Research

Learning Where to Look: Delaunay Matching for Policy Choice and Data Collection [Draft] [Abstract]

This paper studies data-driven policy choices from a geometric perspective. A sample from a donor population fully exposed to an innovation informs a policymaker (PM) on whether to innovate groups in a target population where the innovation was not introduced. Any wrong decision must be compensated from a finite budget, and the PM seeks an estimator of the innovation's effect that guarantees an affordable compensation cost. I focus on matching estimators with positive weights and derive affordability guarantees when finite and large samples of the populations of interest are available. In the latter case, Delaunay interpolants, whose properties are well-known from results in computational geometry, deliver the smallest budget that covers the compensation cost uniformly over the admissible target populations, and conditional on the donor collection design. This result informs where to look for new donor observations to decrease the worst-case compensation cost the most. In an empirical application, I show that such collection plans halve the cost by adding three donor units, while random sampling fails to reach the same target within twenty additions.

Status: Collecting Feedback

Producing Policy Recommendations: from Statistical Decision Theory to Empirical Practice [Draft] [Abstract]

Applied research in economics is intrinsically motivated by broad normative objectives. However, it is not obvious how a researcher should direct their efforts to produce evidence toward such objectives. This paper reviews recent theoretical developments on research design for policy choice and provides new tools applied researchers can use to guide their design choices and communicate their policy recommendations. First, I focus on theoretical contributions in econometrics and provide a general framework that nests all the contexts and results reviewed using a coherent notation and narrative. Then, I present two diagrams applied researchers can use to navigate the theoretical literature starting from concrete scenarios to make thoughtful design choices. Finally, I introduce a new R package that produces one table and two figures applied researchers can plug in their `policy implications' section to provide evidence on the performance of different policy recommendations coming out of their study. The use of such tools is illustrated with an example in development economics.

Status: Collecting Feedback

Better Measurement or Larger Samples? Data Collection for Policy Learning with Unobserved Heterogeneity [Draft] [Slides] [Abstract]

Empirical research shows that individuals' responses to treatments vary along latent characteristics, such as innate ability or motivation. Therefore, a policymaker seeking to maximize welfare may consider designing policies based on observed characteristics and estimated latent traits. I characterize how the estimates' precision affects the worst-case performance of policies deriving rate-sharp regret bounds for assignment rules that include or exclude them, highlighting new trade-offs with the policy space complexity. I then study how a policymaker can solve such trade-offs by designing tailored data collections, and derive the minimax optimal collection plan. In an empirical application in development economics, I show that including a proxy for entrepreneurs' business skills in targeting cash transfers increases welfare by 5%, and halves the probability of generating welfare losses. Moreover, I estimate the optimal allocation of resources between improving the precision of the proxy via repeated measurements, and increasing sample size.

Winner of Unicredit Young Economist Best Presentation Award
Status: Submitted

BallotBot: Can AI Chatbots Lower Voter-Information Barriers? with Elliott Ash and Sergio Galletta [Draft] [VoxEU Column] [Abstract]

This study examines the potential of AI-powered chatbots to increase engagement with political information. We develop and evaluate BallotBot, an AI chatbot with access to official voter guide information from the November 2024 referendums in California. In a pre-registered three-wave survey experiment in the weeks around election day, participants (California voters) were randomly assigned to use either BallotBot or a traditional digital voter guide to answer questions about ballot initiatives. We find that BallotBot access lowered the perceived cost of acquiring information for less-informed participants, fostered greater engagement with political information, while improving participants' ability to correctly answer in-depth questions about ballot measures.

Status: R&R at The Economic Journal