I am a labor and urban economist at UC Berkeley Haas. I study how place and policies of the past shape inequality in the United States. I received my Ph.D. in Economics from MIT in 2024. Before joining Berkeley, I was a postdoctoral fellow at Stanford.

Name pronunciation tip — my last name is two Chinese characters (危吴) and spoken as way-woo

UPDATE: I am co-organizing the All-California Labor Economics Conference at UC Berkeley on September 10-11 with Conrad Miller and Na’ama Shenhav.

Working Papers

Unequal Access: Racial Segregation and the Distributional Impacts of Interstate Highways in Cities

Revision requested at American Economic Review

PDF Appendix PDF + Appendix Data

This paper measures the Interstate Highway System's impact on welfare inequality in cities separately by race and education. I quantify the incidence of commute benefits from connecting residences to workplaces, costs where highways are built, and equilibrium responses using novel data on commuting and historical maps nationwide. Given the concentration of benefits in suburbs and costs in the urban core, differential suburbanization by race, more pronounced than by education, contributes to racial gaps in welfare gains. Using a spatial equilibrium framework, opening access to peripheral areas for minority families eliminates the majority of the gap while preserving aggregate welfare improvements.

Best Student Paper, Urban Economics Association.
Allan Nevins Dissertation Prize, Economic History Association

Opportunity in Motion: Infrastructure, Job Access, and Intergenerational Mobility

PDF

This paper studies how transportation infrastructure improves job access and, in turn, shapes neighborhood economic development and the intergenerational outcomes of children. I use a newly linked administrative dataset covering the near-universe of families with children born between 1964 and 1979. Increased commuting access from Interstate highway construction raises average neighborhood income through both direct income gains for existing residents and compositional changes driven by selective migration. Using a movers design, I show that increases in neighborhood income benefit children and reduce intergenerational inequality, with larger gains for children from lower-income families. However, migration responses generate negative spillovers: areas with smaller access improvements experience declines in peer quality, adversely affecting children in those locations. I develop an accounting framework to quantify the aggregate effects on intergenerational mobility and find that the direct economic benefits of improved access outweigh these negative spillovers. Nevertheless, changes in neighborhood composition increase the spatial inequality of opportunity.

Russell Sage Foundation Pipeline Grant, Upjohn Institute Early Career Research Award

Changing Intergenerational Mobility and Family Structure: Evidence from New Linkages for the Mid-20th Century

with Martha Stinson

Measuring Winners and Losers from Increasing Housing Supply

with Vincent Rollet

PDF

Local governments often restrict the construction of new housing through zoning and permitting hurdles. This resistance suggests that increasing the housing stock in an area may harm some of its existing residents. Understanding these losses is key to facilitating new housing construction. In this paper, we aim to measure who gains and who loses from a local increase in the housing supply, and why. We build a model of household location choices over time, allowing us to measure the effect of construction on amenities, prices, and local taxes. To estimate this model, we employ detailed data describing individual housing units, households and their migration patterns, local public finances, and zoning regulations.

Publications

Essays on Inequality in Cities: Evidence from the Interstate Highway System

Dissertation summary for the Allan Nevins Prize for best dissertation in American Economic History.
Journal of Economic History, 2025, 85(2), 549-554

Works in Progress

New Origins, New Impacts? Immigration and Local Labor Markets

with Ran Abramitzky, Leah Boustan, and Yulia Valdivia Rivera

The Intergenerational Effects of Local Shocks: Income, Migration, and Human Capital

with Martha Stinson and Sean Wang

We study the channels through which changes in local economic conditions during early childhood affect long-run outcomes for children from differing economic backgrounds. We exploit geographic variation across counties in the decline of manufacturing employment during the 1979 to 1984 period with microdata from the Longitudinal Business Database. To assess the exogeneity of local labor market shocks, we construct additional shocks by combining industry-level energy intensity with spikes in oil prices as a result of the 1979 energy crisis. With administrative and survey data that trace the full trajectory of the children’s lives, we measure how these local changes impact educational attainment, income, and the quality of the firm of employment in the modern day. We explore how migration of parents away from counties experiencing declines and changes in parental income during childhood are central mediators for our findings.

Is Public Housing Good or Bad for Families?

with Beau Bressler and Dani Sandler

Other Papers

Intergenerational Linkages between Historical IRS 1040 Data and the Numident: 1964-1979 Cohorts

with Martha Stinson. Census Bureau Center for Economic Studies (CES) Technical Note

Link

We construct novel parent-child linkages between the universe of parent tax filers in IRS 1040 forms in 1974 and 1979 and the universe of children from the Census Numident in the cohorts of 1964 to 1979. Variables used for matching are parent names of children and names of parent tax filers, which are obtained from a restricted name file provided by the Social Security Administration. Applying name-matching techniques that incorporate supervised learning methods, we flexibly compare parent names and disambiguate parent-to-parent matches. This report documents the iterative process for identifying matches and the algorithm that is used for assessing the likelihood of a match. We provide match rates for different demographic groups and validate the accuracy of the linkages.