We analyze how poverty and a country’s fiscal space impact policy and welfare in times of a pandemic. We introduce a subsistence level of consumption into a tractable heterogeneous agent framework, and use this framework to characterize optimal joint policies of a lockdown and transfer payments. In our model, a more stringent lockdown helps fighting the pandemic, but it also deepens the recession, which implies that poorer parts of society find it harder to subsist. This reduces their compliance with the lockdown, and may cause deprivation of the very poor, giving rise to an excruciating trade-off between saving lives from the pandemic and from deprivation. Transfer payments help mitigate this trade-off. We show that, ceteris paribus, the optimal lockdown is stricter in richer countries and the aggregate death burden and welfare losses smaller. We then consider a government borrowing constraint and show that limited fiscal space lowers the optimal lockdown and welfare, and increases the aggregate death burden during the pandemic. This is particularly true in societies where a larger fraction of the population is in poverty. We discuss evidence from the literature and provide reduced-form regressions that support the relevance of our main mechanisms. We finally discuss distributional consequences and the political economy of fighting a pandemic.
Technological improvement is the most important cause of long-term economic growth. In standard growth models, technology is treated in the aggregate, but an economy can also be viewed as a network in which producers buy goods, convert them to new goods, and sell the production to households or other producers. We develop predictions for how this network amplifies the effects of technological improvements as they propagate along chains of production, showing that longer production chains for an industry bias it toward faster price reduction and that longer production chains for a country bias it toward faster growth. These predictions are in good agreement with data from the World Input Output Database and improve with the passage of time. The results show that production chains play a major role in shaping the long-term evolution of prices, output growth, and structural change.
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.
Cambridge Elements are a new concept in academic publishing and scholarly communication, combining the best features of books and journals. They consist of original, concise, authoritative, and peer-reviewed scholarly and scientific research, organised into focused series edited by leading scholars, and provide comprehensive coverage of the key topics in disciplines spanning the arts and sciences.
Regularly updated and conceived from the start for a digital environment, they provide a dynamic reference resource for graduate students, researchers, and practitioners.
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.
This paper constructs a two-stage sequential game model to shed light on the spillover effect of inward FDI on the efficiency of domestic firms in host countries. Our model shows that, given an optimal joint-venture policy made by foreign firms, the impact of the spillover effect of inward FDI is contingent upon the productivity gap between the domestic firms and foreign ones. In particular, we demonstrate that the spillover effect of inward FDI varies negatively with the productivity gap between domestic low-productivity firms and foreign firms but works in the opposite way for high-productivity firms. This suggests that once the productivity gap widens, the entry of foreign firms will increase the efficiency of high-productivity firms but reduce the efficiency of low-productivity firms. In support of our theoretical model, we provide robust empirical results by using the dataset of annual survey of Chinese industrial enterprises.
As the literature has studied the financing method of Chinese-listed firms for a long time, but with inconclusive indications, this research thus adopts non-financial Chinese-listed firms’ data from 2003 to 2015 to investigate the relationship between long-term debt financing and financing deficit. We pay particular attention to three channels (ownership concentration, market timing, and state ownership) that potentially affect the adoption of long-term debt financing when there is a financing deficit. The empirical analysis documents a positive relationship between financing deficit and changes in the long-term debt ratio in our sampled firms for both static and dynamic panel models. Moreover, among the three channels we show that state ownership has the strongest positive impact on the adoption of long-term debt financing, followed by ownership concentration, while the weakest channel is the market timing’s negative effect. In general, our empirical analysis finds that the important external financing method of long-term debt is most likely to be impacted by the state ownership aspect.
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.
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.
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.
By exploiting variation both in mortgage payoffs and mortgage interest rate resets, we find that a decline in mortgage payments induces a significant increase in nondurable goods spending, even when households have substantial amounts of liquidity. Following mortgage payoff, households increase consumption expenditures by 61% of the original payment. In comparison, households increase consumption by only 36% in response to a transitory payment adjustment induced by interest rate changes. Households with a higher payment-to-income ratio have a significantly lower marginal propensity to consume (MPC). These results have practical implications for policy markers seeking to design consumption boosting policies and are important for understanding how changes in monetary policy may affect consumer spending patterns.
We use aggregated and anonymized information based on international expenditures through corporate payment cards to map the network of global business travel. We combine this network with information on the industrial composition and export baskets of national economies. The business travel network helps to predict which economic activities will grow in a country, which new activities will develop and which old activities will be abandoned. In statistical terms, business travel has the most substantial impact among a range of bilateral relationships between countries, such as trade, foreign direct investments and migration. Moreover, our analysis suggests that this impact is causal: business travel from countries specializing in a specific industry causes growth in that economic activity in the destination country. Our interpretation of this is that business travel helps to diffuse knowledge, and we use our estimates to assess which countries contribute or benefit the most from the diffusion of knowledge through global business travel.
The conventional paradigm about development banks is that these institutions exist to target well-identified market failures. However, market failures are not directly observable and can only be ascertained with a suitable learning process. Hence, the question is how do the policymakers know what activities should be promoted; how do they learn about the obstacles to the creation of new activities? Rather than assuming that the government has arrived at the right list of market failures and uses development banks to close some well-identified market gaps, we suggest that development banks can be in charge of identifying these market failures through their loan-screening and lending activities to guide their operations and provide critical inputs for the design of productive development policies. In fact, they can also identify government failures that stand in the way of development and call for needed public inputs. This intelligence role of development banks is similar to the role that modern theories of financial intermediation assign to banks as institutions with a comparative advantage in producing and processing information. However, while private banks focus on information on private returns, development banks would potentially produce and organize information about social returns.
We combine a sequence of machine-learning techniques, together called Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in the dynamics of complex systems. Here, we deploy this method on the task of automating the development of new theory of economic growth. Traditionally, economic growth is modelled with a few aggregate quantities derived from simplified theoretical models. PriSDA, by contrast, identifies important quantities. Applied to 55 years of data on countries’ exports, PriSDA finds that what most distinguishes countries’ export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity. The second dimension of variation is proficiency in machinery relative to agriculture. PriSDA then infers the dynamics of these two quantities and of per capita income. The inferred model predicts that diversification drives growth in income, that diversified middle-income countries will grow the fastest, and that countries will converge onto intermediate levels of income and specialization. PriSDA is generalizable and may illuminate dynamics of elusive quantities such as diversity and complexity in other natural and social systems.
As individuals specialize in specific knowledge areas, a society’s know-how becomes distributed across different workers. To use this distributed know-how, workers must be coordinated into teams that, collectively, can cover a wide range of expertise. This paper studies the interdependencies among co-workers that result from this process in a population-wide dataset covering educational specializations of millions of workers and their co-workers in Sweden over a 10-year period. The analysis shows that the value of what a person knows depends on whom that person works with. Whereas having co-workers with qualifications similar to one’s own is costly, having co-workers with complementary qualifications is beneficial. This co-worker complementarity increases over a worker’s career and offers a unifying framework to explain seemingly disparate observations, answering questions such as “Why do returns to education differ so widely?” “Why do workers earn higher wages in large establishments?” “Why are wages so high in large cities?”