Research
My primary fields are labor and development, with a focus on topics in education.
Job Market Paper
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When a student is weak in a skill, a key question is why: is it because the skill is hard to learn (costs) or because it has low value to the family (benefits)? Parents have insight into which skills hold value for their child, but large class sizes and informal communication limit teachers' access to this information. In this paper, I provide teachers with structured parent information through a field experiment. I survey 3,404 parents across five private schools in India to measure parents' perceptions of their children's skill levels and preferences for improvement across academic and socioemotional domains. Parents vary in their preferences over which skills to improve, but on average prefer improving their children's weaker skills. I develop a structural model of skill development showing that this pattern indicates learning costs, rather than family values, primarily drive observed specialization. I elicit teachers' beliefs about parent preferences and find little alignment with actual parent views, even at the classroom level. I randomize teacher access to parent survey data via a web portal. Treatment shifts student specialization toward parent-prioritized skills, with larger effects where baseline teacher beliefs were most inaccurate. Structural estimation corroborates these patterns and enables policy counterfactuals quantifying welfare gains from better cost-benefit alignment. The results demonstrate that structured parent feedback enables teachers to target instruction toward what families value most.
Works in Progress
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Philosophical perspectives on human desires and values vary; economic theory-driven measurement techniques can provide relevant empirical evidence. We elicited over half a million stated preference choices over 126 dimensions or "aspects" of well-being from a sample of 896 online respondents. We also elicited, via self-reported well-being (SWB) questions, respondents' current levels of the aspects. From the stated preference data, we estimate for each aspect its relative marginal utility per point on our 0-100 response scale. We validate these estimates by comparing them to alternative methods for estimating preferences, and we offer a range of estimates between those that take self-reports at face value and those that (over-)correct for potential social-desirability reporting bias. Our findings suggest that our respondents want, first and foremost, three basic things: family, money, and health (not necessarily in this order). While commonly studied concepts such as happiness, life satisfaction, where life ranks on a ladder, and meaning, are all important, respondents place the highest marginal utilities on aspects related to family well-being and health, and financial freedom and security. We document substantial heterogeneity in preferences across respondents within (but not between) demographic groups, with current SWB levels accounting for a significant portion of the variation.
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Explainable AI and Human Decision Making: Preferences, Beliefs, and Biases [Slides]
Increasingly, AI is being used as a gatekeeper to key areas that affect economic mobility. AI is screening applicants for jobs, loans, healthcare and housing. Generative AI has accelerated this trend; its pre-trained models can readily be deployed across a variety of contexts. However, there are concerns that these models discriminate against protected groups. We construct a model of applicant selection that distinguishes between different forms of discrimination -- taste-based discrimination, statistical discrimination, and biased beliefs -- at the employer or recruiter level. We collect data that allow us to record resume review and hiring outcomes for applicant profiles and overcome the selection issue of observing hiring outcomes only for interviewed applicants. We compare AI decision making to the distribution of human decision makers and use the model to simulate policies such as blinding resume characteristics ("ban the box") and to build non-discriminatory screening algorithms.
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Closing the Last Mile: Norms and Expectations in Women's Job Uptake
We study the "last-mile" constraint to female labor force participation (FLFP) among graduates of the Calcutta Foundation's (CF) vocational programs in Kolkata, India. Low FLFP is a particular puzzle for India, as it remains low despite rising educational rates for women -- a stark contrast to other countries where rising female education has been accompanied by a commensurate increase in FLFP. Our focus is on a select sample of women who do not face well-documented barriers to work, as we survey CF graduates who have completed vocational training, report high willingness and family permission to work, and yet still remain out of the labor force. Our aim is to understand why, focusing on two possible levers: (i) second-order beliefs about community support for women's work, and (ii) expectations about the costs and benefits of work. We ask: can repeated, public community-engagement events increase job search and employment for women by (i) correcting women's beliefs about community support for women's work and/or (ii) correcting misaligned expectations about wages/ job conditions? We propose distinct activities during these events that help disentangle mechanisms, and reveal which lever is more dominant.
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Preferences and Educational Choices of the Youth(Funded by EDUCA Flagship, Research Council of Finland)
We study how youths’ preferences over life outcomes—e.g., financial gains, social status, health—relate to educational choices and gaps across demographic groups. We elicit preferences via a hypothetical choice task adapted from a stated-preference framework (Benjamin et al., 2025), link them to registry data, and analyze (i) how preferences vary by gender and migration background and map to actual choices, and (ii) how peer composition and network homophily shape preferences, validating patterns in culturally polarized classrooms (Anderberg et al., 2025).
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Well-being in School and Academic Achievement(Funded by EDUCA Flagship, Research Council of Finland)
We examine how students’ subjective well-being relates to grades. We construct a multidimensional well-being index combining self-reports with stated preferences over key life aspects (Benjamin et al., 2017; Benjamin et al., 2025). Using linked registry data, we (i) characterize well-being distributions and associations with achievement—especially for minority and disadvantaged students—and (ii) leverage unexpected family shocks (e.g., labor-market or health shocks) to quantify the well-being channel in academic outcomes.
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TeachAIde - Improving Teacher Agency and Student Outcomes through Hypercontextualized Generative AI Chatbots(Pilot and Scoping Ongoing)