Academic Research

Economic complexity is an active field with a growing number of methodologies and applications. Among the different paradigms, the Hausmann-Hidalgo economic complexity framework offers a way to quantify the sophistication and productive knowledge embedded in an economy. In this work, we provide an overview of its foundational concepts, empirical applications, policy uses, and directions for future research. We aim to equip readers with a basic understanding of this framework in simple words and to help them navigate the vast literature. We argue that the Hausmann-Hidalgo economic complexity serves as a flexible framework for understanding the dynamics of knowledge diversification across multiple economic domains and provides a starting point for the design of place-based policies. 

Japan remains one of the world’s most technologically sophisticated economies, yet its labor productivity has been stagnant for more than two decades. This paper investigates the apparent contradiction between Japan’s high R&D intensity and its weak productivity performance by examining the allocation, composition, and effectiveness of innovation across industries. Using industry-level data from the OECD, patent-level data linked across technology and industry classifications, and a set of nine technological taxonomies, we document that Japan disproportionately concentrates R&D in mid-technology manufacturing sectors—such as motor vehicles, electrical equipment, and chemicals—that generate relatively low productivity spillovers. High-technology sectors, including ICT, pharmaceuticals, scientific R&D, and advanced digital services, receive a significantly smaller share of investment and exhibit much higher productivity contributions in other countries. We further show that Japan’s indirect, tax-based system of R&D support reinforces this equilibrium by favoring large incumbents and under-supporting SMEs. We conclude by assessing the potential of Japan’s new 17-sector strategy to reorient the innovation system toward frontier technologies.

The energy transition offers countries that can manufacture clean energy technologies substantial opportunities for sustainable economic growth. This paper provides a framework for context-aware industrial policy by applying economic complexity theory to a newly constructed dataset of twelve key clean energy supply chains (CESCs). We find that CESCs are diverse but highly interdependent; they are also growing faster and are more concentrated than other industries. CESCs exhibit substantial entry, exit and competitive churn, and countries are more likely to enter CESC industries that are related to their existing productive capabilities. We also explore changing global competitiveness and country positioning in these industries, and draw out implications of these patterns for industrial policymakers.

Economic development hinges on structural change, that is, transformations in what an economy produces. The field of economic complexity has investigated this process through two related but distinct branches: one studying how economies diversify, the other how the complexity of an economy is reflected in its output. However, a formal connection between these approaches, and their relationship to classic accounts of structural transformation (for example, from agriculture to manufacturing), remains unclear. Here we introduce a simple dynamical model that links these perspectives through one core idea: economies diversify preferentially into activities related to those they already do. Studying this model yields three main results: It generates quantities resembling economic complexity metrics, suggests these metrics summarize long-term structural change rather than directly infer an economy’s complexity, and reproduces stylized facts of development. Our framework formally connects the field’s conceptual strands, bridges short and long timescales of change, and adds granularity to classic descriptions of development.

Diversified economies are critical for cities to sustain their growth and development, but they are also costly because diversification often requires expanding a city’s capability base. We analyze how cities manage this trade-off by measuring the coherence of the economic activities they support, defined as the technological distance between randomly sampled productive units in a city. We use this framework to study how the US urban system developed over almost two centuries, from 1850 to today. To do so, we rely on historical census data, covering over 600M individual records to describe the economic activities of cities between 1850 and 1940, as well as 8 million patent records and detailed occupational and industrial profiles of cities for more recent decades. Despite massive shifts in the economic geography of the United States over this 170-year period, average coherence in its urban system remains unchanged. Moreover, across different time periods, datasets, and relatedness measures, coherence falls with city size at the exact same rate, pointing to constraints to diversification that are governed by a city’s size in universal ways.

We develop a novel framework to study the interaction between monetary policy and trade. Our New Keynesian open economy model incorporates international production networks, sectoral heterogeneity in price rigidities, and trade distortions. We decompose the general equilibrium response to trade shocks into distinct channels that account for demand shifts, policy effects, exchange rate adjustments, expectations, price stickiness, and input–output linkages. Tariffs act simultaneously as demand and supply shocks, leading to endogenous fragmentation through changes in trade and production network linkages. We show that the net impact of tariffs on domestic inflation, output, employment, and the dollar depends on the endogenous monetary policy response in both the tariff-imposing and tariff-exposed countries, within a global general equilibrium framework. Our quantitative exercise replicates the observed effects of the 2018 tariffs on the U.S. economy and predicts a 1.6 pp decline in U.S. output, a 0.8 pp rise in inflation, and a 4.8% appreciation of the dollar in response to a retaliatory trade war linked to tariffs announced on “Liberation Day.” Tariff threats, even in the absence of actual implementation, are self-defeating— leading to a 4.1% appreciation of the dollar, 0.6% deflation, and a 0.7 pp decline in output, as agents re-optimize in anticipation of future distortions. Dollar appreciates less or even can depreciate under retaliation, tariff threats, and increased global uncertainty.

