Assignment models in trade predict that countries with higher productivity levels are assortatively matched to industries that make better use of these higher levels. Here, we assume that the driver of productivity differences is the differential distribution of factors among countries. Utilizing such a structure, we define and estimate the average factor level (AFL) for countries and products using only the information about the production patterns. Interestingly, our estimates coincide with the complexity variables of (Hidalgo and Hausmann, 2009), providing an underlying economic rationale. We show that AFL is highly correlated with country-level characteristics and predictive of future economic growth.
Economic growth is associated with the diversification of economic activities, which can be observed via the evolution of product export baskets. Exporting a new product is dependent on having, and acquiring, a specific set of capabilities, making the diversification process path-dependent. Taking an agnostic view on the identity of the capabilities, here we derive a probabilistic model for the directed dynamical process of capability accumulation and product diversification of countries. Using international trade data, we identify the set of pre-existing products, the product Ecosystem, that enables a product to be exported competitively. We construct a directed network of products, the Eco Space, where the edge weight corresponds to capability overlap. We uncover a modular structure, and show that low- and middle-income countries move from product communities dominated by small Ecosystem products to advanced (large Ecosystem) product clusters over time. Finally, we show that our network model is predictive of product appearances.
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 evolution of comparative advantage. We build a simple Ricardian-inspired model and show that hidden information on inter-industry and inter-location relatedness can be captured by simple correlations between the observed structure of industries across locations, or the structure of locations across industries. We then use this recovered information to calculate a measure of implied comparative advantage, and show that it 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). In each of these cases, the deviations between the observed and implied comparative advantage in the past 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.
Non-Pharmaceutical Interventions (NPIs) have been for most countries the key policy instrument utilized to contain the impact of the COVID-19 pandemic. In this article, we conduct an empirical analysis of the impact of these policies on the virus’ transmission and death toll, for a panel of 152 countries, from the start of the pandemic through December 31, 2020. We find that lockdowns tend to significantly reduce the spread of the virus and the number of related deaths. We also show that this benign impact declines over time: after four months of strict lockdown, NPIs have a significantly weaker contribution in terms of their effect in reducing COVID-19 related fatalities. Part of the fading effect of quarantines could be attributed to an increasing non-compliance with mobility restrictions, as reflected in our estimates of a declining effect of lockdowns on measures of actual mobility. However, we additionally find that a reduction in de facto mobility also exhibits a diminishing effect on health outcomes, which suggests that lockdown fatigues may have introduce broader hurdles to containment policies.
We examine how inequality and openness interact in shaping the long-run growth prospects of developing countries. To this end, we develop a Schumpeterian growth model with heterogeneous households and non-homothetic preferences for quality. We show that inequality affects growth very differently in an open economy as opposed to a closed economy: If the economy is close to the technological frontier, the positive demand effect of inequality on growth found in closed-economy models may be amplified by international competition. In countries with a larger distance to the technology frontier, however, rich households satisfy their demand for high quality via importing, and the effect of inequality on growth is smaller than in a closed economy and may even be negative. We show that this theoretical prediction holds up in the data, both when considering growth in export quality at the industry level and when considering growth in GDP per capita.
With the exception of some flashpoints in Northern and Southern Africa, the continent has been largely spared from the direct health effect of Covid-19. However, the African economy has been significantly hurt by the economic consequences. This eBook summarises recent research on the economic effect of the Covid-19 pandemic in the continent covering a wide array of topics focusing on the response of firms, households, governments, and international organisations.
COVID-19 pandemic had a devastating effect on both lives and livelihoods in 2020. The arrival of effective vaccines can be a major game changer. However, vaccines are in short supply as of early 2021 and most of them are reserved for the advanced economies. We show that the global GDP loss of not inoculating all the countries, relative to a counterfactual of global vaccinations, is higher than the cost of manufacturing and distributing vaccines globally. We use an economic-epidemiological framework that combines a SIR model with international production and trade networks. Based on this framework, we estimate the costs for 65 countries and 35 sectors. Our estimates suggest that up to 49 percent of the global economic costs of the pandemic in 2021 are borne by the advanced economies even if they achieve universal vaccination in their own countries.
The literature on wage gaps between Chiapas and the rest of Mexico revolves around individual factors, such as education and ethnicity. Yet, twenty years after the Zapatista rebellion, the schooling gap has shrunk while the wage gap has widened, and we find no evidence indicating that Chiapas indigenes are worse-off than their likes elsewhere in Mexico. We explore a different hypothesis and argue that place-specific characteristics condition the choices and behaviors of individuals living in Chiapas and explain persisting income gaps. Most importantly, they limit the necessary investments at the firm level in dynamic capabilities. Based on census data, we calculate the economic complexity index, a measure of the knowledge agglomeration embedded in the economic activities at the municipal level. Economic complexity explains a larger fraction of the wage gap than any individual factor. Our results suggest that the problem is Chiapas, and not Chiapanecos.
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.
