I am a Ph.D. candidate in Economics at Yale. My research examines the economic impacts of digital technologies. I completed my B.A. in Applied Mathematics at Harvard College in 2019 and an M.Sc. in Global Governance and Diplomacy at Oxford University under a Clarendon Fellowship in 2020.
I will join the Department of Economics at Miami Herbert Business School as an Assistant Professor in Fall 2026.
Working papers
Endogenous Transfer Networks Under Spatial Risk [Draft] (Job Market Paper)
This paper studies how digital payment technologies affect the amount of insurance households can attain under spatially-correlated shocks when risk-sharing links are endogenously formed. I develop a quantitative model in which households form transfer networks over space, treating their own partnerships as substitutable and others' linking decisions as strategic complements to their own. Households jointly agree to strengthen their partnerships but can unilaterally weaken them, leading to multiple stable network configurations. I use the model to quantify welfare gains from informal transfer networks in Tanzania after the 2008 introduction of mobile money, which lowered the costs of transporting money across space. By 2028, the diffusion of mobile money generates average welfare gains of 0.9%, about two-thirds of which arise from network reorganization in response to the technology. Welfare gains are progressive, driven by an enhanced redistributive role of transfers but diminished insurance benefits.
AI and Scale: A Quantitative Task-Based Theory of Automation [Draft]
with Danial Lashkari, Wensu Li, and Neil Thompson
We develop a quantitative task-based model of automation in which machines feature task-level fixed costs, e.g., application-specific training or fine-tuning costs in the case of AI models. Machine's comparative advantage over workers across tasks reflects both the conventional marginal-productivity differences as well as a novel scale advantage (whether task scale justifies the fixed cost). We characterize the resulting production function given the firm's task composition, deriving expressions for the degree of machine-labor elasticity of substitution, nonhomotheticity, and returns to scale. We illustrate the quantitative potential of the model in an application to computer vision AI automation. Using scaling laws that map computing requirements to task characteristics, estimated from a fine-tuning experiment and LLM-based task descriptions, we recover the patterns of AI comparative advantage for 1,920 vision tasks across the U.S. economy. Calibrated to 2023 firm-level adoption rates, the model projects the future path of automation under current trends in computing prices. Aggregate output rises by about 18% by 2075; substitutability is high early on but falls as automation deepens, real wages increase throughout, and the labor share follows a U-shape, declining initially before recovering as AI and labor gradually become complements.
Publications
Regionalized liquidity: A cross-country analysis of mobile money deployment and inflation in developing economies [Paper]
World Development, 2022
Mobile money has been hailed as a serious innovation in the pursuit of financial inclusion and poverty alleviation; however, its macroeconomic effect is not fully understood. This study presents a regional theory of inflation and argues that limited market integration contributes to mobile money's inflationary effects. Household survey data from Kenya confirms increased use of mobile money after village- and supra-village-level shocks due to risk-sharing between liquidity-flexible and liquidity-constrained regions. A difference-in-differences empirical assessment indicates that mobile money deployment increases national consumer price indices. Findings support that the power to distribute equals the power to generate money supply in developig countries.
Work in progress
Dynamic Management of Supplier Capital
with Will Jianyu Lu
This paper develops a theory of supplier capital, treating the geographic distribution of a firm’s suppliers as a stock subject to depreciation. The framework rests on two principles: first, supplier relationships show persistence over time, and second, a supplier set’s geographic properties affect input price levels and volatility. In the model, each location offers a continuous menu of supplier qualities indexed by volatility. Firms adjust supplier capital along two margins: deepening relationships within a location by accessing lower-volatility suppliers, and diversifying across locations by creating new supplier ties. Diversification substitutes for deepening, while deepening displays increasing returns. The model generates state-dependent shifts in firm focus between profitability and resilience, as well as path-dependent firm costs of deepening supplier capital. Moreover, endogenous profitability shapes risk attitudes, leading firms to organize their supply chains to be more resilient as they mature.