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Various factors influence why some countries are more open to immigration than others. Policy is only one of them. We design country-specifc measures of openness to immigration that aim to capture de facto levels of openness to immigration, complementing existing de jure measures of immigration, based on enacted immigration laws and policy measures. We estimate these for 148 countries and three years (2000, 2010, and 2020). For a subset of countries, we also distinguish between openness towards tertiary-educated migrants and less than tertiary-educated migrants. Using the measures, we show that most places in the World today are closed to immigration, and a few regions are very open. The World became more open in the first decade of the millennium, an opening mainly driven by the Western World and the Gulf countries. Moreover, we show that other factors equal, countries that increased their openness to immigration, reduced their old-age dependency ratios, and experienced slower real wage growth, arguably a sign of relaxing labor and skill shortages.

Explore the country rankings in our interactive visualization website and learn more about the project, Leveraging the Global Talent Pool to Jumpstart Prosperity in Emerging Economies.

We introduce a record linkage algorithm that allows one to (1) efficiently match hundreds of millions of records based not just on demographic characteristics but also name similarity, (2) make statistical choices regarding the trade-off between match quality and representativity and (3) automatically generate a ground truth of true and false matches, suitable for training purposes, based on networked family relationships. Given the recent availability of hundreds of millions of digitized census records, this algorithm significantly reduces computational costs to researchers while allowing them to tailor their matching design towards their research question at hand (e.g. prioritizing external validity over match quality). Applied to U.S Census Records from 1850 to 1940, the algorithm produces two sets of matches, one designed for representativity and one designed to maximize the number of matched individuals. At the same level of accuracy as commonly used methods, the algorithm tends to have a higher level of representativity and a larger pool of matches. The algorithm also allows one to match harder-to-match groups with less bias (e.g. women whose names tend to change over time due to marriage). 

Do refugees settle in destinations that are ecologically similar to their origins? We assess the relevance of “climate matching” theories of migration for Venezuelan refugees in South America. Leveraging social media data, we build and validate the first local bilateral matrix of Venezuelan flows across the region. We measure bilateral ecological similarities in terms of temperature, precipitation, elevation, and distance to the coastline. Performing Poisson Pseudo-Maximum Likelihood gravity models of migration, we show that Venezuelan flows are more likely between ecologically similar areas. Model predictions explain independent measurements of Venezuelans’ settlement choices at both bilateral and destination levels. 

Technological know-how in a country shapes its growth potential and competitiveness. Scientific publications, patents, and international trade data offer complementary insights into how ideas from science, technology, and production evolve, combine, and are transformed into capabilities. Analyzing their trajectories enables a more comprehensive and multifaceted understanding of the whole innovation process, from generating ideas to internationally commercializing products. We analyze the production patterns in these three domains, documenting the differences between advanced and emerging market economies. We find that future income, patenting, and publishing growth correlate with the economic complexity indices calculated from these domains. Capabilities embedded in the country also shape future diversification opportunities and make the innovation process path dependent. Lastly, we also show that diversification opportunities can be inferred across innovation domains.

Recent geopolitical challenges have revived the implementation of industrial and innovation policies. Ongoing discussions focus on supporting cutting-edge industries and strategic technologies but hardly pay attention to their impact on economic growth. In light of this, we discuss the design of innovation policies to address current development challenges while considering the complex nature of productive activities. Our approach conceives economic development and technological progress as a process of accumulation and diversification of knowledge. This process is limited by the tacit nature of knowledge and by countries’ binding constraints to growth. Consequently, effective innovation policies should be place-based and multidimensional, leveraging countries’ existing capabilities and addressing countries’ current problems. This contrasts policies that lead to economic efficiencies, such as copying other countries’ solutions to problems that countries do not currently have.

The purpose of the GLocal dataset is to enable research in international development that requires both global scope and local precision. Leveraging modern geospatial analysis tools, we process a diverse array of sources to provide researchers with a growing set of economic, demographic, ecological and socio-political variables for geographic units relevant to public policy. We provide separate data files for different levels of administrative and periodic aggregation, along with ad-hoc files with more detailed information on specific topics. In this data descriptor paper, we discuss both our data processing methodologies and validation pipelines, and provide a short case study to illustrate the research potential of the dataset. We also introduce a simple web app, glocal.streamlit.app, which offers a user-friendly interface for exploring and visualizing the dataset. Given the growing number of public and granular sources of relevance for international development research, we hope to continue adding features and expand the GLocal dataset in the future.

