The Pennsylvania State University
Grant: $213,786 - National Science Foundation - Aug. 15, 2009
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Award Description: The market for illicit drugs is seen as the cause of many social ills in the United States, and massive amounts of resources are devoted to interfering with the drugs market, the so-called war on drugs. This massive intervention takes place without serious consideration of the retail market structure. The prevailing conception of the retail drugs market is, by default, a Walrasian one: a centralized market with the usual demand and supply curves, and a market-clearing price. The PIs argue that Walrasian paradigm fails to capture a number of empirical stylized facts, and propose to develop another model which does.
Project Description: The model is based on three basic facts, none of which is accounted for by the Walrasian paradigm. First, retail transactions for illegal drugs are subject to significant moral hazard. Second fact we observe in the data: the formation of long-term relationships between buyers and sellers. Third, the data reveal a large amount of price dispersion; the same dollar amount fetches very different amounts of pure drugs in different transactions. To account for these three facts, the PIs propose to study a search-theoretic model of repeated trade with unobservable quality. The PIs further propose to compare the models predictions to available data. It is hoped that useful policy prescription will be obtained from this analysis, possibly aiding us in more effectively fighting the war on drugs. Finally, the PIs intend to extend the analysis beyond the market for illicit drugs, to more general settings of repeated moral hazard with long-term relationships and matching frictions.
Jobs Summary: A Principal Investigator and part-time graduate student were assigned to work on this research project. This provides a learning and work-study opportunity for the student, while facilitating the objectives of the project. (Total jobs reported: 1)
Project Status: Less Than 50% Completed
This award's data was last updated on Aug. 15, 2009. Help expand these official descriptions using the wiki below.
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