Job Market Paper
Illegal Drug Use and Government Policy: Evidence from a Darknet Marketplace
with Priyanka Goonetilleke, Anastasia Karpova, and Peter Meylakhs
This paper develops a structural model of demand for illegal drug varieties and studies how consumers substitute between different types of drugs in response to government policies.
We use a unique longitudinal dataset on prices, quantities, and individual decisions that we obtained by scraping a darknet marketplace that covered the majority of the retail illegal drug trade in Russia. Our estimation procedure exploits a novel set of micro-level moment conditions to identify correlations in preferences for specific drug types and the degree of attachment to them. We find that the median own-price elasticity of demand for illegal drugs is -3.6, and there is high substitution within two classes of drugs: medium-risk stimulants and cannabis. We validate our estimates using exogenous variation in the price of hashish caused by increased policing. The estimated model is used to evaluate counterfactual drug policies. We find that the legalization of cannabis has the benefit of decreasing the use of riskier drugs while increasing cannabis use. For every 4 additional doses of cannabis consumed, 1 less dose of another drug is consumed. Our estimates show that the recent introduction of a new family of synthetic drugs has increased total drug demand in the country by 40%, suggesting that governments should allocate resources to prevent the introduction of new drug products. Finally, our model helps identify the optimal drugs to target for interdiction, specifically those without close substitutes, such as α-PVP.
An Economy of Neural Networks: Learning from Heterogeneous Experiences
PIER Working Paper No. 21-027
AbstractThis 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.
Hydra: Lessons from the World’s Largest Darknet Market
with Priyanka Goonetilleke and Alex Knorre, Criminology & Public Policy, November 2023