Welcome! I study development economics with a focus on the environment, gender, and education.
I am a Prize Fellow in Economics, History, and Politics at the Center for History and Economics at Harvard and a Postdoctoral Fellow at J-PAL at MIT. In 2026, I will start as an Assistant Professor in the Brown Department of Economics and Institute at Brown for Environment and Society. I received my Ph.D. in economics from Harvard in 2024.
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Working Papers
A Rosetta Stone for Human Capital (Updated draft coming soon!)
(with Justin Sandefur)
Abstract
Comparing human capital across different measures is a central challenge in many empirical economics settings, from quantifying local schools' contribution to neighborhood effects to understanding the causes and consequences of global education gaps. Leveraging insights from item response theory with simple data collection, we develop a new methodology to non-parametrically translate performance measured across arbitrarily different scales. We implement this approach to link four of the world's largest standardized tests using a hybrid exam we developed and administered to students in India and the United States. Armed with this learning "Rosetta Stone", we apply our translations out of sample to microdata from 600,000 pupils across 80 countries and match their socio-economic status to moments of the global income distribution, establishing four new facts: (i) students with the same household income score significantly higher if they live in richer countries; (ii) the income-test score gradient is steeper in countries with greater income inequality; (iii) girls read better than boys at all incomes but only outperform them in mathematics at the lowest deciles of the global income distribution, and (iv) the test-score gap between public and private schools increases with inequality, partially due to a rise in socio-economic sorting across school types.
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Coverage
The Economist
Floods
[Online Interactive Guide: How to Measure Floods from Space]
Abstract
Floods threaten a quarter of the world's population, most of whom live in poor countries. How do floods impact economic development, and how do households adapt? To answer these questions, I first combine methods from geophysics and machine learning in the analysis of satellite data to detect inundation at a granular geographic level anywhere every day for the past two decades. Using this approach in Bangladesh, I find that floods cause a persistent decline in economic activity and force structural change by pushing employment out of agriculture, spurring migration, and shifting children into school. Places with recent exposure to floods experience less harm after subsequent inundation. Using a simple model of experience-driven adaptation, I derive empirical tests for two mechanisms underpinning this pattern and find evidence for both. In a survey of rural farmers, I first show that past flood exposure increases the perceived marginal benefit of adaptation investment by raising households' beliefs about future disaster risk and damages. I next find that the marginal cost of coping with floods via temporary urban migration declines in inundation experience. Consistent with this "learning-by-doing" channel, reduced mobility frictions identified from quasi-random variation in Colonial-era transportation networks mediate the differential treatment effects of past flood exposure. Together, my results indicate that endogenous adaptation will significantly reduce the damage from future flooding.
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Coverage
VoxDev Podcast
Learning About a Warming World: Attention and Adaptation in Agriculture
Abstract
Global warming threatens the livelihoods of 600 million low-income agricultural workers. I study how farmers learn about the environment and the consequences for climate change adaptation. Rice farmers in Bangladesh must form beliefs about their plot's soil salinity, a climate danger exacerbated by rising sea levels that can be mitigated by planting salinity-tolerant seeds. Comparing beliefs about salt levels to agronomic readings, I document both significant over- and underestimation of soil salinity across individuals. I explain this pattern using a conceptual framework of belief formation featuring an identification problem: farmers must learn about multiple unobserved environmental threats from ambiguous signals. As a result, farmers endogenously process data in support of their priors, e.g., someone worried about high salinity will interpret low yield as a sign of too much salt. Climate change amplifies this process by systematically altering the environmental risks farmers consider most threatening. I test and confirm the framework's predictions using a lab-in-the-field exercise and two natural experiments that isolate salient shocks that capture attention (e.g., tidal flooding) and subtle shifts that go unnoticed (e.g., irrigation water contamination through rising sea-levels). Despite equal effects on true salt levels, salient saltwater floods increase salinity beliefs substantially more than does subtle irrigation intrusion. These experiences shape how farmers interpret new data: past exposure to salient shocks increases the mental link between low yield and salinity while subtle shocks reduce the perceived diagnosticity of salinity clues. In large-scale field experiments, I show that correcting misperceptions significantly alters farmers' demand for salinity-tolerant seeds with substantial consequences for profits. I use this experimental variation to estimate and validate a structural model of seed choice that allows me to simulate counterfactual policies and underscores the major economic impacts of environmental beliefs.
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Survey Instruments
Baseline (English) | Baseline (Bangla) | Endline (English) | Endline (Bangla) | Dec. 2023 Follow-Up (English) | Dec. 2023 Follow-Up (Bangla)
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Coverage
World Bank Blog | VoxDev Podcast
What Jobs Come to Mind? Stereotypes About Fields of Study
(with John J. Conlon)
Abstract
Using both large-scale nationally representative data and surveys administered among undergraduates at the Ohio State University, we measure how US freshmen perceive the relationship between college majors and occupations. We show that students stereotype fields of study, greatly exaggerating the likelihood that majors lead to their distinctive jobs (e.g., counselor for psychology, journalist for journalism). Estimates from a structural model suggest that such stereotyping distorts decisions because students have strong preferences for future occupations when choosing their major. In a field experiment, we find that reducing stereotyping has significant effects on students' intentions about what to study as well as the classes and majors in which they enroll. Misperceptions also skew towards the careers and majors of people students know personally, consistent with a recall-based model of belief formation.
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Coverage
Econimate Video Summary
Publications
Texts Don't Nudge: An Adaptive Trial to Prevent the Spread of COVID-19 in India
(with Girija Bahety, Sebastian Bauhoff, and James Potter)
Journal of Development Economics, 2021
Abstract
We conduct an adaptive randomized controlled trial to evaluate the impact of a SMS-based information campaign on the adoption of social distancing and handwashing in rural Bihar, India, six months into the COVID-19 pandemic. We test 10 arms that vary in delivery timing and message framing, changing content to highlight gains or losses for either one's own family or community. We identify the optimal treatment separately for each targeted behavior by adaptively allocating shares across arms over 10 experimental rounds using exploration sampling. Based on phone surveys with nearly 4,000 households and using several elicitation methods, we do not find evidence of impact on knowledge or adoption of preventive health behavior, and our confidence intervals cannot rule out positive effects as large as 5.5 percentage points, or 16%. Our results suggest that SMS-based information campaigns may have limited efficacy after the initial phase of a pandemic.
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The New Era of Unconditional Convergence
(with Arvind Subramanian and Justin Sandefur)
Journal of Development Economics, 2021
Abstract
The central fact that has motivated the empirics of economic growth—namely unconditional divergence—is no longer true and has not been so for decades. Across a range of data sources, poorer countries have in fact been catching up with richer ones, albeit slowly, since the mid-1990s. This new era of convergence does not stem primarily from growth moderation in the rich world but rather from accelerating growth in the developing world, which has simultaneously become remarkably less volatile and more persistent. Debates about a "middle-income trap" also appear anachronistic: middle-income countries have exhibited higher growth rates than all others since the mid-1980s.
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Replication Data and Code | Figures from the Paper
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Coverage
Bloomberg (Noah Smith) | The Economist | The New York Times (Paul Krugman)
Teaching
Syllabus for my 2021 course on the economics of gender inequality
PDF
Other
Bangladesh Data Resources
Bangladesh Labor Force Survey 2016-17
Roshni Islam translated the 2016-17 questionnaire to English. The underlying data can be acquired by contacting the Bangladesh Bureau of Statistics. For more details, visit this link.
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