economics

I’m an applied microeconomist who thinks about gaps in access to opportunity, application systems, and supporting students’ growth. While I am a labor and education economist at my core, I also filter in and out of many other fields — like market design, IO, econometrics, development, theory, and behavioral — using their tools for the questions I want to answer.

job market paper

The Winner's Curse in Test-Blind Admissions (link)

with Nagisa Tadjfar and Kartik Vira

When universities admit students without considering exam scores, how does this impact test effort? How universities screen students? The overall match? We examine these with evidence from the UK.

abstract

High-stakes final exams shape students’ educational paths in two ways: universities can screen applicants with more information about their ability, changing the equilibrium match, and exams may create incentives for students to exert effort. We study these forces using the staggered introduction of unconditional offers in UK university admissions, which allowed students to matriculate regardless of results from their age-18 exams (A-levels). Students who accept an unconditional offer score 0.14 SD below observably similar students who decline one, and so programs that make unconditional offers face a winner’s curse. We show this gap reflects students’ private information rather than reduced effort: using a two-way fixed effects design, we find that exposure to unconditional offers has a precisely estimated null effect on students’ age-18 exam performance. Unconditional offers have positive match effects for recipients: exposure increases matriculation by 1.1pp, largely at middle-tercile universities. Using a structural model, we find that unconditional offers are anti-meritocratic, as they benefit the lowest-achieving quartile of students at the expense of higher-achieving students, lowering total welfare by 0.5% relative to a ban. Post-qualification admissions, under which students apply after receiving their scores, raise welfare by 14.2% through better sorting. This gain is mostly attributable to increased information for both sides of the market rather than to the change to a single-stage admissions system. Our results inform current policy debates in the UK, highlighting how the distributional effects of admissions policies depend on student information.

reflections

This paper’s come a long way from its beginnings, and I’m very happy with how it’s turned out. There’s a lot more I’m now interested in studying in the UK, but I think this work answers some important questions about how university admissions should be designed. Excited for this to be my Job Market Paper!

other research

working papers (with drafts)

Identifying and Nurturing Math Talent: Evidence from Tamil Nadu (link)

with Esther Duflo, Glenn Ellison, Sara Fisher Ellison, and Harini Kannan · AEA pre-registry

How do we find talented but underserved students? How can we support their growth? How far can that growth go?

abstract

This paper presents evidence from a randomized controlled trial designed to test how to best identify and support gifted students from disadvantaged backgrounds. To do so, we offered access to an advanced online prealgebra course and an internet-enabled tablet to 7th-grade public-school students from Tamil Nadu, whom we identified as having high potential using several alternative strategies. Our primary finding is that we were able to successfully identify students who succeeded in challenging coursework originally designed as enrichment material for students with much stronger math preparation.

We randomly assigned identification methods, which allowed us to investigate their relative effectiveness. We find that the combination of existing administrative scores and engagement with an extract of the textbook distributed as a physical booklet best predicts engagement and success with the course material. We additionally cross-randomized treatments to evaluate program design (including group and individual tutoring, incentives for students, access to computer labs), and find that the simplest-to-scale version without tutoring led to the highest engagement.

reflections

My coauthors and I are running an RCT with 2,400 gifted 7th-grade students in Tamil Nadu and an online self-paced prealgebra class from AoPS. We’re currently in the middle of Phase 2, and are excited to see the long-term effects. Presented by coauthor at the NBER Economics of Talent Meeting (Fall 2025), NBER Economics of Education Meeting (Spring 2026).

A Practitioner's Guide to Estimating School Effects in Centralized Assignment Systems (link)

We can identify school effects from lotteries in centralized school assignment. But what parts of the lottery do we actually need to do so? I show when and how propensity scores and causal estimates can be identified.

abstract

Centralized school assignment with lottery tie-breaking generates quasi-experiments that identify school effects, but existing methods require applicants’ rank-order lists and lottery numbers, data that is not always available. I characterize which data from a match identify a propensity score under which offers are ignorable. When each school draws its own lottery, offers among applicants who reach a school are randomly assigned, so comparing applicants offered and rejected there requires only waitlists. Under a single lottery, this comparison is biased, and the order of each applicant’s rejections is also needed.

Rank-order lists identify the score without lottery numbers, while the assignment and published aggregates do not in general. I show how to compute each score in finite markets, how waitlist order reveals which schools share a lottery, and how to handle priorities and screened schools. In simulations, all valid designs recover the true effect, and richer data yields more precise estimates.

reflections

I do a lot of causal effects estimation using centralized assignment.¹ But we don’t always have all of the data about centralized assignment. In this paper, I’ve put together a list of when we can and can’t recover causal effects.

