About Me
I am a PhD Candidate in the Economics Department at UC Berkeley. I will be a Postdoctoral Fellow at the Stanford King Center during the 2025–2026 academic year, and in July 2026 I will join the Economics Department at Boston University as an Assistant Professor.
My research and teaching interests are development economics, industrial organization, and healthcare economics.
You can find my CV here.
Working Papers
Who calls the shots? Financial incentives and provider influence in the adoption of a new health technology (Job Market Paper)
Abstract
The choice to adopt an effective healthcare product is often a joint decision between the patient and their medical professional. Many governments and payers use patient subsidies and provider incentives to increase the adoption of new health technologies. Using data from a randomized field experiment in Kenya, I estimate a structural model of patient demand and provider advice for a new contraceptive method. I then use the model to study the welfare effects to the patient from the introduction of demand and supply side incentives to adopt the new technology. This approach allows the study of channels that promote diffusion, including the roles of provider advice, financial incentives and altruism, as well as patient preferences. Taken together, the results suggest that changes in provider advice due to their altruism and financial incentives are key to increasing adoption of the new technology and making incentive programs effective, regardless of whether the incentive targets the patient or the provider. In fact, changes in provider advice account for 79% of the welfare benefits of a policy that reduces the price to the patient. To be effective, incentive policies need to account for the central role that the provider takes in medical decision-making.Using Diagnosis Contingent Incentive Contracts to Improve Malaria Treatment
With Maria Dieci, Paul Gertler, and Jonathan Kolstad
Abstract
In this project, we study the welfare effects of diagnosis-contingent contracts designed to improve malaria care. These contracts aim to incentivize the use of rapid diagnostic tests (RDTs) to determine malaria status before making a treatment decision. A key concern in our context is over-treatment: as many as 66–90% of patients who purchase anti-malarials are, in fact, malaria-negative. The contracts vary along two dimensions: (1) whether they target patient or provider incentives, and (2) whether they offer direct incentives to test or incentives to treat conditional on a positive test result (i.e., diagnosis-contingent incentives). Using data from a cluster-randomized field experiment with 140 pharmacies in malaria-endemic regions of Kenya, we find that the contracts significantly increased RDT uptake. Across all arms, the incentives led to a 25 percentage point increase in RDT use and a 14 percentage point decline in anti-malarial (ACT) purchases. Using a model of patient choice, we estimate that diagnosis-contingent contracts increase social welfare substantially relative to program costs, with a rate of return of at least 50% across all contract types tested. The primary welfare gain comes from reducing unnecessary ACT use among patients who test negative and therefore do not require treatment. Counterfactual analysis allows us to compare contract designs and identify which maximizes social welfare. We find that patient subsidies for treatment, contingent on a positive test result, are by far the most cost-effective. This is because patients substantially overestimate their likelihood of having malaria and thus respond strongly to these conditional incentives, even though their expected value is low due to the low malaria positivity rate in our setting.Publications
Targeting Impact versus Deprivation (Forthcoming, American Economic Review)
With Johannes Haushofer, Edward Miguel, Michael Walker and Paul Niehaus
Abstract
A large literature has examined how best to target anti-poverty programs to those most deprived in some sense (e.g., consumption). We examine the potential tradeoff between this objective and targeting those most impacted by such programs. We work in the context of an NGO cash transfer program in Kenya, employing recent advances in machine learning methods and dynamic outcome data to learn proxy means tests that jointly target both objectives. Targeting solely on the basis of deprivation is not attractive in this setting under standard social welfare criteria unless the planner’s preferences are extremely redistributive.Works in progress
Demand for generic medications and unobserved product quality in Mexico
With Adrian Rubli
Optimal incentive contracts for malaria case-management
With Maria Dieci, Paul Gertler, and Jonathan Kolstad