Research

This paper provides causal evidence that the neighborhoods where students grow up play a significant role in shaping their college major choices, focusing on STEM fields. Using administrative data from Texas and variation in the timing of school moves across counties and districts, we estimate the impact of neighborhood exposure on the likelihood of pursuing a STEMmajor. We find that students who move to high-STEM neighborhoods—defined by the share of non-moving peers who earn STEM degrees—are increasingly likely to major in STEM with each additional year of exposure. We also show that neighborhood STEMexposure is strongly tied to the local occupational landscape, especially the concentration of residents working in STEM fields, with the highest-STEM areas clustered around major research and technology hubs. This suggests that exposure to local STEM careers is a key mechanism behind the observed effects. Importantly, the benefits of STEM-rich neighborhoods extend to underrepresented groups, including students from economically disadvantaged backgrounds, women, and racial minorities. These findings underscore the critical role of neighborhood environments in shaping educational pathways and highlight their importance in addressing educational inequality and strengthening the STEM workforce.

We examine public school districts capital spending. Using 2609 school districts over 40 years, we estimate a cointegrated relationship that shows how capital spending in the long run depends on school district population, enrollment, and per capita income. Assuming that capital spending is endogenous to these variables, we estimate an error correction model that shows how capital spending adjusts to shocks to population, income, and enrollment. We f ind that school spending reacts slowly to shocks and in particular, converges very slowly to the long-run stochastic equilibrium. These patterns are illustrated using graphically using impulse response functions. We find that school districts that make frequent use of borrowing have relatively higher school capital. These results challenge the belief that school districts operate on identical education production possibilities frontiers, suggesting that estimates of the return to school capital may be biased by different district objectives or administrative practices.

I investigate the impact of standardized test classifications on students' college major decisions. Focusing on Texas' STAAR Algebra I exam, which categorizes students into tiers based on performance, I examine whether these classifications influence self-perception and academic choices. Using a regression discontinuity design, the causal effect of being classified just above or below the threshold on high school graduation, college enrollment, and STEM major choice. I find no significant discontinuities at the classification cutoff, suggesting that these labels do not affect student choices. Further analysis on subgroups, including high and low STEM areas, grade at which a student takes the exam, and race, gender, and socio-economic status, reveals no patterns based on these factors. These results may imply that students do not place any weight on standardized test exam results, and that other factors play a more pivotal role in their decision-making

Teacher quality is a central determinant of student achievement, yet identifying effective teachers remains difficult because observable characteristics such as advanced degrees or years of experience explain little of the variation in classroom performance. This paper exploits the institutional setting of school closures to study how districts respond when they possess direct information about teacher effectiveness. Using administrative data from Texas that links students, teachers, and test performance from 2012–2019, I estimate teacher value-added following Chetty, Friedman, and Rockoff (2014a, 2014b) and track teachers displaced by closures. I document whether high–value-added teachers remain in the profession at higher rates than their lower-performing peers, and whether districts selectively retain more effective teachers during reallocation. After seeing that retention is seemingly random, I design an optimal reallocation model, and show how schools and students would be better off if they followed this model.