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Costas Arkolakis Publications

Discussion Paper
Abstract

We introduce a new methodology to detect and measure economic activity using geospatial data and apply it to steel production, a major industrial pollution source worldwide. Combining plant output data with geospatial data, such as ambient air pollutants, nighttime lights, and temperature, we train machine learning models to predict plant locations and output. We identify about 40% (70%) of plants missing from the training sample within a 1 km (5 km) radius and achieve R2 above 0.8 for output prediction at a 1 km grid and at the plant level, as well as for both regional and time series validations. Our approach can be adapted to other industries and regions, and used by policymakers and researchers to track and measure industrial activity in near real time.

American Economic Review: Insights
Abstract

We consider a broad class of spatial models where there are many types of interactions across a large number of locations. We provide a new theorem that offers an iterative algorithm for calculating an equilibrium and sufficient and "globally necessary" conditions under which the equilibrium is unique. We show how this theorem enables the characterization of equilibrium properties for one important spatial system: an urban model with spillovers across a large number of different types of agents. An online appendix provides 12 additional examples of both spatial and nonspatial economic frameworks for which our theorem provides new equilibrium characterizations.

Journal of Economic Perspectives
Abstract

What do recent advances in economic geography teach us about the spatial distribution of economic activity? We show that the equilibrium distribution of economic activity can be determined simply by the intersection of labor supply and demand curves. We discuss how to estimate these curves and highlight the importance of global geography—the connections between locations through the trading network—in determining how various policy relevant changes to geography shape the spatial economy.

Abstract

We examine multi-product exporters and use firm-product-destination data to quantify export entry barriers. Our general-equilibrium model of multi-product firms generalizes earlier models. To match main facts about multi-product exporters, we estimate our model with rich demand and access cost shocks for Brazilian firms. The estimates document that additional products farther from a firm’s core competency incur higher unit costs, but face lower market access costs. We find that these market access costs differ across destinations and evaluate a scenario that standardizes market access between countries. The resulting welfare gains are similar to eliminating all current tariffs.