategies that minimize the total time needed to diversify an economy target highly connected activities during a narrow and specific time window. We compare the strategies suggested by our model with the strategies followed by countries in the diversification of their exports and research activities, finding that countries follow strategies that are close to the ones suggested by the model. These findings add to our understanding of economic diversification and also to our general understanding of diffusion in networks.

Are there Marshallian externalities in job search? We study how workers who lose their jobs in establishment closures in Germany cope with their loss of employment. About a fifth of these displaced workers do not return to social-security covered employment within the next three years. Among those who do get re-employed, about two-thirds leave their old industry and one-third move out of their region. However, which of these two types of mobility responses workers will choose depends on the local industry mix in ways that are suggestive of Marshallian benefits to job search. In particular, large concentrations of one’ s old industry make finding new jobs easier: in regions where the pre-displacement industry is large, displaced workers suffer relatively small earnings losses and find new work faster. In contrast, large local industries skill-related to the pre-displacement industry increase earnings losses but also protect against long-term unemployment. Analyzed through the lens of a job-search model, the exact spatial and industrial job-switching patterns reveal that workers take these Marshallian externalities into account when deciding how to allocate search efforts among industries.

A country’s mix of products predicts its subsequent pattern of diversification and economic growth. But does this product mix also predict income inequality? Here we combine methods from econometrics, network science, and economic complexity to show that countries exporting complex products—as measured by the Economic Complexity Index—have lower levels of income inequality than countries exporting simpler products. Using multivariate regression analysis, we show that economic complexity is a significant and negative predictor of income inequality and that this relationship is robust to controlling for aggregate measures of income, institutions, export concentration, and human capital.

Recent work has shown that a country's productive structure constrains its level of economic growth and income inequality. In this paper, we compare the productive structure of countries in Latin American and the Caribbean (LAC) with that of China and other High-Performing Asian Economies (HPAE) to expose the increasing gap in their productive capabilities. Moreover, we use the product space and the Product Gini Index to reveal the structural constraints on income inequality. Our network maps reveal that HPAE have managed to diversify into products typically produced by countries with low levels of income inequality, while LAC economies have remained dependent on products related with high levels of income inequality. We also introduce the Xgini, a coefficient that captures the constraints on income inequality imposed by the mix of products a country makes. Finally, we argue that LAC countries need to emphasize a smart combination of social and economic policies to overcome the structural constraints for inclusive growth.

What is economic growth? And why, historically, has it occurred in only a few places? Previous efforts to answer these questions have focused on institutions, geography, finances, and psychology. But according to MIT's antidisciplinarian César Hidalgo, understanding the nature of economic growth demands transcending the social sciences and including the natural sciences of information, networks, and complexity. To understand the growth of economies, Hidalgo argues, we first need to understand the growth of order.

The search to identify factors that might explain the great heterogeneity in economic development and the quality of life of countries or regions always challenged social scientists. This is particularly important in Brazil, a country characterized by huge and persistent inequalities. One of the most striking faces of Brazilian inequality is regional inequality, with the South and Southeast regions concentrating most of the economic activity and income and providing the best levels of education, health, infrastructure and quality of life. As an alternative approach in the debate about the differences in growth patterns between countries, the Product Space methodology use export data to establish associations for identifying new products that can leverage the economic development of each locality, considering what it already exports. The Product Space methodology was applied to foreign trade data of Brazilian municipalities. The paper analyzes the evolution of Brazilian exports and sophistication in the period 2002-2014, in order to also identify whether there is evidence of spatial autocorrelation in the level of sophistication of the municipalities. From the exploratory analysis of spatial data exports, diversity and sophistication in all Brazilian municipalities, this paper contributes to the debate about regional inequality in Brazil.

The Amenity Space, was built by using a dataset summarizing the precise location of millions of amenities, introducing a clustering algorithm to identify neighborhoods, and then using the identified neighborhoods to map the probability that two amenities will be co-located in one of them. The Amenity Space is used to build a recommender system that identifies the amenities missing in a neighborhood given its current pattern of specialization.

Over two decades since independence, upper-middle income Kazakhstan—a large, landlocked, sparsely populated but resource-rich country—remains an economy in transition.

The literature on knowledge diffusion shows that knowledge decays strongly with distance. In this paper we document that the probability that a product is added to a country's export basket is, on average, 65% larger if a neighboring country is a successful exporter of that same product.

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.

Much of the analysis of economic growth has focused on the study of aggregate output. Here, we deviate from this tradition and look instead at the structure of output embodied in the network connecting countries to the products that they export.

We introduce The Economic Complexity Observatory, a tool for helping users understand the evolution of countries' productive structures and trade partners. Here we bridge the gap of harnessing the raw computational power of cycling through thousands of entries of data with the analytical, decision making qualities of the human mind through the use of information visualization “apps”.

What are Southern and East Africa’s industrial opportunities? In this article we explore this question by using the product space to study the productive structure of five Southern and East African countries: Kenya, Mozambique, Rwanda, Tanzania, and Zambia.

How does the productive structure of countries' changes over time? In this paper we explore this question by combining techniques of networks science with 42 years of trade data and find that, while the Product Space remains relatively stable during this period, the dynamics of countries' productive structures is characterized by a few highly dynamic economies.

For Adam Smith, wealth was related to the division of labor. As people and firms specialize in different activities, economic efficiency increases, suggesting that development is associated with an increase in the number of individual activities and with the complexity that emerges from the interactions between them.

Does the type of product a country exports matter for subsequent economic performance?

Economies grow by upgrading the products they produce and export. The technology, capital, institutions, and skills needed to make newer products are more easily adapted from some products than from others.