Working Papers

Target What? The Foundations of Poverty Targeting

Rema Hanna ⓡ Emily Breza ⓡ Benjamin A. Olken ⓡ Abhijit Banerjee ⓡ Arun G. Chandrasekhar ⓡ Gideon Moore

NBER Working Paper 35984, November 2026

Abstract: Most government transfer programs aim to target the poor. Doing so implicitly assumes that the lowest-consumption households have the highest marginal social valuation, i.e., have the highest social welfare gain from an additional dollar. But this need not be the case. In 600 Indonesian villages, we ask over 5,500 villagers to rank other villagers on how well off they are and how much a modest cash transfer would help them, and compare these rankings to consumption and self-assessed welfare. Targeting consumption misses 77% of households with the highest marginal social valuation. Targeting self-assessed welfare misses 57%. Even targeting those the community ranks worst off in levels, not margins, misses 25%. Divergences between different notions of neediness are statistically predictable. In a randomized controlled trial, proxy means tests best target consumption, self-targeting ordeals capture self-assessed welfare levels, and community targeting comes closest to marginal social valuation. Targeting strategies inescapably embed normative stances.

The Political Content of College Courses

Joint work with Jacob Light and Samuel Thau.

SIEPR Working Paper 26-19, August 2026

Abstract: We measure two dimensions of politics in college courses - the amount of political content and its partisan orientation - using course descriptions from nearly 1,000 U.S. colleges and universities over the past 25 years. Using a novel text-as-data method, we separate the amount of “political content,” the extent to which a course engages with topics salient to politics and public policy, from “slant,” the partisan direction of that content. Since 2000, the average course has 0.15 SD more political content and 0.13 SD more liberal slant. Changes are most pronounced in the tails of the distribution, particularly for slant, and are modest relative to persistent cross-field differences. Following instructors who move between institutions, we find that instructors account for 60-65% of cross-sectional differences in course political content and slant. Using enrollment data, we find students show no preference for liberal content before the 2010s; demand for liberal content rises over the following decade, peaks in 2020, and falls slightly thereafter.

Time Travel on Professional Profiles

Joint work with Nicholas Bloom, Lisa K. Simon, and Caelan Wilkie-Rogers.

NBER Working Paper 35546, July 2026

Abstract: Economists increasingly use professional profile data to reconstruct employment histories and measure skill supply. We show that these records are not fixed historical snapshots, but mutable accounts that workers revise over time. Using monthly vintages of Revelio Labs data from 2020–2026, we document that 19.7 percent of established U.S. LinkedIn users retroactively edit the title or description of a job they have already left. These “time-travel” edits are closely tied to labor market transitions: around such edits, workers are much more likely to change employers as compared to later-editing users. This mutability can bias historical measures of skills, but it also reveals workers’ beliefs about which skills are in demand. Retroactive edits show sharp post-2022 increases in AI-related language and recent reductions in work-from-home and DEI language. Finally, LLM-associated writing markers surge after ChatGPT, especially among less-educated groups and MBAs from lower-ranked programs, revealing heterogeneous AI-assisted profile editing.

Medicare Home Health Fraud: How Much, Where, and Who?

Joint work with Liran Einav, Amy Finkelstein, Yunan Ji, and Neale Mahoney.

NBER Working Paper 35280, June 2026

Abstract: How much fraud is there in Medicare and who commits it? We provide an answer for Medicare home health, a setting widely considered especially rife with fraud. We define a home health agency (HHA) as fraudulent if it was prosecuted by a federal strike force. Combining Medicare claims data on all HHAs with hand-collected prosecution records from the nine federal judicial districts where strike forces operated between 2009 and 2013, we train a machine learning model to predict, out of sample, the probability that each HHA in the remaining 85 districts would have been prosecuted had a strike force been present. We estimate that in 2008, 3.4% of Medicare home health spending — about $520 million — was billed by fraudulent HHAs. The strike forces were well-targeted: their nine districts contained only 40% of home health spending but 65% of fraudulent spending. Fraudulent HHAs display intuitive characteristics: they are more likely to rely on extremely high-volume referring physicians, to exhibit unusually uniform patterns of care, and to serve healthier-than-average patients.

Works in Progress

Learning-by-Doing and the Life Cycle of Innovation

Joint work with Janet Stefanov and Samuel Thau.

Abstract: Orville and Wilbur Wright developed their famous glider while working as bicycle repairmen—despite neither finishing high school. Today, innovations in flight are not so easy to come by: in 2025 Lockheed Martin employed more than 30,000 PhDs as part of the nearly 2 billion dollars it spends on research each year. We show this shift—from practitioner learning-by-doing to dedicated research—is emblematic of a broader “life cycle” of innovation within an industry. Using a novel text-as-data method to identify idea sources based on patent content, we document a decline in learning-by-doing both within and across fields from 1976-2010; this trend has reversed in recent years. Motivated by this fact, we develop a novel growth model incorporating both sources of innovation. This model implies optimal innovation policy depends on the maturity of an industry: young industries benefit from procurement policies which harness the benefits of learning-by-doing, while more mature industries warrant more dedicated R&D subsidies.

Dormant Projects

Estimating Substitution Using Text Embeddings: Evidence from the Film Industry

Spring 2024. My second-year paper for the Economics PhD. Replication files are available on GitHub.

Abstract: Using text descriptions of films in conjunction with weekly box office receipts, I develop a novel model of characteristic-space competition in the film industry. By exploiting plausibly exogenous variation in film release windows, I identify the impact of competitor characteristics on film revenue. As films become more similar, the impact of competition increases. Due to the film industry’s thin profit margins and high fixed costs, replacing a competitor in the 10th percentile of similarity with one in the 90th percentile can reduce profit by as much as 47%.

A Competitive Market for Kidney Patients

Spring 2020. My term paper for Simplicity and Complexity in Economic Theory taught by Mohammad Akbarpour and Paul Milgrom. Replication files are available on GitHub.

Abstract: Despite the economies of scale present in kidney exchanges, the market for kidneys remains highly fragmented. Moreover, despite theoretical evidence supporting the virtues of “patient” matching algorithms, there is not yet a consensus on periodic matching: exchanges run their matches anywhere between quarterly and daily. I argue that given positive waiting costs, ``easy to match’’ recipient/donor pairs will prefer greedy exchanges to more patient options. Thus, exchanges are privately incentivized to match more frequently than would be socially optimal, as this draws in the most desirable donors. In simulations, welfare falls in the presence of heterogeneous competing exchanges, and patients who select into frequently matching exchanges are disproportionately easy to match.

Two-Sided Course Allocation: A Modified CEEI

Fall 2019. My term paper for Matching and Market Design taught by Muriel Niederle, Michael Ostrovsky, and Al Roth. Replication files are available on GitHub.

Abstract: I develop a modified version of Budish (2011)’s Approximate Competitive Equilibrium from Equal Incomes (or A-CEEI) which allows school administrators to correct market failures unaddressed in the original mechanism by taxing student enrollment. This system preserves A-CEEI’s incentive compatibility and lack of congestion while maintaining reasonable fairness and efficiency bounds, and adds the ability for matches to contain a degree of two-sidedness. While the lack of an outside good prevents computation of an optimal tax rate, I outline the impact of various types of tax schema changes, as well as advise on how to correct tax rates when presented with an undesired equilibrium. Finally I derive a boundary on market-clearing error when computing this allocation given a set tax schema. As far as I am aware, this is the only mechanism developed which is incentive compatible, uncongested, and two-sided that also provides bounds on fairness and efficiency losses.

Investigating the Effects of Student Debt on Career Outcomes: An Empirical Approach

Spring 2019. My undergraduate thesis; received High Honors from the Economics department. Supervised by John Fitzgerald, and later advised by Matthew Botsch and Dan Stone. Also available at the Bowdoin Digital Commons. Replication files are available on GitHub.

Abstract: High student debt has been hypothesized to affect career choice, causing students to desire stable, high paying jobs. To test this hypothesis, I rely on plausibly exogenous variation in debt due to a federal policy shift. In the summer of 2007, the Higher Education Reconciliation Act (or HERA) expanded the cap for federally subsidized student loans. I examine how variation in debt affects career choice and eventual salary of students using data from the National Longitudinal Survey of Youth 1979 Child and Young Adult Cohort of students who were of college age during the implementation of the policy. I find that student debt has no impact on salary two years after graduation; however, it does seem to shift students’ career choices, leading some to avoid careers in public service industries such as teaching and social work.