Research

Working Papers (with Drafts)

The Intergenerational Wealth Effects of Local Labor Markets
Current draft (Frequently updated. Latest version: November 10, 2022)
Poster (from Stone Center of Inequality Dynamics)

Abstract: Between 1999 and 2019, income and house prices have diverged across local areas in the United States as some cities have seen persistent growth in their labor markets while others have not. These divergent trends across labor and housing markets have an effect on wealth, especially housing wealth, which persists across generations. This paper asks how the local markets of parents shape their children’s wealth and affect wealth inequality. Using an event study style analysis, I find that children who grew up in better local labor markets have, on average, $45,000 higher net-worth as adults. This association is only true for the children of homeowner parents. To measure the aggregate effect of this divergence on wealth inequality, I build a parsimonious, multi-region modeling framework, and find that the dispersion in local labor market growth accounts for 40% of the rise in wealth inequality amongst the bottom 90% of households, primarily because parental concerns about bequests make households in growing areas save disproportionately more.

When the Going Gets Tough, the Rich Get Going: The Effects of Local Labor Demand Shocks on Migration
Current draft (Latest version: March 5th, 2022).

Abstract: This paper explores the role of wealth in mediating the relationship between worker mobility and adjustments to local labor demand shocks within the United States. These shocks affect local population, which leads to changes in house prices. In turn, this affects the cost of living of all local workers and the wealth of homeowners. To quantify the wealth effect, I compare the mobility of homeowners and renters between labor markets in a difference-in-differences setting. The results suggest spatial heterogeneity in the way homeowners and renters react to local labor demand shocks: for the same negative shock, homeowners in Metropolitan Statistical Areas (MSAs) that have an inelastic housing supply out-migrate at rates 1.3 percentage points lower than renters, while homeowners and renters in MSAs with more elastic housing supplies out-migrate at similar rates. Further, amongst owners in inelastic MSAs, the effect of the shock is heterogeneous with respect to wealth -- those in the third quartile of the wealth distribution move 1.5 percentage points more than those in the bottom quartile, which points to mobility costs being important. 

Work In Progress

The Effect of Local Labor and Housing Markets on Wealth Portfolios in the United States

Caste Inequality Across Time and Space
with Arpit Gupta and Anup Malani

How Representative Are Tax Data for Research? Comparing Full Universe Tax Filings Data with U.S. Census Data
with Trent Alexander and Katie Genadek

Non-Peer Reviewed/Technical Papers

Updating the Geospatial Data in the PSID Restricted Data Enclave for 2005-2017
with David Johnson, Noura Insolera, and Mohammad Mushtaq
PSID Technical Report. Paper (latest version: April 2022).

Abstract: In 2019, a new geocoding procedure was initiated to prioritize the use of the physical address of the residence. During the past several waves (2005-2017), we found that for a small subset of cases, the U.S. postal mail address provided by the respondent for the mailing of the interview incentive payment was used to determine the geocode, even when a different physical address was provided. The current 2019 geocoding process was updated to use the postal mail address only when there is no physical address. This technical note describes the differences in the geocoded addresses using this new process (physical address) compared to the original process (mailing address for some). 

COVID-19 and Stay-At-Home Orders: Getting the Event Study Right
with Jaedo Choi, Elird Haxhiu, Thomas Helgerman, and Taeuk Seo
Covid Economics 76: 110-137. Paper (Latest Version: April 10, 2021).

Abstract: This paper estimates the dynamic effect of Stay-At-Home (SAH) orders on the transmission of COVID-19 in the United States. Identification in this setting is challenging due to differences between real and reported case data given the imperfect testing environment, as well as the clearly non-random adoption of treatment. We extend a Susceptible-Infected-Recovered (SIR) model from Epidemiology to account for endogenous testing at the county level, and exploit this additional structure to recover identification. With the inclusion of model-derived sufficient statistics and fixed effects, SAH orders have a large and sustained negative effect on the growth of cases under plausible assumptions about the progression of testing. Point estimates range from a 44% to 54% reduction in the growth rate of cases one month after a SAH order. We conclude with a discussion on extending the methodology to later phases of the pandemic.