Research
Job Market Paper
Illegal Drug Use and Government Policy: Evidence from a Darknet Marketplace
with Priyanka Goonetilleke, Anastasia Karpova, and Peter Meylakhs
Abstract:
This paper develops a structural model of demand for illegal drugs to study how consumers substitute between different drugs in response to government policies. We construct a unique longitudinal dataset by scraping a darknet marketplace that dominated retail illegal drug trade in Russia. We exploit novel micro-level moment conditions to identify correlations in preferences across drug types and consumers’ degree of attachment to them. We estimate a median own-price elasticity of demand of -3, and find particularly high substitution within two classes of drugs: medium-risk stimulants and cannabis. We validate our estimates using exogenous variation in hashish prices. Using the estimated model, we find that cannabis legalization reduces the use of riskier drugs while increasing cannabis use: for every four additional doses of cannabis consumed, one fewer dose of another drug is consumed. We also find that the introduction of new synthetic drugs caused a large increase in total drug demand and show that drug enforcement can unintentionally increase total drug market revenue. Finally, we measure the extent to which substitution to other drug types offsets the impact of targeted interdiction.
Working Papers
An Economy of Neural Networks: Learning from Heterogeneous Experiences
PIER Working Paper No. 21-027
Abstract
This paper proposes a new way to model behavioral agents in dynamic macro-financial environments. Agents are described as neural networks and learn policies from idiosyncratic past experiences. I investigate the feedback between irrationality and past outcomes in an economy with heterogeneous shocks similar to Aiyagari (1994). In the model, the rational expectations assumption is seriously violated because learning of a decision rule for savings is unstable. Agents who fall into learning traps save either excessively or save nothing, which provides a candidate explanation for several empirical puzzles about wealth distribution. Neural network agents have a higher average MPC and exhibit excess sensitivity of consumption. Learning can negatively affect intergenerational mobility.Publications
Online Drug Markets and Implications for Addiction Medicine: A Narrative Literature Review
with Nicolas Garel, Priyanka Goonetilleke, and Steven Tate, Journal of Addiction Medicine, April 2025
Abstract
This narrative review examines the evolving landscape of online drug markets, focusing on darknet markets for illegal drugs and their implications for addiction medicine. We provide an overview of the development and current state of these markets, highlighting key features of their operation and the demographics of users. Finally, we address the implications for addiction medicine clinicians, including the need for adapted prevention efforts, new approaches to intervention and relapse prevention, and the potential for leveraging digital platforms in treatment. This review aims to equip addiction medicine professionals with the knowledge needed to navigate the challenges posed by online drug markets and to enhance their ability to provide effective care in this changing environment.
Hydra: Lessons from the World’s Largest Darknet Market
with Priyanka Goonetilleke and Alex Knorre, Criminology & Public Policy, November 2023