This paper presents a structural ranking of countries by their distance to frontier. The ranking is based on comparative advantage. Hence, it reveals information on the productive capabilities of countries that is fundamentally different from GDP per capita. The ranking is centered on the assumption that countries’ capabilities across products are similar to those of other countries with comparable distance to frontier. It can be micro-founded using standard trade models. The estimation strategy provides a general, non-parametric approach to uncovering a log-supermodular structure from the data, and I use it to also derive a structural ranking of products by their complexity. The underlying theory provides a fexible micro-foundation for the Economic Complexity Index (Hidalgo and Hausmann, 2009).

We revisit the well-known fact that richer countries tend to produce a larger variety of goods and analyze economic development through (export) diversifcation. We show that countries are more likely to enter ‘nearby’ industries, i.e., industries that require fewer new occupations. To rationalize this finding, we develop a small open economy (SOE) model of economic development at the extensive industry margin. In our model, industries differ in their input requirements of non-tradeable occupations or tasks. The SOE grows if profit maximizing frms decide to enter new, more advanced industries, which requires training workers in all occupations that are new to the economy. As a consequence, the SOE is more likely to enter nearby industries in line with our motivating fact. We provide indirect evidence in support of our main mechanism and then discuss implications: We show that there may be multiple equilibria along the development path, with some equilibria leading on a pathway to prosperity while others resulting in an income trap, and discuss implications for industrial policy. We finally show that the rise of China has a non-monotonic effect on the growth prospects of other developing countries, and provide suggestive evidence for this theoretical prediction.

After a large growth acceleration within the context of the commodity super cycle (2000-2015), Namibia has been grappling with three interrelated challenges: economic growth, fiscal sustainability, and inclusion. Accelerating technological progress and enhancing Namibia’s knowhow agglomeration is crucial to the process of fostering new engines of growth that will deliver progress across the three targets. Using net exports data at the four-digit level, we estimate the economic complexity of Namibia – a measure of knowhow agglomeration – vis-à-vis its peers. Our results suggest that Namibia’s economy is relatively less complex and attractive opportunities to diversify tend to be more distant. Based on economic complexity metrics, we define a place-specific path for productive diversification, identifying industries with high potential and providing inputs – related to their feasibility and attractiveness in Namibia – for further prioritization. Namibia’s path to structural transformation will likely be steeper than for most peers, calling for a more active policy stance geared towards progressive accumulation of productive capacities, well-targeted “long jumps”, and strengthening state capacity to sort out market failures associated with the process of self-discovery.

Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world.

The empirical literature on the contributions of human capital investments to economic growth shows mixed results. While evidence from OECD countries demonstrates that human capital accumulation is associated with growth accelerations, the substantial efforts of developing countries to improve access to and quality of education, as a means for skill accumulation, did not translate into higher income per capita. In this Element, we propose a framework, building on the principles of 'growth diagnostics', to enable practitioners to determine whether human capital investments are a priority for a country's growth strategy. We then discuss and exemplify different tests to diagnose human capital in a place, drawing on the Harvard Growth Lab's experience in different development context, and discuss various policy options to address skill shortages.

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Estimating the capabilities, or inputs of production, that drive and constrain the economic development of urban areas has remained a challenging goal. We posit that capabilities are instantiated in the complexity and sophistication of urban activities, the know-how of individual workers, and the city-wide collective know-how. We derive a model that indicates how the value of these three quantities can be inferred from the probability that an individual in a city is employed in a given urban activity. We illustrate how to estimate empirically these variables using data on employment across industries and metropolitan statistical areas in the USA. We then show how the functional form of the probability function derived from our theory is statistically superior when compared with competing alternative models, and that it explains well-known results in the urban scaling and economic complexity literature. Finally, we show how the quantities are associated with metrics of economic performance, suggesting our theory can provide testable implications for why some cities are more prosperous than others.

A little over a year ago, the EU’s political leaders agreed on an unprecedented fiscal package – dubbed ‘Next Generation EU’ – to aid Europe’s recovery from the pandemic. Ricardo Hausmann, Miguel Angel Santos, Corrado Macchiarelli and Renato Giacon write that economic complexity theories can provide a useful tool for evaluating whether the recovery and resilience plans submitted by EU member states to receive this funding are well-designed. Assessing the case of Greece, they argue that investments should be tailored toward export-oriented sectors and aim to help close the country’s product complexity gap with other EU states. 

The Growth Lab at Harvard University, with funding provided by the Gordon and Betty Moore Foundation, has undertaken this investigation with the aim of identifying the existing productive capacities in Loreto, as well as the economic activities with potential to drive the structural transformation of its economy. This paper is part of a broader investigation – Promoting Sustainable Economic Growth and Structural Transformation in the Amazon Region of Loreto, Peru – which seeks to contribute with context-specific inputs for the development of national and sub-national public policies that promote productive development and prosperity in this Peruvian state.