Economic development is a path-dependent process in which countries accumulate capabilities that allow them to move into more complex products and industries. Inspired by a theory of capabilities that explains which countries produce which products, these diversification dynamics have been studied in great detail in the literature on economic complexity analysis. However, so far, these capabilities have remained latent and inference is drawn from product spaces that reflect economic outcomes: which products are often exported in tandem. Borrowing a metaphor from biology, such analysis remains phenotypic in nature. In this paper we develop a methodology that allows economic complexity analysis to use capabilities directly. To do so, we interpret the capability requirements of industries as a genetic code that shows how capabilities map onto products. We apply this framework to construct a genotypic product space and to infer countries’ capability bases. These constructs can be used to determine which capabilities a country would still need to acquire if it were to diversify into a given industry. We show that this information is not just valuable in predicting future diversification paths and to advance our understanding of economic development, but also to design more concrete policy interventions that go beyond targeting products by identifying the underlying capability requirements. 

Should exports be an important focus of economic growth strategies? The Washington Consensus, summarised by John Williamson some 35 years ago, would have answered in the negative. In the 1980s, when the consensus was forged, many countries had highly protective trade regimes, multiple exchange rates with large black-market premia, interest rate controls, and high inflation. The consensus was that if countries unified exchange rates, reduced barriers to trade, and brought inflation under control, exports would follow naturally. According to the Lerner symmetry theorem, import tariffs are equivalent to a tax on exports. Exchange controls act as an additional tax by forcing exporters to sell their earnings at an artificially appreciated exchange rate. If such policy-induced distortions were eliminated, the Washington Consensus suggested, exports would naturally reach their efficient level.

Today, many countries have unified their exchange rates, eliminated exchange controls, brought inflation to single digits, reduced trade barriers, and signed free trade agreements with many of their main trade partners, and yet, the median country has not narrowed its income gap with the United States. Export performance matters for growth, with countries that grow exhibiting more than proportional export growth. In many developing and emerging economies, growth is highly correlated with exogenous movements in their export prices and on fluctuations in international capital flows. Moreover, sustained fast-growing economies change the composition of their export basket substantially towards new, more complex products. 

Regional differences in growth and export trajectories confirm these observations. Countries in East Asia – including China — have managed rapid changes to their export baskets, increased their global export shares in new industries, and achieved fast growth. In Latin America, by contrast, even good performers like Chile, Colombia and Peru stabilised inflation, opened their economies to international trade (tariffs are negligible and they have signed numerous free trade agreements) and capital flows. Yet, they have been unable to diversify their export baskets and achieve sustained growth. The experiences of many nations in Africa and the Middle East resemble those of Latin America.

In this paper, I argue that a focus on exports, both at the intensive margin (where existing products increase their volume), but especially at the extensive margin (where new products start being exported), can help countries figure out what policies to adopt in order to achieve sustained growth. I present five stylised facts about growth and its trends in the decades that followed the Washington Consensus. 

Between the mid-19th and mid-20th centuries, the US transformed from an agricultural economy to the frontier in science, technology, and industry. We study how the US transitioned from traditional craftsmanship-based to today’s science-based innovation. To do so, we digitize half a million pages of patent yearbooks that describe inventors, organizations, and technologies on over 1.6M patents and add demographic information from US census records and information on corporate research activities from large-scale repeated surveys on industrial research labs. Starting in 1920, the 19th-century craftsmanship-based invention was, within just 20 years, overtaken by a rapidly emerging new system based on teamwork and a new specialist class of inventors, engineers. This new system relied on a social innovation: industrial research labs. These labs supported high-skill teamwork, replacing the collaborations within families with professional ties in firms and industrial research labs. This shift had wide-ranging consequences. It not only altered the division of labor in invention, but also reshaped the geography of innovation, reestablishing large cities as epicenters of technological progress and introduced new barriers to patenting for women and foreign-born inventors that have persisted into the 21st century.

In this interview article, we embark on a fascinating journey through time alongside the winners of the 2023 DRUID Best Paper Award. DRUID, an annual research conference renowned as the hub of cutting-edge research on innovation and the dynamics of structural, institutional, and geographic change, bestows this award on the most innovative and exceptional conference submission. As longstanding allies of DRUID, Industry and Innovation offers an exclusive peek behind the curtains, unveiling the untold stories that underlie award-winning research.