1. See also my MDRD blog post c: ↩

School-House Shocks: The Effect of Neighborhood-based School Assignment Policies on Intracity Migration

Examining Chicago’s location-based affirmative action — how do admissions policies influence migration and housing prices?

abstract

Affirmative action policies based on geographic characteristics are potentially manipulable by families who move in response to the policy, undermining policy goals and hurting families who the policy is intended to help. I test whether these channels have significant impacts in Chicago, where admissions to selective high schools depend on the demographics of a student’s neighborhood. Using data from Chicago Public Schools and the Cook County Assessor’s office, I find no effects of marginal tier assignment on short-term migration of families or long-term housing prices.

reflections

My second-year paper! I’d love to improve on these estimates using individual-level housing data, though this seems difficult to acquire.

works in progress

From Cubs to Bears: The Evolution of Charter Effectiveness in Chicago

How effective are charters? How has that effectiveness changed over time, and why?

abstract

This paper estimates how the effectiveness of Chicago charter schools has changed between 2009 and 2025. Using random variation from admissions lotteries, I validate earlier research showing high returns to attending Chicago charter schools. I also show effectiveness decreased during the 2010s, but has rebounded in recent years. I explore determinants of these changes, including teacher qualifications, teacher turnover, leadership changes, curricular changes, and policy changes.

reflections

Questions about admission design have gotten much more pertinent given SFFA vs. Harvard, and even more relevant given CPS’s recent interest in neighborhood schools. As a Blueprint Labs research associate, I’m also leading some work on Chicago’s charter schools, with a particular focus on the Noble Network of Charter Schools.

works in “progress” (still marinating on these)

the role of gifted programs in the education system

where does motivation come from? how does one decide they “like” school?

early decision/action as a form of market power

alternative pathways to college/besides college

education

Massachusetts Institute of Technology · PhD in Economics, 2021–2027 (exp.)

NSF Graduate Research Fellowship · NBER Doctoral Dissertation Fellowship, Global Math Talent · Jerry A. Hausman Dissertation Fellowship

reflections

It’s crazy to think that I’m at the end of grad school and am going on the job market. There’s been a lot to do over the last year, but also, I feel like I have a way better sense of direction of what I want in life. In the last year, I think I’ve also “found my groove” as an economist, and have found myself more excited and motivated for research. We’ll see what comes next!

Massachusetts Institute of Technology · SB in Mathematical Economics, 2017–2021

Minor in Public Policy · Phi Beta Kappa

reflections

MIT was a wonderful place to do my undergrad, filled with people who were all excited about something. Here’s a post with many, many reflections on this institution. Three quick reflections for those who don’t want to read the whole thing (and reflections I probably would have figured out anywhere else):

1. At MIT, you learn, mostly in unexpected ways. All of the ways that I changed the most had nothing to do with the content of a class; it was the things I chose to be involved in, the people I met, and the things I figured out about myself.
2. I spent a lot of time in structured non-academic pursuits: deliberate time around others, ESP, science bowl, and much more. During college, I found how necessary it is for me to have non-academic things filling my life; I am not a person who can just do work forever.
3. One of the most difficult parts of MIT is that it sometimes feels bad to do things just for your own sake. Not in the sense that MIT is competitive (it is not), but when you are surrounded by so many smart and high-achieving people who have done amazing things, it can feel like you need to be “productive” all the time. I am still learning how to let go of this feeling.

prior work

undergraduate work

Evaluating Strategic Play: Manipulations with Symmetric Information in the Boston Mechanism (link)

Undergraduate thesis advised by Parag Pathak, 2021

Using some theory and some simulations, examined how manipulable ESP’s lottery mechanism was.

reflections

Inspired by my work with ESP, my feelings on finishing this thesis played a non-zero role in choosing to start grad school.

A Rising Tide for All or Wave for One? The Effect of Charter School Competition on District Achievement (link)

Written for 14.33. MIT Undergraduate Journal of Economics, Vol. 20, 2021

Using favorableness of state laws, I analyzed how charter entry affected non-attending students.

reflections

My first independent research project; while I don’t think this paper is fantastic, I do think that it was a very worthwhile exercise to help me understand the process of research a bit more.

ra work

Optimal Public Transportation Networks: Evidence from the World's Largest Bus Rapid Transit System in Jakarta

RA for Ben Olken, Gabriel Kreindler, Rema Hanna, and Arya Gaduh

A summer RA project where I developed a pipeline to process 60 million riders’ data. My work helped contribute to research on lockdown mobility and the optimal network design.

The Surprising Hybrid Pedigree of Measures of Diversity and Economic Concentration (link)

with Ernst Berndt and Rena Conti · NBER Working Paper No. 26512, 2019

A paper examining the theory behind measuring market concentration, and applying said measures to the pharmaceutical industry.