What is the economic rationale for investing in science? Based on an open economy model of creative destruction, we characterize four key factors of optimal investment in basic research: the stage of economic development, the strength of the manufacturing base, the degree of openness, and the share of foreign‐owned firms. For each of these factors, we analyze its bearings on optimal basic research investment. We then show that the predicted effects are consistent with patterns observed in the data and discuss how the factor‐based approach might inform basic research policies.
Loreto es un lugar de contrastes. Es el departamento más grande del Perú, pero se encuentra entre los de menor densidad poblacional. Su capital, Iquitos, está más cerca de los estados fronterizos de Brasil y Colombia que de las capitales de sus regiones vecinas en el Perú - San Martín y Ucayali. Sólo se puede llegar a Iquitos por vía aérea o fluvial, lo que la convierte en una de las mayores ciudades del mundo sin acceso por carretera. Desde la fundación del departamento, la economía de Loreto ha dependido de la explotación de recursos naturales, desde el boom del caucho a finales del siglo XIX y principios del XX hasta la extracción petrolera y explotación de recursos forestales que predomina en nuestros días. Este modelo ha traído consigo daños ambientales significativos y ha producido un patrón de crecimiento lento y volátil, que ha abierto una brecha cada vez más amplia entre la economía de la región y la del resto del país. Entre 1980 y 2018, Loreto creció a una tasa promedio compuesta anual cuatro veces menor a la del resto del Perú. Es decir, mientras el resto del Perú triplicó el tamaño de su economía, la de Loreto creció algo menos que un tercio.
En la última década (2008-2018), la región también se ha venido distanciando de sus pares amazónicos en el país (Ucayali, San Martín y Madre de Dios), que han crecido a una tasa promedio anual cinco veces mayor. En este período, el ingreso promedio por habitante en Loreto ha pasado de ser tres cuartas partes del promedio nacional en 2008 a menos de la mitad para 2018. Además del rezago económico - o quizás como consecuencia de él -, Loreto también se ubica entre los departamentos con peores indicadores de desarrollo social, anemia y desnutrición infantil del Perú.
En este contexto, el Laboratorio de Crecimiento de la Universidad de Harvard se asoció con la Fundación Gordon and Betty Moore para desarrollar una investigación que proporcionara insumos y recomendaciones de política para acelerar el desarrollo de la región y generar prosperidad de forma sostenible.
The literature extensively discusses the increasing commitment toward comprehensive structural reform of China’s economy as it targets to achieve high quality and sustainable economic growth. This research investigates the inherent relationship between supply-side structural reform (SSSR) and dynamic capital structure adjustment in Chinese-listed firms. Our results show that SSSR’s introduction has significantly improved the adjustment speed toward the optimal debt ratio, especially for firms with high indebtedness and low investment performance. Importantly, China’s bond market plays a crucial role through SSSR for firms’ debt ratio to adjust toward their optimal level. However, there is no such evidence among state-owned enterprises (SOEs), suggesting that the structural reform concerning corporate capital structure for SOEs is more challenging and longstanding when compared with non-SOEs.
This research constructs a simple dynamic model to illustrate the micro‐mechanism of industrial upgrading along the global value chains. Our model predicts that as firms move up from downstream to upstream stages, (a) there is higher profitability if and only if the following three conditions are satisfied. First, the increasing rate of sunk cost (including R&D expenditure) over sequential stages of production cannot be sufficiently large (endogenous sunk cost effect). Second, the decreasing rate of change of intermediate input demand with respect to the price set by firms at a production stage cannot be sufficiently high (intermediate input price effect). Third, the decreasing rate of change of intermediate input demand with respect to the pricing dynamics over the sequential stages of production cannot be sufficiently large (sequential pricing uncertainty effect); (b) total cost is lower if and only if the decreasing rate of change of input demand with respect to the price is sufficiently large; (c) output is higher if and only if and the decreasing rate of change of input demand with respect to the price is not sufficiently large; and (d) the price decreases. We show that the empirical patterns revealed in China are consistent with our model's predictions.
We describe a problem in complex networks we call the Node Vector Distance (NVD) problem, and we survey algorithms currently able to address it. Complex networks are a useful tool to map a non-trivial set of relationships among connected entities, or nodes. An agent—e.g., a disease—can occupy multiple nodes at the same time and can spread through the edges. The node vector distance problem is to estimate the distance traveled by the agent between two moments in time. This is closely related to the Optimal Transportation Problem (OTP), which has received attention in fields such as computer vision. OTP solutions can be used to solve the node vector distance problem, but they are not the only valid approaches. Here, we examine four classes of solutions, showing their differences and similarities both on synthetic networks and real world network data. The NVD problem has a much wider applicability than computer vision, being related to problems in economics, epidemiology, viral marketing, and sociology, to cite a few. We show how solutions to the NVD problem have a wide range of applications, and we provide a roadmap to general and computationally tractable solutions. We have implemented all methods presented in this article in a publicly available open source library, which can be used for result replication.
