Periodic Reporting for period 4 - OPTNETSPACE (Optimal Transport Networks in Spatial Equilibrium)
Okres sprawozdawczy: 2023-07-01 do 2024-06-30
Every year, the world economy invests many resources into the improvement of its transport infrastructure and infrastructure development is one the key tools used by governments and international institutions to foster economic development and international trade. These massive investments have a wide range of impacts on citizens (access to essential goods and services, pollution, migrations) and the economy (location of economic activity, economic growth, domestic and international trade).
How should infrastructure investments be allocated across space to maximize social welfare? How should infrastructure networks be designed while taking into account these various trade-offs? This project seeks to provide an answer to these questions.
In a first part, this project develops novel theoretical and quantitative models to analyze the economic impact of infrastructure projects and study their optimal design. The proposed models build on state-of-the-art models of international trade and economic geography which operate at various scales ranging from the international and domestic scopes to the urban level. The optimal design approach takes into account aspects as varied as how infrastructure affects the flows of goods and people across space, the distribution of economic activity across space, international trade, regional development, migrations and spatial inequalities across and within countries. The objective of the project is to develop tools that can be readily used by researchers and policymakers in charge of infrastructure planning.
In a second part, the project applies these tools to understand real world infrastructure networks. How far are actual and planned infrastructure projects from the social optimum? Are existing infrastructure networks a limit to economic growth in developed and developing economies? The project also investigates the potential sources of real-world inefficiencies, considering for instance the role of political institutions.
How should infrastructure investments be allocated across space to maximize social welfare? How should infrastructure networks be designed while taking into account these various trade-offs? This project seeks to provide an answer to these questions.
In a first part, this project develops novel theoretical and quantitative models to analyze the economic impact of infrastructure projects and study their optimal design. The proposed models build on state-of-the-art models of international trade and economic geography which operate at various scales ranging from the international and domestic scopes to the urban level. The optimal design approach takes into account aspects as varied as how infrastructure affects the flows of goods and people across space, the distribution of economic activity across space, international trade, regional development, migrations and spatial inequalities across and within countries. The objective of the project is to develop tools that can be readily used by researchers and policymakers in charge of infrastructure planning.
In a second part, the project applies these tools to understand real world infrastructure networks. How far are actual and planned infrastructure projects from the social optimum? Are existing infrastructure networks a limit to economic growth in developed and developing economies? The project also investigates the potential sources of real-world inefficiencies, considering for instance the role of political institutions.
The results achieved by the project are exposed in five articles, some already published or submitted to leading economic journals, and others still working papers.
The first significant result is the development of a new methodology which allows to analyze the optimal design of infrastructure networks in general equilibrium spatial models. The results have been published in “Optimal Transport Networks in Spatial Equilibrium” (Econometrica, 2020), joint with P. Fajgelbaum. The theory builds on a quantitative trade model which nests a wide range of international trade, economic geography models and urban models. Locations are arranged on a graph, and goods or people can only travel along the links of the network. The optimal infrastructure planning problem is analyzed through the lens of a planner who chooses how much infrastructure to build in each link, taking into account the private sector’s decisions (where to live/produce, how much/what to produce, optimal shipping/commuting routes and general equilibrium). This novel theory allows the authors to describe the optimal placement of infrastructure taking into account a) the distribution of productivity, resources and amenities, b) the particular geography of a location (availability of land, obstacles like rivers or mountains), c) the characteristics of the transport technology (degree of congestion, economies of scale in transportation). The theory is applied to Europe and evaluates the efficiency of its existing road network. The results broadly suggest a higher degree of infrastructure misallocation and underinvestment in Eastern European countries. An application to US states is also available on the PI’s website. A MATLAB toolkit is available on the PI’s website for the use of other researchers and policymakers.
In the article “Optimal Lockdown in a Commuting Network” (American Economic Review: Insights, 2021) joint with P. Fajgelbaum, A. Khandelwal, W. Kim and C. Mantovani, the project’s optimal network design methodology is applied to an urban environment with an application to COVID-19. The optimal network approach is used to study spatially targeted lockdowns at the level of a large metropolitan area. The theory builds on a standard urban framework in which workers choose to live in various districts of a city and must commute to work. The network is designed to control population flows in a commuting network and achieves a trade-off between saving lives and the economic costs implied by lockdown measures. The article applies its methodology to the metropolitan areas of New York and Seoul. The findings suggests great welfare and economic gains from spatially-targeted lockdowns as opposed to uniform lockdowns and recommends generally stricter lockdowns in those locations compared to reality.
A third paper analyzes how political institutions affect the design of real-world infrastructure project. “Political Preferences and Transport Infrastructure: Evidence from California’s High-Speed Rail” joint with P. Fajgelbaum, C. Gaubert, N. Gorton and E. Morales, uses the particular example of the California High-Speed Rail project to shed light on the role of political economy distortions. Using a quantitative spatial urban model with multiple modes of transport, the PI and his coauthors leverage highly-disaggregated voting data to analyze the voting behavior of Californian citizens and estimate the potential biases of the infrastructure planning authorities. The results suggest an important political bias and a significant overinvestment of resources towards pivotal counties in the election. In a fourth new paper, currently in progress, the PI and his coauthors P. Fajgelbaum and A. Khandelwal, apply the optimal network methodology to analyze China’s Belt and Road Initiative and identifies the potential inefficiencies and political biases present in its design.
