I am an applied microeconomist and PhD Candidate at Cornell University. My research focuses on the impacts of education and immigration policies, which are at the intersection of the economics of education, urban economics, and labor economics.
Prior to attending Cornell, I was a research assistant at the University of Michigan’s Ford School of Public Policy. Before that, I was a research analyst at the University of Chicago’s Consortium on School Research. I graduated from the University of Michigan – Ann Arbor with a M.S. in Applied Statistics and from the University of Chicago with a B.A. in Economics.
I will be available for interviews for the 2021-2022 job market.
PhD in Policy Analysis and Management, 2022 (Expected)
MS in Applied Statistics, 2016
University of Michigan - Ann Arbor
BA in Economics, 2012
University of Chicago
For the past decade, the federal government and an increasing number of states and school districts across the US have begun to focus on the social, learning, and working conditions experienced by students, families, and teachers. Despite this trend, causal research on how various stakeholders value school climate is limited. In this paper, I link real estate transactions data with home loan applications data to investigate the effects of publicly releasing school climate information on the housing market and its potential effects on neighborhood gentrification. Using a plausibly exogenous shock of school climate information in Chicago, I employ event studies and a difference-in-differences framework. I find that providing this information leads to sales price increases for homes zoned to better climate schools. Similarly, I find that the information shock attracts new higher-income homebuyers to move into neighborhoods with better climate scores, even in some of the least expensive neighborhoods. Both information effects dissipate soon after the information release. One mechanism for these dissipating effects is through the change in information salience, making it costlier to find the information even though it remains publicly accessible.
Research on the effects of immigration enforcement on likely-undocumented immigrants’ decisions to invest in their local economies is limited. In this paper, I investigate whether local immigration enforcement policies (287g partnerships) affect targeted groups’ willingness and ability to invest in their local communities through becoming homebuyers. I use event studies and a triple-difference framework with variation in treatment timing to compare counties that successfully applied for partnerships with those who were denied. I find evidence that implementing local 287(g) partnerships lead to large and statistically significant declines of about 12% in the number of home loan applications by Latino applicants (treatment) compared to non-Latino applicants (control). I explore heterogeneity by program partnership type. Additionally, I show that studies that use the sample of counties that apply for and are rejected or accepted by ICE into 287(g) partnerships should account for strong differences in pre-trends between these counties.
In this paper, we use multiple data science tools to create a novel dataset of school board elections in the 1,000 largest U.S. school districts between 2010 and 2019. To our knowledge, this is the only dataset to focus on the largest school districts in the US and to include variation across states and regions. Using this unique data and machine learning techniques, we identify the demographic and political backgrounds of elected school board members. Using regression discontinuity methods, we isolate causal variation in school board composition across a number of dimensions. We use this dataset to investigate the effects of school boards’ political party, race/ethnicity, and gender composition on various outcomes, ranging from financial decisions, student performance, hiring of district leadership (i.e., superintendents), and school choice policies.
Schools aim to retain experienced, high-quality teachers. This is especially difficult in low-income, low-performing schools, which suffer from higher rates of teacher turnover. In this study, I investigate how new school climate information can attract or deter teachers in different types of schools. Using administrative public school teacher records from the Illinois State Board of Education, I employ a difference-in-differences framework to estimate the effects of a plausibly exogenous shock of school climate information.
In this paper, I study the unintended effects of transitioning Chicago’s highly coveted exam schools’ admissions policy from a race-based affirmative action regime to a neighborhood socioeconomic-based regime.
[with Li (Julia) Zhu]
[with Alexandra Cooperstock]
Horowitz Foundation for Social Policy, Dissertation Grant Semi-Finalist (2021)
Russell Sage Foundation Summer Institute on Migration Research Methods (2020)
American Economic Association (AEA) Summer Teaching Fellowship (2019)
State University of New York Graduate Diversity Fellowship (2016)
National Science Foundation (NSF) Graduate Research Fellowship (2014)
University of Chicago - The College Dean’s List (2011)
American Economic Association (AEA) Summer Program Fellowship (2010)
Bill & Melinda Gates Millennium Scholarship (2008)
Lab/Section Instructor: Summer 2020, Spring/Summer 2021 (Cornell University)
Teaching Assistant : Summer 2019 (Michigan State University)