Rene Crespin

Rene Crespin

PhD Candidate

Cornell University

About Me

I am an applied microeconomist and PhD Candidate at Cornell University. I study topics at the intersection of education, public, and labor economics. My developing research portfolio focuses on applying tools of causal inference and data science to study inequality. I currently have ongoing projects on education, housing, and immigration policy.

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 am on the job market during the 2021-2022 academic year.

Interests

  • Economics of Education
  • Public Economics
  • Labor Economics

Education

  • PhD in Policy Analysis and Management, 2022 (Expected)

    Cornell University

  • MS in Applied Statistics, 2016

    University of Michigan - Ann Arbor

  • BA in Economics, 2012

    University of Chicago

Working Papers

For the past decade, the federal government and an increasing number of states and school districts across the US have begun to invest and focus on the social, learning, and working conditions (school climate) experienced by students, families, and teachers. Despite this trend, causal research on whether and how much various stakeholders value school climate is limited. In this paper, I investigate how publicizing school climate information is capitalized into the housing market and how it affects the sorting of homebuyers from different socioeconomic backgrounds. 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 publicly leads to an overall house price increase of 2% for a one-level-higher school climate rating. Additionally, I find a 2% increase in the average income of new homebuyers moving into neighborhoods assigned to a one-level-higher school climate rating. These effects are almost entirely driven by transactions in attendance zones with better-climate schools. These initial effects dissipate over time, as information becomes less salient. The effects are consistent across different types of schools and neighborhoods. I explore various potential mechanisms for these effects. I find evidence that homebuyers value this dimension of school quality that has been understudied in the revealed preferences literature.

Effects of Immigration Enforcement Policies on the Housing Market: Evidence from 287(g) Partnerships

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.

Works in Progress

The Role of Local School Boards on School Finance, Hiring, and School Choice [with Maria Fitzpatrick]

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.

The Effects of School Climate Information on Teacher Mobility

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.

Elite School Choice: Unintended Consequences of Place-Based Affirmative Action

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.

Direct and Spillover Effects of DACA: Evidence from Consumption Patterns and Labor Market Outcomes

[with Li (Julia) Zhu]

Effects of Promise Neighborhoods on Neighborhood Migration and the Housing Market

[with Alexandra Cooperstock]

Fellowships, Honors, and Awards

National Academy of Education/Spencer Dissertation Fellowship (2021)

Outstanding Teaching Assistant Award, Big Data for Big Policy Problems (2021)

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)

Teaching

Big Data for Big Policy Problems

Lab/Section Instructor: Summer 2020, Spring/Summer 2021 (Cornell University)

Intermediate Microeconomic Theory

Teaching Assistant : Summer 2019 (Michigan State University)