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1. Too Good to Be True? Retention Rules for Noisy Agents

   https://doi.org/10.1257/mic.20200472  

   Espinosa, Francisco; Ray, Debraj (May 2023, American Economic Journal: Microeconomics)

   An agent who privately knows his type seeks to be retained by a principal. Agents signal their type with some ambient noise, but can alter this noise, perhaps at some cost. Our main finding is that in equilibrium, the principal treats extreme signals in either direction with suspicion, and retains the agent if and only if the signal falls in some intermediate bounded set. In short, she follows the maxim: “if it seems too good to be true, it probably is.” We consider extensions and applications, including non-normal signal structures, dynamics with term limits, risky portfolio management, and political risk-taking. (JEL D72, D82, G11, G41) 

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2. The Asymmetry of Embeddedness: Illegal Trade Networks and Drug Purchasing Diversity on an Online Illegal Drug Market

   https://doi.org/10.1093/sf/soad134  

   Duxbury, Scott_W; Haynie, Dana_L (October 2023, Social Forces)

   Abstract While economic sociology research and theory argue that excessive network embeddedness depresses competition in illegal markets, prior research does not examine how distinct types of embeddedness may have asymmetric effects on the diversity of purchasing behavior—the range of illegal goods that buyers typically purchase. This study considers how network embeddedness can positively or negatively affect drug purchasing diversity in online drug markets by referring buyers to new vendors or “locking” buyers into recurrent trade for the same products. We analyze novel network data on 16,847 illegal drug exchanges between 7205 actors on one online illegal drug market. Consistent with hypothesized network asymmetry, buyers are more likely to purchase a new type of drug when the transaction is part of an indirect network referral. Although histories of exchange increase the overall frequency of drug purchasing, they are associated with decreases in new drug-type purchases. In the aggregate, these processes either contribute to an integrated market where buyers purchase multiple drugs from multiple vendors (in the case of referrals) or a fragmented market characterized by recurrent trade from the same vendors for the same substances (in the case of repeated trade). We discuss the implications of these findings for research on embeddedness, illegal markets, risky exchange, and drug policy. 

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3. Data-driven Contract Design

   Venkitasubramaniam, Parv; Gupta, Vijay (January 2019, American Control Conference 2019)

   We consider a game in which one player (the principal) seeks to incentivize another player (the agent) to exert effort that is costly to the agent. Any effort exerted leads to an outcome that is a stochastic function of the effort. The amount of effort exerted by the agent is private information for the agent and the principal observes only the outcome; thus, the agent can misreport his effort to gain higher payment. Further, the cost function of the agent is also unknown to the principal and the agent can also misreport a higher cost function to gain higher payment for the same effort. We pose the problem as one of contract design when both adverse selection and moral hazard are present. We show that if the principal and agent interact only finitely many times, it is always possible for the agent to lie due to the asymmetric information pattern and claim a higher payment than if he were unable to lie. However, if the principal and agent interact infinitely many times, then the principal can utilize the observed outcomes to update the contract in a manner that reveals the private cost function of the agent and hence leads to the agent not being able to derive any rent. The result can also be interpreted as saying that the agent is unable to keep his information private if he interacts with the principal sufficiently often. 

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4. The Value of Data Records

   https://doi.org/10.1093/restud/rdad044  

   Galperti, Simone; Levkun, Aleksandr; Perego, Jacopo (March 2024, The Review of Economic Studies)

   Many e-commerce platforms use buyers’ personal data to intermediate their transactions with sellers. How much value do such intermediaries derive from the data record of each single individual? We characterize this value and find that one of its key components is a novel externality between records, which arises when the intermediary pools some records to withhold the information they contain. Our analysis has several implications about compensating individuals for the use of their data, guiding companies’ investments in data acquisition, and more broadly studying the demand side of data markets. Our methods combine modern information design with classic duality theory and apply to a large class of principal-agent problems. 

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5. Simple Delegated Choice

   Khobadahksh, A; Pountourakis, E; Taggart, S (January 2024, LivingOnTrack)

   Woodruff, D (Ed.)

   This paper studies delegation in a model of discrete choice. In the delegation problem, an uninformed principal must consult an informed agent to make a decision. Both the agent and principal have preferences over the decided-upon action which vary based on the state of the world, and which may not be aligned. The principal may commit to a mechanism, which maps reports of the agent to actions. When this mechanism is deterministic, it can take the form of a menu of actions, from which the agent simply chooses upon observing the state. In this case, the principal is said to have delegated the choice of action to the agent. We consider a setting where the decision being delegated is a choice of a utility-maximizing action from a set of several options. We assume the shared portion of the agent's and principal's utilities is drawn from a distribution known to the principal, and that utility misalignment takes the form of a known bias for or against each action. We provide tight approximation analyses for simple threshold policies under three increasingly general sets of assumptions. With independently-distributed utilities, we prove a 3-approximation. When the agent has an outside option the principal cannot rule out, the constant-approximation fails, but we prove a log ρ/ log log ρ-approximation, where ρ is the ratio of the maximum value to the optimal utility. We also give a weaker but tight bound that holds for correlated values, and complement our upper bounds with hardness results. One special case of our model is utility-based assortment optimization, for which our results are new. 

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