Google Scholar profile
The long run effects of anti-immigrant institutional discrimination: Evidence from Philadelphia (Job Market Paper) (link)
Abstract: I estimate the long-run and intergenerational effects of institutional discrimination using a policy targeting Irish immigrants in 19th century Philadelphia. To do this, I construct a panel of US Census data from 1850 to 1910 linked to inmate data from a large prison, and find that the rate of incarceration for Irish men increased following the implementation of the policy. Using a differences-in-differences design, I find that impacted Irish individuals had worse labor market outcomes nearly 25 years after the enactment of the policy, and are more likely to move to a different county. While the effects of the discriminatory policy do not persist across generations on average, first-generation mobility contributes to the persistence of effects. Irish individuals who moved from Philadelphia and their children are less negatively affected than those who stayed, suggesting that institutional discrimination has lasting effects for those unable to move away from the source of the discrimination.
The long-run and intergenerational effects of natural disaster exposure: Evidence from the Galveston Hurricane of 1900 (link)
Abstract: I exploit a natural experiment to examine the long-run and intergenerational effects of a major negative shock, exploring how where we live can have long-lasting impacts. I examine outcomes of individuals impacted by the Galveston Hurricane of 1900, whose landfall was poorly forecasted in the United States. Using historic newspaper records from The Houston Post, I am able to identify towns which sustained significant physical damage or were completely destroyed by the storm. Leveraging panel data of linked US Census records for individuals living in southeast Texas in 1900, I find that individuals in affected towns were less likely to migrate, had lower employment rates, shorter lifespans, and poorer literacy and occupational outcomes compared to similar individuals in nearby unaffected towns. These negative effects persisted into the next generation, pointing to both persistent long-run and intergenerational effects of negative shocks, which may be driven by individuals’ migration behavior.
"The socioeconomic effects of forced displacement: Evidence from the Tennessee Valley Authority" with Andre'nay Harris (link)
Abstract: We examine the socioeconomic effects of forced migration by focusing on individuals who were displaced by the Tennessee Valley Authority (TVA) dam projects in the 1930s. We use data from the relocation program associated with the TVA and link it to US Census data. We compare individuals who were impacted by the dam-induced flooding with individuals in the same counties that were not affected by the dam construction due to their proximity to the Tennessee River. We find evidence that individuals who were impacted by the dam projects are more likely to participate in the labor force, with an influx into unskilled occupations. They are also more likely to pay higher rent prices conditional on renting. We examine racial disparities in outcomes and find that, after the relocation, Black men are more likely to be employed in unskilled occupations compared to White men.
“Male‐biased sex ratios, marriage, and household composition in early twentieth‐century Hawai‘i”, Asia-Pacific Economic History Review (2024), with Sumner La Croix, Timothy Halliday, and Joseph Price (link)
Abstract: Immigration to Hawai‘i between 1870 and 1930 led to a more than six‐fold increase in population and high and rapidly varying sex ratios in the Chinese, Japanese, Korean, Filipino, and Caucasian populations of marriageable age. Using complete populations of the 1910, 1920, and 1930 Territorial Censuses of Hawai‘i, we estimate how male‐biased ethnic sex ratios affected choices of second‐generation men and women of marriageable age. Econometric results indicate that within‐group and extra‐group sex ratios impact the likelihood of males and females to marry, to marry a spouse from another ethnic group, to have children, and to live in larger households.
“Using Linked Census Records to Study Shrinking Cities in the United States from 1900 to 1940”, The Professional Geographer (2022), with Joseph Price and Samuel Otterstrom
Abstract: We develop a data-driven method for linking people in cities over time that can be used in any country that has data tracking the locations of individuals across multiple periods. We apply this process to United States Census data from 1900 through 1940 and find that, of the 1,000 largest cities in 1900, 15 percent experienced a decline in population by 1940. We also use the large data set for this same time period, linking more than 45 million people across adjacent census records to examine which types of people exit a shrinking city and how their eventual socioeconomic outcomes differ from those who stay. Nationally, we find that those who left shrinking cities had longer life spans, greater income, better jobs, and higher education than those who stayed. We note that the regional analyses tend to follow the positive national pattern while indicating the geographic place-based differences of the cities that lost population. We also show the relation of race to the tendency to migrate from different types of cities. This method for linking millions of individuals across censuses has the potential to reveal other important characteristics of past populations, such as multidecade migration patterns and household changes in various regions over time.
"Combining family history and machine learning to link historical records: The Census Tree data set." Explorations in Economic History 80 (2021), with Joseph Price, Kasey Buckles, and Isaac Riley
Abstract: A key challenge for research on many questions in the social sciences is that it is difficult to link records in a way that allows investigators to observe people at different points in their life or across generations. In this paper, we contribute to recent efforts to create these links with a new approach that relies on millions of record links created by individual contributors to a large, public, wiki-style family tree. We use these “true” links both to inform the decisions one needs to make when using automated methods to link records and as a training data set for use in a supervised machine learning approach. We describe our procedure and illustrate its potential by linking individuals across the 100% samples of the US censuses from 1900, 1910, and 1920. When linking adjacent censuses, we obtain an overall match rate of 62-65 percent (for over 88.9 million matches), with a false positive rate that is around 6-7 percent and with links that are similar to the population along observable characteristics. Thus, our method allows us to link records with a combination of a high match rate, precision, and representativeness that is beyond the current frontier. Finally, we demonstrate the potential of the data by estimating the degree of intergenerational transmission of literacy between father-son and mother-daughter pairs.
"The Orphan Train Experiment: The Impact of America's First Large-Scale Child Welfare Program" with Maxwell Bullard