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  5. Spatial Economics
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Spatial Economics

Paper Session

Sunday, Jan. 7, 2024 8:00 AM - 10:00 AM (CST)

Convention Center, 225C
Hosted By: American Economic Association
  • Chair: Stephen Redding, Princeton University and NBER

The Linear Algebra of Economic Geography Models

Benny Kleinman
,
Princeton University
Ernest Liu
,
Princeton University and NBER
Stephen Redding
,
Princeton University and NBER

Abstract

We provide sufficient statistics for nominal and real wage exposure to productivity shocks in a constant elasticity economic geography model. These exposure measures summarize the first-order general equilibrium elasticity of nominal and real wages in each location with respect to productivity shocks in all locations. They are readily computed using commonly-available trade data and the values of trade and migration elasticities. They have an intuitive interpretation in terms of underlying economic mechanisms. Computing these measures for all bilateral pairs of locations involves matrix inversion and therefore remains computational efficient even with an extremely high-dimensional state space. These sufficient statistics provide theory-consistent measures of locations’ exposure to productivity shocks for use in further economic and statistical analysis.

The Traveling Trucker Problem

Treb Allen
,
Dartmouth College and NBER
David Atkin
,
Massachusetts Institute of Technology and NBER
Santiago Cantillo Cleves
,
University of California-San Diego
Carlos Eduardo Hernandez
,
University of the Andes

Abstract

This paper documents three new stylized facts showing that truckers in Colombia frequently choose to make complex chains of shipments in a single trip before returning home. It then provides a new model of optimal trucker trip-chaining with a general geography that is consistent with these facts.

Geographic Segregation, Housing, and Racial Wealth Inequality

Rebecca Diamond
,
Stanford University
William Diamond
,
University of Pennsylvania

Abstract

We study differential investments and rates of return of housing assets by race over the life cycle and decompose these differences into forces driven by credit constraints, liquidity constraints, geography, and housing rental values. We these forces into a model of housing and credit supply and demand to quantify why racial neighborhood segregation can lead to spatially correlated aggregate shocks to liquidity and credit demand. This spatially correlated credit and liquidity shocks lead to differential house price dynamics across neighborhoods based on racial mix. Since racial minorities are more likely to be constrained by liquidity and credit access, housing markets in minority neighborhoods have excess volatility, making them riskier investments. Lower homeownership rates in these neighborhoods may be optimal since their risk-adjustment rate of return is lower.
JEL Classifications
  • R1 - General Regional Economics