Dynamic Spatial Urban Economics
Paper Session
Sunday, Jan. 4, 2026 2:30 PM - 4:30 PM (EST)
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Chairs:
Jeffrey E. Sun, University of Toronto - Andrii Parkhomenko, University of Southern California
Work from Home and the Evolution of Cities
Abstract
We study how the rapid rise of work from home (WFH) since 2020 has transformed cities and affected the welfare of their residents. We develop a dynamic spatial model that includes a large number of locations, forward-looking households, commuting, choice of WFH frequency, costly migration, uninsurable income risk, housing tenure choice, and housing market frictions. We pay particular attention to local public finance and endogenous amenities. We interpret the post-2020 period as a transition toward a new long-run spatial equilibrium and examine changes in residential and commercial real estate prices, the spatial distribution of residents and jobs, income sorting, commuting patterns, and local tax revenues along the transition path. We investigate the possibility of an urban doom loop. We then evaluate several policies that interact with WFH choices, including return-to-office (RTO) mandates, subsidies for RTO, and office-to-residential conversions.Urban Transport Improvements in Dynamic Spatial Equilibrium: Evidence From Buenos Aires
Abstract
How do improvements in the urban transport infrastructure affect the spatial sorting of residents with different levels of income and education within a city? What are the welfare effects of improving urban transit once we take into account these patterns of spatial sorting? In this paper, I study the effects of the construction of a bus rapid transit system (BRT) on the spatial reorganization of residents within the city of Buenos Aires, Argentina. To do so, I leverage an individual level panel data set of more than two million residents with which I can describe intra-city migration patterns. I first find reduced form evidence that the construction of the BRT increased the spatial segregation between high and low-skilled residents within the city. I then develop a dynamic quantitative spatial equilibrium model of a city to quantify the welfare effects of this BRT system. With this quantitative framework, I can measure the averagewelfare gains for residents that were living near the BRT lines before these were built. I find that, welfare gains were very similar between high- and low-skilled workers on average within the city. However, when looking at the spatial variation in these welfare gains, I find that the residents originally living in areas with the highest share of low-skilled residents benefited considerably more than the those living in areas with the highest share of high-skilled workers.
Learning and Expectations in Dynamic Spatial Economies
Abstract
The impact of shocks in dynamic environments depends on how forward-looking agents anticipate the path of future fundamentals that shape their decisions. We incorporate flexible beliefs about future fundamentals into a general class of dynamic spatial models, allowing beliefs to be evolving, uncertain, and heterogeneous across groups of agents. We show how to implement our methodology to study both ex-ante and ex-post shocks to fundamentals. We apply our method to two settings: an ex-ante study of the economic impacts of climate change, and an ex-post evaluation of the China productivity shock on the U.S. economy. In both cases, we study the impact of deviations from perfect foresight on different outcomes.JEL Classifications
- R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location
- C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling