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Spatial Sorting and Economic Transformations

Paper Session

Monday, Jan. 5, 2026 8:00 AM - 10:00 AM (EST)

Philadelphia Marriott Downtown, Room 310
Hosted By: Econometric Society
  • Chair: Jonathan I. Dingel, Columbia University

Dynamic Individuals, Static Neighborhoods: Migration, Earnings Changes, and Concentrated Poverty

Andrew Garin
,
Carnegie Mellon University
Ethan Jenkins
,
University of Notre Dame
Evan Mast
,
University of Notre Dame
Bryan Stuart
,
Federal Reserve Bank of Philadelphia

Abstract

We use administrative data to document a high degree of migration across neighborhoods and neighborhood types defined in terms of poverty rate and median income. Neighborhood quality increases over an individual’s life cycle, and people also move to better neighborhoods in response to earnings improvements. We then develop several implications of these initial
facts for high-poverty neighborhoods. First, resident turnover in these areas is rapid. Second, among the people living in a poor neighborhood at a point in time, the distribution of future concentrated poverty exposure is bimodal. Young people, renters, and those with children tend to spend fewer than half of the next ten years in similar neighborhoods, while older people and homeowners are unlikely to exit concentrated poverty. Third, poor neighborhoods tend to remain poor because of a dynamic process in which initial residents experience high earnings growth but disproportionately out-migrate when earnings improve, contrasting with a pure “poverty trap” understanding of persistent concentrated poverty.

The Spatial Distribution of Income in Cities: Cross-Country Evidence and Theory

Peter Deffebach
,
Boston University
David Lagakos
,
Boston University
Yuhei Miyauchi
,
Boston University
Eiji Yamada
,
Japan International Cooperation Agency

Abstract

We draw on new granular data from 133 cities from 26 countries to study how the spatial distribution of income within cities varies with economic development. We document that in less-developed countries, average incomes of urban residents decline monotonically in distance to the city center, whereas income-distance gradients are flat or increasing in developed economies. Neighborhoods with natural amenities – in particular hills and proximity to a river – are poorer than average in less-developed countries and richer than average in developed ones. We explain these patterns in a spatial model where preferences are non-homothetic in both housing and neighborhood amenities. In cities with low average income levels, the primary consideration of households is job access, and relatively richer households locate closer to city centers, where earning opportunities are better. As income rises, richer urban residents put more weight on amenities and locate in neighborhoods with better ones, further from city centers. When estimated to match our data for U.S. cities, the model predicts that gaps in income levels and commuting costs can explain a large fraction of the difference in urban income distributions between less- and more-developed countries.

Exposure and Spatial Choice: Evidence from Nairobi

Joshua Dean
,
University of Chicago
Gabriel Emanuel Kreindler
,
Harvard University
Oluchi Mbonu
,
Harvard University

Abstract

Economic analysis of access to opportunities (jobs, education, healthcare, goods, etc.) often focuses on the role of prices, quality, and access costs. Building on research from psychology, marketing and neuroscience, we study the additional role of prior exposure with an option for shaping choices, in the context of urban mobility for work. In a sample of 800 casual workers in Nairobi, the median person commutes 7.8 km but has never been to half the neighborhoods within 75 minutes from where they live. To quantify pure preferences for familiar locations, defined as locations visited at least once in the past, we offer short-term employment and experimentally induce familiarity by training participants in either familiar or unfamiliar locations. Participants are willing to travel 3.5 km further or take a pay cut worth 22% of the median daily wage to avoid working in a location never visited before. This differential is fully offset after one visit to such an unfamiliar neighborhood. Individuals are also initially less likely to consider working in an unfamiliar neighborhood without prompting and a single visit closes half of this gap. Participants partially anticipate these effects. These effects persist for a different paid opportunity 2-4 months after the intervention, and participants report visiting training neighborhoods outside our study. Our results are consistent with unfamiliarity making people view neighborhoods more negatively, and we find little evidence that navigation costs or exploration risk can explain these results. Our results suggest that past exposure frictions are an important component of urban mobility costs in cities like Nairobi.

Remote Work and Consumer Cities

Franklin Qian
,
University of North Carolina-Chapel Hill
Yichen Su
,
Southern Methodist University

Abstract

The rapid adoption of remote work reduced the physical presence of workers in urban centers, weakening cities’ traditional role as centers of production. We highlight that cities’ role as centers of consumption remained robust and, with greater time flexibility from workers, may have grown in importance. We present a stylized model showing that the amenity value premium of dense urban areas can serve as an anchoring force for urban foot traffic despite residential suburbanization. Using detailed mobile-device foot traffic data, we find that while remote work reduced visits to former commuting destinations, it
simultaneously increased visits to amenity-rich urban hot spots. Our findings suggest that remote work
accelerated the transition of urban centers from commuting destinations to leisure destinations.

Discussant(s)
Patrick Bayer
,
Duke University
Christopher Severen
,
Federal Reserve Bank of Philadelphia
Victor Couture
,
University of Toronto
Jordan Rappaport
,
Federal Reserve Bank of Kansas City
JEL Classifications
  • R1 - General Regional Economics