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on Urban Economics and Policy |
| By: | José García-Montalvo |
| Abstract: | Do housing markets price flood risk only when a disaster makes it salient? I combine 3.4 million notarial housing transactions in Spain (2007–2025) with official flood-hazard maps and use the October 2024 Valencia DANA, which killed 232 people, as a nationwide shock to the salience of long-available risk information. After the DANA, prices per square meter in 10-year flood zones fell by about 8 % relative to unexposed dwellings, even outside the affected municipalities; repeat-sales estimates imply a decrease of 4-5 %. Before the event, these zones sold at no discount. Within the affected municipalities, demand shifted toward nearby safe housing, increasing prices in those areas and decreasing housing prices in affected areas. |
| Keywords: | capitalization, climate change, flood risk, housing prices, natural disasters, risk salience, valencia |
| JEL: | Q54 R31 Q51 R21 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1600 |
| By: | Daniel J. Wilson; Xiaoqing Zhou |
| Abstract: | From early 2021 through early 2024, the U.S. experienced an unprecedented boom in unauthorized immigration, with substantial heterogeneity across local economies. Using administrative microdata, we construct new measures of national and local unauthorized immigration flows and exploit exogenous variation using multiple empirical designs. Unauthorized immigrant worker flows increased local employment approximately one-for-one, with mixed evidence of wage depression. They also raised house prices and rents without expanding housing supply. Finally, these flows reduced labor income per capita and government transfers. These findings help inform debates surrounding how unauthorized immigration impacts labor and housing markets and public finances in the U.S. |
| Keywords: | immigration; labor markets; housing markets; post-pandemic |
| JEL: | E24 H53 J11 J21 J22 J23 J31 J61 R31 |
| Date: | 2026–10–15 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedfwp:103940 |
| By: | Tim Ederer |
| Abstract: | This paper studies how career dynamics shape teacher spatial sorting using data on Peru's centralized teacher allocation. I develop a dynamic two-sided matching model and identify teacher and school preferences from observed assignments and schools' discretionary evaluations. I use the estimated model to correct teacher value-added estimates for two-sided endogenous selection. The analysis reveals the existence of a spatial job ladder that dynamically sorts more effective teachers into urban schools, contributing to spatial inequality in student achievement. Targeted mobility restrictions can reduce the urban-rural effectiveness gap by 76 percent, but avoiding adverse selection requires compensating teachers for the implied welfare loss, highlighting an inherent recruitment–retention trade-off. |
| Keywords: | teacher spatial sorting, dynamic matching, teacher value-added |
| JEL: | J45 J62 I24 R23 C78 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13063 |
| By: | Megan Evans (Max Planck Institute for Demographic Research, Rostock, Germany); Noli Brazil |
| Abstract: | Urban residents increasingly rely on online tools to searchfor housing, raising new questions as to the role of digital tools in interrupting or reinforcing racialized housing searches. Generative Artificial Intelligence (GenAI), in particular, represents a new intermediary in how neighborhood knowledge is created and distributed in housing platforms and search engines. This study examines whether ChatGPT engages in racial matching by systematically varying user demographics in housing queries across 10 large U.S. cities. Using 14, 400 unique queries per city, we asked GPT-4o for neighborhood recommendations and areas to avoid. Merging AI generated recommendations with census data, we investigate racial patterns in how GPT-4o incorporate user characteristics and neighborhood demographics into their housing guidance. Findings suggest that AI recommendations reflect and may amplify existing patterns of residential segregation through systematic differences in how neighborhoods are recommended to different users. Results have important implications for informing both fair housing policy and the responsible development and regulation of AI systems in the housing market. |
| JEL: | J1 Z0 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:dem:wpaper:wp-2026-041 |
| By: | Guillaume Chapelle; Julia Paul-Venturine; Camille Urvoy (CY Cergy Paris Université, THEMA) |