In 2023, this coveted DRUID prize was awarded to a paper by Ljubica Nedelkoska, Shreyas Gadgin Matha, James McNerney, Andre Assumpcao, Dario Diodato, and Frank Neffke. Their work stands out through an impressive data collection effort and the exploration of a compelling and urgent research question – how technological change has impacted the gender pay gap. Throughout this interview, the author team takes us down memory lane, retelling the story behind their research project. On this journey through time, we trace the genesis of the authors’ innovative ideas and the intricate pathways they navigated in their quest to understand the past as a means of unravelling the future of work and its implications for gender inequality in the labour market. This journey not only takes us back in time but also points to potential avenues for future research and open questions that lie ahead.

Do women apply more for jobs when they know the hiring probability of female job seekers directly from employers? I implemented a randomized control trial and a double-incentivized resume rating to elicit the preferences of employers and job seekers for candidates and vacancies in Iraq. The treatment reveals the job offer rate for women, calculated using the employers’ selection of women divided by the total number of female candidates. After revealing the treatment, the women applied for jobs by three more percentage points than the men in the control group. This paper highlights the value of revealing employers’ preferences to improve the match between female candidates and employers when women underestimate the chances of finding a job. 

We study whether, when, and how better connectivity through nonstop flights leads to positive innovation outcomes for firms in the global context. Using unique data of all flights emanating from 5,015 airports around the globe from 2005 to 2015 and exploiting a regression discontinuity framework, we report that a 10% increase in nonstop flights between two locations leads to a 3.4% increase in citations and a 1.4% increase in the production of collaborative patents between those locations. This effect is driven primarily by firms as opposed to academic institutions. We further study the characteristics of firms and firm locations that are salient to the relation between nonstop flights and innovation outcomes across countries. Using a gravity model, we posit and find that the positive effect of nonstop flights on innovation is stronger for firms and subsidiaries with greater innovation mass (e.g., stocks of inventors and R&D spending), located in innovation hubs or countries that are deemed technology leaders, and that are separated by large cultural or temporal distance.

Research Summary: The Role of Nonstop Flights in Fostering Global Firm Innovation

We analyze the effects of bans on exports at the level of 5,000 products and show how our results can inform economic sanctions against Russia after its invasion of Ukraine. We begin with characterizing export restrictions imposed by the EU and the US until mid May 2022. We then propose a theoretically-grounded criterion for targeting export bans at the 6-digit HS level. Our results show that the cost to Russia are highly convex in the market share of the sanctioning parties, i.e., there are large benefits from coordinating export bans among a broad coalition of countries. Applying our results to Russia, we find that sanctions imposed by the EU and the US are not systematically related to our arguments once we condition on Russia’s total imports of a product from participating countries. Quantitative evaluations of the export bans show (i) that they are very effective with the welfare loss typically ∼100 times larger for Russia than for the sanctioners. (ii) Improved coordination of the sanctions and targeting sanctions based on our criterion allows to increase the costs to Russia by about 60% with little to no extra cost to the sanctioners. (iii) There is scope for increasing the cost to Russia further by expanding the set of sanctioned products.

Wall Street Journal: Sanctions Against Russia Could Be Better, These Harvard Economists Say

Video summary: How can sanctions against Russia be more effective?

We analyze the effects of bans on exports at the level of 5,000 products and show how our results can inform economic sanctions against Russia after its invasion of Ukraine. We begin with characterizing export restrictions imposed by the EU and the US until mid May 2022. We then propose a theoretically-grounded criterion for targeting export bans at the 6-digit HS level. Our results show that the cost to Russia are highly convex in the market share of the sanctioning parties, i.e., there are large benefits from coordinating export bans among a broad coalition of countries. Applying our results to Russia, we find that sanctions imposed by the EU and the US are not systematically related to our arguments once we condition on Russia’s total imports of a product from participating countries. Quantitative evaluations of the export bans show (i) that they are very effective with the welfare loss typically ∼100 times larger for Russia than for the sanctioners. (ii) Improved coordination of the sanctions and targeting sanctions based on our criterion allows to increase the costs to Russia by about 60% with little to no extra cost to the sanctioners. (iii) There is scope for increasing the cost to Russia further by expanding the set of sanctioned products.

Wall Street Journal: Sanctions Against Russia Could Be Better, These Harvard Economists Say

Video summary: How can sanctions against Russia be more effective?