Ethiopia will need to increase the diversity of its export basket to guarantee a sustainable growth path. Ethiopia has shown stellar growth performance throughout the last two decades, but, in this period, export growth has been insufficient to finance the country’s balance of payments needs. As argued in our Growth Diagnostic report,1 Ethiopia’s growth decelerated as a result of the increasing external imbalances which have resulted in a foreign exchange constraint. This macroeconomic imbalance is now slowing the rate of economic growth, job creation and poverty alleviation across the country. Although export growth will not be rapid enough to address the foreign exchange constraint on its own in the short-term, the only way for the country to achieve macroeconomic balance as it grows in the longer term is to increase its exports per capita. With only limited opportunities to expand its exports on the intensive margin, the Government of Ethiopia (GoE) will have to strategically support the diversification of its economy to expand its exports base.
This report applies the theory of Economic Complexity in order to describe the base of productive knowhow and assess the opportunities and constraints to diversification in Ethiopia’s economy. The theory of Economic Complexity offers tools to capture and quantitatively estimate the diversity and sophistication of productive knowhow in an economy and to analyze the potential to develop comparative advantage in new industries. These tools provide valuable inputs for informing diversification strategies and the use of state resources by providing rigorous information on the risks and potential returns of government industrial policies in support of different sectors.
Sembrado en el flanco oeste de la selva amazónica, Loreto se encuentra entre los departamentos más pobres y con peores indicadores sociales del Perú. El desarrollo enfrenta allí un sinfín de barreras, pero no todas son igualmente limitantes y tampoco hay recursos para atender todos los problemas a la vez. El Laboratorio de Crecimiento de la Universidad de Harvard, bajo el auspicio de la Fundación Gordon and Betty Moore, ha desarrollado un Diagnóstico de Crecimiento que buscar identificar las restricciones más limitantes, y priorizar las intervenciones de políticas públicas alrededor de un número reducido de factores con el mayor impacto. La investigación, que se fundamenta en análisis de bases de datos nacionales e internacionales, e incluye factores cuantitativos y cualitativos derivados de las visitas de campo, identifica a la conectividad de transporte, los problemas de coordinación asociados al autodescubrimiento, y la energía eléctrica, como las restricciones más vinculantes para el desarrollo de Loreto. De acuerdo con nuestras conclusiones, mejoras en la provisión de estos tres factores tendrían un mayor impacto sobre el desarrollo sostenible de la región que mejores en la educación y los niveles de capital humano, el acceso a financiamiento, y otros sospechosos habituales. Este reporte es el segundo de una investigación más amplia – Transformación estructural y restricciones limitantes a la prosperidad en Loreto, Perú – que busca aportar insumos para el desarrollo de políticas públicas a escala nacional y regional que contribuyan a promover el desarrollo productivo y la prosperidad de la región.
We analyze Ukraine's opportunities to participate in European value chains, using traditional gravity models, combined with tools from Economic Complexity Analysis to study international trade (exports) and Foreign Direct Investment (FDI). This toolbox is shown to be predictive of the growth and entry of new exports to the EU's Single Market, as well as foreign direct investments from the Single Market in Ukraine. We find that Ukraine has suffered from a decline of trade with Russia, which has led not only to a quantitative but also a qualitative deterioration in Ukrainian exports. Connecting to western European value chains is in principle possible, with several opportunities in the automotive, information technology and other sectors. However, such a shift may lead to a spatial restructuring of the Ukrainian economy and a mismatch between the geographical supply of and demand for labor.
We analyze how globalization affects the allocation of talent across competing teams in large matching markets. Focusing on amplified superstar effects, we show that a convex transformation of payoffs promotes positive assortative matching. This result holds under minimal assumptions on how skills translate into competition outcomes and how competition outcomes translate into payoffs. Our analysis covers many interesting special cases, including simple extensions of Rosen (1981) and Melitz (2003) with competing teams. It also provides new insights on the distributional consequences of globalization, and on the role of technological change, urban agglomeration, and taxation for the composition of teams.
El Laboratorio de Crecimiento de la Universidad de Harvard, bajo el auspicio de la Fundación Gordon and Betty Moore, ha desarrollado esta investigación para identificar las capacidades productivas existentes en Loreto y las actividades económicas con potencial para liderar la transformación estructural de su economía. Este reporte forma parte de una investigación más amplia – Transformación estructural y restricciones limitantes a la prosperidad en Loreto, Perú – que busca aportar insumos para el desarrollo de políticas públicas a escala nacional y regional que contribuyan a promover el desarrollo productivo y la prosperidad de la región, tomando en cuenta sus características particulares.
What does it take for a sub-national unit to become an autonomous engine of growth? This issue is particularly relevant to large cities, as they tend to display larger and more complex know-how agglomerations and may have access to a broader set of policy tools. To approximate an answer to this question, specific to the case of Buenos Aires, Harvard’s Growth Lab engaged in a research project from December 2018 to June 2019, collaborating with the Center for Evidence-based Evaluation of Policies (CEPE) of Universidad Torcuato di Tella, and the Development Unit of the Secretary of Finance of the City of Buenos Aires. Together, we have developed research agenda that seeks to provide inputs for a policy plan aimed at decoupling Buenos Aires’s growth trajectory from the rest of Argentina’s.