| Abstract: | When a national government relaxes a local regulation, do local ocials comply, or find other ways to keep the same rules in place? We study a 2014 French reform that abolished floor area ratios (FAR), a widely used cap on building density, with the explicit goal of expanding housing construction. We assemble a new geospatial panel tracking three zoning tools floor area ratios, building heights, and coverage ratios | over 15 years across 118 municipalities in the Paris region, linked to building permits, housing stock, and transaction prices. Comparing municipalities where FAR existed before the reform to those where it did not, we show that local governments o set the deregulation by tightening the two instruments they retained: maximum heights fell, coverage ratios tightened, and permit denials rose. Density did not increase in previously regulated areas, and we detect no e ect on prices, consistent with the limited change in supply. The regulatory tightening was strongest where mayors faced closer elections and where property values are higher, not where mayors belong to parties opposing the reforming government, pointing to electoral accountability rather than partisanship. Top-down deregulation can therefore be substantially undercut when local governments retain complementary instruments, suggesting reforms must either limit local discretion directly or realign local incentives. |
| Keywords: | Decentralization, Fiscal federalism, Local political incentives, Upzoning, Land use regulations |
| JEL: | H77 R52 R38 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ema:worpap:2026-12 |
| By: | Cecilia H. Moreira; Steven L. Puller; Ini Umosen |
| Abstract: | School choice can weaken the link between residential neighborhood and educational opportunity only if students can reach the schools available to them. We study school choice in Chicago, where high schoolers can choose among schools throughout the city but generally rely on public transit, making the structure of the transit network a key determinant of opportunity. Using student-level rankings and public transit travel times, we document two systematic patterns: Black students are less likely to rank top-rated schools first and face longer public transit times to such schools. We estimate a discrete choice model to separate the value placed on schools from the costs of reaching them. We find that high schools vary substantially in how they are valued and that there is a high degree of agreement across racial groups in their relative ranking. However, transit times vary significantly across neighborhoods, generating racial differences in demand for top-rated schools. Counterfactually, giving Black students the travel times of white students increases demand for top-rated schools by 23 percentage points. Finally, we identify underserved neighborhoods where public transit access constrains demand and quantify how targeted bus service would increase demand for high-quality schools. Our counterfactuals quantify how transportation expands school access. |
| JEL: | I21 I24 R4 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35915 |
| By: | Garret Christensen; John Voorheis |
| Abstract: | We measure the effects of housing quality and living in renter- versus owner-occupied housing as a child on later life outcomes. Homeownership is the primary source of most US households’ wealth, and homeownership and housing quality potentially have large impacts on children, yet we know relatively little about any long-term effects. Ownership and housing quality are correlated with income, neighborhood characteristics, and many other socioeconomic characteristics, making causal estimation difficult. Using the US Census Bureau’s data linkage infrastructure, we create a new data linkage between the American Housing Survey (1995-2002) and administrative tax data (2016-2019), to investigate adult outcomes such as earnings and having a mortgage or owning a home, while comprehensively controlling for a wide variety of variables, including housing quality. We find strong positive correlations with growing up in a parent-owned home, approximately $2, 000 higher incomes and a 6 percentage point increased likelihood of ownership. However, we find no evidence of causal effects: unobservables that are significantly less correlated with outcome and treatment than observables would drive the coefficients of interest to zero, and family fixed effects models that compare siblings to each other show no effect of years of childhood spent in an owned home or being born into an owned home. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:26-54 |
| By: | Kirill Borusyak; Rafael Dix-Carneiro; Brian K. Kovak |
| Abstract: | A commonly used migration regression relates population changes to local labor demand shocks, often yielding small or statistically insignificant coefficients despite sizable effects on labor market outcomes. We argue that such findings need not imply that mobility is unresponsive to local economic conditions. Rather, conventional migration regressions can be biased, as they do not reflect how workers choose among competing locations. In a parsimonious equilibrium model, local population growth depends on differences between local shocks and those in relevant destinations; when these shocks are correlated, estimated responses are attenuated. Moreover, with industry-level shocks, standard shift-share measures misrepresent locations’ effective exposure, leading to further attenuation. Using simulation experiments based on Brazilian and German administrative data, we show attenuation can exceed 50% and that conventional regression estimates can be highly imprecise. We propose theory-grounded empirical approaches that correct the biases, deliver estimates with a clear structural interpretation, and improve the precision of estimated mobility responses. |
