My research focuses on the forces that create and maintain disparities in educational outcomes. 

Immigration Enforcement and Student Achievement

The position of unauthorized immigrants within the United States was a hotly debated issue during the 2016 presidential campaign. Since his inauguration, President Trump has continued advocating for and implementing immigration enforcement policies designed to remove larger numbers of unauthorized immigrants from the United States. My dissertation explores the spillover effects of increasing immigration enforcement on the children of immigrants' educational achievement. Approximately 5.1 million children residing in the United States have at least one unauthorized parent; roughly 80 percent of these children were born in the United States and are U.S. citizens.  Immigration enforcement policies may, therefore, have long-term consequences for the education levels of the U.S. workforce, as well as increase pre-existing disparities between different groups of students. I investigate these questions using quasi-experimental methods at two different levels: using measures of average school district achievement, available across the United States, as well as data on all students in North Carolina, available at the individual-level.  

Food Instability and Student Outcomes

In work with Anna Gassman-Pines, we use a natural experiment in North Carolina to analyze how recency of Supplemental Nutrition Assistance Program (SNAP) benefit transfer affects students' test scores (forthcoming in American Educational Research Journal) and disciplinary incidents. Results suggest that the relationship between students' test scores and SNAP transfer is roughly curvilinear, with test scores peaking in the third week post transfer (shown below). We argue that families are likely spending the majority of their benefit during the first half of the month; after families deplete their resources, families may experience increased stress or children may have access to less nutritious food, either of which affects children's ability to learn. Our work contributes to a growing body of evidence that increasing SNAP amounts might have spillover beneficial effects for children (op-ed and additional press coverage). 

Teacher Preparation Programs

Good teachers matter. But most beginning teachers are not very good. Understanding how to train beginning teachers has therefore been a goal of many interested in education policy. Indeed, sixteen states hold teacher preparation programs (TPPs) accountable for the test scores of their trainees' students. However, even though the desire to improve teacher training is laudable, this practice may be faulty. 

  1. With Jane Lincove, Cynthia Osborne, and Nick Mills, we find that, even though teachers from independent non-profits TPPs outperform teachers from other TPPs, these trainees teach only in very select areas of Texas (published in The Journal of Teacher Education). 
  2. With Paul von Hippel, Jane Lincove, Cynthia Osborne, and Nick Mills, we find that it is very difficult to distinguish between TPPs in Texas - differences are small, and estimates are noisy (published in Economics of Education Review). As shown below, few TPPs appear to be significantly different from the average when we correct for multiple testing, and the distribution of TPP effects very closely resembles the null distribution - the distribution of what estimates would look like under homogeneity (if estimates differed only due to random estimation error). 
  3. Paul von Hippel and I apply similar statistical techniques from our Texas study to previous studies in five other states. We find, like in Texas, that detectable differences between teachers from different TPPs are negligible (published in Economics of Education Review; article in Education Next; additional press coverage). We also release a Stata program (ssc install caterpillar) that allows users to replicate our methods, as well as easily calculate null distributions more generally.

 In blue are value-added estimates for every teacher preparation program in Texas, along with 95% confidence intervals for those estimates. In pink is the null distribution, or the pattern estimates should follow if programs were actually identical and ranked on estimation error alone. 

In blue are value-added estimates for every teacher preparation program in Texas, along with 95% confidence intervals for those estimates. In pink is the null distribution, or the pattern estimates should follow if programs were actually identical and ranked on estimation error alone.