Nishaad Rao

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.

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). 

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.