I study the problem of optimally distributing treatments among individuals on a network in the presence of spillovers in the effect of treatment across linked individuals. In this paper, I consider the problem of a planner who needs to distribute a limited number of preventative treatments (e.g., vaccines) for a deadly infectious disease among individuals in a target village in order to maximize population welfare. Since the planner does not know the extent of spillovers or the heterogeneity in treatment effects, she uses data coming from an experiment conducted in a separate pilot village. By placing restrictions on how others’ treatments affect one’s outcome on the contact network, I derive theoretical limits on how the data from the experiment could be used to best allocate the treatments when the planner observes the contact network structure in both the target and pilot village. For this purpose, I extend the empirical welfare maximization (EWM) procedure to derive an optimal statistical treatment rule. Under restrictions on the shape of the contact network, I provide finite sample bounds for the uniform regret (a measure of the effectiveness of a treatment rule). The main takeaway is that the uniform regret associated with EWM, extended to account for spillovers, converges to 0 at the parametric rate as the size of the pilot experiment grows. I also show that no statistical treatment rule admits a faster rate of convergence for the uniform regret, suggesting that the EWM procedure is rate-optimal.
In this paper, I present a game theoretic model of Joint Liability Lending (JLL) microfinance programs with endogenous peer pressure to repay. I also describe a role for institutional pressure applied by microfinance institutions (MFI). This model helps better explain two important empirical findings in the literature. Firstly, observed repayment rates in not-for-profit microfinance programs are very high. Secondly, the productive capacity of participants does not significantly increase. The most striking finding is that when (risk-averse) participants can choose between low risk-low reward and high risk-high reward investments, and the MFI prefers to set low interest rates, the resulting equilibrium boasts inefficiently high repayment rates. This inefficiently transfers the burden of risk onto the participants who respond by inefficiently choosing low risk-low reward investments. Thus, counter to the main purpose of these programs of poverty alleviation, this model suggest that growth generating investments (high risk-high reward) are left under funded in equilibrium.
Works in Progress
Sharp Identification Region from Pairwise Stable Networks with Francesca Molinari
Optimal Seeding of Office Lite on Collaborator Networks with Francesca Molinari and Sida Peng
The Effect of Evictions in the Azure Spot Market with Francesca Molinari, Sida Peng and Will Wang
Awards and Honors
Tapan Mitra Economics Prize, 2018
L.R. “Red” Wilson MA ‘67 Excellence in Economics Medal, 2018
Michael Brunn Family Goldman Sachs Scholarship, 2020