A fifth working paper, “Zipf’s Law and Fractal City Networks” develops a new theory to explain the pervasive observation that the city-size distribution in many countries follows a Power law distribution with a coefficient close to -1. The theory first establishes that fractal city networks satisfy a Zipf’s law with a coefficient equal to minus the fractal dimension of the network. The theory goes on to establish conditions under which fractal networks are optimal and feature a fractal dimension close to 1. An empirical study is currently underway to test the theory’s predictions.
The first significant result is the development of a new methodology which allows to analyze the optimal design of infrastructure networks in general equilibrium spatial models. The results have been published in “Optimal Transport Networks in Spatial Equilibrium” (Econometrica, 2020), joint with P. Fajgelbaum. The theory builds on a quantitative trade model which nests a wide range of international trade, economic geography models and urban models. Locations are arranged on a graph, and goods or people can only travel along the links of the network. The optimal infrastructure planning problem is analyzed through the lens of a planner who chooses how much infrastructure to build in each link, taking into account the private sector’s decisions (where to live/produce, how much/what to produce, optimal shipping/commuting routes and general equilibrium). This novel theory allows the authors to describe the optimal placement of infrastructure taking into account a) the distribution of productivity, resources and amenities, b) the particular geography of a location (availability of land, obstacles like rivers or mountains), c) the characteristics of the transport technology (degree of congestion, economies of scale in transportation). The theory is applied to Europe and evaluates the efficiency of its existing road network. The results broadly suggest a higher degree of infrastructure misallocation and underinvestment in Eastern European countries. An application to US states is also available on the PI’s website. A MATLAB toolkit is available on the PI’s website for the use of other researchers and policymakers.
In the article “Optimal Lockdown in a Commuting Network” (American Economic Review: Insights, 2021) joint with P. Fajgelbaum, A. Khandelwal, W. Kim and C. Mantovani, the project’s optimal network design methodology is applied to an urban environment with an application to COVID-19. The optimal network approach is used to study spatially targeted lockdowns at the level of a large metropolitan area. The theory builds on a standard urban framework in which workers choose to live in various districts of a city and must commute to work. The network is designed to control population flows in a commuting network and achieves a trade-off between saving lives and the economic costs implied by lockdown measures. The article applies its methodology to the metropolitan areas of New York and Seoul. The findings suggests great welfare and economic gains from spatially-targeted lockdowns as opposed to uniform lockdowns and recommends generally stricter lockdowns in those locations compared to reality.
A third paper analyzes how political institutions affect the design of real-world infrastructure project. “Political Preferences and Transport Infrastructure: Evidence from California’s High-Speed Rail” joint with P. Fajgelbaum, C. Gaubert, N. Gorton and E. Morales, uses the particular example of the California High-Speed Rail project to shed light on the role of political economy distortions. Using a quantitative spatial urban model with multiple modes of transport, the PI and his coauthors leverage highly-disaggregated voting data to analyze the voting behavior of Californian citizens and estimate the potential biases of the infrastructure planning authorities. The results suggest an important political bias and a significant overinvestment of resources towards pivotal counties in the election. In a fourth new paper, currently in progress, the PI and his coauthors P. Fajgelbaum and A. Khandelwal, apply the optimal network methodology to analyze China’s Belt and Road Initiative and identifies the potential inefficiencies and political biases present in its design.
A fifth working paper, “Zipf’s Law and Fractal City Networks” develops a new theory to explain the pervasive observation that the city-size distribution in many countries follows a Power law distribution with a coefficient close to -1. The theory first establishes that fractal city networks satisfy a Zipf’s law with a coefficient equal to minus the fractal dimension of the network. The theory goes on to establish conditions under which fractal networks are optimal and feature a fractal dimension close to 1. An empirical study is currently underway to test the theory’s predictions.
The development of theoretical and quantitative models to optimally design infrastructure networks in general equilibrium economic models is novel and pushes beyond state-of-the-art methods to evaluate infrastructure projects. The methodology developed in the project exploits tools from convex optimization and optimal transport, making the computation of optimal networks feasible. The project’s modeling approach also brings the power of modern general equilibrium models of trade and economic geography to infrastructure planning.
The project demonstrates the usefulness of the methodology by applying it to various contexts: misallocation of road infrastructure in Europe/US, COVID-19, the role of political institutions in infrastructure design, Zipf’s law, etc. The implications of the theory are wide and far-reaching. The PI and his coauthors are planning to keep exploring those implications in the near future.
The project demonstrates the usefulness of the methodology by applying it to various contexts: misallocation of road infrastructure in Europe/US, COVID-19, the role of political institutions in infrastructure design, Zipf’s law, etc. The implications of the theory are wide and far-reaching. The PI and his coauthors are planning to keep exploring those implications in the near future.