I am a Ph.D. candidate in Economics at Princeton University.
I work on macroeconomics and international trade. I am particularly interested in knowledge diffusion, firm dynamics and economic growth.
I will be a postdoc at Einaudi Institute for Economics and Finance (EIEF), Rome, in the fall of 2023. Later, I will join the Economics Department of Hong Kong University of Science and Technology (HKUST) as an assistant professor.
You can find my CV here.
Economic Growth and the Rise of Large Firms [Job Market Paper] STEG Ph.D. Grant
Rich and poor countries differ in the size distribution of business firms. In this paper, I document that the right tail of the firm size distribution systematically grows thicker with economic development, both within countries over time and across countries. I develop a simple idea diffusion model with both endogenous growth and an endogenous firm size distribution. The economy features an asymptotic balanced growth path. Along the transition, Gibrat’s law holds at each date, and the right tail of the firm size distribution becomes monotonically thicker. The firm size distribution converges to Zipf’s distribution. Despite its parsimony, the model provides a good quantitative fit to the US GDP per capita growth. I prove that, in a general class of idea diffusion models, Gibrat’s law holds if and only if the right tail of the firm size distribution grows thicker. The simple model is the only one consistent with Gibrat’s law and a thickening tail under common functional form assumptions. Finally, I show that policies favoring large firms can improve welfare due to the externality associated with idea diffusion.
Export by Cohort [Paper]
with Qing Huang.
Using Chinese customs data, we document that, in a destination market, firms entering later tend to perform better at the same age. We interpret this fact through the lens of structural models of new exporter dynamics, among which two competing theories stand out: learning about demand and customer accumulation. We show that the relationship between sales lifecycle and cohort is informative about the main driver of post-entry exporter growth. A major class of demand learning models à la Jovanovic (1982) predict flatter lifecycles for later cohorts, which are in- consistent with the parallel lifecycles seen in the data. On the other hand, we show analytically that customer accumulation models can generate parallel lifecycles. Guided by the qualitative analysis, we build a tractable customer base accumulation model with advertising, estimate it structurally and validate its capability to replicate the empirical cross-cohort exporter lifecycles. The model estimates suggest that the cohort effect is a combination of productivity and demand effects: exporters entering one cohort later on average gain 0.2% in measured productivity and start with a 6.7% larger customer base.
Knowledge Diffusion Through Networks [Paper]
with Treb Allen, Kamran Bilir and Christopher Tonetti.
How do geography and other barriers to the free flow of information shape the rate of knowledge diffusion? To address this question, we develop an empirical model of product discrete choice with Bayesian learning on a social network. Estimating this model using monthly data on the cholesterol-drug prescription decisions of over 50,000 U.S. physicians during January 2000 through December 2010, we find that the evolution of product choice efficiency is highly responsive to network structure changes, particularly targeted friction reductions that strengthen the strongest bilateral links.