| JEL: | C21 F16 J61 R23 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35921 |
| By: | Martimort, David; Li, Yue; Rama, Martin; Straub, Stéphane |
| Abstract: | We develop a theory of urban governance based on the allocation of residual claims over urban surplus. A developer undertakes non-verifiable investments that attract productive residents and generate agglomeration benefits. We compare five urban governance modes: two single-agent modes, public and private ownership of the developer, and three hybrid arrangements. Public ownership internalizes urban rents but weakens investment incentives. Private ownership restores these incentives but restricts access because the developer does not capture property-market rents. We characterize this trade-off and study the three hybrid arrangements. Performance contracts subsidize access, equity contributions make quality observable and contractible, and rights transfers consolidate access and property rights. These arrangements provide alternative ways to improve urban development when investment is non-verifiable. |
| Keywords: | Urban Development; Urban Governance; Public–Private Partnerships;; Contract Theory |
| JEL: | D86 H54 L33 R58 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:132243 |
| By: | Stark, Philipp (Department of Human Geography, Lund University); Sopasakis, Alexandros (Centre for Mathematical Sciences, Lund University); Hall, Ola (Department of Human Geography, Lund University); Grillitsch, Markus (Department of Human Geography, Lund University) |
| Abstract: | Regional opportunities for industrial development arise when local conditions favor particular economic activities. These conditions depend in part on a region’s existing industry profile and on the economic relationships among its activities. Heterogeneous data sources on mobility, production, and firm ownership provide complementary evidence about this underlying industrial structure. We therefore develop a multi-view representation-learning framework for industries that integrates these different sources into a shared latent representation. By using graph diffusion, the framework captures both direct and indirect connections between industries. Applied to Swedish industries over three decades and three classification versions, the learned representation places industries from different classification periods within a common latent space. To evaluate this operationalization of opportunity, we construct an opportunity-density measure by combining latent similarity with local industry size. Higher opportunity density is consistently associated with industry entry and startup formation and with lower industry exit. Across classification periods, a one-standard-deviation increase in opportunity density is associated with 5.3–6.2 percentage-point higher industry-entry probabilities (118–136% of baseline rates), 8.9–13.8 percentage-point lower industry-exit probabilities (147–209%), and 1.1–1.7 percentage-point higher startup-formation probabilities (131–187%). Opportunity density remains associated with these outcomes in 41 of 42 models after accounting for information contained in the individual data sources. Our machine-learning framework shows how heterogeneous data can be integrated into a common latent structure and used to operationalize regional opportunity spaces, providing a new approach to studying industrial path development. |
| Keywords: | Regional opportunity spaces; Industrial path development; Industry relatedness; Multi-view representation learning; Regional diversification |
| JEL: | C45 L16 R11 |
| Date: | 2026–10–13 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lucirc:2026_012 |
| By: | James Horton (Methodist University, Charles M. Reeves School of Business and Economics); Jamie Bologna Pavlik (Texas Tech University, Free Market Institute) |
| Abstract: | This paper examines how large, sustained changes in the share of religious adherents affect county-level income in the United States using a stacked difference-in-differences design with inverse propensity score weights. Counties with large declines in adherent shares experience a roughly four percent decline in net income per capita at the time of treatment and a seven percent decline a decade later. The effects of increases in adherents are positive but slower to appear, with income rising by five to seven percent after one to two decades. Part of this relationship reflects changes in county demographics. Increases in adherents are accompanied by increases in the retirement-age population and, in turn, Social Security, Medicare, and asset income. Declines produce the opposite pattern. However, demographics do not explain the full effect. Jobs and net earnings per capita also rise with increases in adherents and fall with declines, and these results hold once we exclude counties with large contemporaneous changes in their retirement-age population. |
| Keywords: | religion, religious adherence, county income, demographics, stacked difference-in-differences |
| JEL: | Z12 R11 J11 H55 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:mtd:wpaper:003 |