PUBLICATIONS and FORTHCOMING
- Media Coverage: The Atlantic, Washington Post
- Previous version: NBER Working Paper No. 24933
- Previously Circulated as "Who Benefited from Women's Suffrage?" and "Women's Enfranchisement and Children's Education: The Long-Run Impact of the U.S. Suffrage Movement"
WORKING PAPERS
- Featured in NBER The Bulletin on Health, Issue 2, June 2022
"This paper evaluates the effects of maternal access to the largest federal preschool program in the U.S., Head Start, on infant health at birth using Vital Statistics Natality data. Using variation in the year of Head Start's introduction in each county from 1965 to 1980, I nd that mother's access to Head Start during childhood improves infant health. As potential pathways to improvements in infant health, I show that Head Start availability increases the probability that a new mother is more educated, reduces the number of births, reduces likelihood of smoking and drinking during pregnancy. These findings suggest that the total benefits of the early childhood programs are long lasting."
"We introduce a new data source on the War on Poverty programs from 1965-1969, using a collection of newly discovered and digitized Poverty Program Information (PPI) books. This grant-level data set has four primary contributions. First, it includes the name of each grant recipient, the type of program funded, and the amount of funding that was appropriated. Second, it indicates whether the grant coverage was nationwide, multiple state, individual county, or for multiple counties. Multiple county program listings provide information about the recipient counties by local agencies for the first time. Third, for several programs, it provides novel information on enrollment. Finally, it includes information on a new set of War on Poverty programs that have not been previously studied due to lack of data."
"In this paper, we produce population estimates for the 1960s at a newly-available level of detail, by age, year, and county, covering ages 1 through 21. To do so we train an artificial neural network on a rich set of demographic features on data from the 1970s and 1980s. We train our model to predict reported population counts from the Survey of Epidemiology and End Results. The contributions of this paper are twofold. First, we produce and make publicly available our new population estimates. These estimates are better than a linear interpolation benchmark - using an out of sample testing data set, the median absolute percentage error is about 47% smaller. Our second contribution is to frame the population estimation exercise as a prediction problem, and to demonstrate that tools from the machine learning literature can give improved estimates for this type of problem. This new tool has promise for additional population prediction problems."
SELECTED RESEARCH IN PROGRESS
- Women's Suffrage and Children's Education (with Elira Kuka and Na'ama Shenhav), American Economic Journal: Economic Policy, Vol. 13(3): 374–405, 2021 (Ungated PDF)
- Media Coverage: The Atlantic, Washington Post
- Previous version: NBER Working Paper No. 24933
- Previously Circulated as "Who Benefited from Women's Suffrage?" and "Women's Enfranchisement and Children's Education: The Long-Run Impact of the U.S. Suffrage Movement"
- Public Investments in Early Childhood Education and Academic Performance: Evidence from Head Start in Texas, 2023Forthcoming at the Journal of Human Resources
- Head Start Funding Expansions and Program Inputs, 2023 (with Chris Herbst) (Forthcoming at the Public Finance Review)
WORKING PAPERS
- Does the Delivery of Primary Health Care Improve Birth Outcomes? Evidence from the Rollout of Community Health Centers, May 2022, (with Siobhan O'Keefe and Maria Rosales-Rueda) (Revise and Resubmit at the Journal of Human Resources)
- Featured in NBER The Bulletin on Health, Issue 2, June 2022
- The Intergenerational Effects of Head Start on Infant Health, January 2022
"This paper evaluates the effects of maternal access to the largest federal preschool program in the U.S., Head Start, on infant health at birth using Vital Statistics Natality data. Using variation in the year of Head Start's introduction in each county from 1965 to 1980, I nd that mother's access to Head Start during childhood improves infant health. As potential pathways to improvements in infant health, I show that Head Start availability increases the probability that a new mother is more educated, reduces the number of births, reduces likelihood of smoking and drinking during pregnancy. These findings suggest that the total benefits of the early childhood programs are long lasting."
- New Data on War on Poverty Programs in the 1960s (with Henry Manley and Doug Miller), October 2022
"We introduce a new data source on the War on Poverty programs from 1965-1969, using a collection of newly discovered and digitized Poverty Program Information (PPI) books. This grant-level data set has four primary contributions. First, it includes the name of each grant recipient, the type of program funded, and the amount of funding that was appropriated. Second, it indicates whether the grant coverage was nationwide, multiple state, individual county, or for multiple counties. Multiple county program listings provide information about the recipient counties by local agencies for the first time. Third, for several programs, it provides novel information on enrollment. Finally, it includes information on a new set of War on Poverty programs that have not been previously studied due to lack of data."
- Backcasting Population Data in the 1960s with Supervised Learning (with Henry Manley and Doug Miller), October 2022
"In this paper, we produce population estimates for the 1960s at a newly-available level of detail, by age, year, and county, covering ages 1 through 21. To do so we train an artificial neural network on a rich set of demographic features on data from the 1970s and 1980s. We train our model to predict reported population counts from the Survey of Epidemiology and End Results. The contributions of this paper are twofold. First, we produce and make publicly available our new population estimates. These estimates are better than a linear interpolation benchmark - using an out of sample testing data set, the median absolute percentage error is about 47% smaller. Our second contribution is to frame the population estimation exercise as a prediction problem, and to demonstrate that tools from the machine learning literature can give improved estimates for this type of problem. This new tool has promise for additional population prediction problems."
SELECTED RESEARCH IN PROGRESS
- The Life-Course Effects of Access to WIC (with Marianne Bitler, Danea Horn, Maria Rosales-Rueda and Arian Seifoddini)
- Head Start's Long-Term Impacts on Children (with Doug Miller)
- Construction of the County-Year Head Start Spending Data (with Doug Miller)